diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0653c9fa08e673b7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0653c9fa08e673b7.sql new file mode 100644 index 0000000000000000000000000000000000000000..351d2525f9ecc3778f86842768ce076363919808 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0653c9fa08e673b7.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c10_0653c9fa08e673b7 +-- problem_id: v2p_c10_fac06ec52865343f +-- realization_mode: agent +-- source_kind: agent +SELECT "c5", + AVG(CASE WHEN "s1" = '2' THEN 1 ELSE 0 END) AS condition_rate +FROM "c10" +GROUP BY "c5" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0c8c55d78fa3d2db.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0c8c55d78fa3d2db.sql new file mode 100644 index 0000000000000000000000000000000000000000..7327e694c5c0a503db4b4f22d9b8562b7efc6342 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0c8c55d78fa3d2db.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c10_0c8c55d78fa3d2db +-- problem_id: v2p_c10_02d15dab3de77794 +-- realization_mode: agent +-- source_kind: agent +SELECT "s1", SUM(CAST("s1" AS NUMERIC)) AS total_measure +FROM "c10" +GROUP BY "s1" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_150e25373331ab7e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_150e25373331ab7e.sql new file mode 100644 index 0000000000000000000000000000000000000000..bf88d8d496f398d3b1f6c6a4e13ce328d81ae1fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_150e25373331ab7e.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c10_150e25373331ab7e +-- problem_id: v2p_c10_4601737b54944315 +-- realization_mode: agent +-- source_kind: agent +SELECT "c2", "s3", + SUM(CAST("c3" AS REAL)) AS "total_measure", + SUM(CAST("c3" AS REAL)) * 100.0 / SUM(SUM(CAST("c3" AS REAL))) OVER (PARTITION BY "c2") AS "share_within_group" +FROM "c10" +GROUP BY "c2", "s3" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_3bf2d02a0e3ba0b5.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_3bf2d02a0e3ba0b5.sql new file mode 100644 index 0000000000000000000000000000000000000000..70001dc4aabd1c29193ee6b6797f0e054f3dfba2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_3bf2d02a0e3ba0b5.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c10_3bf2d02a0e3ba0b5 +-- problem_id: v2p_c10_77a80f3f1fd79566 +-- realization_mode: agent +-- source_kind: agent +SELECT "c2", "s3", + SUM(CAST("c3" AS REAL)) AS "total_measure", + SUM(CAST("c3" AS REAL)) * 100.0 / SUM(SUM(CAST("c3" AS REAL))) OVER (PARTITION BY "c2") AS "share_within_group" +FROM "c10" +GROUP BY "c2", "s3" +ORDER BY "share_within_group" DESC +LIMIT 15; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_57dcf1ec83f9d50b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_57dcf1ec83f9d50b.sql new file mode 100644 index 0000000000000000000000000000000000000000..c1b5904dfc6c781aa9e421e7bb1d2303e737116e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_57dcf1ec83f9d50b.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c10_57dcf1ec83f9d50b +-- problem_id: v2p_c10_a552459169e616ef +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "s1" AS value_label, COUNT(*) AS support + FROM "c10" + GROUP BY "s1" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_64ca0079f85ff2dc.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_64ca0079f85ff2dc.sql new file mode 100644 index 0000000000000000000000000000000000000000..172e4d423ac4ac7e24c4349497c1149da042dd1a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_64ca0079f85ff2dc.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c10_64ca0079f85ff2dc +-- problem_id: v2p_c10_2d74a1d86d0f031e +-- realization_mode: agent +-- source_kind: agent +SELECT "s3", "c3", + SUM(CAST("s4" AS INTEGER)) AS total_measure, + SUM(CAST("s4" AS INTEGER)) * 100.0 / SUM(SUM(CAST("s4" AS INTEGER))) OVER (PARTITION BY "s3") AS share_within_group +FROM "c10" +GROUP BY "s3", "c3" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_6834ece4ea14b5f6.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_6834ece4ea14b5f6.sql new file mode 100644 index 0000000000000000000000000000000000000000..b422e7836e61e66aa47b7042ed999386b2986819 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_6834ece4ea14b5f6.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c10_6834ece4ea14b5f6 +-- problem_id: v2p_c10_2bd53f44d2f67401 +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN CAST("s5" AS REAL) <= 3.25 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold" +FROM "c10"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_7303da6d47482102.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_7303da6d47482102.sql new file mode 100644 index 0000000000000000000000000000000000000000..9625886471853ca45d165195bc55fc5f2a8eec0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_7303da6d47482102.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c10_7303da6d47482102 +-- problem_id: v2p_c10_2e21e4f1d3ece7d2 +-- realization_mode: agent +-- source_kind: agent +SELECT + "s4", + "c4", + SUM(CAST("s5" AS REAL)) AS total_measure, + SUM(CAST("s5" AS REAL)) * 100.0 / SUM(SUM(CAST("s5" AS REAL))) OVER (PARTITION BY "s4") AS share_within_group +FROM "c10" +GROUP BY "s4", "c4" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_b0c331e4e07d5956.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_b0c331e4e07d5956.sql new file mode 100644 index 0000000000000000000000000000000000000000..90a0b8ba6646f68ff21cdbe62619264ac7566f2a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_b0c331e4e07d5956.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c10_b0c331e4e07d5956 +-- problem_id: v2p_c10_f0154b5d7b0adaad +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "s1", SUM(CAST("c4" AS REAL)) AS group_value + FROM "c10" + GROUP BY "s1" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."s1", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_ba8cf43a42128a91.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_ba8cf43a42128a91.sql new file mode 100644 index 0000000000000000000000000000000000000000..3c2397457513a96e509105f50677650d390c4b6d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_ba8cf43a42128a91.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c10_ba8cf43a42128a91 +-- problem_id: v2p_c10_3663d741630d026b +-- realization_mode: agent +-- source_kind: agent +SELECT "s3", SUM(CAST("s3" AS INTEGER)) AS total_measure +FROM "c10" +GROUP BY "s3" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_be136feed7ec761a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_be136feed7ec761a.sql new file mode 100644 index 0000000000000000000000000000000000000000..176343a045b77754cc6a7e48f11ac92f0c92d47f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_be136feed7ec761a.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c10_be136feed7ec761a +-- problem_id: v2p_c10_05eb3b08974ecb6c +-- realization_mode: agent +-- source_kind: agent +SELECT "s4", SUM(CAST("s4" AS INTEGER)) AS total_measure +FROM "c10" +GROUP BY "s4" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_cdf07da8d4b3547b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_cdf07da8d4b3547b.sql new file mode 100644 index 0000000000000000000000000000000000000000..8517cce39aadba5c55bb314e5f1fd3617cfeb28b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_cdf07da8d4b3547b.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c10_cdf07da8d4b3547b +-- problem_id: v2p_c10_ee461d6fadb1d5a4 +-- realization_mode: agent +-- source_kind: agent +SELECT "c4", SUM(CAST("c4" AS INTEGER)) AS "total_measure" +FROM "c10" +GROUP BY "c4" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_e95c606c8b51b765.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_e95c606c8b51b765.sql new file mode 100644 index 0000000000000000000000000000000000000000..cb34b45a8cab0508ea66d48cfc6ac70822fa3bda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_e95c606c8b51b765.sql @@ -0,0 +1,59 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c10_e95c606c8b51b765 +-- problem_id: v2p_c10_d23e683e564e668e +-- realization_mode: agent +-- source_kind: agent +WITH "ordered" AS ( + SELECT + "s5", + CAST("c3" AS REAL) AS "measure_value", + ROW_NUMBER() OVER ( + PARTITION BY "s5" + ORDER BY CAST("c3" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "s5" + ) AS "cnt" + FROM "c10" +), +"targets" AS ( + SELECT DISTINCT + "s5", + (1.0 + 0.9 * ("cnt" - 1)) AS "target_pos", + CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) AS "lower_rn", + CASE + WHEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) < (1.0 + 0.9 * ("cnt" - 1)) + THEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + 1 + ELSE CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + END AS "upper_rn" + FROM "ordered" +), +"picked" AS ( + SELECT + o."s5", + t."target_pos", + MAX(CASE WHEN o."rn" = t."lower_rn" THEN o."measure_value" END) AS "lower_val", + MAX(CASE WHEN o."rn" = t."upper_rn" THEN o."measure_value" END) AS "upper_val" + FROM "ordered" AS o + JOIN "targets" AS t + ON o."s5" = t."s5" + GROUP BY o."s5", t."target_pos" +) +SELECT + "s5", + CASE + WHEN "lower_val" IS NULL THEN NULL + WHEN "upper_val" IS NULL THEN "lower_val" + WHEN "target_pos" = CAST("target_pos" AS INTEGER) THEN "lower_val" + ELSE "lower_val" + ("target_pos" - CAST("target_pos" AS INTEGER)) * ("upper_val" - "lower_val") + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_f6fe13b810157e4b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_f6fe13b810157e4b.sql new file mode 100644 index 0000000000000000000000000000000000000000..e87e24d886f45ed8fcacc19a881e0ed816554cc8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_f6fe13b810157e4b.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c10_f6fe13b810157e4b +-- problem_id: v2p_c10_712d3c61d5512ba8 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "s4", + SUM(CASE WHEN "s3" = '2' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "s3" = '1' THEN 1 ELSE 0 END) AS denominator_count + FROM "c10" + GROUP BY "s4" +) +SELECT "s4", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_fb57cf013d307b6b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_fb57cf013d307b6b.sql new file mode 100644 index 0000000000000000000000000000000000000000..1295bb8959b16207a825188cd2727aa0da1d7364 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_fb57cf013d307b6b.sql @@ -0,0 +1,23 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_a +-- sql_source_dataset_id: c10 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c10_fb57cf013d307b6b +-- problem_id: v2p_c10_13360e72f3bb3704 +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "s1", + NTILE(10) OVER (ORDER BY CAST("s1" AS INTEGER) DESC) AS "tail_bucket" + FROM "c10" +) +SELECT "s1" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("s1" AS INTEGER) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..167aac870af446d953387eb58df18737043e9c03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9605, "bytes_utf8": 9605, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 428, "bytes_utf8": 428, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14739, "cached_input_tokens": 13696, "output_tokens": 485, "reasoning_output_tokens": 367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7de7dd3b52b554979e12a5b4a50e04378590b1bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_0143799233bedfc5", + "api_calls": 0, + "input_tokens": 14739, + "cached_input_tokens": 13696, + "output_tokens": 485, + "total_tokens": 15224, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12525.53, + "sql_execution_elapsed_ms_total": 6.62, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2a25a4ab38244260ecd83345e54384223c6a5e6d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:07.130754+00:00", + "ended_at": "2026-05-19T15:43:19.656321+00:00", + "elapsed_ms": 12525.53, + "prompt_metrics": { + "chars": 9605, + "bytes_utf8": 9605, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 803, + "bytes_utf8": 803, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 428, + "bytes_utf8": 428, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14739, + "cached_input_tokens": 13696, + "output_tokens": 485, + "reasoning_output_tokens": 367 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1a6c0263e635bc2b611b84d98d9c743baec9f508 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=last_new_job.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "experience", + "group_col_2": "last_new_job", + "predicate_col": "last_new_job", + "predicate_op": "=", + "predicate_value": "4", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e5e2b8ae97046d3de3d6c977d3e25b63ed1d471c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-b47d-7041-bc2f-fac52d830844"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"last_new_job\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"last_new_job\\\" = '4'\\nGROUP BY \\\"experience\\\", \\\"last_new_job\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the planned filtered two-dimensional group count template with \\\"experience\\\" and \\\"last_new_job\\\" as the grouping columns, restricted to rows where \\\"last_new_job\\\" = '4'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14739,"cached_input_tokens":13696,"output_tokens":485,"reasoning_output_tokens":367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..266e0e023c69b917ebd5b0ad97ef4278ec465750 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"last_new_job\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE \"last_new_job\" = '4'\nGROUP BY \"experience\", \"last_new_job\"\nORDER BY \"row_count\" DESC;","notes":"Uses the planned filtered two-dimensional group count template with \"experience\" and \"last_new_job\" as the grouping columns, restricted to rows where \"last_new_job\" = '4'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1659fe1b4edd09db82940ad55b3fe6a324f89db1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9380, "bytes_utf8": 9380, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 440, "bytes_utf8": 440, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14659, "cached_input_tokens": 12032, "output_tokens": 311, "reasoning_output_tokens": 197}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ae7201a03ce62d5199000080e9ac6d7b8f012431 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_03d55ad136bfea10", + "api_calls": 0, + "input_tokens": 14659, + "cached_input_tokens": 12032, + "output_tokens": 311, + "total_tokens": 14970, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8392.9, + "sql_execution_elapsed_ms_total": 32.73, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8df86a9e30c62843ccb0cd91aa157d1d8937e6c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:33.110209+00:00", + "ended_at": "2026-05-19T16:08:41.503135+00:00", + "elapsed_ms": 8392.9, + "prompt_metrics": { + "chars": 9380, + "bytes_utf8": 9380, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 799, + "bytes_utf8": 799, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 440, + "bytes_utf8": 440, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14659, + "cached_input_tokens": 12032, + "output_tokens": 311, + "reasoning_output_tokens": 197 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee35fd5d4bc32c67c3b2b5fa49274a75df87b893 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0fe47694b1a903c75eec818ee7dd493700d9045e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-fd64-7820-8a73-52d63a1bdeb1"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n \\\"experience\\\",\\n AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"experience\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"experience\\\" and measure_col=\\\"enrollee_id\\\". CAST is used because \\\"enrollee_id\\\" is stored as TEXT in the schema but contains numeric-like values.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14659,"cached_input_tokens":12032,"output_tokens":311,"reasoning_output_tokens":197}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4bc6191844ed0ad96e44092efa5fff4a435b720e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n \"experience\",\n AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"experience\") AS avg_measure\nFROM \"m9\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"experience\" and measure_col=\"enrollee_id\". CAST is used because \"enrollee_id\" is stored as TEXT in the schema but contains numeric-like values."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..47508798ffdc042938abfccc25025a047437334d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9618, "bytes_utf8": 9618, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 498, "bytes_utf8": 498, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14740, "cached_input_tokens": 13696, "output_tokens": 641, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f7b013bd751561013c384305bb102d42049e2367 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_0b646637c9fd1427", + "api_calls": 0, + "input_tokens": 14740, + "cached_input_tokens": 13696, + "output_tokens": 641, + "total_tokens": 15381, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19037.11, + "sql_execution_elapsed_ms_total": 9.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e5501b1750a0b469590825fbf3d896663f2e602f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:19.672520+00:00", + "ended_at": "2026-05-19T15:43:38.709669+00:00", + "elapsed_ms": 19037.11, + "prompt_metrics": { + "chars": 9618, + "bytes_utf8": 9618, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1206, + "bytes_utf8": 1206, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 498, + "bytes_utf8": 498, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14740, + "cached_input_tokens": 13696, + "output_tokens": 641, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..59d754d80f92080bb455a54e7926fa280b662612 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=training_hours.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "experience", + "group_col_2": "training_hours", + "predicate_col": "training_hours", + "predicate_op": ">=", + "predicate_value": 88.0, + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..28ac7a2b8f09031dcb8dc4bc7bcfa94f34adbf59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e7-e57d-7103-8002-64c3f0e0a52c"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15375. Please try again in 461ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"training_hours\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE CAST(\\\"training_hours\\\" AS REAL) >= 88.0\\nGROUP BY \\\"experience\\\", \\\"training_hours\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Used the provided filtered two-dimensional group-count template with \\\"experience\\\" and \\\"training_hours\\\". CAST(\\\"training_hours\\\" AS REAL) is needed because the schema stores it as TEXT while the predicate value is numeric.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14740,"cached_input_tokens":13696,"output_tokens":641,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c3d0603e0d987abd0af41556d058915963eeafb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"training_hours\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE CAST(\"training_hours\" AS REAL) >= 88.0\nGROUP BY \"experience\", \"training_hours\"\nORDER BY \"row_count\" DESC;","notes":"Used the provided filtered two-dimensional group-count template with \"experience\" and \"training_hours\". CAST(\"training_hours\" AS REAL) is needed because the schema stores it as TEXT while the predicate value is numeric."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a530c52cffbd80690f23e7407241ef96ff850a59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9405, "bytes_utf8": 9405, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 637, "bytes_utf8": 637, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14666, "cached_input_tokens": 12032, "output_tokens": 682, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ec4f932638b1326d8ba61f0eb845491ca7a5f313 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_0d1d5d0b6b0ef65a", + "api_calls": 0, + "input_tokens": 14666, + "cached_input_tokens": 12032, + "output_tokens": 682, + "total_tokens": 15348, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12741.36, + "sql_execution_elapsed_ms_total": 66.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d64f986eabe3741eb9dcb47711590b719fc93553 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:31.519396+00:00", + "ended_at": "2026-05-19T16:06:44.260783+00:00", + "elapsed_ms": 12741.36, + "prompt_metrics": { + "chars": 9405, + "bytes_utf8": 9405, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1011, + "bytes_utf8": 1011, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 637, + "bytes_utf8": 637, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14666, + "cached_input_tokens": 12032, + "output_tokens": 682, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7ea4e60e3075019441cecd0ca09749bdb5eaef59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..124be036e12ebe4cb255e266ff30f1869dd47aea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-226e-7e53-8e85-3ca82cf2b29b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"city_development_index\\\",\\n AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"city_development_index\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col \\\"city_development_index\\\" and measure_col \\\"enrollee_id\\\". Cast the text-valued identifier to REAL for SQLite averaging and filtered empty values to keep the view stable.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14666,"cached_input_tokens":12032,"output_tokens":682,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..777ab4e9840e0e68e17442cf664620127cff7743 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"city_development_index\",\n AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"city_development_index\") AS avg_measure\nFROM \"m9\"\nWHERE \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col \"city_development_index\" and measure_col \"enrollee_id\". Cast the text-valued identifier to REAL for SQLite averaging and filtered empty values to keep the view stable."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4d296fb0eb655e23e35d28301fff7d5d29d40233 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9223, "bytes_utf8": 9223, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 315, "bytes_utf8": 315, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14624, "cached_input_tokens": 13696, "output_tokens": 327, "reasoning_output_tokens": 244}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8537d78d2d0c6af3232b9276497ef1377c738c53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_126d5fc6d858efd2", + "api_calls": 0, + "input_tokens": 14624, + "cached_input_tokens": 13696, + "output_tokens": 327, + "total_tokens": 14951, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11424.96, + "sql_execution_elapsed_ms_total": 8.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..47a985cb352ab73cd85816ea5487a450e866d662 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:07.906876+00:00", + "ended_at": "2026-05-19T15:32:19.331865+00:00", + "elapsed_ms": 11424.96, + "prompt_metrics": { + "chars": 9223, + "bytes_utf8": 9223, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 665, + "bytes_utf8": 665, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 315, + "bytes_utf8": 315, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14624, + "cached_input_tokens": 13696, + "output_tokens": 327, + "reasoning_output_tokens": 244 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..39856cfe170e52739df8cb07b38f13375a0d512b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=city_development_index.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "city_development_index", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..14fd4446041e9e1615214fc496b9486dbe67b2d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-a56e-7bd1-b115-35267fa6b027"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"city_development_index\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the planned grouped-count template with group_col bound to city_development_index on the single table m9.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14624,"cached_input_tokens":13696,"output_tokens":327,"reasoning_output_tokens":244}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f67619e52416e7fb6f5711b14f897409119bb758 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"city_development_index\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nORDER BY \"row_count\" DESC;","notes":"Uses the planned grouped-count template with group_col bound to city_development_index on the single table m9."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fd73b2d6bfb563a75b622eadf5fb8a911388ec2c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9227, "bytes_utf8": 9227, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 367, "bytes_utf8": 367, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14639, "cached_input_tokens": 12032, "output_tokens": 423, "reasoning_output_tokens": 312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..75d6e35148babe3b17799b4a3035b92f7549dde1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_14826b2ffd2eecc5", + "api_calls": 0, + "input_tokens": 14639, + "cached_input_tokens": 12032, + "output_tokens": 423, + "total_tokens": 15062, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11129.65, + "sql_execution_elapsed_ms_total": 5.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..316ea4db51a8cb2618850fd8f157243d0aebf47c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:08.123146+00:00", + "ended_at": "2026-05-19T16:03:19.252833+00:00", + "elapsed_ms": 11129.65, + "prompt_metrics": { + "chars": 9227, + "bytes_utf8": 9227, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 711, + "bytes_utf8": 711, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 367, + "bytes_utf8": 367, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14639, + "cached_input_tokens": 12032, + "output_tokens": 423, + "reasoning_output_tokens": 312 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..22f049f926e3b000841dfe5a9580ae8bee82547f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "enrollee_id", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3ac467569e97c1f39fa5ad247996792e4c9e58cd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-07e6-7e40-aaa8-08fcd423cf2b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf.\\nSELECT AVG(CASE WHEN CAST(\\\"enrollee_id\\\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template with measure_col bound to \\\"enrollee_id\\\" and threshold 25169.75; CAST to REAL is used because the schema stores the column as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14639,"cached_input_tokens":12032,"output_tokens":423,"reasoning_output_tokens":312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f6726076668d859e2df086115a630a97151b84e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf.\nSELECT AVG(CASE WHEN CAST(\"enrollee_id\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m9\";","notes":"Applied the Threshold Rarity CDF template with measure_col bound to \"enrollee_id\" and threshold 25169.75; CAST to REAL is used because the schema stores the column as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_14826b2ffd2eecc5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..709a7a3f3384e03eca1326cecafef158c9b39dbc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9729, "bytes_utf8": 9729, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 682, "bytes_utf8": 682, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14774, "cached_input_tokens": 12032, "output_tokens": 1232, "reasoning_output_tokens": 1034}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bc7b5c8b9ffea82ef568d0c394250724dbc8d677 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_15eb58bf509589a4", + "api_calls": 0, + "input_tokens": 14774, + "cached_input_tokens": 12032, + "output_tokens": 1232, + "total_tokens": 16006, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 23249.69, + "sql_execution_elapsed_ms_total": 69.98, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..26771d7569103edfcd4da3a3b18d773055955a55 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:34:09.698449+00:00", + "ended_at": "2026-05-19T15:34:32.948158+00:00", + "elapsed_ms": 23249.69, + "prompt_metrics": { + "chars": 9729, + "bytes_utf8": 9729, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1077, + "bytes_utf8": 1077, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 682, + "bytes_utf8": 682, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14774, + "cached_input_tokens": 12032, + "output_tokens": 1232, + "reasoning_output_tokens": 1034 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dca7caa3d49b0403e0c888584a5956716e28173c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "item_col": "enrollee_id", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a8698f5ecc65a9d185b4b9a0597a1869866511d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40df-8123-70d0-9cc1-9232017e6829"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"city_development_index\\\", \\\"enrollee_id\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"enrollee_id\\\" AS REAL))) OVER (PARTITION BY \\\"city_development_index\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\", \\\"enrollee_id\\\"\\nORDER BY \\\"share_within_group\\\" DESC\\nLIMIT 19;\",\"notes\":\"Used the requested Within-Group Share of Total template with group_col=\\\"city_development_index\\\" and item_col/measure_col=\\\"enrollee_id\\\". CAST(\\\"enrollee_id\\\" AS REAL) is needed because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14774,"cached_input_tokens":12032,"output_tokens":1232,"reasoning_output_tokens":1034}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c01e0d8201ea3df1d6aa460dd28606daadaa322 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"city_development_index\", \"enrollee_id\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"enrollee_id\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"enrollee_id\" AS REAL))) OVER (PARTITION BY \"city_development_index\") AS \"share_within_group\"\nFROM \"m9\"\nGROUP BY \"city_development_index\", \"enrollee_id\"\nORDER BY \"share_within_group\" DESC\nLIMIT 19;","notes":"Used the requested Within-Group Share of Total template with group_col=\"city_development_index\" and item_col/measure_col=\"enrollee_id\". CAST(\"enrollee_id\" AS REAL) is needed because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_15eb58bf509589a4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fa922b005aa4b2bd0bc95b197b7b79eebdbfb897 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9463, "bytes_utf8": 9463, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 585, "bytes_utf8": 585, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14699, "cached_input_tokens": 12032, "output_tokens": 464, "reasoning_output_tokens": 315}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2b81eb097ce00e3f82317212b329cf90b0a521c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_18af7d0afaf31093", + "api_calls": 0, + "input_tokens": 14699, + "cached_input_tokens": 12032, + "output_tokens": 464, + "total_tokens": 15163, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11313.69, + "sql_execution_elapsed_ms_total": 32.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..05cb22443b5506603deda95fafbbcb74c4f9f658 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:08.046541+00:00", + "ended_at": "2026-05-19T15:45:19.360254+00:00", + "elapsed_ms": 11313.69, + "prompt_metrics": { + "chars": 9463, + "bytes_utf8": 9463, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 949, + "bytes_utf8": 949, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 585, + "bytes_utf8": 585, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14699, + "cached_input_tokens": 12032, + "output_tokens": 464, + "reasoning_output_tokens": 315 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5997017e78e5ff57db6f84c43229e1650e45e49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=training_hours.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "training_hours", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac9112d1151f4af23e050bc7f94bb2f72df1d5a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-8cdb-7543-99ca-c48f5b511817"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT \\\"training_hours\\\",\\n NTILE(10) OVER (ORDER BY CAST(NULLIF(\\\"training_hours\\\", '') AS REAL) DESC) AS tail_bucket\\n FROM \\\"m9\\\"\\n WHERE NULLIF(\\\"training_hours\\\", '') IS NOT NULL\\n)\\nSELECT \\\"training_hours\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"training_hours\\\" AS REAL) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"training_hours\\\". Casted the TEXT field to REAL for numeric ordering and excluded blank values before bucketing.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14699,"cached_input_tokens":12032,"output_tokens":464,"reasoning_output_tokens":315}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2b0c300f684e7ecfd9cbbfdd418f7e7f6cf38f1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT \"training_hours\",\n NTILE(10) OVER (ORDER BY CAST(NULLIF(\"training_hours\", '') AS REAL) DESC) AS tail_bucket\n FROM \"m9\"\n WHERE NULLIF(\"training_hours\", '') IS NOT NULL\n)\nSELECT \"training_hours\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"training_hours\" AS REAL) DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"training_hours\". Casted the TEXT field to REAL for numeric ordering and excluded blank values before bucketing."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_18af7d0afaf31093/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..da210472eb1784a77520ff7aad035bb5f759bce5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10128, "bytes_utf8": 10128, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 645, "bytes_utf8": 645, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14855, "cached_input_tokens": 13696, "output_tokens": 684, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1ccd97f847377206fc1ee29c90062f4d841e8853 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_19a7dbe29f1919bc", + "api_calls": 0, + "input_tokens": 14855, + "cached_input_tokens": 13696, + "output_tokens": 684, + "total_tokens": 15539, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13697.45, + "sql_execution_elapsed_ms_total": 23.94, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3105e59061464a5fee5e2316ff8dd77f7b608255 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:41.673394+00:00", + "ended_at": "2026-05-19T15:41:55.370881+00:00", + "elapsed_ms": 13697.45, + "prompt_metrics": { + "chars": 10128, + "bytes_utf8": 10128, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1008, + "bytes_utf8": 1008, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 645, + "bytes_utf8": 645, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14855, + "cached_input_tokens": 13696, + "output_tokens": 684, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..01de55d4d913885acb45ee6c1271da66aa7fa38c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=company_size, condition_col=education_level.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "company_size", + "condition_col": "education_level", + "condition_value": "Graduate", + "positive_value": "Graduate", + "negative_value": "Masters", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..75f8c89a5ec56b751efa24d290ea9a5832c5c4ba --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-66d9-7a12-918c-fee22e3668c1"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions.\\nWITH grouped AS (\\n SELECT\\n \\\"company_size\\\",\\n SUM(CASE WHEN \\\"education_level\\\" = 'Graduate' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"education_level\\\" = 'Masters' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m9\\\"\\n GROUP BY \\\"company_size\\\"\\n)\\nSELECT\\n \\\"company_size\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the Graduate-to-Masters ratio within each company_size group using the requested template and bindings.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14855,"cached_input_tokens":13696,"output_tokens":684,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae75a61118886b1c6a2dcc3b8c4f374a44389474 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions.\nWITH grouped AS (\n SELECT\n \"company_size\",\n SUM(CASE WHEN \"education_level\" = 'Graduate' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"education_level\" = 'Masters' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m9\"\n GROUP BY \"company_size\"\n)\nSELECT\n \"company_size\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Computes the Graduate-to-Masters ratio within each company_size group using the requested template and bindings."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_19a7dbe29f1919bc/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b2e9ab09a067c6457c876f1a7ce6a780c1ed4eaa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10118, "bytes_utf8": 10118, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 742, "bytes_utf8": 742, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14862, "cached_input_tokens": 13696, "output_tokens": 646, "reasoning_output_tokens": 422}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..076b238fcfd78d6ae3717a6f6997d157b747f286 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_1a398fef0ebdc7be", + "api_calls": 0, + "input_tokens": 14862, + "cached_input_tokens": 13696, + "output_tokens": 646, + "total_tokens": 15508, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14106.79, + "sql_execution_elapsed_ms_total": 20.7, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f991a03e48ab26ec1cdf5dae418053a992cb9a87 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:24.045382+00:00", + "ended_at": "2026-05-19T15:40:38.152208+00:00", + "elapsed_ms": 14106.79, + "prompt_metrics": { + "chars": 10118, + "bytes_utf8": 10118, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1149, + "bytes_utf8": 1149, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 742, + "bytes_utf8": 742, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14862, + "cached_input_tokens": 13696, + "output_tokens": 646, + "reasoning_output_tokens": 422 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f7de1947bde64795197f3a295ecd384e6517075 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=relevent_experience, condition_col=last_new_job.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "relevent_experience", + "condition_col": "last_new_job", + "condition_value": "1", + "positive_value": "1", + "negative_value": ">4", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f94513d691f17853738496adf81b8dea3fa1b32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-376d-7d03-ad3b-1f8df2019c6a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"relevent_experience\\\",\\n SUM(CASE WHEN \\\"last_new_job\\\" = '1' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"last_new_job\\\" = '>4' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"relevent_experience\\\"\\n)\\nSELECT\\n \\\"relevent_experience\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Computes, for each \\\"relevent_experience\\\" group, the ratio of rows where \\\"last_new_job\\\" = '1' to rows where \\\"last_new_job\\\" = '>4', using NULLIF to avoid division by zero.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14862,"cached_input_tokens":13696,"output_tokens":646,"reasoning_output_tokens":422}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b6cebb484d46ecf2b26d42e051882ec69985fab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n SELECT\n \"relevent_experience\",\n SUM(CASE WHEN \"last_new_job\" = '1' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"last_new_job\" = '>4' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m9\"\n GROUP BY \"relevent_experience\"\n)\nSELECT\n \"relevent_experience\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;","notes":"Computes, for each \"relevent_experience\" group, the ratio of rows where \"last_new_job\" = '1' to rows where \"last_new_job\" = '>4', using NULLIF to avoid division by zero."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1a398fef0ebdc7be/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4e5ddc50760a883a52d1f90bae8204ccb1280dde --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9474, "bytes_utf8": 9474, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9474, "bytes_utf8": 9474, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 1553, "bytes_utf8": 1555, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14686, "cached_input_tokens": 13696, "output_tokens": 2004, "reasoning_output_tokens": 1537}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5e620ffd842558a486b2d75b0a5b685ec8de12a2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_1aaf6b7ff4c9d23d", + "api_calls": 0, + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 2004, + "total_tokens": 16690, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 37969.48, + "sql_execution_elapsed_ms_total": 55.41, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..35e12c71d29f3ba952bb680e76d9cf687330d04b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:41.573451+00:00", + "ended_at": "2026-05-19T15:56:44.697628+00:00", + "elapsed_ms": 3124.15, + "returncode": 1, + "prompt_metrics": { + "chars": 9474, + "bytes_utf8": 9474, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a1eeb7ea42fffd57e17d2255aebc89c63199900a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:45.700229+00:00", + "ended_at": "2026-05-19T15:57:20.545596+00:00", + "elapsed_ms": 34845.33, + "prompt_metrics": { + "chars": 9474, + "bytes_utf8": 9474, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2136, + "bytes_utf8": 2138, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1553, + "bytes_utf8": 1555, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 2004, + "reasoning_output_tokens": 1537 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..851b64b6336aab54435d6afa7ee2f5c23339bd32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..851b64b6336aab54435d6afa7ee2f5c23339bd32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..62e3d768def9fda948bb82f785631d2ac2c6b0f9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-21d5-7190-9c5c-00ef0d42b2f1"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd77035a779a2b34c1fc5ffc700de4fb81c0fb47 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-21d5-7190-9c5c-00ef0d42b2f1"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a4d7f60a63fce8adf2f333c1153c9b758b9110f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-3218-7042-bd0a-e0e51acdfb4e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"experience\\\",\\n CAST(\\\"enrollee_id\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"experience\\\"\\n ORDER BY CAST(\\\"enrollee_id\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"experience\\\") AS \\\"cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"experience\\\" IS NOT NULL\\n AND \\\"experience\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"experience\\\",\\n \\\"measure_value\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n 1 + ((\\\"cnt\\\" - 1) * 0.9) AS \\\"pos\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n \\\"experience\\\",\\n \\\"cnt\\\",\\n MAX(CASE WHEN \\\"rn\\\" = CAST(\\\"pos\\\" AS INTEGER) THEN \\\"measure_value\\\" END) AS \\\"lower_value\\\",\\n MAX(CASE WHEN \\\"rn\\\" = CAST(\\\"pos\\\" AS INTEGER) + 1 THEN \\\"measure_value\\\" END) AS \\\"upper_value\\\",\\n MAX(\\\"pos\\\") AS \\\"pos\\\"\\n FROM \\\"bounds\\\"\\n GROUP BY \\\"experience\\\", \\\"cnt\\\"\\n)\\nSELECT\\n \\\"experience\\\",\\n CASE\\n WHEN \\\"cnt\\\" = 1 THEN \\\"lower_value\\\"\\n WHEN CAST(\\\"pos\\\" AS INTEGER) = \\\"pos\\\" THEN \\\"lower_value\\\"\\n ELSE \\\"lower_value\\\" + (\\\"pos\\\" - CAST(\\\"pos\\\" AS INTEGER)) * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Emulates the template’s 0.9 percentile in SQLite via window functions and linear interpolation on CAST(\\\"enrollee_id\\\" AS REAL), excluding blank/null experience and enrollee_id values.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14686,"cached_input_tokens":13696,"output_tokens":2004,"reasoning_output_tokens":1537}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..995fa3299a24fe25f68fc6cb7b01ba9cca82882a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"experience\",\n CAST(\"enrollee_id\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"experience\"\n ORDER BY CAST(\"enrollee_id\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"experience\") AS \"cnt\"\n FROM \"m9\"\n WHERE \"experience\" IS NOT NULL\n AND \"experience\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n),\n\"bounds\" AS (\n SELECT\n \"experience\",\n \"measure_value\",\n \"rn\",\n \"cnt\",\n 1 + ((\"cnt\" - 1) * 0.9) AS \"pos\"\n FROM \"ordered\"\n),\n\"picked\" AS (\n SELECT\n \"experience\",\n \"cnt\",\n MAX(CASE WHEN \"rn\" = CAST(\"pos\" AS INTEGER) THEN \"measure_value\" END) AS \"lower_value\",\n MAX(CASE WHEN \"rn\" = CAST(\"pos\" AS INTEGER) + 1 THEN \"measure_value\" END) AS \"upper_value\",\n MAX(\"pos\") AS \"pos\"\n FROM \"bounds\"\n GROUP BY \"experience\", \"cnt\"\n)\nSELECT\n \"experience\",\n CASE\n WHEN \"cnt\" = 1 THEN \"lower_value\"\n WHEN CAST(\"pos\" AS INTEGER) = \"pos\" THEN \"lower_value\"\n ELSE \"lower_value\" + (\"pos\" - CAST(\"pos\" AS INTEGER)) * (\"upper_value\" - \"lower_value\")\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"Emulates the template’s 0.9 percentile in SQLite via window functions and linear interpolation on CAST(\"enrollee_id\" AS REAL), excluding blank/null experience and enrollee_id values."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1aaf6b7ff4c9d23d/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c76eb55aebf76b950507aa8efa01ac99ab126c2a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:26.978326+00:00", + "ended_at": "2026-05-19T16:03:30.311831+00:00", + "elapsed_ms": 3333.47, + "returncode": 1, + "prompt_metrics": { + "chars": 9229, + "bytes_utf8": 9229, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..014df1ab59d7188dc54c559a22d76d3783ed3cd3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:31.314097+00:00", + "ended_at": "2026-05-19T16:03:34.204977+00:00", + "elapsed_ms": 2890.83, + "returncode": 1, + "prompt_metrics": { + "chars": 9229, + "bytes_utf8": 9229, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c70e31b6c54a72ddad6d73f95a151da6aa40d766 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=training_hours.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "training_hours", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c70e31b6c54a72ddad6d73f95a151da6aa40d766 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=training_hours.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "training_hours", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f40151971c0ca2433e3de05353ad277021f8ab8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-51a8-7a73-ba77-6c1339838e95"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..328507cc37023ed627bc4cc32dd5b0b77832cf38 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-51a8-7a73-ba77-6c1339838e95"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f1e4f851c5eaeb25066dfca7a0b52252af9a07ce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-628b-7b83-850e-411f36a79eb2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0842a210c05bc055975ed7425d03da8c0f1bbee3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-628b-7b83-850e-411f36a79eb2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1b3881da9a53e227/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7d36376dfe909c596b8c253810d8e20236d65cf7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9324, "bytes_utf8": 9324, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 475, "bytes_utf8": 475, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14650, "cached_input_tokens": 12032, "output_tokens": 634, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bb7330f45474dc8881a0fa4b824e35f658a65e19 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_1d72320a89b76321", + "api_calls": 0, + "input_tokens": 14650, + "cached_input_tokens": 12032, + "output_tokens": 634, + "total_tokens": 15284, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 28473.5, + "sql_execution_elapsed_ms_total": 13.31, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..18860f56d0b780e52eaaf829fd0ae115cd405c80 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:03.989384+00:00", + "ended_at": "2026-05-19T15:30:32.462911+00:00", + "elapsed_ms": 28473.5, + "prompt_metrics": { + "chars": 9324, + "bytes_utf8": 9324, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2169, + "bytes_utf8": 2169, + "lines": 8, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 475, + "bytes_utf8": 475, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14650, + "cached_input_tokens": 12032, + "output_tokens": 634, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b707c224f90f15cd85a7fe077c04f32c5ec8105 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..86c307ba073f951ab44c12ddbbffdf1c41451f13 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,8 @@ +{"type":"thread.started","thread_id":"019e40db-c161-74d3-bd05-4bda271260f5"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1995234, Requested 15285. Please try again in 315ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15285. Please try again in 458ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1998393, Requested 15285. Please try again in 410ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15285. Please try again in 458ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"education_level\\\", SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"education_level\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the planned grouped-sum template with \\\"education_level\\\" as the grouping column and casts text-valued \\\"city_development_index\\\" to REAL for SQLite-compatible summation; blank measure values are treated as NULL and ignored by SUM.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14650,"cached_input_tokens":12032,"output_tokens":634,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b32f75f89480b285d415a832808a35c696413a38 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"education_level\", SUM(CAST(NULLIF(\"city_development_index\", '') AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"education_level\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the planned grouped-sum template with \"education_level\" as the grouping column and casts text-valued \"city_development_index\" to REAL for SQLite-compatible summation; blank measure values are treated as NULL and ignored by SUM."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1d72320a89b76321/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..05bb29a5422a857153c8f0b4b0176800e1b0b208 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9720, "bytes_utf8": 9720, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 651, "bytes_utf8": 651, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14770, "cached_input_tokens": 12032, "output_tokens": 877, "reasoning_output_tokens": 694}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..792553de7e5e9da671a784a772dac75fe873fdb4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_1fcfeb0362f1c7eb", + "api_calls": 0, + "input_tokens": 14770, + "cached_input_tokens": 12032, + "output_tokens": 877, + "total_tokens": 15647, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15633.23, + "sql_execution_elapsed_ms_total": 23.47, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5d6861689b381923892ab64b988e161b7b0be902 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:41.743027+00:00", + "ended_at": "2026-05-19T15:35:57.376290+00:00", + "elapsed_ms": 15633.23, + "prompt_metrics": { + "chars": 9720, + "bytes_utf8": 9720, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1035, + "bytes_utf8": 1035, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 651, + "bytes_utf8": 651, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14770, + "cached_input_tokens": 12032, + "output_tokens": 877, + "reasoning_output_tokens": 694 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..37d0b74790249eb2771092057471735b10488549 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "item_col": "experience", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e9616bd41efb0226cd936cc99e719bb6482b959 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-e8b6-78a3-862b-aeb123017b23"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"enrolled_university\\\", \\\"experience\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC)) AS total_measure,\\n SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC)) * 100.0 / SUM(SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC))) OVER (PARTITION BY \\\"enrolled_university\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\", \\\"experience\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"enrolled_university\\\", item_col=\\\"experience\\\", and measure_col=\\\"enrollee_id\\\". CAST is used because the schema stores \\\"enrollee_id\\\" as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14770,"cached_input_tokens":12032,"output_tokens":877,"reasoning_output_tokens":694}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..03456ee119fd7eaf8d281a936e88d4e44d4c6826 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"enrolled_university\", \"experience\",\n SUM(CAST(\"enrollee_id\" AS NUMERIC)) AS total_measure,\n SUM(CAST(\"enrollee_id\" AS NUMERIC)) * 100.0 / SUM(SUM(CAST(\"enrollee_id\" AS NUMERIC))) OVER (PARTITION BY \"enrolled_university\") AS share_within_group\nFROM \"m9\"\nGROUP BY \"enrolled_university\", \"experience\"\nORDER BY share_within_group DESC;","notes":"Applied the Within-Group Share of Total template with group_col=\"enrolled_university\", item_col=\"experience\", and measure_col=\"enrollee_id\". CAST is used because the schema stores \"enrollee_id\" as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_1fcfeb0362f1c7eb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..128282294f53537ddfde7cf32450210533c366ed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9206, "bytes_utf8": 9206, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 291, "bytes_utf8": 291, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14621, "cached_input_tokens": 12032, "output_tokens": 338, "reasoning_output_tokens": 264}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e0af9429b28ba9e8571944a9823c60303de1dc4e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_22971f0ddd0d2767", + "api_calls": 0, + "input_tokens": 14621, + "cached_input_tokens": 12032, + "output_tokens": 338, + "total_tokens": 14959, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14039.73, + "sql_execution_elapsed_ms_total": 7.21, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5f5e3b87d9b2cd6639c91865f19b2bfc16fc82e3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:13.462433+00:00", + "ended_at": "2026-05-19T15:33:27.502199+00:00", + "elapsed_ms": 14039.73, + "prompt_metrics": { + "chars": 9206, + "bytes_utf8": 9206, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 969, + "bytes_utf8": 969, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 291, + "bytes_utf8": 291, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14621, + "cached_input_tokens": 12032, + "output_tokens": 338, + "reasoning_output_tokens": 264 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2be2265c04dacee371c5c856b2af9d7c5d34d49d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=major_discipline.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "major_discipline", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a5c78fc5a873445a63a26906f5e4956f7b5f3e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40de-a591-7973-ad3d-b796738b0e1e"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1985806, Requested 15256. Please try again in 31ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"major_discipline\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Grouped count distribution for \\\"major_discipline\\\" using the provided Grouped Count by Category template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14621,"cached_input_tokens":12032,"output_tokens":338,"reasoning_output_tokens":264}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0667b4d0bd29ad974b74e4def1199499b41be442 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"major_discipline\", COUNT(*) AS row_count\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY row_count DESC;","notes":"Grouped count distribution for \"major_discipline\" using the provided Grouped Count by Category template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_22971f0ddd0d2767/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..93f502d77baf4c41f00cbb04562db799f33e185d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10140, "bytes_utf8": 10140, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 716, "bytes_utf8": 716, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14862, "cached_input_tokens": 13696, "output_tokens": 670, "reasoning_output_tokens": 481}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bc8b41793d0834e22bb065e6c54bf2d60e3fc450 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_26fd625d21bebadb", + "api_calls": 0, + "input_tokens": 14862, + "cached_input_tokens": 13696, + "output_tokens": 670, + "total_tokens": 15532, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15918.79, + "sql_execution_elapsed_ms_total": 25.22, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3378368815d32c95252730d6172695a57811fdce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:55.680483+00:00", + "ended_at": "2026-05-19T15:40:11.599304+00:00", + "elapsed_ms": 15918.79, + "prompt_metrics": { + "chars": 10140, + "bytes_utf8": 10140, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1077, + "bytes_utf8": 1077, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 716, + "bytes_utf8": 716, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14862, + "cached_input_tokens": 13696, + "output_tokens": 670, + "reasoning_output_tokens": 481 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa6f525129feee0ebce14b01fe888a13a63ddacf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=city_development_index, condition_col=major_discipline.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "city_development_index", + "condition_col": "major_discipline", + "condition_value": "STEM", + "positive_value": "STEM", + "negative_value": "", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf434c49a202502c5704970e2342935bc39ac726 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-c8b6-7f91-b837-1730d4bf4506"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"city_development_index\\\",\\n SUM(CASE WHEN \\\"major_discipline\\\" = 'STEM' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"major_discipline\\\" = '' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m9\\\"\\n GROUP BY \\\"city_development_index\\\"\\n)\\nSELECT \\\"city_development_index\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes, for each city development index, the ratio of rows with major_discipline = 'STEM' to rows with blank major_discipline, using NULLIF to avoid division by zero.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14862,"cached_input_tokens":13696,"output_tokens":670,"reasoning_output_tokens":481}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6c15fae06cf1f4525f040c8266be0c43d560f0cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"city_development_index\",\n SUM(CASE WHEN \"major_discipline\" = 'STEM' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"major_discipline\" = '' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m9\"\n GROUP BY \"city_development_index\"\n)\nSELECT \"city_development_index\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Computes, for each city development index, the ratio of rows with major_discipline = 'STEM' to rows with blank major_discipline, using NULLIF to avoid division by zero."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_26fd625d21bebadb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..92bce9f7ccdd02319caca4c0fd6c953b8efdab8d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9502, "bytes_utf8": 9502, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9502, "bytes_utf8": 9502, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 2137, "bytes_utf8": 2137, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14688, "cached_input_tokens": 12032, "output_tokens": 2498, "reasoning_output_tokens": 1807}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..da8e0d8e220880ab4a9bbaca1a2cd2333045f9ed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_2716dec92da40a34", + "api_calls": 0, + "input_tokens": 14688, + "cached_input_tokens": 12032, + "output_tokens": 2498, + "total_tokens": 17186, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 42450.48, + "sql_execution_elapsed_ms_total": 78.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89524278e50c0acef25b06b5edc365ae3ebde95 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:54:14.448088+00:00", + "ended_at": "2026-05-19T15:54:17.652248+00:00", + "elapsed_ms": 3204.13, + "returncode": 1, + "prompt_metrics": { + "chars": 9502, + "bytes_utf8": 9502, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fefa41a413abbc486c02c592ed2896750c5e184e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:54:18.654847+00:00", + "ended_at": "2026-05-19T15:54:57.901252+00:00", + "elapsed_ms": 39246.35, + "prompt_metrics": { + "chars": 9502, + "bytes_utf8": 9502, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2786, + "bytes_utf8": 2786, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2137, + "bytes_utf8": 2137, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14688, + "cached_input_tokens": 12032, + "output_tokens": 2498, + "reasoning_output_tokens": 1807 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..877da3540f38659b1c07eca5ec4bffd14bb7ceea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..877da3540f38659b1c07eca5ec4bffd14bb7ceea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..763f111672eb16373ac3d5647fc0e7cd130c98d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-e355-7140-aa50-e533207b104d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..aff750e9696dc6fdcc5382ddcdb14b0bb92c0a9a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-e355-7140-aa50-e533207b104d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc998130b3df382daa4c2581cc2f280321bc355e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-f3ac-7a00-8b96-db8d9233ed33"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"education_level\\\" AS \\\"group_col\\\",\\n CAST(\\\"city_development_index\\\" AS REAL) AS \\\"measure_col\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"education_level\\\" IS NOT NULL\\n AND \\\"education_level\\\" <> ''\\n AND \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n),\\n\\\"ranked\\\" AS (\\n SELECT\\n \\\"group_col\\\",\\n \\\"measure_col\\\",\\n ROW_NUMBER() OVER (PARTITION BY \\\"group_col\\\" ORDER BY \\\"measure_col\\\") AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"group_col\\\") AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"group_col\\\",\\n \\\"cnt\\\",\\n (0.95 * (\\\"cnt\\\" - 1)) + 1 AS \\\"pos\\\",\\n CAST((0.95 * (\\\"cnt\\\" - 1)) + 1 AS INTEGER) AS \\\"lo_rn\\\",\\n CASE\\n WHEN ((0.95 * (\\\"cnt\\\" - 1)) + 1) = CAST((0.95 * (\\\"cnt\\\" - 1)) + 1 AS INTEGER)\\n THEN CAST((0.95 * (\\\"cnt\\\" - 1)) + 1 AS INTEGER)\\n ELSE CAST((0.95 * (\\\"cnt\\\" - 1)) + 1 AS INTEGER) + 1\\n END AS \\\"hi_rn\\\"\\n FROM \\\"ranked\\\"\\n WHERE \\\"cnt\\\" >= 5\\n GROUP BY \\\"group_col\\\", \\\"cnt\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n b.\\\"group_col\\\",\\n b.\\\"pos\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = b.\\\"lo_rn\\\" THEN r.\\\"measure_col\\\" END) AS \\\"lo_val\\\",\\n MAX(CASE WHEN r.\\\"rn\\\" = b.\\\"hi_rn\\\" THEN r.\\\"measure_col\\\" END) AS \\\"hi_val\\\"\\n FROM \\\"bounds\\\" AS b\\n JOIN \\\"ranked\\\" AS r\\n ON r.\\\"group_col\\\" = b.\\\"group_col\\\"\\n GROUP BY b.\\\"group_col\\\", b.\\\"pos\\\"\\n)\\nSELECT\\n \\\"group_col\\\" AS \\\"education_level\\\",\\n CASE\\n WHEN \\\"lo_val\\\" IS NULL THEN NULL\\n WHEN \\\"hi_val\\\" IS NULL OR \\\"lo_val\\\" = \\\"hi_val\\\" THEN \\\"lo_val\\\"\\n ELSE \\\"lo_val\\\" + ((\\\"pos\\\" - CAST(\\\"pos\\\" AS INTEGER)) * (\\\"hi_val\\\" - \\\"lo_val\\\"))\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this emulates the 0.95 percentile for each education_level by casting city_development_index to REAL, ordering values within each group, and linearly interpolating between the bounding ranks. Blank and NULL values are excluded, and groups are limited to at least 5 rows.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14688,"cached_input_tokens":12032,"output_tokens":2498,"reasoning_output_tokens":1807}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb75e87d62e0f8020382c598f9a3bebde5dd2a99 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"education_level\" AS \"group_col\",\n CAST(\"city_development_index\" AS REAL) AS \"measure_col\"\n FROM \"m9\"\n WHERE \"education_level\" IS NOT NULL\n AND \"education_level\" <> ''\n AND \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n),\n\"ranked\" AS (\n SELECT\n \"group_col\",\n \"measure_col\",\n ROW_NUMBER() OVER (PARTITION BY \"group_col\" ORDER BY \"measure_col\") AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"group_col\") AS \"cnt\"\n FROM \"base\"\n),\n\"bounds\" AS (\n SELECT\n \"group_col\",\n \"cnt\",\n (0.95 * (\"cnt\" - 1)) + 1 AS \"pos\",\n CAST((0.95 * (\"cnt\" - 1)) + 1 AS INTEGER) AS \"lo_rn\",\n CASE\n WHEN ((0.95 * (\"cnt\" - 1)) + 1) = CAST((0.95 * (\"cnt\" - 1)) + 1 AS INTEGER)\n THEN CAST((0.95 * (\"cnt\" - 1)) + 1 AS INTEGER)\n ELSE CAST((0.95 * (\"cnt\" - 1)) + 1 AS INTEGER) + 1\n END AS \"hi_rn\"\n FROM \"ranked\"\n WHERE \"cnt\" >= 5\n GROUP BY \"group_col\", \"cnt\"\n),\n\"picked\" AS (\n SELECT\n b.\"group_col\",\n b.\"pos\",\n MAX(CASE WHEN r.\"rn\" = b.\"lo_rn\" THEN r.\"measure_col\" END) AS \"lo_val\",\n MAX(CASE WHEN r.\"rn\" = b.\"hi_rn\" THEN r.\"measure_col\" END) AS \"hi_val\"\n FROM \"bounds\" AS b\n JOIN \"ranked\" AS r\n ON r.\"group_col\" = b.\"group_col\"\n GROUP BY b.\"group_col\", b.\"pos\"\n)\nSELECT\n \"group_col\" AS \"education_level\",\n CASE\n WHEN \"lo_val\" IS NULL THEN NULL\n WHEN \"hi_val\" IS NULL OR \"lo_val\" = \"hi_val\" THEN \"lo_val\"\n ELSE \"lo_val\" + ((\"pos\" - CAST(\"pos\" AS INTEGER)) * (\"hi_val\" - \"lo_val\"))\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this emulates the 0.95 percentile for each education_level by casting city_development_index to REAL, ordering values within each group, and linearly interpolating between the bounding ranks. Blank and NULL values are excluded, and groups are limited to at least 5 rows."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2716dec92da40a34/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..101efb5047030dbaff0069839bbfb7627d1f6b93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9278, "bytes_utf8": 9278, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9278, "bytes_utf8": 9278, "lines": 262, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 316, "bytes_utf8": 316, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14656, "cached_input_tokens": 12032, "output_tokens": 266, "reasoning_output_tokens": 179}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3e257b8538c6513cf5de111c7ea766674619acd5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_2bd920d79f7b2d74", + "api_calls": 0, + "input_tokens": 14656, + "cached_input_tokens": 12032, + "output_tokens": 266, + "total_tokens": 14922, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12015.76, + "sql_execution_elapsed_ms_total": 9.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e1ac612ddf349118d95e594939c97e7017804d71 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:56.208368+00:00", + "ended_at": "2026-05-19T16:05:59.425970+00:00", + "elapsed_ms": 3217.58, + "returncode": 1, + "prompt_metrics": { + "chars": 9278, + "bytes_utf8": 9278, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5a076a10a374bf028ddfd937fb8ce62f5e14d182 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:00.429207+00:00", + "ended_at": "2026-05-19T16:06:09.227438+00:00", + "elapsed_ms": 8798.18, + "prompt_metrics": { + "chars": 9278, + "bytes_utf8": 9278, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 669, + "bytes_utf8": 669, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 316, + "bytes_utf8": 316, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14656, + "cached_input_tokens": 12032, + "output_tokens": 266, + "reasoning_output_tokens": 179 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3d0936bc45f11bd4f2067e7f5ee6699ced244cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=experience.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "experience", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3d0936bc45f11bd4f2067e7f5ee6699ced244cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=experience.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "experience", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7e84f27a290c1e1c9bfd170bdda91ee8e3c48923 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-9874-78b2-8359-0d89fac7bbc4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ff5c439c06ecf93717dcfdc8fb5f1a3ddbdc350 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-9874-78b2-8359-0d89fac7bbc4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f372fa1b97e9750bb0229ba07a92481d269934ca --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-a935-7c81-b7bd-88cbf667103f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"experience\\\",\\n COUNT(*) AS support\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\"\\nORDER BY support ASC, \\\"experience\\\"\\nLIMIT 11;\",\"notes\":\"Uses the provided Low-Support Group Count template with group_col bound to \\\"experience\\\" and top_k=11.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14656,"cached_input_tokens":12032,"output_tokens":266,"reasoning_output_tokens":179}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..58ec9a9d2f8132630225a13787ede9bf7baa90e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"experience\",\n COUNT(*) AS support\nFROM \"m9\"\nGROUP BY \"experience\"\nORDER BY support ASC, \"experience\"\nLIMIT 11;","notes":"Uses the provided Low-Support Group Count template with group_col bound to \"experience\" and top_k=11."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2bd920d79f7b2d74/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b0b644e09bc437a37f2faf51b9c22e325adbeddc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:26.238093+00:00", + "ended_at": "2026-05-19T16:08:29.226900+00:00", + "elapsed_ms": 2988.78, + "returncode": 1, + "prompt_metrics": { + "chars": 9381, + "bytes_utf8": 9381, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5c42e78db276541f01e69140b4fca3031aaf5aa5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:30.228453+00:00", + "ended_at": "2026-05-19T16:08:33.106840+00:00", + "elapsed_ms": 2878.35, + "returncode": 1, + "prompt_metrics": { + "chars": 9381, + "bytes_utf8": 9381, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b09939cf2ee05dfee776c32214049ede9fb8117 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b09939cf2ee05dfee776c32214049ede9fb8117 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..af5cc2a9537d691374ebdfa6353c95c583248429 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-e27b-7503-8a2b-74cf8c9e4cd7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..938a1ad4b3d44c6dc23c14fe026a1e7dfb133fae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-e27b-7503-8a2b-74cf8c9e4cd7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9774f26fbe37c0cc33c0166cf4ed2d57fcf0790e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-f20e-7bd2-b542-b4174731cefb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..51a16cb7ca1e9059bc488444b9471aa5a6635cc9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-f20e-7bd2-b542-b4174731cefb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2de055a3833dfc2b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..223af01f6cc6e8d28275a6465229302038101844 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9855, "bytes_utf8": 9855, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 690, "bytes_utf8": 690, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14782, "cached_input_tokens": 13696, "output_tokens": 650, "reasoning_output_tokens": 455}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8e77756b318a0ad684c0e35d6c95a59d22624a4d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_2fb04cf42eeead01", + "api_calls": 0, + "input_tokens": 14782, + "cached_input_tokens": 13696, + "output_tokens": 650, + "total_tokens": 15432, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15884.75, + "sql_execution_elapsed_ms_total": 8.35, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3f2781cdab04adcfaf3439cfc100d6b9b129238c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:53.492452+00:00", + "ended_at": "2026-05-19T15:50:09.377235+00:00", + "elapsed_ms": 15884.75, + "prompt_metrics": { + "chars": 9855, + "bytes_utf8": 9855, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1110, + "bytes_utf8": 1110, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 690, + "bytes_utf8": 690, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14782, + "cached_input_tokens": 13696, + "output_tokens": 650, + "reasoning_output_tokens": 455 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..25150253bb92899e35cc1941da8d5f6bb4e2c53c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..69c628bf9ccf2f4504221e27eb9455b5121d4731 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-e81b-7e23-963f-9b790d387d4a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"company_type\\\", SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"company_type\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"company_type\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the required template with group_col=\\\"company_type\\\" and measure_col=\\\"training_hours\\\". CAST to REAL is applied because \\\"training_hours\\\" is stored as TEXT in the SQLite schema snapshot.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14782,"cached_input_tokens":13696,"output_tokens":650,"reasoning_output_tokens":455}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c325ca130ea29529cbe87485cfa71a38afd7a68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"company_type\", SUM(CAST(\"training_hours\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n GROUP BY \"company_type\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"company_type\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the required template with group_col=\"company_type\" and measure_col=\"training_hours\". CAST to REAL is applied because \"training_hours\" is stored as TEXT in the SQLite schema snapshot."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_2fb04cf42eeead01/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c476533b8bdc8a51b2ef9c9f582dc3883b7e167b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:20.606443+00:00", + "ended_at": "2026-05-19T15:57:23.734748+00:00", + "elapsed_ms": 3128.28, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..855843e74f1fff8f8c8ce7e2afc538d820e8d4af --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:24.737417+00:00", + "ended_at": "2026-05-19T15:57:27.917927+00:00", + "elapsed_ms": 3180.46, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..87b1cc2c69153706fbf99aef59b6505c13a8d737 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..87b1cc2c69153706fbf99aef59b6505c13a8d737 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..47073bddf0d5d28da93cfe4bbe7cc5171cf4f059 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ba5b-7d53-81f3-f512cbf47eca"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4f2dc62782850ef9b31c6444ff6aab487cdf06f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ba5b-7d53-81f3-f512cbf47eca"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e7dfdb9e0467dc1def5dbb5cdb7af5a04d89b0c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ca9f-7120-a4c6-a94a63c1f547"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b556f1517edbdd948c6e2243af015ed0b686493a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-ca9f-7120-a4c6-a94a63c1f547"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3118ae6c80eb666b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ce5ac9541ec3de36bf2d1e3b23bc7f9824521851 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9849, "bytes_utf8": 9849, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 695, "bytes_utf8": 695, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14784, "cached_input_tokens": 13696, "output_tokens": 929, "reasoning_output_tokens": 732}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fbbbcc6e4400050da4c515499e1d5834fa509847 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_335ae5b066dd48bd", + "api_calls": 0, + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 929, + "total_tokens": 15713, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16756.66, + "sql_execution_elapsed_ms_total": 15.5, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a0f9484890c8fc2c9dc5a37a4a6ea273b52ac96c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:48:54.229580+00:00", + "ended_at": "2026-05-19T15:49:10.986279+00:00", + "elapsed_ms": 16756.66, + "prompt_metrics": { + "chars": 9849, + "bytes_utf8": 9849, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1115, + "bytes_utf8": 1115, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 695, + "bytes_utf8": 695, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 929, + "reasoning_output_tokens": 732 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c24a861ea3edf485bdfda8bd41d29a0d4a4be5d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..be98a5343ab5f92cc690c7783af91ae0d8546cc0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-0070-7383-bc2a-3f019333019b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"experience\\\",\\n COUNT(\\\"enrollee_id\\\") AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"experience\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"experience\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned relative-to-total threshold template on \\\"experience\\\". For count_distribution semantics, used COUNT(\\\"enrollee_id\\\") as the group measure instead of summing the identifier-like field.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14784,"cached_input_tokens":13696,"output_tokens":929,"reasoning_output_tokens":732}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3f2c564e5ddd79fe5e993a97e1e8a95cf5c2117e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"experience\",\n COUNT(\"enrollee_id\") AS \"group_value\"\n FROM \"m9\"\n GROUP BY \"experience\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"experience\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Applied the planned relative-to-total threshold template on \"experience\". For count_distribution semantics, used COUNT(\"enrollee_id\") as the group measure instead of summing the identifier-like field."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_335ae5b066dd48bd/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..15048c4863b64d251c22a9bf1a2fa89b15d99ef9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9462, "bytes_utf8": 9462, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 614, "bytes_utf8": 614, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14703, "cached_input_tokens": 12032, "output_tokens": 434, "reasoning_output_tokens": 258}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..13f1c45712d18320b29715c4233b153e1a1d070e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_38a7d957278baec1", + "api_calls": 0, + "input_tokens": 14703, + "cached_input_tokens": 12032, + "output_tokens": 434, + "total_tokens": 15137, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11678.49, + "sql_execution_elapsed_ms_total": 43.81, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f64dea8b1278a4ee340e172473e511f5111f42a7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:03.608103+00:00", + "ended_at": "2026-05-19T15:44:15.286622+00:00", + "elapsed_ms": 11678.49, + "prompt_metrics": { + "chars": 9462, + "bytes_utf8": 9462, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 987, + "bytes_utf8": 987, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 614, + "bytes_utf8": 614, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14703, + "cached_input_tokens": 12032, + "output_tokens": 434, + "reasoning_output_tokens": 258 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..894556ff8d7c82c08aba380b38a2bd9355148d1e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "enrollee_id", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..163011d9705cbeb6aa602bd32efdc361cf6cc40b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e8-912b-7033-8556-100c8f0fc81a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT\\n \\\"enrollee_id\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC) AS tail_bucket\\n FROM \\\"m9\\\"\\n WHERE \\\"enrollee_id\\\" IS NOT NULL AND \\\"enrollee_id\\\" <> ''\\n)\\nSELECT \\\"enrollee_id\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"enrollee_id\\\" and num_tiles=10. Cast to INTEGER for correct descending numeric tail slicing because the schema stores \\\"enrollee_id\\\" as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14703,"cached_input_tokens":12032,"output_tokens":434,"reasoning_output_tokens":258}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5f02e03b9dbae97f6b5dd05b9f9fb3901dfaae2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT\n \"enrollee_id\",\n NTILE(10) OVER (ORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC) AS tail_bucket\n FROM \"m9\"\n WHERE \"enrollee_id\" IS NOT NULL AND \"enrollee_id\" <> ''\n)\nSELECT \"enrollee_id\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"enrollee_id\" and num_tiles=10. Cast to INTEGER for correct descending numeric tail slicing because the schema stores \"enrollee_id\" as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_38a7d957278baec1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2fdcb208b37484743242830c376ae94134282713 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9540, "bytes_utf8": 9540, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9540, "bytes_utf8": 9540, "lines": 267, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 394, "bytes_utf8": 394, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14704, "cached_input_tokens": 12032, "output_tokens": 377, "reasoning_output_tokens": 273}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4f388a9a917415070b684ca46ddf81783bb5d164 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_3cc6043271b19bf1", + "api_calls": 0, + "input_tokens": 14704, + "cached_input_tokens": 12032, + "output_tokens": 377, + "total_tokens": 15081, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12712.29, + "sql_execution_elapsed_ms_total": 10.05, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cae4a6c6fc3dc6237a0bb62ae1528d7888839f44 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:10.625045+00:00", + "ended_at": "2026-05-19T16:02:13.634360+00:00", + "elapsed_ms": 3009.29, + "returncode": 1, + "prompt_metrics": { + "chars": 9540, + "bytes_utf8": 9540, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c2c7df4669ee78ead400d55295c08465f292cfff --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:14.637176+00:00", + "ended_at": "2026-05-19T16:02:24.340228+00:00", + "elapsed_ms": 9703.0, + "prompt_metrics": { + "chars": 9540, + "bytes_utf8": 9540, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 749, + "bytes_utf8": 749, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 394, + "bytes_utf8": 394, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14704, + "cached_input_tokens": 12032, + "output_tokens": 377, + "reasoning_output_tokens": 273 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b04ba1c300ce4a446cdb91655a4f8c5212ee4a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=company_type, condition_col=gender.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "company_type", + "condition_col": "gender", + "condition_value": "Male", + "positive_value": "Male", + "negative_value": "", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b04ba1c300ce4a446cdb91655a4f8c5212ee4a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=company_type, condition_col=gender.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "company_type", + "condition_col": "gender", + "condition_value": "Male", + "positive_value": "Male", + "negative_value": "", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..06c52dbb24d556ab298cd991821f7ed112864798 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-2769-71f3-86f8-e97145c6951e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4271749c2165daec3c51677c07ef0caa41112760 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-2769-71f3-86f8-e97145c6951e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b7bc3bf6b3385fd2505ae083896be92b2edb47b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-36f6-79c2-baf7-922de16f6597"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"company_type\\\",\\n AVG(CASE WHEN \\\"gender\\\" = 'Male' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_type\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Computes the within-group proportion of rows where \\\"gender\\\" is 'Male' for each \\\"company_type\\\", following the provided Grouped Condition Rate template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14704,"cached_input_tokens":12032,"output_tokens":377,"reasoning_output_tokens":273}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff489a05d4070c5ce79e29d22cb28ffab6fdda6a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"company_type\",\n AVG(CASE WHEN \"gender\" = 'Male' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m9\"\nGROUP BY \"company_type\"\nORDER BY condition_rate DESC;","notes":"Computes the within-group proportion of rows where \"gender\" is 'Male' for each \"company_type\", following the provided Grouped Condition Rate template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cc6043271b19bf1/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a1d639e27b961a537d22653378a9e321a8db5e7e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10114, "bytes_utf8": 10114, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 726, "bytes_utf8": 726, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14863, "cached_input_tokens": 13696, "output_tokens": 718, "reasoning_output_tokens": 501}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5da654e2f9facdec8d71e404620cfea6241be0cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_3cf01897659feb3f", + "api_calls": 0, + "input_tokens": 14863, + "cached_input_tokens": 13696, + "output_tokens": 718, + "total_tokens": 15581, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14377.96, + "sql_execution_elapsed_ms_total": 12.88, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c5869a3db997376b105294c6890fd2a0ff856ce8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:38.182248+00:00", + "ended_at": "2026-05-19T15:40:52.560242+00:00", + "elapsed_ms": 14377.96, + "prompt_metrics": { + "chars": 10114, + "bytes_utf8": 10114, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1121, + "bytes_utf8": 1121, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 726, + "bytes_utf8": 726, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14863, + "cached_input_tokens": 13696, + "output_tokens": 718, + "reasoning_output_tokens": 501 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc7c8dab35892460f987dfafd637eb9a4e1c8598 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=enrolled_university, condition_col=target.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "enrolled_university", + "condition_col": "target", + "condition_value": "0.0", + "positive_value": "0.0", + "negative_value": "1.0", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0d2a90cf8a1da2b7621f44f53942d936d576565b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-6ec8-7d90-92a1-a6bb3191b4d2"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"enrolled_university\\\",\\n SUM(CASE WHEN \\\"target\\\" = '0.0' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"target\\\" = '1.0' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"enrolled_university\\\"\\n)\\nSELECT\\n \\\"enrolled_university\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Computes the ratio of rows with target='0.0' to rows with target='1.0' for each enrolled_university group. Groups with zero target='1.0' rows return NULL for the ratio.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14863,"cached_input_tokens":13696,"output_tokens":718,"reasoning_output_tokens":501}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6acee3135f67bb7aefba9b0867576cc9695555f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n SELECT\n \"enrolled_university\",\n SUM(CASE WHEN \"target\" = '0.0' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"target\" = '1.0' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m9\"\n GROUP BY \"enrolled_university\"\n)\nSELECT\n \"enrolled_university\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;","notes":"Computes the ratio of rows with target='0.0' to rows with target='1.0' for each enrolled_university group. Groups with zero target='1.0' rows return NULL for the ratio."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3cf01897659feb3f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f67dfd356689f66523ba288017277365e2d6dbf9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9577, "bytes_utf8": 9577, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 373, "bytes_utf8": 373, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14711, "cached_input_tokens": 12032, "output_tokens": 348, "reasoning_output_tokens": 246}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6279c95d34444ea7375a88112db3e2fc11565106 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_3f5d3394bd5c4a6c", + "api_calls": 0, + "input_tokens": 14711, + "cached_input_tokens": 12032, + "output_tokens": 348, + "total_tokens": 15059, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8525.97, + "sql_execution_elapsed_ms_total": 8.34, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..26bb342a79084db43fbe7d891abe31da94992da0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:22.502088+00:00", + "ended_at": "2026-05-19T16:00:31.028100+00:00", + "elapsed_ms": 8525.97, + "prompt_metrics": { + "chars": 9577, + "bytes_utf8": 9577, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 728, + "bytes_utf8": 728, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 373, + "bytes_utf8": 373, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14711, + "cached_input_tokens": 12032, + "output_tokens": 348, + "reasoning_output_tokens": 246 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6bbd314684d84bccb59f90212ccd8dd02b87a17c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=enrolled_university, condition_col=education_level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "enrolled_university", + "condition_col": "education_level", + "condition_value": "Graduate", + "positive_value": "Graduate", + "negative_value": "Masters", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbf029c7c68f3d5353eb7cc22ae4475d92c0e3a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-80fa-7032-b8d6-47b024a48553"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"enrolled_university\\\",\\n AVG(CASE WHEN \\\"education_level\\\" = 'Graduate' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Computes the rate of rows with education_level = 'Graduate' within each enrolled_university group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14711,"cached_input_tokens":12032,"output_tokens":348,"reasoning_output_tokens":246}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f5a1fab17923e492279a119791178d7b739c5722 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"enrolled_university\",\n AVG(CASE WHEN \"education_level\" = 'Graduate' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY \"condition_rate\" DESC;","notes":"Computes the rate of rows with education_level = 'Graduate' within each enrolled_university group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_3f5d3394bd5c4a6c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bda493e70d92ce5ec48206c3cce8b449b47d0ae4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9319, "bytes_utf8": 9319, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 463, "bytes_utf8": 463, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14650, "cached_input_tokens": 13696, "output_tokens": 391, "reasoning_output_tokens": 270}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3b80717848a2cd1bf94341ca2abed0113524b6f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4088565feebc9016", + "api_calls": 0, + "input_tokens": 14650, + "cached_input_tokens": 13696, + "output_tokens": 391, + "total_tokens": 15041, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9969.74, + "sql_execution_elapsed_ms_total": 11.62, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d03ae6bcb07be7ffae35d945c62bef27e3a7baa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:31:57.920895+00:00", + "ended_at": "2026-05-19T15:32:07.890671+00:00", + "elapsed_ms": 9969.74, + "prompt_metrics": { + "chars": 9319, + "bytes_utf8": 9319, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 827, + "bytes_utf8": 827, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 463, + "bytes_utf8": 463, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14650, + "cached_input_tokens": 13696, + "output_tokens": 391, + "reasoning_output_tokens": 270 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee06d04284ad073e778bdbe242e9519bd0380ca1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1e1c53e405b25642f12fb5bad7ed57922eb40a58 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-7e8f-7411-a9ac-0ea93a444645"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n \\\"company_size\\\",\\n SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_size\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the planned grouped numeric sum template with group_col=\\\"company_size\\\" and measure_col=\\\"city_development_index\\\". Casts the text-valued measure to REAL and treats empty strings as NULL so SQLite can sum it correctly.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14650,"cached_input_tokens":13696,"output_tokens":391,"reasoning_output_tokens":270}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5319d4f1d68efbf39335be4329902624298f94ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT\n \"company_size\",\n SUM(CAST(NULLIF(\"city_development_index\", '') AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"company_size\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the planned grouped numeric sum template with group_col=\"company_size\" and measure_col=\"city_development_index\". Casts the text-valued measure to REAL and treats empty strings as NULL so SQLite can sum it correctly."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4088565feebc9016/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6ccc67eaebf25bc5508b6c6f622abae5dad246a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9461, "bytes_utf8": 9461, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 478, "bytes_utf8": 478, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14703, "cached_input_tokens": 13696, "output_tokens": 550, "reasoning_output_tokens": 408}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5da838e4b043c8ce70fe7596bd8502eb8bc675e2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_41350f98b5babc7a", + "api_calls": 0, + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 550, + "total_tokens": 15253, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11527.29, + "sql_execution_elapsed_ms_total": 25.8, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f1e0268f9c5553fe33e4e52b252c8407878fda5f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:43.804289+00:00", + "ended_at": "2026-05-19T15:44:55.331609+00:00", + "elapsed_ms": 11527.29, + "prompt_metrics": { + "chars": 9461, + "bytes_utf8": 9461, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 841, + "bytes_utf8": 841, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 478, + "bytes_utf8": 478, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 550, + "reasoning_output_tokens": 408 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf3f67088990e8130d1af35536d4727afdd8b158 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f76e3abd1198e983a7aca65db0148cc2ba749257 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-2e1b-7752-9486-581b99525f02"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT \\\"enrollee_id\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC) AS tail_bucket\\n FROM \\\"m9\\\"\\n)\\nSELECT \\\"enrollee_id\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with 10 tiles on \\\"m9\\\" and used a numeric cast for \\\"enrollee_id\\\" because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14703,"cached_input_tokens":13696,"output_tokens":550,"reasoning_output_tokens":408}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..16609fd6b86b85d5140c7c942a79a78e7b3f73a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT \"enrollee_id\",\n NTILE(10) OVER (ORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC) AS tail_bucket\n FROM \"m9\"\n)\nSELECT \"enrollee_id\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC;","notes":"Applied the Quantile Tail Slice template with 10 tiles on \"m9\" and used a numeric cast for \"enrollee_id\" because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_41350f98b5babc7a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..685ef2539c9965d40109047f5472260bbf56f562 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9479, "bytes_utf8": 9479, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 744, "bytes_utf8": 744, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14703, "cached_input_tokens": 12032, "output_tokens": 505, "reasoning_output_tokens": 312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..de62c332ac975170322842837c73c4fcbd02caf1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4648f676e11eb886", + "api_calls": 0, + "input_tokens": 14703, + "cached_input_tokens": 12032, + "output_tokens": 505, + "total_tokens": 15208, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13979.34, + "sql_execution_elapsed_ms_total": 34.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..09e23f905421326348c67610c5c31f029f097106 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:35.409500+00:00", + "ended_at": "2026-05-19T15:45:49.388860+00:00", + "elapsed_ms": 13979.34, + "prompt_metrics": { + "chars": 9479, + "bytes_utf8": 9479, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1126, + "bytes_utf8": 1126, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 744, + "bytes_utf8": 744, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14703, + "cached_input_tokens": 12032, + "output_tokens": 505, + "reasoning_output_tokens": 312 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e0f9503a28cb6bb07a0d990313f700939f2668c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "city_development_index", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..67f44b5ef8d9834f29854148f9cf9ba472fb880a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-f7bb-7e03-9e31-d6afd56349cf"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"city_development_index\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"city_development_index\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n)\\nSELECT \\\"city_development_index\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"city_development_index\\\" AS REAL) DESC;\",\"notes\":\"Used the requested Quantile Tail Slice template with measure_col bound to city_development_index and num_tiles=10. Cast city_development_index to REAL for correct numeric ordering because the SQLite column is stored as TEXT, and excluded blank values from bucket assignment.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14703,"cached_input_tokens":12032,"output_tokens":505,"reasoning_output_tokens":312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b4ee96627b26003d4b1b4fa9fbe6b9ea220081f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n \"city_development_index\",\n NTILE(10) OVER (ORDER BY CAST(\"city_development_index\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"m9\"\n WHERE \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n)\nSELECT \"city_development_index\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY CAST(\"city_development_index\" AS REAL) DESC;","notes":"Used the requested Quantile Tail Slice template with measure_col bound to city_development_index and num_tiles=10. Cast city_development_index to REAL for correct numeric ordering because the SQLite column is stored as TEXT, and excluded blank values from bucket assignment."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4648f676e11eb886/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1cc92df644c5640559395c2a6d3eacd4b7e10104 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9864, "bytes_utf8": 9864, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 846, "bytes_utf8": 846, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14786, "cached_input_tokens": 13696, "output_tokens": 665, "reasoning_output_tokens": 435}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..326abbe5204daf2c74c5b0eb1141a298f4dacd81 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4706c75934b09268", + "api_calls": 0, + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 665, + "total_tokens": 15451, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15737.49, + "sql_execution_elapsed_ms_total": 15.44, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e43d61db41a466ba9a8a3e1b841e30546e5e80bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:48:22.399765+00:00", + "ended_at": "2026-05-19T15:48:38.137292+00:00", + "elapsed_ms": 15737.49, + "prompt_metrics": { + "chars": 9864, + "bytes_utf8": 9864, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1257, + "bytes_utf8": 1257, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 846, + "bytes_utf8": 846, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 665, + "reasoning_output_tokens": 435 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f45d6d7fae687c258b4e6dcdabfaf473531ed131 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9444ebe56418327f7cdb757cf662665988894f33 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ec-841d-7fd0-9a3a-360dca7fb7c9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"major_discipline\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"major_discipline\\\" IS NOT NULL\\n AND \\\"major_discipline\\\" <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND \\\"training_hours\\\" <> ''\\n GROUP BY \\\"major_discipline\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT\\n g.\\\"major_discipline\\\",\\n g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned threshold template with group_col=major_discipline and measure_col=training_hours, casting the TEXT measure to REAL and filtering blank/null values for a stable grouped view.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14786,"cached_input_tokens":13696,"output_tokens":665,"reasoning_output_tokens":435}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..32f919e1a9cd07141b7d005b9b69dad17d5edc45 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT\n \"major_discipline\",\n SUM(CAST(\"training_hours\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE \"major_discipline\" IS NOT NULL\n AND \"major_discipline\" <> ''\n AND \"training_hours\" IS NOT NULL\n AND \"training_hours\" <> ''\n GROUP BY \"major_discipline\"\n), total AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM grouped\n)\nSELECT\n g.\"major_discipline\",\n g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the planned threshold template with group_col=major_discipline and measure_col=training_hours, casting the TEXT measure to REAL and filtering blank/null values for a stable grouped view."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4706c75934b09268/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dca743cb272657d98c66a8acfb18ed0e84437c45 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9474, "bytes_utf8": 9474, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1979, "bytes_utf8": 1979, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14686, "cached_input_tokens": 12032, "output_tokens": 2407, "reasoning_output_tokens": 1746}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a1ead591aa78d0ae29d225cef97a6ea5bfcbb2f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4c8980cb676340ca", + "api_calls": 0, + "input_tokens": 14686, + "cached_input_tokens": 12032, + "output_tokens": 2407, + "total_tokens": 17093, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 40264.34, + "sql_execution_elapsed_ms_total": 68.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..749f1613a2147ed4e777a9feba1f81367b4c4c1f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:01.235327+00:00", + "ended_at": "2026-05-19T15:56:41.499697+00:00", + "elapsed_ms": 40264.34, + "prompt_metrics": { + "chars": 9474, + "bytes_utf8": 9474, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2672, + "bytes_utf8": 2672, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1979, + "bytes_utf8": 1979, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14686, + "cached_input_tokens": 12032, + "output_tokens": 2407, + "reasoning_output_tokens": 1746 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d5f1b78d9f6a95961370352a1cf3d99fe6eb63d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..edd4e61951055005c7212c9524642f7fc3719333 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-8466-7643-8516-4a04d2c69675"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"experience\\\",\\n CAST(\\\"enrollee_id\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"experience\\\"\\n ORDER BY CAST(\\\"enrollee_id\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"experience\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"experience\\\" IS NOT NULL\\n AND \\\"experience\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n),\\n\\\"bounds\\\" AS (\\n SELECT DISTINCT\\n \\\"experience\\\",\\n (1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS \\\"rank_pos\\\",\\n CAST((1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) AS \\\"lower_rn\\\",\\n CASE\\n WHEN (1.0 + (0.95 * (\\\"cnt\\\" - 1))) = CAST((1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER)\\n THEN CAST((1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER)\\n ELSE CAST((1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) + 1\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ranked\\\"\\n),\\n\\\"points\\\" AS (\\n SELECT\\n \\\"b\\\".\\\"experience\\\",\\n \\\"b\\\".\\\"lower_rn\\\",\\n \\\"b\\\".\\\"upper_rn\\\",\\n (\\\"b\\\".\\\"rank_pos\\\" - \\\"b\\\".\\\"lower_rn\\\") AS \\\"frac\\\",\\n \\\"lr\\\".\\\"measure_value\\\" AS \\\"lower_value\\\",\\n \\\"ur\\\".\\\"measure_value\\\" AS \\\"upper_value\\\"\\n FROM \\\"bounds\\\" AS \\\"b\\\"\\n JOIN \\\"ranked\\\" AS \\\"lr\\\"\\n ON \\\"lr\\\".\\\"experience\\\" = \\\"b\\\".\\\"experience\\\"\\n AND \\\"lr\\\".\\\"rn\\\" = \\\"b\\\".\\\"lower_rn\\\"\\n JOIN \\\"ranked\\\" AS \\\"ur\\\"\\n ON \\\"ur\\\".\\\"experience\\\" = \\\"b\\\".\\\"experience\\\"\\n AND \\\"ur\\\".\\\"rn\\\" = \\\"b\\\".\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"experience\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_value\\\"\\n ELSE \\\"lower_value\\\" + \\\"frac\\\" * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"points\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite-compatible emulation of PERCENTILE_CONT(0.95) by experience using window functions and linear interpolation; casts \\\"enrollee_id\\\" to REAL and excludes blank/null group or measure values.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14686,"cached_input_tokens":12032,"output_tokens":2407,"reasoning_output_tokens":1746}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9fb4f1fd2c0426a3633655db61f47b69be0bb1bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"experience\",\n CAST(\"enrollee_id\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"experience\"\n ORDER BY CAST(\"enrollee_id\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"experience\"\n ) AS \"cnt\"\n FROM \"m9\"\n WHERE \"experience\" IS NOT NULL\n AND \"experience\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n),\n\"bounds\" AS (\n SELECT DISTINCT\n \"experience\",\n (1.0 + (0.95 * (\"cnt\" - 1))) AS \"rank_pos\",\n CAST((1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER) AS \"lower_rn\",\n CASE\n WHEN (1.0 + (0.95 * (\"cnt\" - 1))) = CAST((1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER)\n THEN CAST((1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER)\n ELSE CAST((1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER) + 1\n END AS \"upper_rn\"\n FROM \"ranked\"\n),\n\"points\" AS (\n SELECT\n \"b\".\"experience\",\n \"b\".\"lower_rn\",\n \"b\".\"upper_rn\",\n (\"b\".\"rank_pos\" - \"b\".\"lower_rn\") AS \"frac\",\n \"lr\".\"measure_value\" AS \"lower_value\",\n \"ur\".\"measure_value\" AS \"upper_value\"\n FROM \"bounds\" AS \"b\"\n JOIN \"ranked\" AS \"lr\"\n ON \"lr\".\"experience\" = \"b\".\"experience\"\n AND \"lr\".\"rn\" = \"b\".\"lower_rn\"\n JOIN \"ranked\" AS \"ur\"\n ON \"ur\".\"experience\" = \"b\".\"experience\"\n AND \"ur\".\"rn\" = \"b\".\"upper_rn\"\n)\nSELECT\n \"experience\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_value\"\n ELSE \"lower_value\" + \"frac\" * (\"upper_value\" - \"lower_value\")\n END AS \"percentile_measure\"\nFROM \"points\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite-compatible emulation of PERCENTILE_CONT(0.95) by experience using window functions and linear interpolation; casts \"enrollee_id\" to REAL and excludes blank/null group or measure values."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4c8980cb676340ca/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..52d3fe7baa271ec110c3ce5836d68499bb55bb9a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9717, "bytes_utf8": 9717, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 870, "bytes_utf8": 870, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14768, "cached_input_tokens": 13696, "output_tokens": 916, "reasoning_output_tokens": 668}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7d458b0bcebfaa7e2471e25e6006af7947d02073 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4e6a7a50e53f1c56", + "api_calls": 0, + "input_tokens": 14768, + "cached_input_tokens": 13696, + "output_tokens": 916, + "total_tokens": 15684, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16753.32, + "sql_execution_elapsed_ms_total": 62.32, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9e4ae3f60bb70175ef02e76a5c84aa35d55c0ae8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:41.631911+00:00", + "ended_at": "2026-05-19T15:36:58.385263+00:00", + "elapsed_ms": 16753.32, + "prompt_metrics": { + "chars": 9717, + "bytes_utf8": 9717, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1298, + "bytes_utf8": 1298, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 870, + "bytes_utf8": 870, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14768, + "cached_input_tokens": 13696, + "output_tokens": 916, + "reasoning_output_tokens": 668 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6200e340e004c03de674cb12236733b23f43bbe4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "item_col": "enrollee_id", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..50370eee04e72690613940a65ab6e67a156c7ea3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-d297-7760-9025-3849538350c5"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"major_discipline\\\",\\n \\\"enrollee_id\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"training_hours\\\" AS REAL))) OVER (PARTITION BY \\\"major_discipline\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"major_discipline\\\" IS NOT NULL\\n AND \\\"major_discipline\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND \\\"training_hours\\\" <> ''\\nGROUP BY \\\"major_discipline\\\", \\\"enrollee_id\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"major_discipline\\\", item_col=\\\"enrollee_id\\\", and measure_col=\\\"training_hours\\\". Cast \\\"training_hours\\\" from TEXT to REAL for aggregation and excluded blank keys/measures.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14768,"cached_input_tokens":13696,"output_tokens":916,"reasoning_output_tokens":668}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b083c922e886a21764ec4029ce8df7c380f976f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"major_discipline\",\n \"enrollee_id\",\n SUM(CAST(\"training_hours\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"training_hours\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"training_hours\" AS REAL))) OVER (PARTITION BY \"major_discipline\") AS \"share_within_group\"\nFROM \"m9\"\nWHERE \"major_discipline\" IS NOT NULL\n AND \"major_discipline\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n AND \"training_hours\" IS NOT NULL\n AND \"training_hours\" <> ''\nGROUP BY \"major_discipline\", \"enrollee_id\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the provided template with group_col=\"major_discipline\", item_col=\"enrollee_id\", and measure_col=\"training_hours\". Cast \"training_hours\" from TEXT to REAL for aggregation and excluded blank keys/measures."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4e6a7a50e53f1c56/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..414b04e8d8b7c7bc960fc1198823b56d5b2cd95a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9482, "bytes_utf8": 9482, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 959, "bytes_utf8": 959, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14686, "cached_input_tokens": 12032, "output_tokens": 776, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0580471cb6fff856c5a1bfa736faf72005499b95 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_4fe32b6b75ad6eae", + "api_calls": 0, + "input_tokens": 14686, + "cached_input_tokens": 12032, + "output_tokens": 776, + "total_tokens": 15462, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14908.62, + "sql_execution_elapsed_ms_total": 54.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9aead2851051ff375e14e3b48af0129eb34ebdc1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:51:45.433346+00:00", + "ended_at": "2026-05-19T15:52:00.342027+00:00", + "elapsed_ms": 14908.62, + "prompt_metrics": { + "chars": 9482, + "bytes_utf8": 9482, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1391, + "bytes_utf8": 1391, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 959, + "bytes_utf8": 959, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14686, + "cached_input_tokens": 12032, + "output_tokens": 776, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c07cbfebeb50e179df2331373cd24f7799d2e4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0f54e21ae8c904db722082bd2e2cea6286295e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ef-9d4a-79d3-9b74-969b4675d630"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH ranked AS (\\n SELECT\\n \\\"gender\\\",\\n CAST(\\\"city_development_index\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"gender\\\"\\n ORDER BY CAST(\\\"city_development_index\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"gender\\\") AS \\\"cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"gender\\\" IS NOT NULL\\n AND \\\"gender\\\" <> ''\\n AND \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n)\\nSELECT\\n \\\"gender\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM ranked\\nWHERE CAST(\\\"rn\\\" AS REAL) / \\\"cnt\\\" >= 0.9\\nGROUP BY \\\"gender\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a grouped nearest-rank 90th percentile approximation with window functions and casts \\\"city_development_index\\\" from TEXT to REAL; empty/null gender and measure values are excluded.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14686,"cached_input_tokens":12032,"output_tokens":776,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c21edb9c64152941ca3037941ec1c5ba7a58d9b8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH ranked AS (\n SELECT\n \"gender\",\n CAST(\"city_development_index\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"gender\"\n ORDER BY CAST(\"city_development_index\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"gender\") AS \"cnt\"\n FROM \"m9\"\n WHERE \"gender\" IS NOT NULL\n AND \"gender\" <> ''\n AND \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n)\nSELECT\n \"gender\",\n MIN(\"measure_value\") AS \"percentile_measure\"\nFROM ranked\nWHERE CAST(\"rn\" AS REAL) / \"cnt\" >= 0.9\nGROUP BY \"gender\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a grouped nearest-rank 90th percentile approximation with window functions and casts \"city_development_index\" from TEXT to REAL; empty/null gender and measure values are excluded."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_4fe32b6b75ad6eae/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4e23ad134e4a8848bda92b351f8cf07a8cf625f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9724, "bytes_utf8": 9724, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 881, "bytes_utf8": 881, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14769, "cached_input_tokens": 13696, "output_tokens": 754, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..66174533c8a7efeedadd25ab285bda61335ba134 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_504cec37ca19abaa", + "api_calls": 0, + "input_tokens": 14769, + "cached_input_tokens": 13696, + "output_tokens": 754, + "total_tokens": 15523, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19163.3, + "sql_execution_elapsed_ms_total": 81.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ed4ca9f809a2859720d81a952bf057454cd2cde2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:58.452967+00:00", + "ended_at": "2026-05-19T15:37:17.616296+00:00", + "elapsed_ms": 19163.3, + "prompt_metrics": { + "chars": 9724, + "bytes_utf8": 9724, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1616, + "bytes_utf8": 1616, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 881, + "bytes_utf8": 881, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14769, + "cached_input_tokens": 13696, + "output_tokens": 754, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e10f87ed4eb5db97f86b425ac6f6281840905123 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "item_col": "enrollee_id", + "top_k": 19, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..797d373c11d923778f81fc1617e28e2520c037f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e2-1458-78c1-849a-d755cd92c6b9"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1988079, Requested 15404. Please try again in 104ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"major_discipline\\\",\\n \\\"enrollee_id\\\",\\n SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) AS total_measure,\\n SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) * 100.0\\n / SUM(SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL))) OVER (PARTITION BY \\\"major_discipline\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nWHERE NULLIF(\\\"major_discipline\\\", '') IS NOT NULL\\n AND NULLIF(\\\"enrollee_id\\\", '') IS NOT NULL\\n AND NULLIF(\\\"training_hours\\\", '') IS NOT NULL\\nGROUP BY \\\"major_discipline\\\", \\\"enrollee_id\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the requested template with group_col=\\\"major_discipline\\\", item_col=\\\"enrollee_id\\\", and measure_col=\\\"training_hours\\\". Since \\\"training_hours\\\" is stored as TEXT, the query casts non-empty values to REAL and filters out empty strings before aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14769,"cached_input_tokens":13696,"output_tokens":754,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..81fae6c2d82e03c324cc21a2e5b221f60654fc25 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"major_discipline\",\n \"enrollee_id\",\n SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) AS total_measure,\n SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) * 100.0\n / SUM(SUM(CAST(NULLIF(\"training_hours\", '') AS REAL))) OVER (PARTITION BY \"major_discipline\") AS share_within_group\nFROM \"m9\"\nWHERE NULLIF(\"major_discipline\", '') IS NOT NULL\n AND NULLIF(\"enrollee_id\", '') IS NOT NULL\n AND NULLIF(\"training_hours\", '') IS NOT NULL\nGROUP BY \"major_discipline\", \"enrollee_id\"\nORDER BY share_within_group DESC;","notes":"Uses the requested template with group_col=\"major_discipline\", item_col=\"enrollee_id\", and measure_col=\"training_hours\". Since \"training_hours\" is stored as TEXT, the query casts non-empty values to REAL and filters out empty strings before aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_504cec37ca19abaa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ceae65eeb6aac2a980e383be5cf4c4d4ddc6426d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9199, "bytes_utf8": 9199, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 283, "bytes_utf8": 283, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14618, "cached_input_tokens": 12032, "output_tokens": 225, "reasoning_output_tokens": 151}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..31c3bdc88a4b043171545b52a03d2594277a954f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_52930b19a0530d19", + "api_calls": 0, + "input_tokens": 14618, + "cached_input_tokens": 12032, + "output_tokens": 225, + "total_tokens": 14843, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7147.43, + "sql_execution_elapsed_ms_total": 9.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..46d88af146c46a96a60acbe85e1134ea0e139d99 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:27.514272+00:00", + "ended_at": "2026-05-19T15:33:34.661729+00:00", + "elapsed_ms": 7147.43, + "prompt_metrics": { + "chars": 9199, + "bytes_utf8": 9199, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 633, + "bytes_utf8": 633, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 283, + "bytes_utf8": 283, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14618, + "cached_input_tokens": 12032, + "output_tokens": 225, + "reasoning_output_tokens": 151 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c764513c0f5e06386765bf6840cc24afa340e884 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=experience.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "experience", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4af03673973990b0979d266636412b5e93fee762 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40de-dc70-71c0-b4ad-e108fa8d80dc"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"experience\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Applied the provided grouped-count template with group_col bound to \\\"experience\\\" on the single table \\\"m9\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14618,"cached_input_tokens":12032,"output_tokens":225,"reasoning_output_tokens":151}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..971eea01eae95189dd8608a19a2f7de221011586 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"experience\", COUNT(*) AS row_count\nFROM \"m9\"\nGROUP BY \"experience\"\nORDER BY row_count DESC;","notes":"Applied the provided grouped-count template with group_col bound to \"experience\" on the single table \"m9\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_52930b19a0530d19/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a3f6453eb558441ed0912996b3b7fbc11cac6521 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9216, "bytes_utf8": 9216, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 321, "bytes_utf8": 321, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14623, "cached_input_tokens": 12032, "output_tokens": 258, "reasoning_output_tokens": 174}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..be08bcc0f4a3b050fd89c5b79075aabb7296c79b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_54e2ae1c7a2dc453", + "api_calls": 0, + "input_tokens": 14623, + "cached_input_tokens": 12032, + "output_tokens": 258, + "total_tokens": 14881, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15512.93, + "sql_execution_elapsed_ms_total": 7.52, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0f22afb7c2d0e26337fcb13ef5798af0ec2a041c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:50.072606+00:00", + "ended_at": "2026-05-19T15:33:05.585561+00:00", + "elapsed_ms": 15512.93, + "prompt_metrics": { + "chars": 9216, + "bytes_utf8": 9216, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1364, + "bytes_utf8": 1364, + "lines": 6, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 321, + "bytes_utf8": 321, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14623, + "cached_input_tokens": 12032, + "output_tokens": 258, + "reasoning_output_tokens": 174 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1cba6f57b166c578ed7b6222be6967ca3a439d5a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=enrolled_university.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "enrolled_university", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..366d62719f086ab1a3a7d184be78a78cefc9112f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,6 @@ +{"type":"thread.started","thread_id":"019e40de-4a39-7a62-8d0c-87144e09f1a4"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15258. Please try again in 457ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Request too large for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Requested 15258. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"enrolled_university\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Grouped count by \\\"enrolled_university\\\" using the planned template; empty strings, if present, will appear as their own category.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14623,"cached_input_tokens":12032,"output_tokens":258,"reasoning_output_tokens":174}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bbcdf9e6d4a6dc5577204feb6d59ad768716f52c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"enrolled_university\", COUNT(*) AS row_count\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY row_count DESC;","notes":"Grouped count by \"enrolled_university\" using the planned template; empty strings, if present, will appear as their own category."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_54e2ae1c7a2dc453/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3b1d884f8a8aa49ec9ee4957a6c9ebf684932934 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9618, "bytes_utf8": 9618, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 395, "bytes_utf8": 395, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14737, "cached_input_tokens": 12032, "output_tokens": 331, "reasoning_output_tokens": 228}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4672522d957f7123fbf2eabc30df632af0cc320b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_559d61ebf9e1508e", + "api_calls": 0, + "input_tokens": 14737, + "cached_input_tokens": 12032, + "output_tokens": 331, + "total_tokens": 15068, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18644.09, + "sql_execution_elapsed_ms_total": 3.25, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9c0b6de64cd9a405133f36cd484dad84eb0965a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:48.478529+00:00", + "ended_at": "2026-05-19T15:43:07.122646+00:00", + "elapsed_ms": 18644.09, + "prompt_metrics": { + "chars": 9618, + "bytes_utf8": 9618, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1757, + "bytes_utf8": 1757, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 395, + "bytes_utf8": 395, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14737, + "cached_input_tokens": 12032, + "output_tokens": 331, + "reasoning_output_tokens": 228 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..da3b9761a22bdc7f5ec6bcf68861579fec09fb61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=company_type.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "experience", + "group_col_2": "company_type", + "predicate_col": "company_type", + "predicate_op": "=", + "predicate_value": "Public Sector", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b87d870c02c13fdd88298d2ca16b69cb5e2c8cd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40e7-6bb5-7533-90dc-77a76a38a945"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1991578, Requested 15372. Please try again in 208ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15372. Please try again in 461ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990764, Requested 15372. Please try again in 184ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"company_type\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nWHERE \\\"company_type\\\" = 'Public Sector'\\nGROUP BY \\\"experience\\\", \\\"company_type\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the planned template exactly with group_col=\\\"experience\\\", group_col_2=\\\"company_type\\\", and predicate company_type = 'Public Sector'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14737,"cached_input_tokens":12032,"output_tokens":331,"reasoning_output_tokens":228}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2103656a83536ee5367dff74d3b32a0e1257ae6b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"company_type\", COUNT(*) AS row_count\nFROM \"m9\"\nWHERE \"company_type\" = 'Public Sector'\nGROUP BY \"experience\", \"company_type\"\nORDER BY row_count DESC;","notes":"Uses the planned template exactly with group_col=\"experience\", group_col_2=\"company_type\", and predicate company_type = 'Public Sector'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_559d61ebf9e1508e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d67190463b58803b283ce6b89b41e91fc90acb44 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9296, "bytes_utf8": 9296, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9296, "bytes_utf8": 9296, "lines": 262, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 354, "bytes_utf8": 354, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14658, "cached_input_tokens": 13696, "output_tokens": 604, "reasoning_output_tokens": 500}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3c33ff3da25505c52dfaebab500d6953b62a395b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_59ffdbe430c482fa", + "api_calls": 0, + "input_tokens": 14658, + "cached_input_tokens": 13696, + "output_tokens": 604, + "total_tokens": 15262, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15199.53, + "sql_execution_elapsed_ms_total": 6.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b82e345653575f5febe05e5a514293bab1dbdad9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:55.936727+00:00", + "ended_at": "2026-05-19T16:04:58.771983+00:00", + "elapsed_ms": 2835.23, + "returncode": 1, + "prompt_metrics": { + "chars": 9296, + "bytes_utf8": 9296, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..22957d831c6b15c6b742853ccc89b13fba1f4fd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:59.774297+00:00", + "ended_at": "2026-05-19T16:05:12.138640+00:00", + "elapsed_ms": 12364.3, + "prompt_metrics": { + "chars": 9296, + "bytes_utf8": 9296, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 715, + "bytes_utf8": 715, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 354, + "bytes_utf8": 354, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14658, + "cached_input_tokens": 13696, + "output_tokens": 604, + "reasoning_output_tokens": 500 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0b935630a8a48a6900729c1f6113f5f94e64468 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=enrolled_university.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "enrolled_university", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0b935630a8a48a6900729c1f6113f5f94e64468 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=enrolled_university.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "enrolled_university", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0dac20fa0dfac0c576ca4b96935f6acfc09de9b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-ad07-76d2-a3e7-24a8b9d62f21"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d64bdace3e1cfce23946d4ac649e65940ab710f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-ad07-76d2-a3e7-24a8b9d62f21"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb43356adae2a1c726d3c67014eaa710980bc023 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-bbf6-7f03-83a2-2ece8f7b6fdf"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"enrolled_university\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY \\\"support\\\" ASC, \\\"enrolled_university\\\"\\nLIMIT 13;\",\"notes\":\"Uses the Low-Support Group Count template with group_col bound to \\\"enrolled_university\\\" and top_k set to 13.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14658,"cached_input_tokens":13696,"output_tokens":604,"reasoning_output_tokens":500}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b02eae16ec8f5b63978f7d5ed28d0e20ae58cfd1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"enrolled_university\",\n COUNT(*) AS \"support\"\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY \"support\" ASC, \"enrolled_university\"\nLIMIT 13;","notes":"Uses the Low-Support Group Count template with group_col bound to \"enrolled_university\" and top_k set to 13."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_59ffdbe430c482fa/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a54af7ef5cd57697bcbbcce4f6ff04580469bccc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9552, "bytes_utf8": 9552, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9552, "bytes_utf8": 9552, "lines": 267, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 414, "bytes_utf8": 414, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14708, "cached_input_tokens": 12032, "output_tokens": 535, "reasoning_output_tokens": 427}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d5558d85ebf9620f1749522b7af13926fa4d6b06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_5a6c150bef37dcbf", + "api_calls": 0, + "input_tokens": 14708, + "cached_input_tokens": 12032, + "output_tokens": 535, + "total_tokens": 15243, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16149.01, + "sql_execution_elapsed_ms_total": 8.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..26de3bc4e22aadb81e203a0a2747cf3c31586d80 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:12.878670+00:00", + "ended_at": "2026-05-19T16:01:15.981941+00:00", + "elapsed_ms": 3103.24, + "returncode": 1, + "prompt_metrics": { + "chars": 9552, + "bytes_utf8": 9552, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5e08e4f9f2fbb18429ebcfe8ac0856822b5ea646 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:16.984099+00:00", + "ended_at": "2026-05-19T16:01:30.029918+00:00", + "elapsed_ms": 13045.77, + "prompt_metrics": { + "chars": 9552, + "bytes_utf8": 9552, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 777, + "bytes_utf8": 777, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 414, + "bytes_utf8": 414, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14708, + "cached_input_tokens": 12032, + "output_tokens": 535, + "reasoning_output_tokens": 427 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b60bbc77e42f5d22939c9b74093c508cadbe5b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=major_discipline, condition_col=company_type.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "major_discipline", + "condition_col": "company_type", + "condition_value": "", + "positive_value": "Pvt Ltd", + "negative_value": "", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b60bbc77e42f5d22939c9b74093c508cadbe5b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=major_discipline, condition_col=company_type.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "major_discipline", + "condition_col": "company_type", + "condition_value": "", + "positive_value": "Pvt Ltd", + "negative_value": "", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..48673a6fd1e9a0ba466f6692eda8e3dcd0385fad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-45bc-7040-bd9e-6e205018aa95"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a15fc8523903e10789ce36744e4582b56b17e037 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-45bc-7040-bd9e-6e205018aa95"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc080b0e58900d806fac71435cbd261c57fb2986 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-55b6-70a3-b650-bf59ce115938"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"major_discipline\\\",\\n AVG(CASE WHEN \\\"company_type\\\" = '' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Computes the within-group proportion of rows with empty \\\"company_type\\\" for each \\\"major_discipline\\\", following the bound Grouped Condition Rate template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14708,"cached_input_tokens":12032,"output_tokens":535,"reasoning_output_tokens":427}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..06d0b8155e6f1fcef3da40e8ec3cb3bced6752d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"major_discipline\",\n AVG(CASE WHEN \"company_type\" = '' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY \"condition_rate\" DESC;","notes":"Computes the within-group proportion of rows with empty \"company_type\" for each \"major_discipline\", following the bound Grouped Condition Rate template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6c150bef37dcbf/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d320f95772fce197481abd1df6e7fb2c8d3f3ce0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:19.263450+00:00", + "ended_at": "2026-05-19T16:03:22.559870+00:00", + "elapsed_ms": 3296.39, + "returncode": 1, + "prompt_metrics": { + "chars": 9246, + "bytes_utf8": 9246, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..37b24cb056c07ca9ebd7781267ee88f2e7af3e44 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:23.562208+00:00", + "ended_at": "2026-05-19T16:03:26.973419+00:00", + "elapsed_ms": 3411.16, + "returncode": 1, + "prompt_metrics": { + "chars": 9246, + "bytes_utf8": 9246, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ef765a6e41e08e8605bd80f2ae00bc74528ba1e2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "city_development_index", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ef765a6e41e08e8605bd80f2ae00bc74528ba1e2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "city_development_index", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c04afe69898aaad7e8255320e20a22591255d1b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-3373-7ed0-b179-9ac8c76bd65c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..936b532020908aa6f265cb564978d59d9382d7c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-3373-7ed0-b179-9ac8c76bd65c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3b5774e765c0eec6ebf4f0ed0853a76876d03ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-4447-7171-8d1d-3528f0fc3544"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..90fc870296727b946d086b879a0f8a1b1d7cc8a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-4447-7171-8d1d-3528f0fc3544"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5a6f475ca820eda1/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3107d3efbd2e37b6123c7bc0f1729bac611e7b01 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9308, "bytes_utf8": 9308, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 393, "bytes_utf8": 393, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14648, "cached_input_tokens": 12032, "output_tokens": 516, "reasoning_output_tokens": 413}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d654e6cbdab48ebde200b0a68c3c10c514eb2ae7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_5b13630789372daa", + "api_calls": 0, + "input_tokens": 14648, + "cached_input_tokens": 12032, + "output_tokens": 516, + "total_tokens": 15164, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14566.39, + "sql_execution_elapsed_ms_total": 9.02, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9e0ce55ff3eadd6eab48407a5ea2fde4f5aa0465 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:32.481343+00:00", + "ended_at": "2026-05-19T15:30:47.047763+00:00", + "elapsed_ms": 14566.39, + "prompt_metrics": { + "chars": 9308, + "bytes_utf8": 9308, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 759, + "bytes_utf8": 759, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 393, + "bytes_utf8": 393, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14648, + "cached_input_tokens": 12032, + "output_tokens": 516, + "reasoning_output_tokens": 413 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9397884974da821b8f72074eaccb7eb6afae381a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..92ab0c797af7c11cf445661622bdfdc8d8688e11 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dc-30a9-7931-85e9-d0f9a7368b21"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"major_discipline\\\", SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Grouped numeric sum using \\\"major_discipline\\\" as the group column and \\\"training_hours\\\" as the measure; casts \\\"training_hours\\\" from TEXT to REAL for SQLite aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14648,"cached_input_tokens":12032,"output_tokens":516,"reasoning_output_tokens":413}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b7c1d507ba5cb5074ebeb2c637385f9313dbab0f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"major_discipline\", SUM(CAST(\"training_hours\" AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY \"total_measure\" DESC;","notes":"Grouped numeric sum using \"major_discipline\" as the group column and \"training_hours\" as the measure; casts \"training_hours\" from TEXT to REAL for SQLite aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5b13630789372daa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..26147e8b6ab7eb3a82b2197747353a468a8faa84 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:48.120487+00:00", + "ended_at": "2026-05-19T16:05:51.450306+00:00", + "elapsed_ms": 3329.79, + "returncode": 1, + "prompt_metrics": { + "chars": 9288, + "bytes_utf8": 9288, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7bdc556945f03916d62c3485038524263e6572d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:52.452527+00:00", + "ended_at": "2026-05-19T16:05:56.204737+00:00", + "elapsed_ms": 3752.17, + "returncode": 1, + "prompt_metrics": { + "chars": 9288, + "bytes_utf8": 9288, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6013ba556aa796cbb235002f2cafc75cea722157 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=major_discipline.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "major_discipline", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6013ba556aa796cbb235002f2cafc75cea722157 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=major_discipline.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "major_discipline", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..da7fc9ccdf3437530adff3a6e193dcf657ba0d04 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-78d0-76d0-911e-09c951f523a7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c025ec0f0b21901217389af3fca5c2432816ebdd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-78d0-76d0-911e-09c951f523a7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f3ad21b6011edf6842e94afba36daec0fb062486 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-89da-7a11-948b-aa43bf431262"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee66d809284ab7a1bc38c3cc5114e3403a6048ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-89da-7a11-948b-aa43bf431262"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_5d63791d94b36e53/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f24015901b0fd1ae21d01d2b7a1d9caf9f95af08 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9583, "bytes_utf8": 9583, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 438, "bytes_utf8": 438, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14712, "cached_input_tokens": 12032, "output_tokens": 404, "reasoning_output_tokens": 292}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7c8b4766228e94ee257703e2ca12d583b027e6cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_632ae20f9a8d55ae", + "api_calls": 0, + "input_tokens": 14712, + "cached_input_tokens": 12032, + "output_tokens": 404, + "total_tokens": 15116, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10340.89, + "sql_execution_elapsed_ms_total": 19.5, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..577beaca6bc0435f49a5d914ac4eac3050720710 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:31.040908+00:00", + "ended_at": "2026-05-19T16:00:41.381837+00:00", + "elapsed_ms": 10340.89, + "prompt_metrics": { + "chars": 9583, + "bytes_utf8": 9583, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 793, + "bytes_utf8": 793, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 438, + "bytes_utf8": 438, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14712, + "cached_input_tokens": 12032, + "output_tokens": 404, + "reasoning_output_tokens": 292 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c0b95ac00d20b5e25db8583734f6d6d7a79b2b31 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=enrolled_university, condition_col=education_level.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "enrolled_university", + "condition_col": "education_level", + "condition_value": "Masters", + "positive_value": "Graduate", + "negative_value": "Masters", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a9761be5fb28018eea89b790ff583f91ac1a354 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-a264-7732-94fe-eab59df969df"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"enrolled_university\\\",\\n AVG(CASE WHEN \\\"education_level\\\" = 'Masters' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the grouped condition rate template exactly as planned: for each enrolled_university group, computes the proportion of rows where education_level is 'Masters'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14712,"cached_input_tokens":12032,"output_tokens":404,"reasoning_output_tokens":292}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1135461e9f329278fb735caeb61ee2a5bfed54c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"enrolled_university\",\n AVG(CASE WHEN \"education_level\" = 'Masters' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the grouped condition rate template exactly as planned: for each enrolled_university group, computes the proportion of rows where education_level is 'Masters'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_632ae20f9a8d55ae/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..024d74f2f813ee228348586ac4175c9328199a0d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:24.606722+00:00", + "ended_at": "2026-05-19T16:05:27.822454+00:00", + "elapsed_ms": 3215.7, + "returncode": 1, + "prompt_metrics": { + "chars": 9284, + "bytes_utf8": 9284, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..8428579158ca49285252d8d18f70788c3ecd2c1d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-1d0d-7ca2-8dc8-fcb36f881db6"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6d398c56a4001f93/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1b85e12b6fb8fc534dec3e8c82e99e19c1b84a0e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9228, "bytes_utf8": 9228, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 335, "bytes_utf8": 335, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14639, "cached_input_tokens": 13696, "output_tokens": 254, "reasoning_output_tokens": 155}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7c6277ff0dc7dd5407a932d59916e76ba8dc982b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_6e7e6c4413c14105", + "api_calls": 0, + "input_tokens": 14639, + "cached_input_tokens": 13696, + "output_tokens": 254, + "total_tokens": 14893, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7897.55, + "sql_execution_elapsed_ms_total": 4.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..83711b6c1517e1c8f8ded9043a98e7103a75d63b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:31.905747+00:00", + "ended_at": "2026-05-19T16:02:39.803321+00:00", + "elapsed_ms": 7897.55, + "prompt_metrics": { + "chars": 9228, + "bytes_utf8": 9228, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 675, + "bytes_utf8": 675, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 335, + "bytes_utf8": 335, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14639, + "cached_input_tokens": 13696, + "output_tokens": 254, + "reasoning_output_tokens": 155 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e11d7d7d4c8bcf2e39ece72e55315d245eb8bfed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..46f879d23e462a29694fc610b9a5eb412efd2be4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-7a5f-7c52-9a67-8f94f9ee601e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"enrollee_id\\\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\\nFROM \\\"m9\\\";\",\"notes\":\"Used the required Threshold Rarity CDF template with measure_col bound to \\\"enrollee_id\\\" and cast it to REAL because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14639,"cached_input_tokens":13696,"output_tokens":254,"reasoning_output_tokens":155}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..60f169b2488368d22cfc08f75fec162834f8004f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"enrollee_id\" AS REAL) <= 25169.75 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM \"m9\";","notes":"Used the required Threshold Rarity CDF template with measure_col bound to \"enrollee_id\" and cast it to REAL because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e7e6c4413c14105/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6490a84347a58b51ba386b479070e85e22cb1190 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9589, "bytes_utf8": 9589, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 366, "bytes_utf8": 366, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14735, "cached_input_tokens": 12032, "output_tokens": 370, "reasoning_output_tokens": 268}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a367cc40a3431f08d4546ec53b62b1bd1b8ae544 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_6e80fcf66868f9be", + "api_calls": 0, + "input_tokens": 14735, + "cached_input_tokens": 12032, + "output_tokens": 370, + "total_tokens": 15105, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8917.93, + "sql_execution_elapsed_ms_total": 5.42, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1483f60fd71b19df3c29772cef7ffc1a03c81309 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:38.728887+00:00", + "ended_at": "2026-05-19T15:43:47.646851+00:00", + "elapsed_ms": 8917.93, + "prompt_metrics": { + "chars": 9589, + "bytes_utf8": 9589, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 733, + "bytes_utf8": 733, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 366, + "bytes_utf8": 366, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14735, + "cached_input_tokens": 12032, + "output_tokens": 370, + "reasoning_output_tokens": 268 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b935433ef0d31690959476543dd68447138a3688 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=target.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "experience", + "group_col_2": "target", + "predicate_col": "target", + "predicate_op": "=", + "predicate_value": "1.0", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..db4be3b336b4b9ace9b19156e118e1ff216a0a6d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e8-2fed-7361-a41d-fc2fb489b562"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"target\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nWHERE \\\"target\\\" = '1.0'\\nGROUP BY \\\"experience\\\", \\\"target\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the planned filtered two-dimensional group count template with \\\"experience\\\" and \\\"target\\\", filtering rows where \\\"target\\\" = '1.0'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14735,"cached_input_tokens":12032,"output_tokens":370,"reasoning_output_tokens":268}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..89ebb4457ecbc6846c96f284a1b4a84297013d0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"target\", COUNT(*) AS row_count\nFROM \"m9\"\nWHERE \"target\" = '1.0'\nGROUP BY \"experience\", \"target\"\nORDER BY row_count DESC;","notes":"Uses the planned filtered two-dimensional group count template with \"experience\" and \"target\", filtering rows where \"target\" = '1.0'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_6e80fcf66868f9be/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..277e1080d4bf7e6814236e97c7737bab7b9d7e2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9717, "bytes_utf8": 9717, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 624, "bytes_utf8": 624, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14771, "cached_input_tokens": 13696, "output_tokens": 696, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1430f95b0132710800ad620ba768a4b109bd8c03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_7294117e92cfa6f4", + "api_calls": 0, + "input_tokens": 14771, + "cached_input_tokens": 13696, + "output_tokens": 696, + "total_tokens": 15467, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15134.84, + "sql_execution_elapsed_ms_total": 45.05, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d46137a13614edd59c61612b495acf99bf55816b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:40.493403+00:00", + "ended_at": "2026-05-19T15:39:55.628278+00:00", + "elapsed_ms": 15134.84, + "prompt_metrics": { + "chars": 9717, + "bytes_utf8": 9717, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1008, + "bytes_utf8": 1008, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 624, + "bytes_utf8": 624, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14771, + "cached_input_tokens": 13696, + "output_tokens": 696, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9b306e4c6bd989b504690d8aa66dacc51b5c132 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=last_new_job, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "last_new_job", + "measure_col": "enrollee_id", + "item_col": "training_hours", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb2432851a9563eba1991c70a3b8b18cb01d353f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-8d49-72b0-b14b-23c564d36929"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"last_new_job\\\", \\\"training_hours\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"enrollee_id\\\" AS REAL))) OVER (PARTITION BY \\\"last_new_job\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nGROUP BY \\\"last_new_job\\\", \\\"training_hours\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the required template with group_col=\\\"last_new_job\\\", item_col=\\\"training_hours\\\", and measure_col=\\\"enrollee_id\\\". Since \\\"enrollee_id\\\" is stored as TEXT in SQLite, it is cast to REAL before SUM().\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14771,"cached_input_tokens":13696,"output_tokens":696,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2d7cadf8beb86853db4d183e6d717c9ba9dbb6a3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"last_new_job\", \"training_hours\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS total_measure,\n SUM(CAST(\"enrollee_id\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"enrollee_id\" AS REAL))) OVER (PARTITION BY \"last_new_job\") AS share_within_group\nFROM \"m9\"\nGROUP BY \"last_new_job\", \"training_hours\"\nORDER BY share_within_group DESC;","notes":"Uses the required template with group_col=\"last_new_job\", item_col=\"training_hours\", and measure_col=\"enrollee_id\". Since \"enrollee_id\" is stored as TEXT in SQLite, it is cast to REAL before SUM()."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7294117e92cfa6f4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d37390398c4ce68c502c40245b73848174924ec4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9486, "bytes_utf8": 9486, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1942, "bytes_utf8": 1942, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14686, "cached_input_tokens": 13696, "output_tokens": 2479, "reasoning_output_tokens": 1843}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bdbe26a275c2b2a30f551ae0dc69c052d1fc75ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_72b6191fdbae7aa9", + "api_calls": 0, + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 2479, + "total_tokens": 17165, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 36792.38, + "sql_execution_elapsed_ms_total": 67.9, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d17cb7136c99fa56a3bd4847f3bfd65354a6eaa5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:55:16.736181+00:00", + "ended_at": "2026-05-19T15:55:53.528585+00:00", + "elapsed_ms": 36792.38, + "prompt_metrics": { + "chars": 9486, + "bytes_utf8": 9486, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2562, + "bytes_utf8": 2562, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1942, + "bytes_utf8": 1942, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 2479, + "reasoning_output_tokens": 1843 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0d8dc336c224802ed960384d74daa2290220a67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3fa926501c68ce0f27f864883bd6921b5f501f99 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f2-d691-7ea0-a8b4-50af7bc81411"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"major_discipline\\\",\\n CAST(\\\"training_hours\\\" AS REAL) AS \\\"training_hours_num\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"major_discipline\\\"\\n ORDER BY CAST(\\\"training_hours\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"major_discipline\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"major_discipline\\\" <> ''\\n AND \\\"training_hours\\\" <> ''\\n),\\n\\\"bounds\\\" AS (\\n SELECT DISTINCT\\n \\\"major_discipline\\\",\\n (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER) AS \\\"lower_rn\\\",\\n CASE\\n WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) = CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER)\\n THEN CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER)\\n ELSE CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER) + 1\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n b.\\\"major_discipline\\\",\\n b.\\\"pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"training_hours_num\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"upper_rn\\\" THEN o.\\\"training_hours_num\\\" END) AS \\\"upper_val\\\"\\n FROM \\\"bounds\\\" AS b\\n JOIN \\\"ordered\\\" AS o\\n ON o.\\\"major_discipline\\\" = b.\\\"major_discipline\\\"\\n GROUP BY\\n b.\\\"major_discipline\\\",\\n b.\\\"pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"major_discipline\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite-compatible emulation of PERCENTILE_CONT(0.9) by major discipline using ordered CAST(\\\"training_hours\\\" AS REAL) values and linear interpolation; blank \\\"major_discipline\\\" and \\\"training_hours\\\" values are excluded.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14686,"cached_input_tokens":13696,"output_tokens":2479,"reasoning_output_tokens":1843}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b40a53e8b6724e2b1db86b51b31c518d98b194ee --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"major_discipline\",\n CAST(\"training_hours\" AS REAL) AS \"training_hours_num\",\n ROW_NUMBER() OVER (\n PARTITION BY \"major_discipline\"\n ORDER BY CAST(\"training_hours\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"major_discipline\"\n ) AS \"cnt\"\n FROM \"m9\"\n WHERE \"major_discipline\" <> ''\n AND \"training_hours\" <> ''\n),\n\"bounds\" AS (\n SELECT DISTINCT\n \"major_discipline\",\n (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER) AS \"lower_rn\",\n CASE\n WHEN (1.0 + 0.9 * (\"cnt\" - 1)) = CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER)\n THEN CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER)\n ELSE CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER) + 1\n END AS \"upper_rn\"\n FROM \"ordered\"\n),\n\"picked\" AS (\n SELECT\n b.\"major_discipline\",\n b.\"pos\",\n b.\"lower_rn\",\n b.\"upper_rn\",\n MAX(CASE WHEN o.\"rn\" = b.\"lower_rn\" THEN o.\"training_hours_num\" END) AS \"lower_val\",\n MAX(CASE WHEN o.\"rn\" = b.\"upper_rn\" THEN o.\"training_hours_num\" END) AS \"upper_val\"\n FROM \"bounds\" AS b\n JOIN \"ordered\" AS o\n ON o.\"major_discipline\" = b.\"major_discipline\"\n GROUP BY\n b.\"major_discipline\",\n b.\"pos\",\n b.\"lower_rn\",\n b.\"upper_rn\"\n)\nSELECT\n \"major_discipline\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n ELSE \"lower_val\" + (\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\")\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite-compatible emulation of PERCENTILE_CONT(0.9) by major discipline using ordered CAST(\"training_hours\" AS REAL) values and linear interpolation; blank \"major_discipline\" and \"training_hours\" values are excluded."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_72b6191fdbae7aa9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f4aca438e731768efafd53080e88a42fad26666d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9319, "bytes_utf8": 9319, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 417, "bytes_utf8": 417, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14654, "cached_input_tokens": 12288, "output_tokens": 526, "reasoning_output_tokens": 414}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8dd3f073805763678becc64c58dfba0d56dc8a54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_73725bcdc4e67730", + "api_calls": 0, + "input_tokens": 14654, + "cached_input_tokens": 12288, + "output_tokens": 526, + "total_tokens": 15180, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14091.67, + "sql_execution_elapsed_ms_total": 13.01, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9699a260c50cb42187761e85347de282f8925d78 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:18.178089+00:00", + "ended_at": "2026-05-19T15:28:32.269795+00:00", + "elapsed_ms": 14091.67, + "prompt_metrics": { + "chars": 9319, + "bytes_utf8": 9319, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 775, + "bytes_utf8": 775, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 417, + "bytes_utf8": 417, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14654, + "cached_input_tokens": 12288, + "output_tokens": 526, + "reasoning_output_tokens": 414 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..81f3db8508e3a3be4f11a8829f9b70c074f4692c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8759589e79a12f969671e00327c36c3e50a094c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-2411-7f23-974a-081c9df80546"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"city_development_index\\\", SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the requested grouped numeric sum template with group_col=\\\"city_development_index\\\" and measure_col=\\\"enrollee_id\\\". CAST is applied because \\\"enrollee_id\\\" is stored as TEXT in the schema.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14654,"cached_input_tokens":12288,"output_tokens":526,"reasoning_output_tokens":414}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..36f1166c651d639d28340c5020a79209adc25f72 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"city_development_index\", SUM(CAST(\"enrollee_id\" AS REAL)) AS total_measure\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nORDER BY total_measure DESC;","notes":"Used the requested grouped numeric sum template with group_col=\"city_development_index\" and measure_col=\"enrollee_id\". CAST is applied because \"enrollee_id\" is stored as TEXT in the schema."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73725bcdc4e67730/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..87c60bb17528d6298e8d2c569fc126503e1524db --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9546, "bytes_utf8": 9546, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 395, "bytes_utf8": 395, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14708, "cached_input_tokens": 12032, "output_tokens": 261, "reasoning_output_tokens": 154}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..9f2f151cde5f3ccf3eb8d50851fc77ddbecb3800 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_73740aaf7c2ea120", + "api_calls": 0, + "input_tokens": 14708, + "cached_input_tokens": 12032, + "output_tokens": 261, + "total_tokens": 14969, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7589.23, + "sql_execution_elapsed_ms_total": 10.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f129653b75d29565f44adc880f83b06ac020d3aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:37.976863+00:00", + "ended_at": "2026-05-19T16:01:45.566123+00:00", + "elapsed_ms": 7589.23, + "prompt_metrics": { + "chars": 9546, + "bytes_utf8": 9546, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 758, + "bytes_utf8": 758, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 395, + "bytes_utf8": 395, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14708, + "cached_input_tokens": 12032, + "output_tokens": 261, + "reasoning_output_tokens": 154 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..450c60a00fb47834c5682be5a2aafc67569dc451 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=experience, condition_col=last_new_job.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "experience", + "condition_col": "last_new_job", + "condition_value": ">4", + "positive_value": "1", + "negative_value": ">4", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..01d5ff3a7a599e49de1c306354e09549eb19cf13 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-a7c0-77e2-aa90-693d4a0892a8"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"experience\\\",\\n AVG(CASE WHEN \\\"last_new_job\\\" = '>4' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Applied the Grouped Condition Rate template with group_col=\\\"experience\\\" and condition_col=\\\"last_new_job\\\", using the bound condition_value '>4'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14708,"cached_input_tokens":12032,"output_tokens":261,"reasoning_output_tokens":154}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b6e3eb92b2dd35e5f5d130e56a223d75a2bc1e24 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"experience\",\n AVG(CASE WHEN \"last_new_job\" = '>4' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"experience\"\nORDER BY \"condition_rate\" DESC;","notes":"Applied the Grouped Condition Rate template with group_col=\"experience\" and condition_col=\"last_new_job\", using the bound condition_value '>4'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_73740aaf7c2ea120/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cda99231cbe679b9cbc57aca3097ebdb2584b33a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:24.443629+00:00", + "ended_at": "2026-05-19T16:06:27.447372+00:00", + "elapsed_ms": 3003.71, + "returncode": 1, + "prompt_metrics": { + "chars": 9280, + "bytes_utf8": 9280, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..16f1859ebe0b5bf72cdda518747c95d1ca777b73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:28.449593+00:00", + "ended_at": "2026-05-19T16:06:31.515963+00:00", + "elapsed_ms": 3066.33, + "returncode": 1, + "prompt_metrics": { + "chars": 9280, + "bytes_utf8": 9280, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6c8a1516c873e15c7904fe4b7f248356026a2dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=company_size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "company_size", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6c8a1516c873e15c7904fe4b7f248356026a2dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=company_size.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "company_size", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc12c25258726afcfd282e056f41cde2ecc165f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-06ad-7c62-8bfb-fd34d4cf0cf3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..618f43e73f726ea1dd27684737c37bb430981008 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-06ad-7c62-8bfb-fd34d4cf0cf3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0fa92eeff0711ea5203845bc896e12bd6910d6fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-1660-7401-912c-019d6953ebd2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a114fdd51ab8c67373d66c824befa553d6c8ce76 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-1660-7401-912c-019d6953ebd2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_75532169ae8284db/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ca5bb9364a93cfea50eb0ac5df4328c9afad3206 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:11.604071+00:00", + "ended_at": "2026-05-19T16:08:14.722034+00:00", + "elapsed_ms": 3117.9, + "returncode": 1, + "prompt_metrics": { + "chars": 9390, + "bytes_utf8": 9390, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a11599ed8ac30beea775317d134a44779915ae3a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:15.723980+00:00", + "ended_at": "2026-05-19T16:08:18.986437+00:00", + "elapsed_ms": 3262.41, + "returncode": 1, + "prompt_metrics": { + "chars": 9390, + "bytes_utf8": 9390, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8aad841c9b4bae54c8865eca533f4f4e85ea3960 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..8aad841c9b4bae54c8865eca533f4f4e85ea3960 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e5c3d6c06e6b737cac4a1ca240f407b61877988 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-a944-7763-ad44-0f55b16a6230"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..38e14474328383498b77dce62cfb060a4220fc2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-a944-7763-ad44-0f55b16a6230"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. 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Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb7092042827d21d430c42c3d16cf3a6a04e2505 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-b974-7621-b4d0-2c0e36f4808c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7691ffaf871bb445/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bc8ecad4ecc2d00dc0b2eb4b2da0d8fa25cf1926 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9739, "bytes_utf8": 9739, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 812, "bytes_utf8": 812, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14769, "cached_input_tokens": 12032, "output_tokens": 731, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..06198a6480b5691f3b7ef7cf8a9726400ec0f8e0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_7917a66b4fa7b3ce", + "api_calls": 0, + "input_tokens": 14769, + "cached_input_tokens": 12032, + "output_tokens": 731, + "total_tokens": 15500, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13327.65, + "sql_execution_elapsed_ms_total": 46.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2a10673af30caee3d088a52c56fe7414476efa9e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:11.540429+00:00", + "ended_at": "2026-05-19T15:36:24.868114+00:00", + "elapsed_ms": 13327.65, + "prompt_metrics": { + "chars": 9739, + "bytes_utf8": 9739, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1199, + "bytes_utf8": 1199, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 812, + "bytes_utf8": 812, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14769, + "cached_input_tokens": 12032, + "output_tokens": 731, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1fb9f759e043c12c8bf37deb7a59d9805cdc5523 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "item_col": "training_hours", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..75e370fd6414e88871a5cac04d8947bf7a32bfd2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-5d04-7b01-a729-dbb09569e26c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"education_level\\\",\\n \\\"training_hours\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) * 100.0 /\\n SUM(SUM(CAST(\\\"city_development_index\\\" AS REAL))) OVER (PARTITION BY \\\"education_level\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nWHERE \\\"education_level\\\" <> ''\\n AND \\\"training_hours\\\" <> ''\\n AND \\\"city_development_index\\\" <> ''\\nGROUP BY \\\"education_level\\\", \\\"training_hours\\\"\\nORDER BY share_within_group DESC\\nLIMIT 13;\",\"notes\":\"Filled the Within-Group Share of Total template with group_col=education_level, item_col=training_hours, and measure_col=city_development_index, casting the TEXT measure to REAL and excluding empty strings to avoid zero-like artifacts.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14769,"cached_input_tokens":12032,"output_tokens":731,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f10998174923f6811eac1cf4a59c745ee78297f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"education_level\",\n \"training_hours\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS total_measure,\n SUM(CAST(\"city_development_index\" AS REAL)) * 100.0 /\n SUM(SUM(CAST(\"city_development_index\" AS REAL))) OVER (PARTITION BY \"education_level\") AS share_within_group\nFROM \"m9\"\nWHERE \"education_level\" <> ''\n AND \"training_hours\" <> ''\n AND \"city_development_index\" <> ''\nGROUP BY \"education_level\", \"training_hours\"\nORDER BY share_within_group DESC\nLIMIT 13;","notes":"Filled the Within-Group Share of Total template with group_col=education_level, item_col=training_hours, and measure_col=city_development_index, casting the TEXT measure to REAL and excluding empty strings to avoid zero-like artifacts."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7917a66b4fa7b3ce/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3b4c5059c276add5c63f122e5ab5137952793dc1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9873, "bytes_utf8": 9873, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 785, "bytes_utf8": 785, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14786, "cached_input_tokens": 13696, "output_tokens": 738, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6287a6f881d4fdf222a335a55a2f8b7c1964cc01 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_79a68ff2ed0f5dab", + "api_calls": 0, + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 738, + "total_tokens": 15524, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15451.48, + "sql_execution_elapsed_ms_total": 20.77, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8de48bed7ebd9c43a4e939580f68d2ead7772f0d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:38.012539+00:00", + "ended_at": "2026-05-19T15:49:53.464048+00:00", + "elapsed_ms": 15451.48, + "prompt_metrics": { + "chars": 9873, + "bytes_utf8": 9873, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1209, + "bytes_utf8": 1209, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 785, + "bytes_utf8": 785, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 738, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5eb53554d7b6e0791418c2dca9cfe82614684c65 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..73b1336edfe406d05f7705ae7260f0c20e7f20ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-ab82-7601-b92b-6f6c5d68e018"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"company_size\\\",\\n SUM(CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"company_size\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"company_size\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the requested Relative-to-Total Extreme Threshold template with group_col=\\\"company_size\\\", measure_col=\\\"city_development_index\\\", and fraction_threshold=0.05. CAST/NULLIF is used because \\\"city_development_index\\\" is stored as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14786,"cached_input_tokens":13696,"output_tokens":738,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f7bbeaf2d915636cacc6d0f29b41f36b14f10cdc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"company_size\",\n SUM(CAST(NULLIF(\"city_development_index\", '') AS REAL)) AS \"group_value\"\n FROM \"m9\"\n GROUP BY \"company_size\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"company_size\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Applied the requested Relative-to-Total Extreme Threshold template with group_col=\"company_size\", measure_col=\"city_development_index\", and fraction_threshold=0.05. CAST/NULLIF is used because \"city_development_index\" is stored as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_79a68ff2ed0f5dab/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7c322be374384d7d062f2ea821e779bc8721896e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9479, "bytes_utf8": 9479, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 691, "bytes_utf8": 691, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14703, "cached_input_tokens": 13696, "output_tokens": 653, "reasoning_output_tokens": 468}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..40b1e4e2ee717eaddf66c8dd8d9a2a2779f01579 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_7f9287843a434536", + "api_calls": 0, + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 653, + "total_tokens": 15356, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17302.96, + "sql_execution_elapsed_ms_total": 37.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..61dde41e3413e28b2ad9e753e2d147db97bc9600 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:15.337095+00:00", + "ended_at": "2026-05-19T15:44:32.640085+00:00", + "elapsed_ms": 17302.96, + "prompt_metrics": { + "chars": 9479, + "bytes_utf8": 9479, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1407, + "bytes_utf8": 1407, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 691, + "bytes_utf8": 691, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 653, + "reasoning_output_tokens": 468 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8cd2d704f638bb7c2bca9116a8bb403292e997fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "city_development_index", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d656bd68038d0d02511d289a14f4abc17270564a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e8-bf13-7fd3-ad91-0593907fd8cc"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1992495, Requested 15338. Please try again in 234ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"city_development_index\\\",\\n NTILE(10) OVER (\\n ORDER BY CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL) DESC\\n ) AS \\\"tail_bucket\\\"\\n FROM \\\"m9\\\"\\n WHERE NULLIF(\\\"city_development_index\\\", '') IS NOT NULL\\n)\\nSELECT \\\"city_development_index\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"city_development_index\\\" AS REAL) DESC;\",\"notes\":\"Uses the requested Quantile Tail Slice template with measure_col bound to \\\"city_development_index\\\" and num_tiles=10. Casts the TEXT field to REAL for correct numeric tail ordering and excludes blank values before bucketing.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14703,"cached_input_tokens":13696,"output_tokens":653,"reasoning_output_tokens":468}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f4df62602f6740b75afe30f49bb6593d6ca553c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n \"city_development_index\",\n NTILE(10) OVER (\n ORDER BY CAST(NULLIF(\"city_development_index\", '') AS REAL) DESC\n ) AS \"tail_bucket\"\n FROM \"m9\"\n WHERE NULLIF(\"city_development_index\", '') IS NOT NULL\n)\nSELECT \"city_development_index\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY CAST(\"city_development_index\" AS REAL) DESC;","notes":"Uses the requested Quantile Tail Slice template with measure_col bound to \"city_development_index\" and num_tiles=10. Casts the TEXT field to REAL for correct numeric tail ordering and excludes blank values before bucketing."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_7f9287843a434536/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bac2f01c2034e322c2bb8acbc1de60fbcd19a030 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9316, "bytes_utf8": 9316, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 410, "bytes_utf8": 410, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14650, "cached_input_tokens": 13696, "output_tokens": 387, "reasoning_output_tokens": 284}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e4374c620b7528746ea85df19ec8272a879da418 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_82ef950d3fba8a15", + "api_calls": 0, + "input_tokens": 14650, + "cached_input_tokens": 13696, + "output_tokens": 387, + "total_tokens": 15037, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15667.09, + "sql_execution_elapsed_ms_total": 15.08, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..92d5b0570680145d2fdc9e734ae17c9b04a8fa1c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:31:42.231791+00:00", + "ended_at": "2026-05-19T15:31:57.898919+00:00", + "elapsed_ms": 15667.09, + "prompt_metrics": { + "chars": 9316, + "bytes_utf8": 9316, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 764, + "bytes_utf8": 764, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 410, + "bytes_utf8": 410, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14650, + "cached_input_tokens": 13696, + "output_tokens": 387, + "reasoning_output_tokens": 284 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0865ea81847ac320b64ba8ad8896334802f6e859 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cde30a3644a0befe58755e285bc8170730dc7c98 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-412e-7a03-96df-507159d6b7cc"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"company_size\\\", SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_size\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided grouped numeric sum template with \\\"company_size\\\" as the grouping column and CAST(\\\"city_development_index\\\" AS REAL) because the column is stored as TEXT in the schema snapshot.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14650,"cached_input_tokens":13696,"output_tokens":387,"reasoning_output_tokens":284}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f30b2b22cec7f5e5da1b38ee226d04e34e462f59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"company_size\", SUM(CAST(\"city_development_index\" AS REAL)) AS total_measure\nFROM \"m9\"\nGROUP BY \"company_size\"\nORDER BY total_measure DESC;","notes":"Used the provided grouped numeric sum template with \"company_size\" as the grouping column and CAST(\"city_development_index\" AS REAL) because the column is stored as TEXT in the schema snapshot."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_82ef950d3fba8a15/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e9b30d31a470b862fb1eb39aea48b011372d2456 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9548, "bytes_utf8": 9548, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 371, "bytes_utf8": 371, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14709, "cached_input_tokens": 12032, "output_tokens": 248, "reasoning_output_tokens": 146}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..77eb03b180b2bce0560b514afa98d929de4714f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_830dffe61f3471d1", + "api_calls": 0, + "input_tokens": 14709, + "cached_input_tokens": 12032, + "output_tokens": 248, + "total_tokens": 14957, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7919.63, + "sql_execution_elapsed_ms_total": 9.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..398eb450486a04f742700afc1534bb148f00f5b2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:30.042848+00:00", + "ended_at": "2026-05-19T16:01:37.962505+00:00", + "elapsed_ms": 7919.63, + "prompt_metrics": { + "chars": 9548, + "bytes_utf8": 9548, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 734, + "bytes_utf8": 734, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 371, + "bytes_utf8": 371, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14709, + "cached_input_tokens": 12032, + "output_tokens": 248, + "reasoning_output_tokens": 146 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f90f1b4ce321b9febb69be76b4ef5f8d50083bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=experience, condition_col=last_new_job.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "experience", + "condition_col": "last_new_job", + "condition_value": "1", + "positive_value": "1", + "negative_value": ">4", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d59a1989dc4bf9d0d049aaa79bddfe01f8990cc8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-88b9-7fa3-9486-0a4b81586dc5"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"experience\\\",\\n AVG(CASE WHEN \\\"last_new_job\\\" = '1' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Grouped condition rate over \\\"experience\\\", measuring the within-group proportion of rows where \\\"last_new_job\\\" equals '1'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14709,"cached_input_tokens":12032,"output_tokens":248,"reasoning_output_tokens":146}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2a35b142cc08437e4e209b7b3414c6d2aba15206 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"experience\",\n AVG(CASE WHEN \"last_new_job\" = '1' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"experience\"\nORDER BY \"condition_rate\" DESC;","notes":"Grouped condition rate over \"experience\", measuring the within-group proportion of rows where \"last_new_job\" equals '1'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_830dffe61f3471d1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3bd87355773f11621f23091c9bc4e928bd724a47 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:48.595878+00:00", + "ended_at": "2026-05-19T16:08:51.579760+00:00", + "elapsed_ms": 2983.86, + "returncode": 1, + "prompt_metrics": { + "chars": 9403, + "bytes_utf8": 9403, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..45e6a8f285e12d47f60315c7eb4f70d6a1b3dd07 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:52.581473+00:00", + "ended_at": "2026-05-19T16:08:55.899056+00:00", + "elapsed_ms": 3317.55, + "returncode": 1, + "prompt_metrics": { + "chars": 9403, + "bytes_utf8": 9403, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..702c641ad9df19357d086f5657ab3bf7350d440a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..702c641ad9df19357d086f5657ab3bf7350d440a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 19, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ee475a6cf15df7a589e97169f7d78a7c88112e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-39c8-78f0-acd3-af438ee3c3b9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0583c789b8f84aec2d9157e42538aa094ed3e4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-39c8-78f0-acd3-af438ee3c3b9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b97ca2b5bdf708c5d63812a374303e7c444d27d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-4952-7612-8bb6-5885290bca98"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c029693379a6e733d6a805b90404e529998ab2bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ff-4952-7612-8bb6-5885290bca98"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8465cbaf7645cb24/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7728806c72b9ab4b439cd9fb67374aa28a2b1a8f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9245, "bytes_utf8": 9245, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 495, "bytes_utf8": 495, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14639, "cached_input_tokens": 13696, "output_tokens": 449, "reasoning_output_tokens": 313}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..9398a115df293b54069a2cf8531ccd17429e9184 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_87b197a7201ecee2", + "api_calls": 0, + "input_tokens": 14639, + "cached_input_tokens": 13696, + "output_tokens": 449, + "total_tokens": 15088, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10975.84, + "sql_execution_elapsed_ms_total": 6.02, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fba6efd5427164a8652f2a2bfb31e74243255cf3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:39.812826+00:00", + "ended_at": "2026-05-19T16:02:50.788693+00:00", + "elapsed_ms": 10975.84, + "prompt_metrics": { + "chars": 9245, + "bytes_utf8": 9245, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 843, + "bytes_utf8": 843, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 495, + "bytes_utf8": 495, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14639, + "cached_input_tokens": 13696, + "output_tokens": 449, + "reasoning_output_tokens": 313 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..306e81aeda87d3153e0c464add16a76724bc6490 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "city_development_index", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..acc4e0f7af314a486c639350a37698df3cdd6db3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-996a-7591-9175-bbfa55e3445f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(NULLIF(\\\"city_development_index\\\", '') AS REAL) <= 0.92 THEN 1.0 WHEN NULLIF(\\\"city_development_index\\\", '') IS NOT NULL THEN 0.0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\",\"notes\":\"Uses the planned Threshold Rarity CDF template with measure_col bound to \\\"city_development_index\\\" and threshold 0.92. Because the column is stored as TEXT, the query casts nonblank values to REAL and ignores blank entries.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14639,"cached_input_tokens":13696,"output_tokens":449,"reasoning_output_tokens":313}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6167128714b6982b96faa65d7feb8e52eb3f001c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(NULLIF(\"city_development_index\", '') AS REAL) <= 0.92 THEN 1.0 WHEN NULLIF(\"city_development_index\", '') IS NOT NULL THEN 0.0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m9\";","notes":"Uses the planned Threshold Rarity CDF template with measure_col bound to \"city_development_index\" and threshold 0.92. Because the column is stored as TEXT, the query casts nonblank values to REAL and ignores blank entries."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_87b197a7201ecee2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..65853e6ae973986241a573025eda15e306a3ba62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9604, "bytes_utf8": 9604, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 392, "bytes_utf8": 392, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14737, "cached_input_tokens": 13696, "output_tokens": 377, "reasoning_output_tokens": 271}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2dac7c54f97039ba3e951692d9b50e29980e1bf4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_8d27057783abd9d0", + "api_calls": 0, + "input_tokens": 14737, + "cached_input_tokens": 13696, + "output_tokens": 377, + "total_tokens": 15114, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9317.83, + "sql_execution_elapsed_ms_total": 4.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..da056457242b807211cf433d5b9ef4af2a2000d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:31.325829+00:00", + "ended_at": "2026-05-19T15:42:40.643690+00:00", + "elapsed_ms": 9317.83, + "prompt_metrics": { + "chars": 9604, + "bytes_utf8": 9604, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 759, + "bytes_utf8": 759, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 392, + "bytes_utf8": 392, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14737, + "cached_input_tokens": 13696, + "output_tokens": 377, + "reasoning_output_tokens": 271 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d4350ce679fc8f075b6406f68175ae8aecf14ca1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=major_discipline, group_col_2=target.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "major_discipline", + "group_col_2": "target", + "predicate_col": "experience", + "predicate_op": "=", + "predicate_value": "4", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9ca5f26fb36765dde9b1bebff6cb793706fe7467 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-2886-7491-9385-5adf2d5be3d5"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"major_discipline\\\", \\\"target\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nWHERE \\\"experience\\\" = '4'\\nGROUP BY \\\"major_discipline\\\", \\\"target\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Applied the provided Filtered Two-Dimensional Group Count template with \\\"major_discipline\\\" and \\\"target\\\", filtering rows where \\\"experience\\\" = '4'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14737,"cached_input_tokens":13696,"output_tokens":377,"reasoning_output_tokens":271}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a8b80a2bc44ef6cf7c7a1ef7688d8cc2c912986b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"major_discipline\", \"target\", COUNT(*) AS row_count\nFROM \"m9\"\nWHERE \"experience\" = '4'\nGROUP BY \"major_discipline\", \"target\"\nORDER BY row_count DESC;","notes":"Applied the provided Filtered Two-Dimensional Group Count template with \"major_discipline\" and \"target\", filtering rows where \"experience\" = '4'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d27057783abd9d0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2bc054c9eb4d0bc1a63714979f7219cda7903c17 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9701, "bytes_utf8": 9701, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 654, "bytes_utf8": 654, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14766, "cached_input_tokens": 13696, "output_tokens": 690, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..923573deae000701c8d006583e37d09cfb5a7a5a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_8d50de70e555be2d", + "api_calls": 0, + "input_tokens": 14766, + "cached_input_tokens": 13696, + "output_tokens": 690, + "total_tokens": 15456, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 25739.75, + "sql_execution_elapsed_ms_total": 42.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..530e99ae45762b68b8fc78640d831303049139e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:37:17.704028+00:00", + "ended_at": "2026-05-19T15:37:43.443809+00:00", + "elapsed_ms": 25739.75, + "prompt_metrics": { + "chars": 9701, + "bytes_utf8": 9701, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2041, + "bytes_utf8": 2041, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 654, + "bytes_utf8": 654, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14766, + "cached_input_tokens": 13696, + "output_tokens": 690, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..687a13d666202383b11c1e387414dde94d3eee61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "item_col": "city", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d80e0dc3c95dc7b39a9320ff9a719344a6a6bbff --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40e2-5f88-7211-9cca-5227a988aa85"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1988171, Requested 15401. Please try again in 107ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990666, Requested 15401. Please try again in 182ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15401. Please try again in 462ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"experience\\\", \\\"city\\\",\\n COUNT(\\\"enrollee_id\\\") AS total_measure,\\n COUNT(\\\"enrollee_id\\\") * 100.0 / SUM(COUNT(\\\"enrollee_id\\\")) OVER (PARTITION BY \\\"experience\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\", \\\"city\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned within-group share template with group_col=\\\"experience\\\" and item_col=\\\"city\\\". Because \\\"enrollee_id\\\" is an identifier stored as TEXT, COUNT(\\\"enrollee_id\\\") is used as the group measure instead of SUM(\\\"enrollee_id\\\") to produce a meaningful within-group proportion in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14766,"cached_input_tokens":13696,"output_tokens":690,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2e4407747f1d883f54bfb1af1a7e724b492244c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"experience\", \"city\",\n COUNT(\"enrollee_id\") AS total_measure,\n COUNT(\"enrollee_id\") * 100.0 / SUM(COUNT(\"enrollee_id\")) OVER (PARTITION BY \"experience\") AS share_within_group\nFROM \"m9\"\nGROUP BY \"experience\", \"city\"\nORDER BY share_within_group DESC;","notes":"Used the planned within-group share template with group_col=\"experience\" and item_col=\"city\". Because \"enrollee_id\" is an identifier stored as TEXT, COUNT(\"enrollee_id\") is used as the group measure instead of SUM(\"enrollee_id\") to produce a meaningful within-group proportion in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8d50de70e555be2d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..898c7b12a609a0193c666d9f10f78b2272d9cbcb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9564, "bytes_utf8": 9564, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9564, "bytes_utf8": 9564, "lines": 267, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 405, "bytes_utf8": 405, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14709, "cached_input_tokens": 13696, "output_tokens": 325, "reasoning_output_tokens": 221}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a5326f1741b07fd175933d4cc714e7fb4e11a14c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_8daba880f1fffdde", + "api_calls": 0, + "input_tokens": 14709, + "cached_input_tokens": 13696, + "output_tokens": 325, + "total_tokens": 15034, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12202.57, + "sql_execution_elapsed_ms_total": 15.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cb2f1ce6c4c59d14dde6e2b554dfcbfb234d435a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:08.726384+00:00", + "ended_at": "2026-05-19T15:59:12.109499+00:00", + "elapsed_ms": 3383.09, + "returncode": 1, + "prompt_metrics": { + "chars": 9564, + "bytes_utf8": 9564, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0596891b1a611ed7ac8a5c44cb8d8967ae94da04 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:13.111736+00:00", + "ended_at": "2026-05-19T15:59:21.931267+00:00", + "elapsed_ms": 8819.48, + "prompt_metrics": { + "chars": 9564, + "bytes_utf8": 9564, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 752, + "bytes_utf8": 752, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 405, + "bytes_utf8": 405, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14709, + "cached_input_tokens": 13696, + "output_tokens": 325, + "reasoning_output_tokens": 221 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2762cf8184ce3f8aecaa84957105079ab6626154 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=city_development_index, condition_col=gender.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "city_development_index", + "condition_col": "gender", + "condition_value": "Male", + "positive_value": "Male", + "negative_value": "", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..2762cf8184ce3f8aecaa84957105079ab6626154 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=city_development_index, condition_col=gender.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "city_development_index", + "condition_col": "gender", + "condition_value": "Male", + "positive_value": "Male", + "negative_value": "", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b8f26d22b8b58a149ea34bbdacee07ba3ea15b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-60c6-7ae2-8904-803d93138940"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ecdf7226e1a623e959b80225a3ce289c4d997be6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-60c6-7ae2-8904-803d93138940"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3728e4349027f9631c0b0124dbe4e33ed42e4692 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-71e3-7fb0-b948-cebae17c6b37"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"city_development_index\\\",\\n AVG(CASE WHEN \\\"gender\\\" = 'Male' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Computes the within-group proportion of rows where gender is Male for each city_development_index, following the Grouped Condition Rate template.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14709,"cached_input_tokens":13696,"output_tokens":325,"reasoning_output_tokens":221}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..62262f6208f8ddeae6c82d2e6d8d06ad89215257 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"city_development_index\",\n AVG(CASE WHEN \"gender\" = 'Male' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nORDER BY condition_rate DESC;","notes":"Computes the within-group proportion of rows where gender is Male for each city_development_index, following the Grouped Condition Rate template."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_8daba880f1fffdde/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4bf660ff603691b60836d86d248f472863eac467 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:52.442409+00:00", + "ended_at": "2026-05-19T15:53:55.694175+00:00", + "elapsed_ms": 3251.74, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d7a0be1c072e212bdbdc5a62974bdab1a6310cd2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:56.696534+00:00", + "ended_at": "2026-05-19T15:53:59.873196+00:00", + "elapsed_ms": 3176.61, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..21c821f53e71411f1d829c397e5552055e7a1aa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..21c821f53e71411f1d829c397e5552055e7a1aa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..be7714f84a2cc35e10678343b68c3bb811f38bc9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-8d46-7a71-a14f-eb53732b4da5"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..db9b1c140a514ec8798ad298dbccf22f099a996a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-8d46-7a71-a14f-eb53732b4da5"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6da92a489dc0f139f76d86b5fdeb7ba000492b37 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-9df6-7622-a5b1-79cc9c84eaf0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a2213e686c07c39b1d81c6d8562a0b07ab5f62d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-9df6-7622-a5b1-79cc9c84eaf0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9256061188839702/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9cec75b9226537791766df2708609544d569217d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9860, "bytes_utf8": 9860, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 926, "bytes_utf8": 926, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14786, "cached_input_tokens": 13696, "output_tokens": 755, "reasoning_output_tokens": 493}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f39d778ab8daf662d22394cf1e363dc2b0f718d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_95a13eb726323bed", + "api_calls": 0, + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 755, + "total_tokens": 15541, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13587.17, + "sql_execution_elapsed_ms_total": 9.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9ffe9f232d90458a37a9e6e20b1918f92a1f7a50 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:18.399717+00:00", + "ended_at": "2026-05-19T15:46:31.986921+00:00", + "elapsed_ms": 13587.17, + "prompt_metrics": { + "chars": 9860, + "bytes_utf8": 9860, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1358, + "bytes_utf8": 1358, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 926, + "bytes_utf8": 926, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 755, + "reasoning_output_tokens": 493 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..07c7084bdeb3a0bbe5f6d72781b44b4ecdc52402 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..46b76626ca3bfa250414d8a850be50a930196e5f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-9fc1-7760-b3a9-1cae1029e0c7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"gender\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"gender\\\" IS NOT NULL\\n AND \\\"gender\\\" <> ''\\n AND \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n GROUP BY \\\"gender\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"gender\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the requested template with group_col=gender and measure_col=city_development_index. Because city_development_index is stored as TEXT, it is cast to REAL before summation. Empty and NULL gender/city_development_index values are filtered out to form a stable grouped view.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14786,"cached_input_tokens":13696,"output_tokens":755,"reasoning_output_tokens":493}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2fb39f473eaa543d39ed04db34b7484b71aa8d9f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"gender\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE \"gender\" IS NOT NULL\n AND \"gender\" <> ''\n AND \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n GROUP BY \"gender\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"gender\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the requested template with group_col=gender and measure_col=city_development_index. Because city_development_index is stored as TEXT, it is cast to REAL before summation. Empty and NULL gender/city_development_index values are filtered out to form a stable grouped view."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_95a13eb726323bed/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2149049dae1a77ec9ddba3a4207db1b4a3458ce3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9316, "bytes_utf8": 9316, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 409, "bytes_utf8": 409, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14651, "cached_input_tokens": 12032, "output_tokens": 372, "reasoning_output_tokens": 265}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7364df8c74022b915ca6c994cf43d3f4f6b2ccee --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_96766205f49fc86e", + "api_calls": 0, + "input_tokens": 14651, + "cached_input_tokens": 12032, + "output_tokens": 372, + "total_tokens": 15023, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9675.01, + "sql_execution_elapsed_ms_total": 19.07, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1cd24b409761e4cfdb7350e100a6ba1a1f914719 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:16.560518+00:00", + "ended_at": "2026-05-19T15:29:26.235561+00:00", + "elapsed_ms": 9675.01, + "prompt_metrics": { + "chars": 9316, + "bytes_utf8": 9316, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 763, + "bytes_utf8": 763, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 409, + "bytes_utf8": 409, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14651, + "cached_input_tokens": 12032, + "output_tokens": 372, + "reasoning_output_tokens": 265 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a926b1b8ce1f9f04176a9381554c5e8d2594acf0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f99124beea616b30f8b0d1ee37b99f2aa410ddcc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-081a-72a2-a223-83ba1cc577b0"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"relevent_experience\\\", SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"relevent_experience\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided grouped numeric sum template with \\\"relevent_experience\\\" as the grouping column and cast text-based \\\"training_hours\\\" to REAL, treating empty strings as NULL.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14651,"cached_input_tokens":12032,"output_tokens":372,"reasoning_output_tokens":265}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0690ee1fb4205afa354a817f7f1eada6cec78b36 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"relevent_experience\", SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) AS total_measure\nFROM \"m9\"\nGROUP BY \"relevent_experience\"\nORDER BY total_measure DESC;","notes":"Used the provided grouped numeric sum template with \"relevent_experience\" as the grouping column and cast text-based \"training_hours\" to REAL, treating empty strings as NULL."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96766205f49fc86e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b7d0e6886b9f6d64054cbb9c6469ecd4e91c2263 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9872, "bytes_utf8": 9872, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 905, "bytes_utf8": 905, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14789, "cached_input_tokens": 13696, "output_tokens": 717, "reasoning_output_tokens": 480}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0d10868235af5bcbaaad9c093fef22a5d3dea780 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_96bd9dabe9b751c0", + "api_calls": 0, + "input_tokens": 14789, + "cached_input_tokens": 13696, + "output_tokens": 717, + "total_tokens": 15506, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19202.66, + "sql_execution_elapsed_ms_total": 27.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4ad71a6fed68ccbc096fa3e9b09f4383d972daba --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:53.513745+00:00", + "ended_at": "2026-05-19T15:47:12.716448+00:00", + "elapsed_ms": 19202.66, + "prompt_metrics": { + "chars": 9872, + "bytes_utf8": 9872, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1625, + "bytes_utf8": 1625, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 905, + "bytes_utf8": 905, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14789, + "cached_input_tokens": 13696, + "output_tokens": 717, + "reasoning_output_tokens": 480 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4fb4d1f91f713d5cab0f9b849d98d7af9ff068a7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..72953a9be8f0887afd93f979ac0753ede002c302 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40eb-293f-7b20-87af-5e6e5ec67e30"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1998281, Requested 15424. Please try again in 411ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"relevent_experience\\\", SUM(CAST(\\\"training_hours\\\" AS REAL)) AS group_value\\n FROM \\\"m9\\\"\\n WHERE \\\"relevent_experience\\\" IS NOT NULL\\n AND TRIM(\\\"relevent_experience\\\") <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND TRIM(\\\"training_hours\\\") <> ''\\n GROUP BY \\\"relevent_experience\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"relevent_experience\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the required template with group_col=\\\"relevent_experience\\\" and measure_col=\\\"training_hours\\\", casting the text measure to REAL and filtering null/blank values to form a stable grouped view before applying the 5% relative-to-total threshold.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14789,"cached_input_tokens":13696,"output_tokens":717,"reasoning_output_tokens":480}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..756e5fec080ef2ac575a2db9c048b7a6523c2f0c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"relevent_experience\", SUM(CAST(\"training_hours\" AS REAL)) AS group_value\n FROM \"m9\"\n WHERE \"relevent_experience\" IS NOT NULL\n AND TRIM(\"relevent_experience\") <> ''\n AND \"training_hours\" IS NOT NULL\n AND TRIM(\"training_hours\") <> ''\n GROUP BY \"relevent_experience\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"relevent_experience\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Uses the required template with group_col=\"relevent_experience\" and measure_col=\"training_hours\", casting the text measure to REAL and filtering null/blank values to form a stable grouped view before applying the 5% relative-to-total threshold."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_96bd9dabe9b751c0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ca493b26b967a17bdc830bcf9fac75a06a1608dd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9632, "bytes_utf8": 9632, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 464, "bytes_utf8": 464, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14743, "cached_input_tokens": 12032, "output_tokens": 590, "reasoning_output_tokens": 468}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..20e7bae01f86aad4d228afb5e6629835c551973d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_97769e99c5051056", + "api_calls": 0, + "input_tokens": 14743, + "cached_input_tokens": 12032, + "output_tokens": 590, + "total_tokens": 15333, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11847.5, + "sql_execution_elapsed_ms_total": 9.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9457199aadcf1d6025a359b02295577762007c2d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:05.850215+00:00", + "ended_at": "2026-05-19T15:42:17.697745+00:00", + "elapsed_ms": 11847.5, + "prompt_metrics": { + "chars": 9632, + "bytes_utf8": 9632, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 836, + "bytes_utf8": 836, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 464, + "bytes_utf8": 464, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14743, + "cached_input_tokens": 12032, + "output_tokens": 590, + "reasoning_output_tokens": 468 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7654f94b19aa1b64c7bbdffab66af7a47adbac9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=major_discipline, group_col_2=last_new_job.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "major_discipline", + "group_col_2": "last_new_job", + "predicate_col": "education_level", + "predicate_op": "=", + "predicate_value": "Graduate", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..94ba6ea31a80dfd80aaae21b35c00fd0c9dcf6d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-c524-77a2-bacb-d8539a0136b3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"major_discipline\\\", \\\"last_new_job\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"education_level\\\" = 'Graduate'\\nGROUP BY \\\"major_discipline\\\", \\\"last_new_job\\\"\\nORDER BY \\\"row_count\\\" DESC\\nLIMIT 13;\",\"notes\":\"Uses the requested template with group_col=\\\"major_discipline\\\", group_col_2=\\\"last_new_job\\\", and predicate education_level='Graduate', returning the highest-count graduate slices.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14743,"cached_input_tokens":12032,"output_tokens":590,"reasoning_output_tokens":468}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7344d189e026307e83012f534a356c5fc4d43fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"major_discipline\", \"last_new_job\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE \"education_level\" = 'Graduate'\nGROUP BY \"major_discipline\", \"last_new_job\"\nORDER BY \"row_count\" DESC\nLIMIT 13;","notes":"Uses the requested template with group_col=\"major_discipline\", group_col_2=\"last_new_job\", and predicate education_level='Graduate', returning the highest-count graduate slices."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97769e99c5051056/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7a7beb415d6230520412a678a20b51d8dd023f8f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9295, "bytes_utf8": 9295, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 470, "bytes_utf8": 470, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14648, "cached_input_tokens": 13696, "output_tokens": 636, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e5c6e4e4985445835e9c0953bbeff92042e41bd2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_97d9e1b7f3691dce", + "api_calls": 0, + "input_tokens": 14648, + "cached_input_tokens": 13696, + "output_tokens": 636, + "total_tokens": 15284, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 27413.66, + "sql_execution_elapsed_ms_total": 20.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..80ec2c39ca5eb7fbc2f24a0295ca6615c21c000e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:31:03.464759+00:00", + "ended_at": "2026-05-19T15:31:30.878471+00:00", + "elapsed_ms": 27413.66, + "prompt_metrics": { + "chars": 9295, + "bytes_utf8": 9295, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2156, + "bytes_utf8": 2156, + "lines": 8, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 470, + "bytes_utf8": 470, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14648, + "cached_input_tokens": 13696, + "output_tokens": 636, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..07801bbf23bc553a8253b6a0ef84d2f5de99ed4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..926cca2a3b5d017f77397f4100afa0ee81d49aa2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,8 @@ +{"type":"thread.started","thread_id":"019e40dc-a9b5-7e90-8cb1-b763062a7101"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15283. Please try again in 458ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1995828, Requested 15283. Please try again in 333ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15283. Please try again in 458ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 15283. Please try again in 458ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"experience\\\", SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC)) AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Uses the required grouped numeric sum template with \\\"experience\\\" as the group column. \\\"enrollee_id\\\" is stored as TEXT, so it is cast to NUMERIC for SQLite aggregation; this is syntactically valid, though the measure is identifier-like rather than a business metric.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14648,"cached_input_tokens":13696,"output_tokens":636,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..aebbe4a1351e15fc99d03f1c5dfc832d6ed26021 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"experience\", SUM(CAST(\"enrollee_id\" AS NUMERIC)) AS total_measure\nFROM \"m9\"\nGROUP BY \"experience\"\nORDER BY total_measure DESC;","notes":"Uses the required grouped numeric sum template with \"experience\" as the group column. \"enrollee_id\" is stored as TEXT, so it is cast to NUMERIC for SQLite aggregation; this is syntactically valid, though the measure is identifier-like rather than a business metric."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_97d9e1b7f3691dce/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5d3867146c5c78647e4914f4d6ded645bf80c9b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9606, "bytes_utf8": 9606, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 508, "bytes_utf8": 508, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14720, "cached_input_tokens": 12032, "output_tokens": 452, "reasoning_output_tokens": 319}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6533e5915519b939ca8904aa42d41d3b2b6c80f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9b7e8c4cff94b0a1", + "api_calls": 0, + "input_tokens": 14720, + "cached_input_tokens": 12032, + "output_tokens": 452, + "total_tokens": 15172, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10303.23, + "sql_execution_elapsed_ms_total": 21.76, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ebe72d7ea69849484adb7f1b44d50ff1cc19c4b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:45.754884+00:00", + "ended_at": "2026-05-19T15:59:56.058167+00:00", + "elapsed_ms": 10303.23, + "prompt_metrics": { + "chars": 9606, + "bytes_utf8": 9606, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 871, + "bytes_utf8": 871, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 508, + "bytes_utf8": 508, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14720, + "cached_input_tokens": 12032, + "output_tokens": 452, + "reasoning_output_tokens": 319 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..83f03dd21d72b273924cb2976a37d31466610fff --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=gender, condition_col=relevent_experience.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "gender", + "condition_col": "relevent_experience", + "condition_value": "No relevent experience", + "positive_value": "Has relevent experience", + "negative_value": "No relevent experience", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5f9dbe1416be81ac9428d3e1b1e7b6bf67881213 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-f176-79e0-8410-f90a4d93fbee"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"gender\\\",\\n AVG(CASE WHEN \\\"relevent_experience\\\" = 'No relevent experience' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template with \\\"gender\\\" as the group and the within-group proportion of rows where \\\"relevent_experience\\\" is 'No relevent experience'. Empty gender values, if present, will appear as their own group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14720,"cached_input_tokens":12032,"output_tokens":452,"reasoning_output_tokens":319}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8abae197bd4e2fdf3364513700fc8e857273d22b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"gender\",\n AVG(CASE WHEN \"relevent_experience\" = 'No relevent experience' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"gender\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the planned Grouped Condition Rate template with \"gender\" as the group and the within-group proportion of rows where \"relevent_experience\" is 'No relevent experience'. Empty gender values, if present, will appear as their own group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9b7e8c4cff94b0a1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4bc0daa92996ffe20b96a3d1bafbced5650b728d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9307, "bytes_utf8": 9307, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 383, "bytes_utf8": 383, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14648, "cached_input_tokens": 12032, "output_tokens": 428, "reasoning_output_tokens": 326}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e060d9f3c40474a8a0fbad4571fa5f36715a90bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9be8234aca961fde", + "api_calls": 0, + "input_tokens": 14648, + "cached_input_tokens": 12032, + "output_tokens": 428, + "total_tokens": 15076, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14019.11, + "sql_execution_elapsed_ms_total": 14.95, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3f94b3b605e51a85b936ebb290d86400a0870bde --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:52.250947+00:00", + "ended_at": "2026-05-19T15:29:06.270088+00:00", + "elapsed_ms": 14019.11, + "prompt_metrics": { + "chars": 9307, + "bytes_utf8": 9307, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 745, + "bytes_utf8": 745, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 383, + "bytes_utf8": 383, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14648, + "cached_input_tokens": 12032, + "output_tokens": 428, + "reasoning_output_tokens": 326 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c563131ffce76716f8c2cc0b3bee23438e4fd7a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d8d5b5adc76d0ff085377da42d34107f303e616 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-a939-7281-9ea7-f7b233c6f158"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum.\\nSELECT \\\"gender\\\", SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required grouped numeric sum template with \\\"gender\\\" as the grouping column and cast \\\"city_development_index\\\" from TEXT to REAL so SQLite can sum it numerically.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14648,"cached_input_tokens":12032,"output_tokens":428,"reasoning_output_tokens":326}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4a808072490b6d197c11b6a8e1a7a3c527ced3df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum.\nSELECT \"gender\", SUM(CAST(\"city_development_index\" AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"gender\"\nORDER BY \"total_measure\" DESC;","notes":"Used the required grouped numeric sum template with \"gender\" as the grouping column and cast \"city_development_index\" from TEXT to REAL so SQLite can sum it numerically."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9be8234aca961fde/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6be320f280c4fafe347e7eb1dc54e7ab2d79a10d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10189, "bytes_utf8": 10189, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 797, "bytes_utf8": 797, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14871, "cached_input_tokens": 12032, "output_tokens": 687, "reasoning_output_tokens": 469}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3cd20a285a07b82339c6caab08d16e3656c29498 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9c716372c8e53d66", + "api_calls": 0, + "input_tokens": 14871, + "cached_input_tokens": 12032, + "output_tokens": 687, + "total_tokens": 15558, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14177.5, + "sql_execution_elapsed_ms_total": 8.58, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e70604be73f696492443f54c818e33dae1f06c5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:12.530224+00:00", + "ended_at": "2026-05-19T15:41:26.707758+00:00", + "elapsed_ms": 14177.5, + "prompt_metrics": { + "chars": 10189, + "bytes_utf8": 10189, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1200, + "bytes_utf8": 1200, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 797, + "bytes_utf8": 797, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14871, + "cached_input_tokens": 12032, + "output_tokens": 687, + "reasoning_output_tokens": 469 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..02d56a4c66ca442d5335cb13dcc7f115bdc916af --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=major_discipline, condition_col=relevent_experience.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "major_discipline", + "condition_col": "relevent_experience", + "condition_value": "Has relevent experience", + "positive_value": "Has relevent experience", + "negative_value": "No relevent experience", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..abd0c55cde5bce8e97aa1d57d5f78e065d8a3845 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-f4ec-7653-845a-98a35055316d"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"major_discipline\\\",\\n SUM(CASE WHEN \\\"relevent_experience\\\" = 'Has relevent experience' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"relevent_experience\\\" = 'No relevent experience' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"m9\\\"\\n GROUP BY \\\"major_discipline\\\"\\n)\\nSELECT\\n \\\"major_discipline\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Uses the planned grouped ratio template with group_col=\\\"major_discipline\\\" and condition_col=\\\"relevent_experience\\\", comparing 'Has relevent experience' to 'No relevent experience'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14871,"cached_input_tokens":12032,"output_tokens":687,"reasoning_output_tokens":469}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f642de18e98fc505d90b9f1d8221a858c8111b0c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n SELECT\n \"major_discipline\",\n SUM(CASE WHEN \"relevent_experience\" = 'Has relevent experience' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"relevent_experience\" = 'No relevent experience' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"m9\"\n GROUP BY \"major_discipline\"\n)\nSELECT\n \"major_discipline\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;","notes":"Uses the planned grouped ratio template with group_col=\"major_discipline\" and condition_col=\"relevent_experience\", comparing 'Has relevent experience' to 'No relevent experience'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9c716372c8e53d66/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ee76ae32d07f377f1fdf42ad8e301c793ddcdae8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9876, "bytes_utf8": 9876, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 932, "bytes_utf8": 932, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14792, "cached_input_tokens": 12032, "output_tokens": 762, "reasoning_output_tokens": 494}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d9c84feaa076b90620ccb69e0d880d81104a82fa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9d6bb29cc5632186", + "api_calls": 0, + "input_tokens": 14792, + "cached_input_tokens": 12032, + "output_tokens": 762, + "total_tokens": 15554, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14217.25, + "sql_execution_elapsed_ms_total": 14.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..414c2511bdb3a868711ea1f4a6c3f7d97e763afd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:04.162638+00:00", + "ended_at": "2026-05-19T15:46:18.379920+00:00", + "elapsed_ms": 14217.25, + "prompt_metrics": { + "chars": 9876, + "bytes_utf8": 9876, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1372, + "bytes_utf8": 1372, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 932, + "bytes_utf8": 932, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14792, + "cached_input_tokens": 12032, + "output_tokens": 762, + "reasoning_output_tokens": 494 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..582a3bfbef78393b39f2cfd46437a42828b4134a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b2a360fb55faef22960dea60b6e8cef421a37341 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-6816-7482-803b-513b99f2bd4c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"city_development_index\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n GROUP BY \\\"city_development_index\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"city_development_index\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned relative-to-total threshold template with \\\"city_development_index\\\" as the grouping column and a numeric cast of \\\"enrollee_id\\\" as the measure. Blank and null values are filtered out to keep the grouped view stable.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14792,"cached_input_tokens":12032,"output_tokens":762,"reasoning_output_tokens":494}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b83e2ed67943ab0c9f157d954c92b623da047d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"city_development_index\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n GROUP BY \"city_development_index\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"city_development_index\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Uses the planned relative-to-total threshold template with \"city_development_index\" as the grouping column and a numeric cast of \"enrollee_id\" as the measure. Blank and null values are filtered out to keep the grouped view stable."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d6bb29cc5632186/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5490787825349f0b349f390018b908bd72269cc7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9205, "bytes_utf8": 9205, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 294, "bytes_utf8": 294, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14619, "cached_input_tokens": 12032, "output_tokens": 250, "reasoning_output_tokens": 173}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6379981ff89306ca1d01863ea022b2245b324267 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9d8239258a6175aa", + "api_calls": 0, + "input_tokens": 14619, + "cached_input_tokens": 12032, + "output_tokens": 250, + "total_tokens": 14869, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7837.88, + "sql_execution_elapsed_ms_total": 16.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..036ae727389681cd5a9ac8070bdd1eb279582756 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:05.598427+00:00", + "ended_at": "2026-05-19T15:33:13.436344+00:00", + "elapsed_ms": 7837.88, + "prompt_metrics": { + "chars": 9205, + "bytes_utf8": 9205, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 644, + "bytes_utf8": 644, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 294, + "bytes_utf8": 294, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14619, + "cached_input_tokens": 12032, + "output_tokens": 250, + "reasoning_output_tokens": 173 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..00f447e94f69a5a7f092771aed9f6f4f31a76e91 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=education_level.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "education_level", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..13c4d3b9bd8c5bdf5b38bc12e33899fe3a751291 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40de-86bb-79c1-a1cb-089eb0cf67b3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"education_level\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nGROUP BY \\\"education_level\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the planned grouped-count template with group_col bound to \\\"education_level\\\" on the single table \\\"m9\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14619,"cached_input_tokens":12032,"output_tokens":250,"reasoning_output_tokens":173}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..90703b39e87013a4de18bbc2524df7f2b481e7d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"education_level\", COUNT(*) AS row_count\nFROM \"m9\"\nGROUP BY \"education_level\"\nORDER BY row_count DESC;","notes":"Uses the planned grouped-count template with group_col bound to \"education_level\" on the single table \"m9\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9d8239258a6175aa/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..de6530c3620f2dc96fe4c2f475e03afe8fb544c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9486, "bytes_utf8": 9486, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 2131, "bytes_utf8": 2131, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14686, "cached_input_tokens": 13696, "output_tokens": 3630, "reasoning_output_tokens": 2935}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7dd142856b62b4d1d563114c9f8ada68985cdcda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_9e8ac55f8903ed27", + "api_calls": 0, + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 3630, + "total_tokens": 18316, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 59911.59, + "sql_execution_elapsed_ms_total": 16.1, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..caafca91791c5b758fac1a148b79343be750bf4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:52:00.402272+00:00", + "ended_at": "2026-05-19T15:53:00.313891+00:00", + "elapsed_ms": 59911.59, + "prompt_metrics": { + "chars": 9486, + "bytes_utf8": 9486, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 3121, + "bytes_utf8": 3121, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2131, + "bytes_utf8": 2131, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14686, + "cached_input_tokens": 13696, + "output_tokens": 3630, + "reasoning_output_tokens": 2935 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4ebbe8280ca4dc4fc64bd31a2f941ceeacf18b3d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..53682157fd5fbd0e883d0645e9599b7f9f08edfc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40ef-d7a9-7220-bd47-acfffd9fe163"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1996842, Requested 15321. Please try again in 364ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"gender\\\",\\n CAST(\\\"city_development_index\\\" AS REAL) AS \\\"measure\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"target\\\" = '1.0'\\n AND \\\"gender\\\" <> ''\\n AND \\\"city_development_index\\\" <> ''\\n),\\n\\\"ordered\\\" AS (\\n SELECT\\n \\\"gender\\\",\\n \\\"measure\\\",\\n ROW_NUMBER() OVER (PARTITION BY \\\"gender\\\" ORDER BY \\\"measure\\\") AS \\\"rn\\\",\\n COUNT(*) OVER (PARTITION BY \\\"gender\\\") AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"gender\\\",\\n \\\"cnt\\\",\\n (1.0 + (\\\"cnt\\\" - 1) * 0.95) AS \\\"pos\\\",\\n CAST((1.0 + (\\\"cnt\\\" - 1) * 0.95) AS INTEGER) AS \\\"lower_rn\\\",\\n CAST((1.0 + (\\\"cnt\\\" - 1) * 0.95) AS INTEGER) +\\n CASE\\n WHEN (1.0 + (\\\"cnt\\\" - 1) * 0.95) > CAST((1.0 + (\\\"cnt\\\" - 1) * 0.95) AS INTEGER) THEN 1\\n ELSE 0\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n p.\\\"gender\\\",\\n p.\\\"cnt\\\",\\n p.\\\"pos\\\",\\n p.\\\"lower_rn\\\",\\n p.\\\"upper_rn\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN o.\\\"measure\\\" END) AS \\\"lower_value\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN o.\\\"measure\\\" END) AS \\\"upper_value\\\"\\n FROM \\\"positions\\\" AS p\\n JOIN \\\"ordered\\\" AS o\\n ON o.\\\"gender\\\" = p.\\\"gender\\\"\\n GROUP BY p.\\\"gender\\\", p.\\\"cnt\\\", p.\\\"pos\\\", p.\\\"lower_rn\\\", p.\\\"upper_rn\\\"\\n)\\nSELECT\\n b.\\\"gender\\\",\\n CASE\\n WHEN b.\\\"lower_rn\\\" = b.\\\"upper_rn\\\" THEN b.\\\"lower_value\\\"\\n ELSE b.\\\"lower_value\\\" + (b.\\\"pos\\\" - b.\\\"lower_rn\\\") * (b.\\\"upper_value\\\" - b.\\\"lower_value\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\" AS b\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this query emulates the 0.95 continuous percentile for \\\"city_development_index\\\" within each \\\"gender\\\" using window functions and linear interpolation. It casts the TEXT measure to REAL, excludes blank \\\"gender\\\" and blank measure values, enforces the bound minimum group size of 5, and interprets focused_target_view as filtering to positive target rows where \\\"target\\\" = '1.0'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14686,"cached_input_tokens":13696,"output_tokens":3630,"reasoning_output_tokens":2935}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..07536f6a9934f399a07499fef89abf9e5dc7fbbe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"gender\",\n CAST(\"city_development_index\" AS REAL) AS \"measure\"\n FROM \"m9\"\n WHERE \"target\" = '1.0'\n AND \"gender\" <> ''\n AND \"city_development_index\" <> ''\n),\n\"ordered\" AS (\n SELECT\n \"gender\",\n \"measure\",\n ROW_NUMBER() OVER (PARTITION BY \"gender\" ORDER BY \"measure\") AS \"rn\",\n COUNT(*) OVER (PARTITION BY \"gender\") AS \"cnt\"\n FROM \"base\"\n),\n\"positions\" AS (\n SELECT DISTINCT\n \"gender\",\n \"cnt\",\n (1.0 + (\"cnt\" - 1) * 0.95) AS \"pos\",\n CAST((1.0 + (\"cnt\" - 1) * 0.95) AS INTEGER) AS \"lower_rn\",\n CAST((1.0 + (\"cnt\" - 1) * 0.95) AS INTEGER) +\n CASE\n WHEN (1.0 + (\"cnt\" - 1) * 0.95) > CAST((1.0 + (\"cnt\" - 1) * 0.95) AS INTEGER) THEN 1\n ELSE 0\n END AS \"upper_rn\"\n FROM \"ordered\"\n WHERE \"cnt\" >= 5\n),\n\"bounds\" AS (\n SELECT\n p.\"gender\",\n p.\"cnt\",\n p.\"pos\",\n p.\"lower_rn\",\n p.\"upper_rn\",\n MAX(CASE WHEN o.\"rn\" = p.\"lower_rn\" THEN o.\"measure\" END) AS \"lower_value\",\n MAX(CASE WHEN o.\"rn\" = p.\"upper_rn\" THEN o.\"measure\" END) AS \"upper_value\"\n FROM \"positions\" AS p\n JOIN \"ordered\" AS o\n ON o.\"gender\" = p.\"gender\"\n GROUP BY p.\"gender\", p.\"cnt\", p.\"pos\", p.\"lower_rn\", p.\"upper_rn\"\n)\nSELECT\n b.\"gender\",\n CASE\n WHEN b.\"lower_rn\" = b.\"upper_rn\" THEN b.\"lower_value\"\n ELSE b.\"lower_value\" + (b.\"pos\" - b.\"lower_rn\") * (b.\"upper_value\" - b.\"lower_value\")\n END AS \"percentile_measure\"\nFROM \"bounds\" AS b\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this query emulates the 0.95 continuous percentile for \"city_development_index\" within each \"gender\" using window functions and linear interpolation. It casts the TEXT measure to REAL, excludes blank \"gender\" and blank measure values, enforces the bound minimum group size of 5, and interprets focused_target_view as filtering to positive target rows where \"target\" = '1.0'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_9e8ac55f8903ed27/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..75736b895da5e7ca2ce0049a86fb0acdd7335437 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9286, "bytes_utf8": 9286, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9286, "bytes_utf8": 9286, "lines": 262, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 393, "bytes_utf8": 393, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14656, "cached_input_tokens": 13696, "output_tokens": 285, "reasoning_output_tokens": 178}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d08410b0ff5a432462b8f9f075c05753c9a6605e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a146e3f95ebe57d9", + "api_calls": 0, + "input_tokens": 14656, + "cached_input_tokens": 13696, + "output_tokens": 285, + "total_tokens": 14941, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11669.21, + "sql_execution_elapsed_ms_total": 8.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f27b433b3a8587cca7a7918452b7b14a0d9f2306 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:35.433649+00:00", + "ended_at": "2026-05-19T16:05:38.361363+00:00", + "elapsed_ms": 2927.68, + "returncode": 1, + "prompt_metrics": { + "chars": 9286, + "bytes_utf8": 9286, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ab49fe9eb111f62fb79f3683a41b34f339b5e664 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:39.363952+00:00", + "ended_at": "2026-05-19T16:05:48.105520+00:00", + "elapsed_ms": 8741.53, + "prompt_metrics": { + "chars": 9286, + "bytes_utf8": 9286, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 754, + "bytes_utf8": 754, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 393, + "bytes_utf8": 393, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14656, + "cached_input_tokens": 13696, + "output_tokens": 285, + "reasoning_output_tokens": 178 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..550379055b180250d5d291331b0d7e49102c1468 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=major_discipline.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "major_discipline", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..550379055b180250d5d291331b0d7e49102c1468 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=major_discipline.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "major_discipline", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac518f6dfe9302d89f229965641ee20709f060a7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-4769-72d1-b199-b880bf10fa93"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..eeaebdf41201a93f36a7c3b45ae47e8c6ca60416 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-4769-72d1-b199-b880bf10fa93"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4a459775f78978a79491e2096aacee1ed10b26aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-56b6-75a1-b151-a0e94985ff9a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"major_discipline\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY \\\"support\\\" ASC, \\\"major_discipline\\\"\\nLIMIT 10;\",\"notes\":\"Uses the Low-Support Group Count template with group_col bound to \\\"major_discipline\\\" and top_k=10 to return the least-supported major_discipline groups.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14656,"cached_input_tokens":13696,"output_tokens":285,"reasoning_output_tokens":178}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ee01c8092a0e516afa039e84f74db569b7f6cb9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"major_discipline\",\n COUNT(*) AS \"support\"\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY \"support\" ASC, \"major_discipline\"\nLIMIT 10;","notes":"Uses the Low-Support Group Count template with group_col bound to \"major_discipline\" and top_k=10 to return the least-supported major_discipline groups."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a146e3f95ebe57d9/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1e2c263ee1440b2ade5a8b34cc9a8a332c423028 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9628, "bytes_utf8": 9628, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 437, "bytes_utf8": 437, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14742, "cached_input_tokens": 13696, "output_tokens": 464, "reasoning_output_tokens": 350}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..587bfefe1f1a33842afc2b8bac442306a53d8707 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a20d7d5089630fd8", + "api_calls": 0, + "input_tokens": 14742, + "cached_input_tokens": 13696, + "output_tokens": 464, + "total_tokens": 15206, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13595.62, + "sql_execution_elapsed_ms_total": 13.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..30a5afc01e2a0ada8cd1df7fe181edc8bb359288 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:17.711691+00:00", + "ended_at": "2026-05-19T15:42:31.307336+00:00", + "elapsed_ms": 13595.62, + "prompt_metrics": { + "chars": 9628, + "bytes_utf8": 9628, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 804, + "bytes_utf8": 804, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 437, + "bytes_utf8": 437, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14742, + "cached_input_tokens": 13696, + "output_tokens": 464, + "reasoning_output_tokens": 350 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d63041d3290834684fd2aac6efc9245cb88c3630 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=major_discipline, group_col_2=training_hours.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "major_discipline", + "group_col_2": "training_hours", + "predicate_col": "major_discipline", + "predicate_op": "=", + "predicate_value": "STEM", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..af0896f8ba6e2a634202d90927e3b5878b86a903 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-f376-7882-b036-56f370034ed8"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"major_discipline\\\", \\\"training_hours\\\", COUNT(*) AS row_count\\nFROM \\\"m9\\\"\\nWHERE \\\"major_discipline\\\" = 'STEM'\\nGROUP BY \\\"major_discipline\\\", \\\"training_hours\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the requested filtered two-dimensional group count template with \\\"major_discipline\\\" and \\\"training_hours\\\", restricted to rows where \\\"major_discipline\\\" is 'STEM'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14742,"cached_input_tokens":13696,"output_tokens":464,"reasoning_output_tokens":350}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..30694a8e1262e376e343afba9ce2a78de02cbf11 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"major_discipline\", \"training_hours\", COUNT(*) AS row_count\nFROM \"m9\"\nWHERE \"major_discipline\" = 'STEM'\nGROUP BY \"major_discipline\", \"training_hours\"\nORDER BY row_count DESC;","notes":"Uses the requested filtered two-dimensional group count template with \"major_discipline\" and \"training_hours\", restricted to rows where \"major_discipline\" is 'STEM'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a20d7d5089630fd8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9bf26bfb49c8a2a5b941da538ca7235b662654fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9858, "bytes_utf8": 9858, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 954, "bytes_utf8": 954, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14784, "cached_input_tokens": 12032, "output_tokens": 1075, "reasoning_output_tokens": 805}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..09fe64a09884aec56b1b1e7e2f2d6472de6121a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a324887aa577ef52", + "api_calls": 0, + "input_tokens": 14784, + "cached_input_tokens": 12032, + "output_tokens": 1075, + "total_tokens": 15859, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 23826.38, + "sql_execution_elapsed_ms_total": 14.12, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e7e1794e66d2f076a603026d9c87a983ec1c3565 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:50:09.389926+00:00", + "ended_at": "2026-05-19T15:50:33.216334+00:00", + "elapsed_ms": 23826.38, + "prompt_metrics": { + "chars": 9858, + "bytes_utf8": 9858, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1417, + "bytes_utf8": 1417, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 954, + "bytes_utf8": 954, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14784, + "cached_input_tokens": 12032, + "output_tokens": 1075, + "reasoning_output_tokens": 805 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d40d8e246a1e7482563ba70807efe5659379b5c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc526cba08019c60a4b5003626c0cbc7a7758940 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ee-25ec-7953-9b3e-3d45a8b0331e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE TRIM(COALESCE(\\\"company_type\\\", '')) <> ''\\n AND TRIM(COALESCE(\\\"training_hours\\\", '')) <> ''\\n GROUP BY \\\"company_type\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n \\\"g\\\".\\\"company_type\\\",\\n \\\"g\\\".\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS \\\"g\\\"\\nCROSS JOIN \\\"total\\\" AS \\\"t\\\"\\nWHERE \\\"g\\\".\\\"group_value\\\" > \\\"t\\\".\\\"total_value\\\" * 0.05\\nORDER BY \\\"g\\\".\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\\\"company_type\\\" and measure_col=\\\"training_hours\\\". Blank company_type/training_hours values are filtered out for a more stable view, and \\\"training_hours\\\" is cast from TEXT to REAL before summation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14784,"cached_input_tokens":12032,"output_tokens":1075,"reasoning_output_tokens":805}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e425c55ed5e3312aaaa91c7ea37aee7ba9ca3d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"company_type\",\n SUM(CAST(\"training_hours\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE TRIM(COALESCE(\"company_type\", '')) <> ''\n AND TRIM(COALESCE(\"training_hours\", '')) <> ''\n GROUP BY \"company_type\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n \"g\".\"company_type\",\n \"g\".\"group_value\"\nFROM \"grouped\" AS \"g\"\nCROSS JOIN \"total\" AS \"t\"\nWHERE \"g\".\"group_value\" > \"t\".\"total_value\" * 0.05\nORDER BY \"g\".\"group_value\" DESC;","notes":"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\"company_type\" and measure_col=\"training_hours\". Blank company_type/training_hours values are filtered out for a more stable view, and \"training_hours\" is cast from TEXT to REAL before summation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a324887aa577ef52/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..084cc04a991239667590170e4cf919685d99b2e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9323, "bytes_utf8": 9323, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 406, "bytes_utf8": 406, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14650, "cached_input_tokens": 12032, "output_tokens": 758, "reasoning_output_tokens": 651}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a7e2a43d6cd647d6a11eb7909bf2db7db7c3159a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a5700007f628b14b", + "api_calls": 0, + "input_tokens": 14650, + "cached_input_tokens": 12032, + "output_tokens": 758, + "total_tokens": 15408, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17684.51, + "sql_execution_elapsed_ms_total": 13.61, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fa764dd01369897cadfdbaca1b556310a14be0f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:46.285737+00:00", + "ended_at": "2026-05-19T15:30:03.970275+00:00", + "elapsed_ms": 17684.51, + "prompt_metrics": { + "chars": 9323, + "bytes_utf8": 9323, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 770, + "bytes_utf8": 770, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 406, + "bytes_utf8": 406, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14650, + "cached_input_tokens": 12032, + "output_tokens": 758, + "reasoning_output_tokens": 651 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fce392ded18a9579aede51c964f3e2f08458f98c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..113b60dba0de622fc9c89f6be80772570fd97db9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-7c36-71a2-997a-80f12a58d0c7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum.\\nSELECT\\n \\\"education_level\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"education_level\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the grouped numeric sum template with \\\"education_level\\\" as the grouping column and casts \\\"city_development_index\\\" from TEXT to REAL so SQLite can sum it correctly.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14650,"cached_input_tokens":12032,"output_tokens":758,"reasoning_output_tokens":651}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0970aabdcad61ea677a570efb9f80afc523b1553 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum.\nSELECT\n \"education_level\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"education_level\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the grouped numeric sum template with \"education_level\" as the grouping column and casts \"city_development_index\" from TEXT to REAL so SQLite can sum it correctly."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5700007f628b14b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..64804ec2792b180ef49b719c4f1fbf02ef74ea15 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9320, "bytes_utf8": 9320, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 356, "bytes_utf8": 356, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14654, "cached_input_tokens": 0, "output_tokens": 402, "reasoning_output_tokens": 305}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..cdd28b09146b4221986dd418dd2e394817380924 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a5cc86f444066e39", + "api_calls": 0, + "input_tokens": 14654, + "cached_input_tokens": 0, + "output_tokens": 402, + "total_tokens": 15056, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10495.27, + "sql_execution_elapsed_ms_total": 13.16, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f8a459800eb57f94dcbf3c28c0e094816c5cec28 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:32.289796+00:00", + "ended_at": "2026-05-19T15:28:42.785102+00:00", + "elapsed_ms": 10495.27, + "prompt_metrics": { + "chars": 9320, + "bytes_utf8": 9320, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 710, + "bytes_utf8": 710, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 356, + "bytes_utf8": 356, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14654, + "cached_input_tokens": 0, + "output_tokens": 402, + "reasoning_output_tokens": 305 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8fe2e21a01662f79865e3186fe7d6e90ae043894 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ad8ca3f0abf7e0c9e8b01cc5e72d08421379488 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-5b30-7111-a087-ae7b984cf25c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"city_development_index\\\", SUM(\\\"enrollee_id\\\") AS total_measure\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the required grouped numeric sum template with group_col=\\\"city_development_index\\\" and measure_col=\\\"enrollee_id\\\" on the single table \\\"m9\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14654,"cached_input_tokens":0,"output_tokens":402,"reasoning_output_tokens":305}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..59103f59f091f1c537dfa80b4aa85b2a910f769a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"city_development_index\", SUM(\"enrollee_id\") AS total_measure\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nORDER BY total_measure DESC;","notes":"Used the required grouped numeric sum template with group_col=\"city_development_index\" and measure_col=\"enrollee_id\" on the single table \"m9\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a5cc86f444066e39/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b0cb6081f00414aed9da9b098809e6ebb2e0e9c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9698, "bytes_utf8": 9698, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 588, "bytes_utf8": 588, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14765, "cached_input_tokens": 13696, "output_tokens": 535, "reasoning_output_tokens": 367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..98dccd7ad164bed47ef800f968b6aba9012e7663 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_a6b27a42e81166a1", + "api_calls": 0, + "input_tokens": 14765, + "cached_input_tokens": 13696, + "output_tokens": 535, + "total_tokens": 15300, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11356.95, + "sql_execution_elapsed_ms_total": 27.91, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bf7c95f6d7040b9130f7d077994f22edfd7010bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:37:43.493910+00:00", + "ended_at": "2026-05-19T15:37:54.850891+00:00", + "elapsed_ms": 11356.95, + "prompt_metrics": { + "chars": 9698, + "bytes_utf8": 9698, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 972, + "bytes_utf8": 972, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 588, + "bytes_utf8": 588, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14765, + "cached_input_tokens": 13696, + "output_tokens": 535, + "reasoning_output_tokens": 367 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..179e5f72965496f2f175ac86eb001f4c471d8335 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "item_col": "city", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2202ef4fc703d8f63759527c172f9558bc62c401 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e2-c434-7ce3-b0d6-bb5b04311427"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"experience\\\", \\\"city\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"enrollee_id\\\" AS REAL))) OVER (PARTITION BY \\\"experience\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nGROUP BY \\\"experience\\\", \\\"city\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"experience\\\", item_col=\\\"city\\\", and measure_col=\\\"enrollee_id\\\". CAST is used because the schema stores \\\"enrollee_id\\\" as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14765,"cached_input_tokens":13696,"output_tokens":535,"reasoning_output_tokens":367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f6a21fb981816c63d958a910c272fa6430fe0d66 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"experience\", \"city\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS total_measure,\n SUM(CAST(\"enrollee_id\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"enrollee_id\" AS REAL))) OVER (PARTITION BY \"experience\") AS share_within_group\nFROM \"m9\"\nGROUP BY \"experience\", \"city\"\nORDER BY share_within_group DESC;","notes":"Applied the Within-Group Share of Total template with group_col=\"experience\", item_col=\"city\", and measure_col=\"enrollee_id\". CAST is used because the schema stores \"enrollee_id\" as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_a6b27a42e81166a1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f0c7d0e7e9b4111ab697d82c886e3b7571656426 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9534, "bytes_utf8": 9534, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9534, "bytes_utf8": 9534, "lines": 267, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 415, "bytes_utf8": 415, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14712, "cached_input_tokens": 12032, "output_tokens": 417, "reasoning_output_tokens": 304}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..31c3f31f0734ae493c1f14193745a47d7f36e120 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_aaf6461496396e2e", + "api_calls": 0, + "input_tokens": 14712, + "cached_input_tokens": 12032, + "output_tokens": 417, + "total_tokens": 15129, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14525.11, + "sql_execution_elapsed_ms_total": 9.69, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..60ac16543940522580839ed6a3533af4f97962ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:55.082016+00:00", + "ended_at": "2026-05-19T16:01:58.338163+00:00", + "elapsed_ms": 3256.12, + "returncode": 1, + "prompt_metrics": { + "chars": 9534, + "bytes_utf8": 9534, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3d44fc6f611799597454459fb923f65d7e606858 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:59.340779+00:00", + "ended_at": "2026-05-19T16:02:10.609820+00:00", + "elapsed_ms": 11268.99, + "prompt_metrics": { + "chars": 9534, + "bytes_utf8": 9534, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 778, + "bytes_utf8": 778, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 415, + "bytes_utf8": 415, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14712, + "cached_input_tokens": 12032, + "output_tokens": 417, + "reasoning_output_tokens": 304 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6aa0a2522305922d340e013577e35bdb281b6785 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=company_size, condition_col=target.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "company_size", + "condition_col": "target", + "condition_value": "1.0", + "positive_value": "0.0", + "negative_value": "1.0", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6aa0a2522305922d340e013577e35bdb281b6785 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=company_size, condition_col=target.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "company_size", + "condition_col": "target", + "condition_value": "1.0", + "positive_value": "0.0", + "negative_value": "1.0", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7ac15725bffe37309e663e9a20318e3a8b4346af --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-ea81-7ec0-9dce-e0c4450042c0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7d15c954d0dd77e06324cf203ad5558358630ae7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-ea81-7ec0-9dce-e0c4450042c0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..53c50ac65cf6bbb148b352d50bce7bf87aca5b5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-fb4e-70d1-80f4-2455a1146cc6"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"company_size\\\",\\n AVG(CASE WHEN \\\"target\\\" = '1.0' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_size\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template with group_col=\\\"company_size\\\" and condition_col=\\\"target\\\", measuring the within-group proportion where target='1.0'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14712,"cached_input_tokens":12032,"output_tokens":417,"reasoning_output_tokens":304}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc98536c919dd9dd66c7f0d14d097ad915c77641 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"company_size\",\n AVG(CASE WHEN \"target\" = '1.0' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"company_size\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the planned Grouped Condition Rate template with group_col=\"company_size\" and condition_col=\"target\", measuring the within-group proportion where target='1.0'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_aaf6461496396e2e/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1a92e01cdf98e1efe991fd5c02c5ef9cf9b7b425 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9713, "bytes_utf8": 9713, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1383, "bytes_utf8": 1383, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14764, "cached_input_tokens": 12032, "output_tokens": 2238, "reasoning_output_tokens": 1857}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5802338cb0aea542ab7e2bc8395cd9f3ffe1a9dd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_abdb029f7f0b96af", + "api_calls": 0, + "input_tokens": 14764, + "cached_input_tokens": 12032, + "output_tokens": 2238, + "total_tokens": 17002, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 31618.03, + "sql_execution_elapsed_ms_total": 55.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b2eb6c5d2ee6b1a93d1d52f2f8a24c3f0dbb81d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:38.387713+00:00", + "ended_at": "2026-05-19T15:39:10.005781+00:00", + "elapsed_ms": 31618.03, + "prompt_metrics": { + "chars": 9713, + "bytes_utf8": 9713, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1885, + "bytes_utf8": 1885, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1383, + "bytes_utf8": 1383, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14764, + "cached_input_tokens": 12032, + "output_tokens": 2238, + "reasoning_output_tokens": 1857 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..66544cf9cc12252c52f8b797357e249da0573457 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "item_col": "experience", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4aba33fddc264499a3e80c8d69def0ce34360477 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-9ab4-7c30-bf32-f26dde7391c9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"experience\\\",\\n CAST(TRIM(\\\"training_hours\\\") AS REAL) AS \\\"training_hours_value\\\"\\n FROM \\\"m9\\\"\\n WHERE NULLIF(TRIM(\\\"company_type\\\"), '') IS NOT NULL\\n AND NULLIF(TRIM(\\\"experience\\\"), '') IS NOT NULL\\n AND NULLIF(TRIM(\\\"training_hours\\\"), '') IS NOT NULL\\n),\\n\\\"grouped\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"experience\\\",\\n SUM(\\\"training_hours_value\\\") AS \\\"total_measure\\\",\\n COUNT(*) AS \\\"support\\\"\\n FROM \\\"base\\\"\\n WHERE \\\"company_type\\\" IN (\\n SELECT \\\"company_type\\\"\\n FROM \\\"base\\\"\\n GROUP BY \\\"company_type\\\"\\n HAVING COUNT(*) >= 5\\n )\\n GROUP BY \\\"company_type\\\", \\\"experience\\\"\\n HAVING COUNT(*) >= 5\\n)\\nSELECT\\n \\\"company_type\\\",\\n \\\"experience\\\",\\n \\\"total_measure\\\",\\n \\\"total_measure\\\" * 100.0 / SUM(\\\"total_measure\\\") OVER (PARTITION BY \\\"company_type\\\") AS \\\"share_within_group\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"share_within_group\\\" DESC\\nLIMIT 12;\",\"notes\":\"Uses the planned template with group_col=\\\"company_type\\\", item_col=\\\"experience\\\", and measure_col=\\\"training_hours\\\". Because \\\"training_hours\\\" is stored as TEXT, the query casts it to REAL, excludes blank values for the grouping/item/measure fields, applies the provided minimum size/support threshold of 5, and uses top_k=12 via LIMIT 12.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14764,"cached_input_tokens":12032,"output_tokens":2238,"reasoning_output_tokens":1857}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..701007bbfe32a4db3c639b967ba7826e03328d94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nWITH \"base\" AS (\n SELECT\n \"company_type\",\n \"experience\",\n CAST(TRIM(\"training_hours\") AS REAL) AS \"training_hours_value\"\n FROM \"m9\"\n WHERE NULLIF(TRIM(\"company_type\"), '') IS NOT NULL\n AND NULLIF(TRIM(\"experience\"), '') IS NOT NULL\n AND NULLIF(TRIM(\"training_hours\"), '') IS NOT NULL\n),\n\"grouped\" AS (\n SELECT\n \"company_type\",\n \"experience\",\n SUM(\"training_hours_value\") AS \"total_measure\",\n COUNT(*) AS \"support\"\n FROM \"base\"\n WHERE \"company_type\" IN (\n SELECT \"company_type\"\n FROM \"base\"\n GROUP BY \"company_type\"\n HAVING COUNT(*) >= 5\n )\n GROUP BY \"company_type\", \"experience\"\n HAVING COUNT(*) >= 5\n)\nSELECT\n \"company_type\",\n \"experience\",\n \"total_measure\",\n \"total_measure\" * 100.0 / SUM(\"total_measure\") OVER (PARTITION BY \"company_type\") AS \"share_within_group\"\nFROM \"grouped\"\nORDER BY \"share_within_group\" DESC\nLIMIT 12;","notes":"Uses the planned template with group_col=\"company_type\", item_col=\"experience\", and measure_col=\"training_hours\". Because \"training_hours\" is stored as TEXT, the query casts it to REAL, excludes blank values for the grouping/item/measure fields, applies the provided minimum size/support threshold of 5, and uses top_k=12 via LIMIT 12."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_abdb029f7f0b96af/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c57318da14ad5b7f79a443e62f4e0b285acd740c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9462, "bytes_utf8": 9462, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 555, "bytes_utf8": 555, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14703, "cached_input_tokens": 13696, "output_tokens": 658, "reasoning_output_tokens": 495}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8b9542d7de8081ef71b389a2890d040d6745833e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_ad39db2dee95ced1", + "api_calls": 0, + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 658, + "total_tokens": 15361, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15982.65, + "sql_execution_elapsed_ms_total": 24.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..60a9581623b488f58a5aa5b860735687ccb10e86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:19.397793+00:00", + "ended_at": "2026-05-19T15:45:35.380474+00:00", + "elapsed_ms": 15982.65, + "prompt_metrics": { + "chars": 9462, + "bytes_utf8": 9462, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 935, + "bytes_utf8": 935, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 555, + "bytes_utf8": 555, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14703, + "cached_input_tokens": 13696, + "output_tokens": 658, + "reasoning_output_tokens": 495 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..228bf02eeee9f700dd3181626ce00d1fdc924cb0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "enrollee_id", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..95c9f9323f49bd859342022f1dea218624e71633 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-b922-7a73-9608-ae68e4371597"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"enrollee_id\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"m9\\\"\\n)\\nSELECT \\\"enrollee_id\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"enrollee_id\\\" AS INTEGER) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"enrollee_id\\\" and num_tiles=10. CAST is used because \\\"enrollee_id\\\" is stored as TEXT in SQLite but represents numeric identifiers.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14703,"cached_input_tokens":13696,"output_tokens":658,"reasoning_output_tokens":495}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc43dfbee9b3d26f54b73bbb67c47906e23a31e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n \"enrollee_id\",\n NTILE(10) OVER (ORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC) AS \"tail_bucket\"\n FROM \"m9\"\n)\nSELECT \"enrollee_id\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY CAST(\"enrollee_id\" AS INTEGER) DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"enrollee_id\" and num_tiles=10. CAST is used because \"enrollee_id\" is stored as TEXT in SQLite but represents numeric identifiers."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ad39db2dee95ced1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3112cac20efe0a430ac8fe5efa1e5b56c93865f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9230, "bytes_utf8": 9230, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 383, "bytes_utf8": 383, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14635, "cached_input_tokens": 12032, "output_tokens": 622, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0da47d05973161f5b1872dc752d8966f5de3829d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_ae2b1f0db34ea4db", + "api_calls": 0, + "input_tokens": 14635, + "cached_input_tokens": 12032, + "output_tokens": 622, + "total_tokens": 15257, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17304.86, + "sql_execution_elapsed_ms_total": 9.17, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..db1fa3c757d5d977f5c80884386c2790bc8065d7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:50.799226+00:00", + "ended_at": "2026-05-19T16:03:08.104122+00:00", + "elapsed_ms": 17304.86, + "prompt_metrics": { + "chars": 9230, + "bytes_utf8": 9230, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 727, + "bytes_utf8": 727, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 383, + "bytes_utf8": 383, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14635, + "cached_input_tokens": 12032, + "output_tokens": 622, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..262f77d56a73ab0b0dd298ebaf7ab17b80b20646 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=training_hours.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "training_hours", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..77fd268c366fce7f59cbde857698156357d95165 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-c435-7bc1-9496-d0e5b2da937b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(NULLIF(\\\"training_hours\\\", '') AS REAL) <= 88.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\",\"notes\":\"Uses the planned Threshold Rarity CDF template on \\\"training_hours\\\" with threshold 88.0, casting from TEXT to REAL and guarding empty strings with NULLIF for SQLite compatibility.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14635,"cached_input_tokens":12032,"output_tokens":622,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f067d141d810c5b52152f89cd4497555f68cde69 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(NULLIF(\"training_hours\", '') AS REAL) <= 88.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m9\";","notes":"Uses the planned Threshold Rarity CDF template on \"training_hours\" with threshold 88.0, casting from TEXT to REAL and guarding empty strings with NULLIF for SQLite compatibility."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ae2b1f0db34ea4db/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3db2768d7f4eddbdc0b476c315f2f6383336f2e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9245, "bytes_utf8": 9245, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9245, "bytes_utf8": 9245, "lines": 262, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 412, "bytes_utf8": 412, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14639, "cached_input_tokens": 12032, "output_tokens": 484, "reasoning_output_tokens": 372}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bd94362ab5165693bf7085b148c2ba5e399dc45f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_af17ec05ff744d92", + "api_calls": 0, + "input_tokens": 14639, + "cached_input_tokens": 12032, + "output_tokens": 484, + "total_tokens": 15123, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17886.28, + "sql_execution_elapsed_ms_total": 11.84, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..68f3f50aba2732cee05a0fb99ca9c0fbac2dc77e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:42.947595+00:00", + "ended_at": "2026-05-19T16:03:48.875775+00:00", + "elapsed_ms": 5928.14, + "returncode": 1, + "prompt_metrics": { + "chars": 9245, + "bytes_utf8": 9245, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..abc1043b1844a89f4c97c10ece15fd175f31c64c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:49.878171+00:00", + "ended_at": "2026-05-19T16:04:01.836368+00:00", + "elapsed_ms": 11958.14, + "prompt_metrics": { + "chars": 9245, + "bytes_utf8": 9245, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 756, + "bytes_utf8": 756, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 412, + "bytes_utf8": 412, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14639, + "cached_input_tokens": 12032, + "output_tokens": 484, + "reasoning_output_tokens": 372 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c38592a86f0070ec3cbd8ab4c3775ca1124fa893 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "city_development_index", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c38592a86f0070ec3cbd8ab4c3775ca1124fa893 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=city_development_index.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "city_development_index", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f8138021062064581e54ee95b432a5fb867d77c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-8ff8-7c80-b75e-5096f11613c2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..803dd1520cd315073e64886c1f059e2df3f5dfde --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-8ff8-7c80-b75e-5096f11613c2"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..444d36f7247f17be0d3c95b5fcfa4880571873f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-aafa-78f2-93f9-4b0338b6c343"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"city_development_index\\\" AS REAL) <= 0.92 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m9\\\";\",\"notes\":\"Used the provided Threshold Rarity CDF template with measure_col bound to \\\"city_development_index\\\" and measure_threshold 0.92. The measure column is cast to REAL so the threshold comparison is numeric in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14639,"cached_input_tokens":12032,"output_tokens":484,"reasoning_output_tokens":372}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5b0cd6301bdc407f658240cd734fa43a4a6935be --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"city_development_index\" AS REAL) <= 0.92 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m9\";","notes":"Used the provided Threshold Rarity CDF template with measure_col bound to \"city_development_index\" and measure_threshold 0.92. The measure column is cast to REAL so the threshold comparison is numeric in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_af17ec05ff744d92/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ce535e7eb9bac0cf3ec73c79cf4a31d0eec88322 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:27.825831+00:00", + "ended_at": "2026-05-19T16:05:30.949580+00:00", + "elapsed_ms": 3123.72, + "returncode": 1, + "prompt_metrics": { + "chars": 9286, + "bytes_utf8": 9286, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e75dda6eb17ab1a8078911c8059562f6cf97272a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:31.951252+00:00", + "ended_at": "2026-05-19T16:05:35.429786+00:00", + "elapsed_ms": 3478.5, + "returncode": 1, + "prompt_metrics": { + "chars": 9286, + "bytes_utf8": 9286, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4132a57a98124af99f7c6fb94beec8626f35a1b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=education_level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "education_level", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..4132a57a98124af99f7c6fb94beec8626f35a1b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=education_level.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "education_level", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb94a423223228c6c462b3fc5a0cf453d18e720d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-2982-76b2-a6b2-04dbb2a97cb9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f1256090308cde9b027cf74da7bcf02c739152f9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-2982-76b2-a6b2-04dbb2a97cb9"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5a618585c7755784445e1ef884e3acdf8c24da2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-39bb-7f33-9094-57ea7e4fde6d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..8858d6533c3dd724044dbbca03a5e1d28d21882c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-39bb-7f33-9094-57ea7e4fde6d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b0338cff30b74497/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..313d920c7e56c548fc32ac27d0fd93f642f40bd6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:44.334606+00:00", + "ended_at": "2026-05-19T16:06:47.456781+00:00", + "elapsed_ms": 3122.14, + "returncode": 1, + "prompt_metrics": { + "chars": 9404, + "bytes_utf8": 9404, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4543001d5dbd962866f6548bea1c5d29c3226cfa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:48.458659+00:00", + "ended_at": "2026-05-19T16:06:51.711470+00:00", + "elapsed_ms": 3252.77, + "returncode": 1, + "prompt_metrics": { + "chars": 9404, + "bytes_utf8": 9404, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..492a9b41c4c366e2596d56e4a972e3ce8bafb34d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..492a9b41c4c366e2596d56e4a972e3ce8bafb34d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..98988487509c020217f9d94a0b7778d1de181e98 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-546b-7592-a74d-d8c87dca8c9a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..52bf27290166600ee2ce5d2b3f7bb80244ed9f1f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-546b-7592-a74d-d8c87dca8c9a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..194e2c515b62d14533233001842ab5dd21d481b4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-6484-7642-90a5-8d243dd26ab8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..afe85d06f7da0eaf3608c2239a4604f0719856fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-6484-7642-90a5-8d243dd26ab8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b049d7fc80d2fb22/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bc78501209a2e881e4e2adbb38b6c5c6421f9e2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:47.203163+00:00", + "ended_at": "2026-05-19T16:07:49.999641+00:00", + "elapsed_ms": 2796.45, + "returncode": 1, + "prompt_metrics": { + "chars": 9409, + "bytes_utf8": 9409, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c48cdb439ab4a9d9e82a4efdd5b3f2c22e2fc551 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:51.001269+00:00", + "ended_at": "2026-05-19T16:07:54.268432+00:00", + "elapsed_ms": 3267.13, + "returncode": 1, + "prompt_metrics": { + "chars": 9409, + "bytes_utf8": 9409, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..465f7a75295f04afccd5e3a07020a73db1b4f611 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..465f7a75295f04afccd5e3a07020a73db1b4f611 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..22f3aaf3eb3ff3f24709c36c158902dc57156405 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-4a02-7a73-a1e9-0c6f06e4616c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..793cdd263f7fcdccc9d5e6dcfe6948312abd84c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-4a02-7a73-a1e9-0c6f06e4616c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..78f7d6d0f50dd5cc2910460dd4f8c92dd3e2e4ba --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-58ee-7cb0-924e-0086f7f3fd2a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9eae3d25fa90daa4462ed2d6ae4b2ee92103a5ed --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-58ee-7cb0-924e-0086f7f3fd2a"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b1f1468746372d9a/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..07f43bc821e0a198159cc10ec2008321239f3a2a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9527, "bytes_utf8": 9527, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14711, "cached_input_tokens": 12032, "output_tokens": 344, "reasoning_output_tokens": 229}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c74beb10b0b53413756927879bbd7a60c7455339 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_b3deab2abab90e4e", + "api_calls": 0, + "input_tokens": 14711, + "cached_input_tokens": 12032, + "output_tokens": 344, + "total_tokens": 15055, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9485.67, + "sql_execution_elapsed_ms_total": 9.93, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..df96c0bad978b6b9b7e225d8c8153dd9a65e77df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:45.581271+00:00", + "ended_at": "2026-05-19T16:01:55.066968+00:00", + "elapsed_ms": 9485.67, + "prompt_metrics": { + "chars": 9527, + "bytes_utf8": 9527, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 779, + "bytes_utf8": 779, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 416, + "bytes_utf8": 416, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14711, + "cached_input_tokens": 12032, + "output_tokens": 344, + "reasoning_output_tokens": 229 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..608903c2baf5710a20bc29ccb0e5a25d6d3dbc7f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=company_size, condition_col=target.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "company_size", + "condition_col": "target", + "condition_value": "0.0", + "positive_value": "0.0", + "negative_value": "1.0", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a08432e03988dae04e6c3c1a0a26e336ad62250a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-c561-7dc2-83cf-69399198739f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"company_size\\\",\\n AVG(CASE WHEN \\\"target\\\" = '0.0' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_size\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the Grouped Condition Rate template with group_col=\\\"company_size\\\" and condition_col=\\\"target\\\", measuring the rate of target='0.0' within each company_size group.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14711,"cached_input_tokens":12032,"output_tokens":344,"reasoning_output_tokens":229}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..11c5fe4b475461dc09921b84db696ce12cdd8b3d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"company_size\",\n AVG(CASE WHEN \"target\" = '0.0' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"company_size\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the Grouped Condition Rate template with group_col=\"company_size\" and condition_col=\"target\", measuring the rate of target='0.0' within each company_size group."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b3deab2abab90e4e/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5f9a98149dd795a92e33719a5e38fdc26e9b32c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:12.149839+00:00", + "ended_at": "2026-05-19T16:05:15.650787+00:00", + "elapsed_ms": 3500.92, + "returncode": 1, + "prompt_metrics": { + "chars": 9298, + "bytes_utf8": 9298, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a0048cf25bd2db8f0d531e7f1055dc946a6e577b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:16.652702+00:00", + "ended_at": "2026-05-19T16:05:19.857828+00:00", + "elapsed_ms": 3205.09, + "returncode": 1, + "prompt_metrics": { + "chars": 9298, + "bytes_utf8": 9298, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0d6732686d00733ae0ac2e6b1b1b9639604f431 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=enrolled_university.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "enrolled_university", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0d6732686d00733ae0ac2e6b1b1b9639604f431 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=enrolled_university.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "enrolled_university", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c3033f6f54fb7178edae6946a824d0975f4b763 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-ec40-7361-91e5-b046f88a4c8b"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a5edb3eb91ca8dfb90e56dd21c0ff8810317f7a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-ec40-7361-91e5-b046f88a4c8b"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1843ee230aa88519673a6327321366e670860711 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-fde8-77f3-9597-be7d4d37e2d8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..784ee983622fc21973e8adf594a989288f9e723f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-fde8-77f3-9597-be7d4d37e2d8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b69e70d76274d9b5/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5c3defaeac8093a34c1b1f1e3eae75fb865e5b62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9868, "bytes_utf8": 9868, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 959, "bytes_utf8": 959, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14791, "cached_input_tokens": 13696, "output_tokens": 1208, "reasoning_output_tokens": 945}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..766b69ed8b92e108e68d89b985659e7beb6078de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_b79b3e19f722a052", + "api_calls": 0, + "input_tokens": 14791, + "cached_input_tokens": 13696, + "output_tokens": 1208, + "total_tokens": 15999, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 22538.33, + "sql_execution_elapsed_ms_total": 10.46, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..264845850b0a2f6c0afca6456e549ce16ee99924 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:12.755039+00:00", + "ended_at": "2026-05-19T15:47:35.293421+00:00", + "elapsed_ms": 22538.33, + "prompt_metrics": { + "chars": 9868, + "bytes_utf8": 9868, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1379, + "bytes_utf8": 1379, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 959, + "bytes_utf8": 959, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14791, + "cached_input_tokens": 13696, + "output_tokens": 1208, + "reasoning_output_tokens": 945 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..aee1055714e68f0c0165544a0543e974d8043ee3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2d2299cd9af5e05b50377e57d0adee0e195cb598 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40eb-7480-7f52-accd-5f782bff35ea"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH grouped AS (\\n SELECT \\\"enrolled_university\\\", SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"enrolled_university\\\" IS NOT NULL\\n AND \\\"enrolled_university\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n GROUP BY \\\"enrolled_university\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT g.\\\"enrolled_university\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the provided relative-to-total threshold template with group_col=\\\"enrolled_university\\\", measure_col=\\\"enrollee_id\\\", and fraction_threshold=0.1. Blank grouped values were filtered out for a stable grouped view, and \\\"enrollee_id\\\" was cast from TEXT to REAL so SQLite can sum it reliably.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14791,"cached_input_tokens":13696,"output_tokens":1208,"reasoning_output_tokens":945}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..418021dcfe79f921c58b7cd391e772d14592a22a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold.\nWITH grouped AS (\n SELECT \"enrolled_university\", SUM(CAST(\"enrollee_id\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE \"enrolled_university\" IS NOT NULL\n AND \"enrolled_university\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n GROUP BY \"enrolled_university\"\n), total AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM grouped\n)\nSELECT g.\"enrolled_university\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Used the provided relative-to-total threshold template with group_col=\"enrolled_university\", measure_col=\"enrollee_id\", and fraction_threshold=0.1. Blank grouped values were filtered out for a stable grouped view, and \"enrollee_id\" was cast from TEXT to REAL so SQLite can sum it reliably."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b79b3e19f722a052/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..527a2b1bc142f412c63004798fe22a24a340257c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9861, "bytes_utf8": 9861, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 697, "bytes_utf8": 697, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14784, "cached_input_tokens": 13696, "output_tokens": 487, "reasoning_output_tokens": 307}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..279c74afb53f18a2dff39998d37003e9667920f1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_b9a42ff63b25a38b", + "api_calls": 0, + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 487, + "total_tokens": 15271, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10785.6, + "sql_execution_elapsed_ms_total": 11.74, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..83491db1200de6aa6341e49a96ef72aa61536715 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:32.001494+00:00", + "ended_at": "2026-05-19T15:46:42.787135+00:00", + "elapsed_ms": 10785.6, + "prompt_metrics": { + "chars": 9861, + "bytes_utf8": 9861, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1070, + "bytes_utf8": 1070, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 697, + "bytes_utf8": 697, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 487, + "reasoning_output_tokens": 307 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5847b7bca1e90993e58c593f9084492f6cf740b4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..63acd42c9c46b650684ed66b364e0a2c16d5a821 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-d4e2-7ee3-99c1-c3e56e4dfc58"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"gender\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS group_value\\n FROM \\\"m9\\\"\\n GROUP BY \\\"gender\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"gender\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the required Relative-to-Total Extreme Threshold template with group_col=\\\"gender\\\" and measure_col=\\\"city_development_index\\\". Since \\\"city_development_index\\\" is stored as TEXT in SQLite, it is cast to REAL before summation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14784,"cached_input_tokens":13696,"output_tokens":487,"reasoning_output_tokens":307}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c9d8352d81f86ef6fbf2838e87f48895bdc01678 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT\n \"gender\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS group_value\n FROM \"m9\"\n GROUP BY \"gender\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"gender\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Uses the required Relative-to-Total Extreme Threshold template with group_col=\"gender\" and measure_col=\"city_development_index\". Since \"city_development_index\" is stored as TEXT in SQLite, it is cast to REAL before summation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_b9a42ff63b25a38b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6addc0313c191a9987793ef98424273aa0bf3694 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9408, "bytes_utf8": 9408, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9408, "bytes_utf8": 9408, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 648, "bytes_utf8": 648, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14661, "cached_input_tokens": 13696, "output_tokens": 538, "reasoning_output_tokens": 382}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..89073e267a30123ed97f69533b38a309d8a9cca7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_bc5870e8544dc5e3", + "api_calls": 0, + "input_tokens": 14661, + "cached_input_tokens": 13696, + "output_tokens": 538, + "total_tokens": 15199, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16278.58, + "sql_execution_elapsed_ms_total": 46.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e7ed4ebf06ba9ba9b691f85a4d731c1bb9b3f62c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:54.271867+00:00", + "ended_at": "2026-05-19T16:07:57.465220+00:00", + "elapsed_ms": 3193.33, + "returncode": 1, + "prompt_metrics": { + "chars": 9408, + "bytes_utf8": 9408, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a4d16cf70c1f6ef073bad3a8c6d5feb74815b266 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:58.467890+00:00", + "ended_at": "2026-05-19T16:08:11.553182+00:00", + "elapsed_ms": 13085.25, + "prompt_metrics": { + "chars": 9408, + "bytes_utf8": 9408, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1014, + "bytes_utf8": 1014, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 648, + "bytes_utf8": 648, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14661, + "cached_input_tokens": 13696, + "output_tokens": 538, + "reasoning_output_tokens": 382 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca30a8341712edb83d77ca53da11622e86f38866 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca30a8341712edb83d77ca53da11622e86f38866 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bfd60898d02a41caa155fdb188fd70b2e3293ab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-6592-7e80-948c-81c69a53f0dc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c9f3c4e7d64b6afdeb14786508d1fba2817bc3f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-6592-7e80-948c-81c69a53f0dc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d105d5a7073b0895489daaaa421affc0bb7859d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-7625-7813-9353-48e35a609db3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"education_level\\\",\\n AVG(CAST(\\\"city_development_index\\\" AS REAL)) OVER (PARTITION BY \\\"education_level\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"education_level\\\" IS NOT NULL\\n AND \\\"education_level\\\" <> ''\\n AND \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=education_level and measure_col=city_development_index; cast the measure to REAL because the schema stores it as TEXT, and excluded blank/null values for cleaner slice averages.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14661,"cached_input_tokens":13696,"output_tokens":538,"reasoning_output_tokens":382}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..69551317b73b6f2b476dd2961827af17463ce362 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"education_level\",\n AVG(CAST(\"city_development_index\" AS REAL)) OVER (PARTITION BY \"education_level\") AS avg_measure\nFROM \"m9\"\nWHERE \"education_level\" IS NOT NULL\n AND \"education_level\" <> ''\n AND \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=education_level and measure_col=city_development_index; cast the measure to REAL because the schema stores it as TEXT, and excluded blank/null values for cleaner slice averages."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bc5870e8544dc5e3/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f0ecdc0472f5221d8547809dbd1e76a476d1db67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:18.989879+00:00", + "ended_at": "2026-05-19T16:08:22.042243+00:00", + "elapsed_ms": 3052.34, + "returncode": 1, + "prompt_metrics": { + "chars": 9395, + "bytes_utf8": 9395, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..524253abeb944ce8695cdf696a9086e765fc8dbd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:08:23.045032+00:00", + "ended_at": "2026-05-19T16:08:26.234565+00:00", + "elapsed_ms": 3189.48, + "returncode": 1, + "prompt_metrics": { + "chars": 9395, + "bytes_utf8": 9395, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c32ba47e55b41028b8df3fffb97551b6c4589c63 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c32ba47e55b41028b8df3fffb97551b6c4589c63 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5142f0f69e0c7c7679d07d18609e7d4fa07ff26f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-c639-7a10-9172-0c04810fc83b"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2ca2ac25743f8d1512360c898318909e0e28d77 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-c639-7a10-9172-0c04810fc83b"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1a6b4b6c533e68c48c52d1094c6c82e14dbc63c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-d60a-7f00-a642-cead5329ff03"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ce4e9d55bfb8870cc8dc6bc49a807df99f9e974 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-d60a-7f00-a642-cead5329ff03"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcbe9cbe9287f27a/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4c4c6a08d4006da366f0e2ea4d1ce6f7a726257e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10116, "bytes_utf8": 10116, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 718, "bytes_utf8": 718, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14857, "cached_input_tokens": 13696, "output_tokens": 502, "reasoning_output_tokens": 320}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d3c66603e1d0f98e48df4daf57d44a2e71ca6721 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_bcf7ab78929086c6", + "api_calls": 0, + "input_tokens": 14857, + "cached_input_tokens": 13696, + "output_tokens": 502, + "total_tokens": 15359, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10428.59, + "sql_execution_elapsed_ms_total": 10.74, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1cf40378e832c3143859df3a6e22b75b88e39386 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:55.405158+00:00", + "ended_at": "2026-05-19T15:42:05.833792+00:00", + "elapsed_ms": 10428.59, + "prompt_metrics": { + "chars": 10116, + "bytes_utf8": 10116, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1087, + "bytes_utf8": 1087, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 718, + "bytes_utf8": 718, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14857, + "cached_input_tokens": 13696, + "output_tokens": 502, + "reasoning_output_tokens": 320 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b464861c5f747986ee70cd55f2119e111a6e9495 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=company_type, condition_col=major_discipline.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "company_type", + "condition_col": "major_discipline", + "condition_value": "STEM", + "positive_value": "STEM", + "negative_value": "", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..421dda691c2839897ccc572f6508dece392dc75e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-9c57-71d0-acdb-1b3ee588b9f0"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"company_type\\\",\\n SUM(CASE WHEN \\\"major_discipline\\\" = 'STEM' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"major_discipline\\\" = '' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m9\\\"\\n GROUP BY \\\"company_type\\\"\\n)\\nSELECT \\\"company_type\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Uses the planned grouped ratio template with group_col=\\\"company_type\\\" and condition_col=\\\"major_discipline\\\", computing the ratio of STEM rows to empty-string major_discipline rows per company_type.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14857,"cached_input_tokens":13696,"output_tokens":502,"reasoning_output_tokens":320}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e33f58380f772e991ab6dc034461bc8d6a3bbaf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"company_type\",\n SUM(CASE WHEN \"major_discipline\" = 'STEM' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"major_discipline\" = '' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m9\"\n GROUP BY \"company_type\"\n)\nSELECT \"company_type\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Uses the planned grouped ratio template with group_col=\"company_type\" and condition_col=\"major_discipline\", computing the ratio of STEM rows to empty-string major_discipline rows per company_type."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bcf7ab78929086c6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b9420af5ce862aa69314f60423fe36b09894bf80 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9494, "bytes_utf8": 9494, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9494, "bytes_utf8": 9494, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 2224, "bytes_utf8": 2224, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14689, "cached_input_tokens": 13696, "output_tokens": 3624, "reasoning_output_tokens": 2895}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e809b0f2e4f5a81c5e86252e8feaf07070276fad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_bd3f35e44639dce9", + "api_calls": 0, + "input_tokens": 14689, + "cached_input_tokens": 13696, + "output_tokens": 3624, + "total_tokens": 18313, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 51005.25, + "sql_execution_elapsed_ms_total": 94.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..881b2bad9b9e74004437a82d6a3daf53d75c4c64 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:00.334602+00:00", + "ended_at": "2026-05-19T15:53:03.901274+00:00", + "elapsed_ms": 3566.64, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..266b5d91c2689b0a4270b2926da46cee364bc075 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:04.904564+00:00", + "ended_at": "2026-05-19T15:53:52.343221+00:00", + "elapsed_ms": 47438.61, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2948, + "bytes_utf8": 2948, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2224, + "bytes_utf8": 2224, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14689, + "cached_input_tokens": 13696, + "output_tokens": 3624, + "reasoning_output_tokens": 2895 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dfe49a2713a59ea56d628d0015ecb7dec433f617 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..dfe49a2713a59ea56d628d0015ecb7dec433f617 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bb7f5f9c6431cc272fcf159508d3787fea90177b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f0-c1b2-7ac2-ac05-62503dbe6483"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5ccdae3ef3d6b65ad7d101ef867a6865d8016c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f0-c1b2-7ac2-ac05-62503dbe6483"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e976618d777597586f9efa7c649f771f3b2c8003 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f0-d399-7cc2-85a2-eefd9695c513"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"relevent_experience\\\" AS \\\"group_col\\\",\\n CAST(\\\"training_hours\\\" AS REAL) AS \\\"measure\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"training_hours\\\" IS NOT NULL\\n AND TRIM(\\\"training_hours\\\") <> ''\\n AND \\\"relevent_experience\\\" IS NOT NULL\\n AND TRIM(\\\"relevent_experience\\\") <> ''\\n),\\n\\\"ordered\\\" AS (\\n SELECT\\n \\\"group_col\\\",\\n \\\"measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"group_col\\\"\\n ORDER BY \\\"measure\\\"\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"group_col\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"pos\\\" AS (\\n SELECT DISTINCT\\n \\\"group_col\\\",\\n 1.0 + (\\\"cnt\\\" - 1) * 0.95 AS \\\"pos\\\",\\n CAST(1.0 + (\\\"cnt\\\" - 1) * 0.95 AS INTEGER) AS \\\"lower_rn\\\",\\n CASE\\n WHEN 1.0 + (\\\"cnt\\\" - 1) * 0.95 = CAST(1.0 + (\\\"cnt\\\" - 1) * 0.95 AS INTEGER)\\n THEN CAST(1.0 + (\\\"cnt\\\" - 1) * 0.95 AS INTEGER)\\n ELSE CAST(1.0 + (\\\"cnt\\\" - 1) * 0.95 AS INTEGER) + 1\\n END AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"bounds\\\" AS (\\n SELECT\\n \\\"pos\\\".\\\"group_col\\\",\\n \\\"pos\\\".\\\"pos\\\",\\n \\\"pos\\\".\\\"lower_rn\\\",\\n \\\"pos\\\".\\\"upper_rn\\\",\\n MAX(CASE WHEN \\\"ordered\\\".\\\"rn\\\" = \\\"pos\\\".\\\"lower_rn\\\" THEN \\\"ordered\\\".\\\"measure\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN \\\"ordered\\\".\\\"rn\\\" = \\\"pos\\\".\\\"upper_rn\\\" THEN \\\"ordered\\\".\\\"measure\\\" END) AS \\\"upper_val\\\"\\n FROM \\\"pos\\\"\\n JOIN \\\"ordered\\\"\\n ON \\\"ordered\\\".\\\"group_col\\\" = \\\"pos\\\".\\\"group_col\\\"\\n GROUP BY\\n \\\"pos\\\".\\\"group_col\\\",\\n \\\"pos\\\".\\\"pos\\\",\\n \\\"pos\\\".\\\"lower_rn\\\",\\n \\\"pos\\\".\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"group_col\\\" AS \\\"relevent_experience\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this emulates the grouped 95th percentile for \\\"training_hours\\\" within each \\\"relevent_experience\\\" group using window functions and linear interpolation after casting \\\"training_hours\\\" to REAL. Blank/null group and measure values are excluded.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14689,"cached_input_tokens":13696,"output_tokens":3624,"reasoning_output_tokens":2895}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c03304b246fb4755bcd6d5d4cc0a8b3c46fe3e09 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"relevent_experience\" AS \"group_col\",\n CAST(\"training_hours\" AS REAL) AS \"measure\"\n FROM \"m9\"\n WHERE \"training_hours\" IS NOT NULL\n AND TRIM(\"training_hours\") <> ''\n AND \"relevent_experience\" IS NOT NULL\n AND TRIM(\"relevent_experience\") <> ''\n),\n\"ordered\" AS (\n SELECT\n \"group_col\",\n \"measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"group_col\"\n ORDER BY \"measure\"\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"group_col\"\n ) AS \"cnt\"\n FROM \"base\"\n),\n\"pos\" AS (\n SELECT DISTINCT\n \"group_col\",\n 1.0 + (\"cnt\" - 1) * 0.95 AS \"pos\",\n CAST(1.0 + (\"cnt\" - 1) * 0.95 AS INTEGER) AS \"lower_rn\",\n CASE\n WHEN 1.0 + (\"cnt\" - 1) * 0.95 = CAST(1.0 + (\"cnt\" - 1) * 0.95 AS INTEGER)\n THEN CAST(1.0 + (\"cnt\" - 1) * 0.95 AS INTEGER)\n ELSE CAST(1.0 + (\"cnt\" - 1) * 0.95 AS INTEGER) + 1\n END AS \"upper_rn\"\n FROM \"ordered\"\n),\n\"bounds\" AS (\n SELECT\n \"pos\".\"group_col\",\n \"pos\".\"pos\",\n \"pos\".\"lower_rn\",\n \"pos\".\"upper_rn\",\n MAX(CASE WHEN \"ordered\".\"rn\" = \"pos\".\"lower_rn\" THEN \"ordered\".\"measure\" END) AS \"lower_val\",\n MAX(CASE WHEN \"ordered\".\"rn\" = \"pos\".\"upper_rn\" THEN \"ordered\".\"measure\" END) AS \"upper_val\"\n FROM \"pos\"\n JOIN \"ordered\"\n ON \"ordered\".\"group_col\" = \"pos\".\"group_col\"\n GROUP BY\n \"pos\".\"group_col\",\n \"pos\".\"pos\",\n \"pos\".\"lower_rn\",\n \"pos\".\"upper_rn\"\n)\nSELECT\n \"group_col\" AS \"relevent_experience\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n ELSE \"lower_val\" + (\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\")\n END AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this emulates the grouped 95th percentile for \"training_hours\" within each \"relevent_experience\" group using window functions and linear interpolation after casting \"training_hours\" to REAL. Blank/null group and measure values are excluded."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd3f35e44639dce9/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f50b3ea995e4952779de843ae208a37e596d4661 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9198, "bytes_utf8": 9198, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 277, "bytes_utf8": 277, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14619, "cached_input_tokens": 12032, "output_tokens": 409, "reasoning_output_tokens": 333}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7b2e0dec07d1c14509f2d1cb977394b2eccc7c85 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_bd949a989eb0fbf6", + "api_calls": 0, + "input_tokens": 14619, + "cached_input_tokens": 12032, + "output_tokens": 409, + "total_tokens": 15028, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14538.72, + "sql_execution_elapsed_ms_total": 8.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5ddf19b844c8e75e4c6e7624f65e3a044796e90d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:34.678416+00:00", + "ended_at": "2026-05-19T15:33:49.217172+00:00", + "elapsed_ms": 14538.72, + "prompt_metrics": { + "chars": 9198, + "bytes_utf8": 9198, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 960, + "bytes_utf8": 960, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 277, + "bytes_utf8": 277, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14619, + "cached_input_tokens": 12032, + "output_tokens": 409, + "reasoning_output_tokens": 333 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d5614021d86a0c483e2e750bb23a170a804144e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=company_size.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "company_size", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8e64a24dfb0b024227785823883bb16f09680a66 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40de-f87d-7151-a8bc-963609734ab5"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1998135, Requested 15254. Please try again in 401ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"company_size\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"company_size\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped count template with group_col bound to company_size on the single table m9.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14619,"cached_input_tokens":12032,"output_tokens":409,"reasoning_output_tokens":333}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..939dd2df9b838de775fdc2a04fca783f48f1fb61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"company_size\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nGROUP BY \"company_size\"\nORDER BY \"row_count\" DESC;","notes":"Uses the grouped count template with group_col bound to company_size on the single table m9."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bd949a989eb0fbf6/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..824f850352e9a86909ffc952ed5d0812295ba0e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9869, "bytes_utf8": 9869, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 718, "bytes_utf8": 718, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14787, "cached_input_tokens": 13696, "output_tokens": 526, "reasoning_output_tokens": 338}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e24b38c4fe2412243f97049ef8d7e1afac4299d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_bf64a03d304de7f8", + "api_calls": 0, + "input_tokens": 14787, + "cached_input_tokens": 13696, + "output_tokens": 526, + "total_tokens": 15313, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10692.73, + "sql_execution_elapsed_ms_total": 9.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..327d195161781edbe5b10fc77e15c8792d5c1f86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:42.805319+00:00", + "ended_at": "2026-05-19T15:46:53.498082+00:00", + "elapsed_ms": 10692.73, + "prompt_metrics": { + "chars": 9869, + "bytes_utf8": 9869, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1089, + "bytes_utf8": 1089, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 718, + "bytes_utf8": 718, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14787, + "cached_input_tokens": 13696, + "output_tokens": 526, + "reasoning_output_tokens": 338 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a753345ec8ff3e176453b64eff6c376d0207f721 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..17ed1d063cb7af8f48c3814d89d1109ea2f83adc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-ff62-7191-a00f-fc5ab2f65bbe"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"relevent_experience\\\", SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) AS group_value\\n FROM \\\"m9\\\"\\n GROUP BY \\\"relevent_experience\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"relevent_experience\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\\\"relevent_experience\\\" and measure_col=\\\"training_hours\\\". \\\"training_hours\\\" is cast from TEXT to REAL with NULLIF to ignore blank values during SUM.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14787,"cached_input_tokens":13696,"output_tokens":526,"reasoning_output_tokens":338}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..46c7ab9fc0985e5658afcfba797bca975e0d981a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"relevent_experience\", SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) AS group_value\n FROM \"m9\"\n GROUP BY \"relevent_experience\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"relevent_experience\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\"relevent_experience\" and measure_col=\"training_hours\". \"training_hours\" is cast from TEXT to REAL with NULLIF to ignore blank values during SUM."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_bf64a03d304de7f8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b89f50555777c33c1d06accbb1f436b15bf017fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9311, "bytes_utf8": 9311, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 459, "bytes_utf8": 459, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14648, "cached_input_tokens": 13696, "output_tokens": 444, "reasoning_output_tokens": 324}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..40c058576e2d2dca525ed24cdbc4652a4af53ce8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_c3a90fe24a3ab805", + "api_calls": 0, + "input_tokens": 14648, + "cached_input_tokens": 13696, + "output_tokens": 444, + "total_tokens": 15092, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16371.89, + "sql_execution_elapsed_ms_total": 21.43, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0ed196c4f65f364a3d51efaef9e932fe13855594 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:47.061787+00:00", + "ended_at": "2026-05-19T15:31:03.433717+00:00", + "elapsed_ms": 16371.89, + "prompt_metrics": { + "chars": 9311, + "bytes_utf8": 9311, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 825, + "bytes_utf8": 825, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 459, + "bytes_utf8": 459, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14648, + "cached_input_tokens": 13696, + "output_tokens": 444, + "reasoning_output_tokens": 324 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8be4843b6b0c58bb14515737ed6b1d87ea07ae80 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7054247eebebf472dd4edc071e2ffcef17443ef2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dc-69b5-7650-8143-657a9fe0cdc3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"major_discipline\\\", SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"major_discipline\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the requested Grouped Numeric Sum template with group_col=\\\"major_discipline\\\" and measure_col=\\\"training_hours\\\". Casted \\\"training_hours\\\" to REAL and applied NULLIF(..., '') so empty text values are ignored in the sum.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14648,"cached_input_tokens":13696,"output_tokens":444,"reasoning_output_tokens":324}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..21157f7c45fec9ae2855e4d0ebc351ab00e5e72f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"major_discipline\", SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"major_discipline\"\nORDER BY \"total_measure\" DESC;","notes":"Used the requested Grouped Numeric Sum template with group_col=\"major_discipline\" and measure_col=\"training_hours\". Casted \"training_hours\" to REAL and applied NULLIF(..., '') so empty text values are ignored in the sum."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c3a90fe24a3ab805/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..44abc7f6731492a168b6bd526031e740b9ad77d8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9464, "bytes_utf8": 9464, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 643, "bytes_utf8": 643, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14699, "cached_input_tokens": 13696, "output_tokens": 493, "reasoning_output_tokens": 322}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fbac01e76b4042bb4dbe6734a22fc64ac2dd81b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_c51df929d8943233", + "api_calls": 0, + "input_tokens": 14699, + "cached_input_tokens": 13696, + "output_tokens": 493, + "total_tokens": 15192, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11086.76, + "sql_execution_elapsed_ms_total": 30.43, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f7ed0bc14fbcab034231307c6c740b395c2eb3f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:32.682331+00:00", + "ended_at": "2026-05-19T15:44:43.769116+00:00", + "elapsed_ms": 11086.76, + "prompt_metrics": { + "chars": 9464, + "bytes_utf8": 9464, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1030, + "bytes_utf8": 1030, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 643, + "bytes_utf8": 643, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14699, + "cached_input_tokens": 13696, + "output_tokens": 493, + "reasoning_output_tokens": 322 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..391fee5207ff399838900c03b8e57c75d3fedd23 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=training_hours.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "training_hours", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc917a02d2e75be69b15d2789379fc1c14f0d9d7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-02bb-7133-830b-2cfa962a72e7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n CAST(NULLIF(\\\"training_hours\\\", '') AS REAL) AS \\\"training_hours\\\",\\n NTILE(10) OVER (\\n ORDER BY CAST(NULLIF(\\\"training_hours\\\", '') AS REAL) DESC\\n ) AS \\\"tail_bucket\\\"\\n FROM \\\"m9\\\"\\n WHERE NULLIF(\\\"training_hours\\\", '') IS NOT NULL\\n)\\nSELECT \\\"training_hours\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"training_hours\\\" DESC;\",\"notes\":\"Uses the Quantile Tail Slice template on \\\"training_hours\\\"; casts the TEXT field to REAL and excludes empty strings so the top decile is computed numerically.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14699,"cached_input_tokens":13696,"output_tokens":493,"reasoning_output_tokens":322}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..57d203e4ad667f8dde9da7ea77ce5a25762a3d6c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n CAST(NULLIF(\"training_hours\", '') AS REAL) AS \"training_hours\",\n NTILE(10) OVER (\n ORDER BY CAST(NULLIF(\"training_hours\", '') AS REAL) DESC\n ) AS \"tail_bucket\"\n FROM \"m9\"\n WHERE NULLIF(\"training_hours\", '') IS NOT NULL\n)\nSELECT \"training_hours\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"training_hours\" DESC;","notes":"Uses the Quantile Tail Slice template on \"training_hours\"; casts the TEXT field to REAL and excludes empty strings so the top decile is computed numerically."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c51df929d8943233/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0ebf797b26b6bb14770a8ee9919eb5ecdcb8dd02 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9315, "bytes_utf8": 9315, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 403, "bytes_utf8": 403, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14653, "cached_input_tokens": 13696, "output_tokens": 425, "reasoning_output_tokens": 309}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6207cf8482e98c3bb3aefc29ffa2f4c48da18ea7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_c52c6918cfe60eff", + "api_calls": 0, + "input_tokens": 14653, + "cached_input_tokens": 13696, + "output_tokens": 425, + "total_tokens": 15078, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11713.62, + "sql_execution_elapsed_ms_total": 8.44, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0748cdc54b1bdc01c8e5888efc4a1d33e28b01d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:34.559237+00:00", + "ended_at": "2026-05-19T15:29:46.272881+00:00", + "elapsed_ms": 11713.62, + "prompt_metrics": { + "chars": 9315, + "bytes_utf8": 9315, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 769, + "bytes_utf8": 769, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 403, + "bytes_utf8": 403, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14653, + "cached_input_tokens": 13696, + "output_tokens": 425, + "reasoning_output_tokens": 309 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..332043e522d8f5c265667741e34815f68254c49b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5fdf84cbc5bbaee856d21452a3f3209d3618a29b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-4e53-7293-be96-835ae96b5690"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"enrolled_university\\\", SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the planned grouped-sum template with group_col=\\\"enrolled_university\\\" and measure_col=\\\"enrollee_id\\\". CAST is added because \\\"enrollee_id\\\" is stored as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14653,"cached_input_tokens":13696,"output_tokens":425,"reasoning_output_tokens":309}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8d78f894c0cc1be1b211be10b73d5faf142bfe48 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"enrolled_university\", SUM(CAST(\"enrollee_id\" AS NUMERIC)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the planned grouped-sum template with group_col=\"enrolled_university\" and measure_col=\"enrollee_id\". CAST is added because \"enrollee_id\" is stored as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c52c6918cfe60eff/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7ab5bd4e8724fce07eb93424d0c57934045817cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:27.922662+00:00", + "ended_at": "2026-05-19T15:57:31.263325+00:00", + "elapsed_ms": 3340.63, + "returncode": 1, + "prompt_metrics": { + "chars": 9498, + "bytes_utf8": 9498, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7ce993ff5336aff4e87c391f3bb6d6ed2124da9e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:32.265610+00:00", + "ended_at": "2026-05-19T15:57:35.518968+00:00", + "elapsed_ms": 3253.31, + "returncode": 1, + "prompt_metrics": { + "chars": 9498, + "bytes_utf8": 9498, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a81399c278ba8eda961b2575fec492923dfd9d86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a81399c278ba8eda961b2575fec492923dfd9d86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=company_size, measure_col=city_development_index.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_size", + "measure_col": "city_development_index", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f527c4f45c5833ce45e1b4118b52aa4804fcd791 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-d6ff-7262-8da7-5b853144ea5f"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..58b978784d4a6ac210f9177f90139e9bfd42d536 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-d6ff-7262-8da7-5b853144ea5f"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ff021f4dfed48d8b8ce0658e5321e7c860ebbc5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-e805-7511-9f3e-9801d2ff0b14"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c31d88b0c07c412e02c104fa21a6f88d2e28fc19 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-e805-7511-9f3e-9801d2ff0b14"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c572571d1f11fd9b/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..957bd79b1fc27bbf6fd56d91cb331188974deffd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 10101, "bytes_utf8": 10101, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 692, "bytes_utf8": 692, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14855, "cached_input_tokens": 13696, "output_tokens": 594, "reasoning_output_tokens": 416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ca6a4990500987212001b19814a77bc605f1da16 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_c5aa48b9b5e80dff", + "api_calls": 0, + "input_tokens": 14855, + "cached_input_tokens": 13696, + "output_tokens": 594, + "total_tokens": 15449, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12396.95, + "sql_execution_elapsed_ms_total": 9.34, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3f0d4c4986cede1fb3d5fa5fe4a4cda246745696 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:11.634119+00:00", + "ended_at": "2026-05-19T15:40:24.031110+00:00", + "elapsed_ms": 12396.95, + "prompt_metrics": { + "chars": 10101, + "bytes_utf8": 10101, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1061, + "bytes_utf8": 1061, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 692, + "bytes_utf8": 692, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14855, + "cached_input_tokens": 13696, + "output_tokens": 594, + "reasoning_output_tokens": 416 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f5422d33bd9bcf4e45cade579266f10e32151ce9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=gender, condition_col=company_type.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "gender", + "condition_col": "company_type", + "condition_value": "Pvt Ltd", + "positive_value": "Pvt Ltd", + "negative_value": "", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..46817f2dfecf8d4b89cb39ea39bbfe307d0db51e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e5-070d-7963-9561-1c3aab5ac715"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"gender\\\",\\n SUM(CASE WHEN \\\"company_type\\\" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"company_type\\\" = '' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"m9\\\"\\n GROUP BY \\\"gender\\\"\\n)\\nSELECT \\\"gender\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Instantiated the provided grouped ratio template with group_col=\\\"gender\\\", condition_col=\\\"company_type\\\", positive_value='Pvt Ltd', and negative_value='' exactly as bound in the problem instance.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14855,"cached_input_tokens":13696,"output_tokens":594,"reasoning_output_tokens":416}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..245b058a8a92921b172ea822c48218b6a8a47f16 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"gender\",\n SUM(CASE WHEN \"company_type\" = 'Pvt Ltd' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"company_type\" = '' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"m9\"\n GROUP BY \"gender\"\n)\nSELECT \"gender\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Instantiated the provided grouped ratio template with group_col=\"gender\", condition_col=\"company_type\", positive_value='Pvt Ltd', and negative_value='' exactly as bound in the problem instance."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_c5aa48b9b5e80dff/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5b375d98fc3168734728454d687c48e0c08c6544 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9400, "bytes_utf8": 9400, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9400, "bytes_utf8": 9400, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 445, "bytes_utf8": 445, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14662, "cached_input_tokens": 13696, "output_tokens": 367, "reasoning_output_tokens": 252}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..10859990395b3f7a4d5db75badd1211151d0b266 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_cad7b4c010a357ce", + "api_calls": 0, + "input_tokens": 14662, + "cached_input_tokens": 13696, + "output_tokens": 367, + "total_tokens": 15029, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12652.0, + "sql_execution_elapsed_ms_total": 38.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..52053d68b6ec75373756dcf1fb5a2a3e7d0f6cfe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:12.667687+00:00", + "ended_at": "2026-05-19T16:07:15.909623+00:00", + "elapsed_ms": 3241.89, + "returncode": 1, + "prompt_metrics": { + "chars": 9400, + "bytes_utf8": 9400, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9bfa35d717c7e0d9527523fffb916157c9055041 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:16.912107+00:00", + "ended_at": "2026-05-19T16:07:26.322252+00:00", + "elapsed_ms": 9410.11, + "prompt_metrics": { + "chars": 9400, + "bytes_utf8": 9400, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 804, + "bytes_utf8": 804, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 445, + "bytes_utf8": 445, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14662, + "cached_input_tokens": 13696, + "output_tokens": 367, + "reasoning_output_tokens": 252 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9810c8bfabf177ea8a42b67be75bdd9cb2d08f94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..9810c8bfabf177ea8a42b67be75bdd9cb2d08f94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5b176577ea9a5ba6a0ecb8dc69aad148c2d1ceec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-c32e-7420-aaff-ad61c67f0cca"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7fa3304d1b88d73faeea6a3c56c95bafeb1c2af1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-c32e-7420-aaff-ad61c67f0cca"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5121e2ce118b541ce08b2d28d7712ab4e9be7332 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-d3c8-7f02-93f0-45f05ac7ccc5"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n \\\"relevent_experience\\\",\\n AVG(CAST(\\\"training_hours\\\" AS REAL)) OVER (PARTITION BY \\\"relevent_experience\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"relevent_experience\\\" and measure_col=\\\"training_hours\\\". CAST to REAL is used because the schema stores \\\"training_hours\\\" as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14662,"cached_input_tokens":13696,"output_tokens":367,"reasoning_output_tokens":252}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..967097ddc990cee43bf44c40a370db819216b2e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n \"relevent_experience\",\n AVG(CAST(\"training_hours\" AS REAL)) OVER (PARTITION BY \"relevent_experience\") AS avg_measure\nFROM \"m9\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"relevent_experience\" and measure_col=\"training_hours\". CAST to REAL is used because the schema stores \"training_hours\" as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_cad7b4c010a357ce/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d3c0f59b9af197144d41fdd915c7d4e17fbc83a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9710, "bytes_utf8": 9710, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 753, "bytes_utf8": 753, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14763, "cached_input_tokens": 13696, "output_tokens": 710, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a75e20c0427e3888bf2c61096de77c52f040527e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_d3e278414d888256", + "api_calls": 0, + "input_tokens": 14763, + "cached_input_tokens": 13696, + "output_tokens": 710, + "total_tokens": 15473, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17612.17, + "sql_execution_elapsed_ms_total": 30.28, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bf2052b81c3c81e06e475823183c6c5fc30a1621 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:10.068528+00:00", + "ended_at": "2026-05-19T15:39:27.680728+00:00", + "elapsed_ms": 17612.17, + "prompt_metrics": { + "chars": 9710, + "bytes_utf8": 9710, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1143, + "bytes_utf8": 1143, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 753, + "bytes_utf8": 753, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14763, + "cached_input_tokens": 13696, + "output_tokens": 710, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..50d700f5e8a24d9e6dabd8942841116ab60e1a8e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "item_col": "experience", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..46e92dda0750aeb959df46b3d64641fabdeb661d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-1691-7090-bbbd-b44d57d90748"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"company_type\\\", \\\"experience\\\",\\n SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) AS total_measure,\\n SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"training_hours\\\", '') AS REAL))) OVER (PARTITION BY \\\"company_type\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nWHERE NULLIF(\\\"training_hours\\\", '') IS NOT NULL\\nGROUP BY \\\"company_type\\\", \\\"experience\\\"\\nORDER BY share_within_group DESC\\nLIMIT 17;\",\"notes\":\"Uses the requested template with group_col=\\\"company_type\\\", item_col=\\\"experience\\\", and measure_col=\\\"training_hours\\\". \\\"training_hours\\\" is stored as TEXT in SQLite, so it is cast to REAL; empty strings are excluded from the measure.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14763,"cached_input_tokens":13696,"output_tokens":710,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e3134d4d72f0fbeb1c96ce5ac953fd13cfcf025c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"company_type\", \"experience\",\n SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) AS total_measure,\n SUM(CAST(NULLIF(\"training_hours\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\"training_hours\", '') AS REAL))) OVER (PARTITION BY \"company_type\") AS share_within_group\nFROM \"m9\"\nWHERE NULLIF(\"training_hours\", '') IS NOT NULL\nGROUP BY \"company_type\", \"experience\"\nORDER BY share_within_group DESC\nLIMIT 17;","notes":"Uses the requested template with group_col=\"company_type\", item_col=\"experience\", and measure_col=\"training_hours\". \"training_hours\" is stored as TEXT in SQLite, so it is cast to REAL; empty strings are excluded from the measure."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d3e278414d888256/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a98c89a1d6a921d996b85fb8452fa7504ab99bfd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9480, "bytes_utf8": 9480, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9480, "bytes_utf8": 9480, "lines": 264, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 1101, "bytes_utf8": 1101, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14684, "cached_input_tokens": 12032, "output_tokens": 1697, "reasoning_output_tokens": 1391}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5458d8425352cf267a23a14e90948737d717d68f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_d42363a79139d199", + "api_calls": 0, + "input_tokens": 14684, + "cached_input_tokens": 12032, + "output_tokens": 1697, + "total_tokens": 16381, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 33320.67, + "sql_execution_elapsed_ms_total": 53.02, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..66ac52eb45e868a77d6103458d18556179a51fb5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:34.343630+00:00", + "ended_at": "2026-05-19T15:58:37.693929+00:00", + "elapsed_ms": 3350.28, + "returncode": 1, + "prompt_metrics": { + "chars": 9480, + "bytes_utf8": 9480, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d3c80307bfabbf9a95ee02ead32cd86d7072a23d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:38.696249+00:00", + "ended_at": "2026-05-19T15:59:08.666693+00:00", + "elapsed_ms": 29970.39, + "prompt_metrics": { + "chars": 9480, + "bytes_utf8": 9480, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1557, + "bytes_utf8": 1557, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1101, + "bytes_utf8": 1101, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14684, + "cached_input_tokens": 12032, + "output_tokens": 1697, + "reasoning_output_tokens": 1391 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5f69b6ff95befa61ab2a384bc1228246e3ba23d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5f69b6ff95befa61ab2a384bc1228246e3ba23d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..885e2f5682edfdaba22909d7e565b2efd3985bb1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-da7d-7b51-9cdd-83e240146658"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d90f7c67bd8c8a38475a2f3ba867ad51eaae95d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-da7d-7b51-9cdd-83e240146658"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b91b7845030eb208c0514f36c9ae5bb3b9f2b4bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-eb94-7e41-8e8c-5c231cbda06c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n CAST(\\\"training_hours\\\" AS REAL) AS \\\"training_hours_num\\\"\\n FROM \\\"m9\\\"\\n WHERE NULLIF(\\\"company_type\\\", '') IS NOT NULL\\n AND NULLIF(\\\"training_hours\\\", '') IS NOT NULL\\n),\\n\\\"ranked\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"training_hours_num\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"company_type\\\"\\n ORDER BY \\\"training_hours_num\\\"\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"company_type\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n)\\nSELECT\\n \\\"company_type\\\",\\n \\\"training_hours_num\\\" AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"cnt\\\" >= 5\\n AND \\\"rn\\\" = CAST((9 * \\\"cnt\\\" + 9) / 10 AS INTEGER)\\nORDER BY \\\"percentile_measure\\\" DESC, \\\"company_type\\\";\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses the nearest-rank 90th percentile per company_type via window functions. It casts training_hours from TEXT to REAL, excludes blank company_type and training_hours values, and applies the provided minimum group size of 5.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14684,"cached_input_tokens":12032,"output_tokens":1697,"reasoning_output_tokens":1391}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..f8ce1abaebae95f048d22fd56075c313289ccb4e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"company_type\",\n CAST(\"training_hours\" AS REAL) AS \"training_hours_num\"\n FROM \"m9\"\n WHERE NULLIF(\"company_type\", '') IS NOT NULL\n AND NULLIF(\"training_hours\", '') IS NOT NULL\n),\n\"ranked\" AS (\n SELECT\n \"company_type\",\n \"training_hours_num\",\n ROW_NUMBER() OVER (\n PARTITION BY \"company_type\"\n ORDER BY \"training_hours_num\"\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"company_type\"\n ) AS \"cnt\"\n FROM \"base\"\n)\nSELECT\n \"company_type\",\n \"training_hours_num\" AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"cnt\" >= 5\n AND \"rn\" = CAST((9 * \"cnt\" + 9) / 10 AS INTEGER)\nORDER BY \"percentile_measure\" DESC, \"company_type\";","notes":"SQLite does not support PERCENTILE_CONT, so this uses the nearest-rank 90th percentile per company_type via window functions. It casts training_hours from TEXT to REAL, excludes blank company_type and training_hours values, and applies the provided minimum group size of 5."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d42363a79139d199/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3ffb2be58f03bb547407bb053fac2d1360cff454 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:05.983514+00:00", + "ended_at": "2026-05-19T16:07:08.897976+00:00", + "elapsed_ms": 2914.43, + "returncode": 1, + "prompt_metrics": { + "chars": 9401, + "bytes_utf8": 9401, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a1b094315aaafcbfbfa863351b797286f6a8d41f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:09.900124+00:00", + "ended_at": "2026-05-19T16:07:12.661826+00:00", + "elapsed_ms": 2761.65, + "returncode": 1, + "prompt_metrics": { + "chars": 9401, + "bytes_utf8": 9401, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..180ce69b7200e42f1b4b87b1b16861e95df034aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..180ce69b7200e42f1b4b87b1b16861e95df034aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c52a687d58ea48a69fe6e8a5b4c0c673358f8a4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-a91a-7910-9c2f-4957e5236012"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ffccb743f440a8014b55e068d03caf2686753273 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-a91a-7910-9c2f-4957e5236012"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a066db7884758bfdc700606ffbe4071e7c15cc3a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-b84d-71b1-85c3-deb8a9f6e564"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..259457f91f44ee325000f46ba178cf98255ac38c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-b84d-71b1-85c3-deb8a9f6e564"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d4a0d62d3e212d89/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c70772a555347586242336426139a860dcd44692 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9394, "bytes_utf8": 9394, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 488, "bytes_utf8": 488, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14663, "cached_input_tokens": 12032, "output_tokens": 344, "reasoning_output_tokens": 220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8d2bd101364cb717eb1f43ce0f56a920913124b2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_d91e1b55d49f5e6b", + "api_calls": 0, + "input_tokens": 14663, + "cached_input_tokens": 12032, + "output_tokens": 344, + "total_tokens": 15007, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9222.19, + "sql_execution_elapsed_ms_total": 35.24, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..68ba991d46500bed6a55deb14e41943067f47687 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:26.366575+00:00", + "ended_at": "2026-05-19T16:07:35.588797+00:00", + "elapsed_ms": 9222.19, + "prompt_metrics": { + "chars": 9394, + "bytes_utf8": 9394, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 846, + "bytes_utf8": 846, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 488, + "bytes_utf8": 488, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14663, + "cached_input_tokens": 12032, + "output_tokens": 344, + "reasoning_output_tokens": 220 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7c23e843d2f3371754d4253a04a1c223d1720063 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c282e986fae2e7678ab07a4a6f10a0c41b5096c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fd-f8a0-7803-843c-38ef1d71f828"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"enrolled_university\\\",\\n AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"enrolled_university\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the requested Window Partition Average template with group_col=\\\"enrolled_university\\\" and measure_col=\\\"enrollee_id\\\". CAST was added because \\\"enrollee_id\\\" is stored as TEXT in the SQLite schema while AVG requires numeric coercion.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14663,"cached_input_tokens":12032,"output_tokens":344,"reasoning_output_tokens":220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a392d4c080eec07f3b8d9908da81c96e7a47a1c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"enrolled_university\",\n AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"enrolled_university\") AS avg_measure\nFROM \"m9\"\nORDER BY avg_measure DESC;","notes":"Used the requested Window Partition Average template with group_col=\"enrolled_university\" and measure_col=\"enrollee_id\". CAST was added because \"enrollee_id\" is stored as TEXT in the SQLite schema while AVG requires numeric coercion."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_d91e1b55d49f5e6b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0f8c6e386b49016d6b2ea290db7302130ac73935 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9732, "bytes_utf8": 9732, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 687, "bytes_utf8": 687, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14775, "cached_input_tokens": 13696, "output_tokens": 927, "reasoning_output_tokens": 730}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..37b527f160ae33e67214836ef850cd9a03013fae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_dfc60077c1267844", + "api_calls": 0, + "input_tokens": 14775, + "cached_input_tokens": 13696, + "output_tokens": 927, + "total_tokens": 15702, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20374.3, + "sql_execution_elapsed_ms_total": 88.22, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5b38129c28f711776470dc07ba45104d1584d5f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:49.230217+00:00", + "ended_at": "2026-05-19T15:34:09.604552+00:00", + "elapsed_ms": 20374.3, + "prompt_metrics": { + "chars": 9732, + "bytes_utf8": 9732, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1070, + "bytes_utf8": 1070, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 687, + "bytes_utf8": 687, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14775, + "cached_input_tokens": 13696, + "output_tokens": 927, + "reasoning_output_tokens": 730 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..da1e67989b23628fa275c111d31481ca6072cb59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "item_col": "enrollee_id", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd09df34ebb4b1263dc6e75a4e625560d6d59f34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40df-3134-7700-b315-ce01e7640b0a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"city_development_index\\\",\\n \\\"enrollee_id\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"enrollee_id\\\" AS REAL))) OVER (PARTITION BY \\\"city_development_index\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\", \\\"enrollee_id\\\"\\nORDER BY share_within_group DESC\\nLIMIT 14;\",\"notes\":\"Used the planned template and bindings with \\\"city_development_index\\\" as group_col and \\\"enrollee_id\\\" as both item_col and measure_col. CAST is applied because the schema stores \\\"enrollee_id\\\" as TEXT while the template requires SUM().\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14775,"cached_input_tokens":13696,"output_tokens":927,"reasoning_output_tokens":730}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7e99b4674250dd07bd09c48926f4ddf48e276d70 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"city_development_index\",\n \"enrollee_id\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS total_measure,\n SUM(CAST(\"enrollee_id\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"enrollee_id\" AS REAL))) OVER (PARTITION BY \"city_development_index\") AS share_within_group\nFROM \"m9\"\nGROUP BY \"city_development_index\", \"enrollee_id\"\nORDER BY share_within_group DESC\nLIMIT 14;","notes":"Used the planned template and bindings with \"city_development_index\" as group_col and \"enrollee_id\" as both item_col and measure_col. CAST is applied because the schema stores \"enrollee_id\" as TEXT while the template requires SUM()."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_dfc60077c1267844/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c071141ef1412cc0266a59a4b565700861a712eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9739, "bytes_utf8": 9739, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1015, "bytes_utf8": 1015, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14772, "cached_input_tokens": 12032, "output_tokens": 790, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4cc33926252c2feb2d618da85d76351b42228153 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_e05a029cf72ca8ce", + "api_calls": 0, + "input_tokens": 14772, + "cached_input_tokens": 12032, + "output_tokens": 790, + "total_tokens": 15562, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15252.92, + "sql_execution_elapsed_ms_total": 35.86, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6f644cce9fedacb50c989a3ed2d3d3725fce4092 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:12.159835+00:00", + "ended_at": "2026-05-19T15:35:27.412793+00:00", + "elapsed_ms": 15252.92, + "prompt_metrics": { + "chars": 9739, + "bytes_utf8": 9739, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1433, + "bytes_utf8": 1433, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1015, + "bytes_utf8": 1015, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14772, + "cached_input_tokens": 12032, + "output_tokens": 790, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..48b1a736801c0658889ee65a57de83c09682d615 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=relevent_experience, measure_col=training_hours.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "relevent_experience", + "measure_col": "training_hours", + "item_col": "city_development_index", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f7c0cd1bb8b290436278232c61c842201b016bd4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-7536-75b3-8fc4-2a36a37e2150"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"relevent_experience\\\",\\n \\\"city_development_index\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) * 100.0\\n / SUM(SUM(CAST(\\\"training_hours\\\" AS REAL))) OVER (PARTITION BY \\\"relevent_experience\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nWHERE \\\"relevent_experience\\\" IS NOT NULL\\n AND \\\"relevent_experience\\\" <> ''\\n AND \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND \\\"training_hours\\\" <> ''\\nGROUP BY \\\"relevent_experience\\\", \\\"city_development_index\\\"\\nORDER BY share_within_group DESC\\nLIMIT 11;\",\"notes\":\"Uses the requested Within-Group Share of Total template with group_col=\\\"relevent_experience\\\", item_col=\\\"city_development_index\\\", and measure_col=\\\"training_hours\\\". CAST is applied because \\\"training_hours\\\" is stored as TEXT in the schema snapshot, and blank values are excluded before aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14772,"cached_input_tokens":12032,"output_tokens":790,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e998f46812d012ec06f110411e9638096b2cb8fc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"relevent_experience\",\n \"city_development_index\",\n SUM(CAST(\"training_hours\" AS REAL)) AS total_measure,\n SUM(CAST(\"training_hours\" AS REAL)) * 100.0\n / SUM(SUM(CAST(\"training_hours\" AS REAL))) OVER (PARTITION BY \"relevent_experience\") AS share_within_group\nFROM \"m9\"\nWHERE \"relevent_experience\" IS NOT NULL\n AND \"relevent_experience\" <> ''\n AND \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n AND \"training_hours\" IS NOT NULL\n AND \"training_hours\" <> ''\nGROUP BY \"relevent_experience\", \"city_development_index\"\nORDER BY share_within_group DESC\nLIMIT 11;","notes":"Uses the requested Within-Group Share of Total template with group_col=\"relevent_experience\", item_col=\"city_development_index\", and measure_col=\"training_hours\". CAST is applied because \"training_hours\" is stored as TEXT in the schema snapshot, and blank values are excluded before aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e05a029cf72ca8ce/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5be98793273741beddc59df84f717f48fd685d56 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9292, "bytes_utf8": 9292, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 380, "bytes_utf8": 380, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14662, "cached_input_tokens": 13696, "output_tokens": 354, "reasoning_output_tokens": 243}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..72b0ebd7ef8d7745bc5b2b5f82c4a3849c985cc8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_e1a797e538210d87", + "api_calls": 0, + "input_tokens": 14662, + "cached_input_tokens": 13696, + "output_tokens": 354, + "total_tokens": 15016, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8521.06, + "sql_execution_elapsed_ms_total": 14.72, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..45d61ddb4288b2bdf7e39e63166b4f9f3716df7b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:39.290023+00:00", + "ended_at": "2026-05-19T16:04:47.811119+00:00", + "elapsed_ms": 8521.06, + "prompt_metrics": { + "chars": 9292, + "bytes_utf8": 9292, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 745, + "bytes_utf8": 745, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 380, + "bytes_utf8": 380, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14662, + "cached_input_tokens": 13696, + "output_tokens": 354, + "reasoning_output_tokens": 243 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..164d295018720be7cbe4bd922036e2c2e87755dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=relevent_experience.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "relevent_experience", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fffb0bcf88d05446ad71f664a82cf31200e6def3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-6bfe-7ad3-836a-6274c73b2d63"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"relevent_experience\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"relevent_experience\\\"\\nORDER BY \\\"support\\\" ASC, \\\"relevent_experience\\\"\\nLIMIT 12;\",\"notes\":\"Implements the planned Low-Support Group Count template using \\\"relevent_experience\\\" as the grouping column on the single table \\\"m9\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14662,"cached_input_tokens":13696,"output_tokens":354,"reasoning_output_tokens":243}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ad8abdc53da1241e916befdfc523f74362c190d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"relevent_experience\",\n COUNT(*) AS \"support\"\nFROM \"m9\"\nGROUP BY \"relevent_experience\"\nORDER BY \"support\" ASC, \"relevent_experience\"\nLIMIT 12;","notes":"Implements the planned Low-Support Group Count template using \"relevent_experience\" as the grouping column on the single table \"m9\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1a797e538210d87/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..25363cdf3ffb5f326c273ee8484799812edd5ca5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9498, "bytes_utf8": 9498, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1083, "bytes_utf8": 1083, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14692, "cached_input_tokens": 13696, "output_tokens": 2392, "reasoning_output_tokens": 2070}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e83ea30eefe168a71e184d482b49cb315208586c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_e1aa0a1f67ea735c", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 2392, + "total_tokens": 17084, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 37666.79, + "sql_execution_elapsed_ms_total": 58.97, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..944f6d08905caa7e573fbe431c1e385eb7e18900 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:50:33.237086+00:00", + "ended_at": "2026-05-19T15:51:10.903912+00:00", + "elapsed_ms": 37666.79, + "prompt_metrics": { + "chars": 9498, + "bytes_utf8": 9498, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1532, + "bytes_utf8": 1532, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1083, + "bytes_utf8": 1083, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 2392, + "reasoning_output_tokens": 2070 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c62d3c7689ceff3aaa486208a397e62cd57efe58 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..52a8910de5124056d6a5bd42bb4b26e9b9e381e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ee-8346-7330-a177-7bed3af7129a"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"__ranked\\\" AS (\\n SELECT\\n \\\"city_development_index\\\",\\n CAST(\\\"enrollee_id\\\" AS REAL) AS \\\"__measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"city_development_index\\\"\\n ORDER BY CAST(\\\"enrollee_id\\\" AS REAL)\\n ) AS \\\"__rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"city_development_index\\\"\\n ) AS \\\"__cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"city_development_index\\\" IS NOT NULL\\n AND TRIM(\\\"city_development_index\\\") <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND TRIM(\\\"enrollee_id\\\") <> ''\\n AND \\\"enrollee_id\\\" NOT GLOB '*[^0-9]*'\\n)\\nSELECT\\n \\\"city_development_index\\\",\\n MIN(\\\"__measure\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"__ranked\\\"\\nWHERE \\\"__rn\\\" >= ((95 * \\\"__cnt\\\" + 99) / 100)\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile approximation with window functions. It casts text \\\"enrollee_id\\\" values to numeric and excludes blank or non-numeric IDs.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14692,"cached_input_tokens":13696,"output_tokens":2392,"reasoning_output_tokens":2070}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a05c7771730f7e529796743748d706e4cdf662f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"__ranked\" AS (\n SELECT\n \"city_development_index\",\n CAST(\"enrollee_id\" AS REAL) AS \"__measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"city_development_index\"\n ORDER BY CAST(\"enrollee_id\" AS REAL)\n ) AS \"__rn\",\n COUNT(*) OVER (\n PARTITION BY \"city_development_index\"\n ) AS \"__cnt\"\n FROM \"m9\"\n WHERE \"city_development_index\" IS NOT NULL\n AND TRIM(\"city_development_index\") <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND TRIM(\"enrollee_id\") <> ''\n AND \"enrollee_id\" NOT GLOB '*[^0-9]*'\n)\nSELECT\n \"city_development_index\",\n MIN(\"__measure\") AS \"percentile_measure\"\nFROM \"__ranked\"\nWHERE \"__rn\" >= ((95 * \"__cnt\" + 99) / 100)\nGROUP BY \"city_development_index\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile approximation with window functions. It casts text \"enrollee_id\" values to numeric and excludes blank or non-numeric IDs."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e1aa0a1f67ea735c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b0fe160c52ffd7b77aebc5d26c9388d3a6ba1a5b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:54:07.091928+00:00", + "ended_at": "2026-05-19T15:54:10.197881+00:00", + "elapsed_ms": 3105.93, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1c897caa75ac7b62dd982c8ab42aa971390e09c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:54:11.201050+00:00", + "ended_at": "2026-05-19T15:54:14.444708+00:00", + "elapsed_ms": 3243.61, + "returncode": 1, + "prompt_metrics": { + "chars": 9494, + "bytes_utf8": 9494, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..146b75528d598945bbdfed31bc3c002fac792df2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..146b75528d598945bbdfed31bc3c002fac792df2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b436d602bd77f852c659c3a2b8d7f8f07920b435 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-c681-7022-8abb-6910eea16bb0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..baa703c66337de7826e2f5e40338531361615855 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-c681-7022-8abb-6910eea16bb0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..62975278976b1ebaaf93d8be2e7d36f09b2703aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-d683-7412-ae8b-55251249d589"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c666bfcce4007bb9ae0c540f4efcd936f335ab0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-d683-7412-ae8b-55251249d589"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e23caf085b15d3d5/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..60451d808662196e872d72b5e20c8edc9ec267c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9312, "bytes_utf8": 9312, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 458, "bytes_utf8": 458, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14653, "cached_input_tokens": 12032, "output_tokens": 342, "reasoning_output_tokens": 218}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f2f8047fa987e13f77cfbb66bed1ba8224743f3c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_e78405547a3b2e49", + "api_calls": 0, + "input_tokens": 14653, + "cached_input_tokens": 12032, + "output_tokens": 342, + "total_tokens": 14995, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8278.27, + "sql_execution_elapsed_ms_total": 11.52, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25d866ca751716502eabd65af4f6364c1d107108 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:26.263623+00:00", + "ended_at": "2026-05-19T15:29:34.541934+00:00", + "elapsed_ms": 8278.27, + "prompt_metrics": { + "chars": 9312, + "bytes_utf8": 9312, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 824, + "bytes_utf8": 824, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 458, + "bytes_utf8": 458, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14653, + "cached_input_tokens": 12032, + "output_tokens": 342, + "reasoning_output_tokens": 218 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..30080d6616c35954bf4d288302832fe27ccdad28 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 25169.75, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3964f80fe55b9e144cdf0f8a6be600437192375 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-2e0d-77d0-9da1-52b5dc514dec"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"enrolled_university\\\", SUM(CAST(\\\"enrollee_id\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"enrolled_university\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required Grouped Numeric Sum template with group_col=\\\"enrolled_university\\\" and measure_col=\\\"enrollee_id\\\". CAST was added because \\\"enrollee_id\\\" is stored as TEXT in SQLite schema but represents numeric values for SUM.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14653,"cached_input_tokens":12032,"output_tokens":342,"reasoning_output_tokens":218}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3f550028b812b306ebd1a60615476e7c8ae576b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"enrolled_university\", SUM(CAST(\"enrollee_id\" AS NUMERIC)) AS \"total_measure\"\nFROM \"m9\"\nGROUP BY \"enrolled_university\"\nORDER BY \"total_measure\" DESC;","notes":"Used the required Grouped Numeric Sum template with group_col=\"enrolled_university\" and measure_col=\"enrollee_id\". CAST was added because \"enrollee_id\" is stored as TEXT in SQLite schema but represents numeric values for SUM."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e78405547a3b2e49/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3befc628a09a84edeca5338f214de09f9bda0a12 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9498, "bytes_utf8": 9498, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1123, "bytes_utf8": 1123, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14692, "cached_input_tokens": 12032, "output_tokens": 1655, "reasoning_output_tokens": 1338}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..85644583aefbd358d12071d4e6dec7e999ac2600 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_e84efb012fbfa8e2", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 12032, + "output_tokens": 1655, + "total_tokens": 16347, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 34407.87, + "sql_execution_elapsed_ms_total": 51.43, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ffd78aad04342d511154890ca8f6af82ccc141c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:51:10.967967+00:00", + "ended_at": "2026-05-19T15:51:45.375860+00:00", + "elapsed_ms": 34407.87, + "prompt_metrics": { + "chars": 9498, + "bytes_utf8": 9498, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1907, + "bytes_utf8": 1907, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1123, + "bytes_utf8": 1123, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14692, + "cached_input_tokens": 12032, + "output_tokens": 1655, + "reasoning_output_tokens": 1338 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c4368478001556c8bc0e3c16f2477b292c2b9ca8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=city_development_index, measure_col=enrollee_id.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "city_development_index", + "measure_col": "enrollee_id", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d70477fb3de946c4dc44de2f23f5d1f71ad2fd1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40ef-16a1-7153-882c-e0cb1b685e3e"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1987448, Requested 15327. Please try again in 83ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"city_development_index\\\",\\n CAST(\\\"enrollee_id\\\" AS REAL) AS \\\"measure_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"city_development_index\\\"\\n ORDER BY CAST(\\\"enrollee_id\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"city_development_index\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"city_development_index\\\" IS NOT NULL\\n AND \\\"city_development_index\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\n)\\nSELECT\\n \\\"city_development_index\\\",\\n MIN(CASE WHEN \\\"rn\\\" >= 0.9 * \\\"cnt\\\" THEN \\\"measure_value\\\" END) AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"cnt\\\" >= 5\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 90th percentile approximation with window functions. It groups by \\\"city_development_index\\\", casts text \\\"enrollee_id\\\" to REAL for ordering, excludes blank keys/measures, and applies the provided minimum group size of 5.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14692,"cached_input_tokens":12032,"output_tokens":1655,"reasoning_output_tokens":1338}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e11b752e3601ff59f7375fc57797076934530aa5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"city_development_index\",\n CAST(\"enrollee_id\" AS REAL) AS \"measure_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"city_development_index\"\n ORDER BY CAST(\"enrollee_id\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"city_development_index\"\n ) AS \"cnt\"\n FROM \"m9\"\n WHERE \"city_development_index\" IS NOT NULL\n AND \"city_development_index\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\n)\nSELECT\n \"city_development_index\",\n MIN(CASE WHEN \"rn\" >= 0.9 * \"cnt\" THEN \"measure_value\" END) AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"cnt\" >= 5\nGROUP BY \"city_development_index\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 90th percentile approximation with window functions. It groups by \"city_development_index\", casts text \"enrollee_id\" to REAL for ordering, excludes blank keys/measures, and applies the provided minimum group size of 5."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_e84efb012fbfa8e2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..71e839fc00d79dbb58cd231cb44f5f8c7d07e370 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9713, "bytes_utf8": 9713, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 618, "bytes_utf8": 618, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14766, "cached_input_tokens": 12032, "output_tokens": 1203, "reasoning_output_tokens": 1034}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..992ad6562076afcf455a069466cc7b2fdd8dc8b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f05afdd3be04aa2c", + "api_calls": 0, + "input_tokens": 14766, + "cached_input_tokens": 12032, + "output_tokens": 1203, + "total_tokens": 15969, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20443.5, + "sql_execution_elapsed_ms_total": 18.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..254b7b521616dc6bf59e78599f365412038dc58d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:34:51.693599+00:00", + "ended_at": "2026-05-19T15:35:12.137128+00:00", + "elapsed_ms": 20443.5, + "prompt_metrics": { + "chars": 9713, + "bytes_utf8": 9713, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1013, + "bytes_utf8": 1013, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 618, + "bytes_utf8": 618, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14766, + "cached_input_tokens": 12032, + "output_tokens": 1203, + "reasoning_output_tokens": 1034 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d82c97105492c29c7f70c01d37d89cb4385413f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=gender, measure_col=city_development_index.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "gender", + "measure_col": "city_development_index", + "item_col": "city", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c73d3d6e53be2916539b8f9a5cdb235427cc6f1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-253f-7e53-9fae-c6bc24bc2cda"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"gender\\\", \\\"city\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"city_development_index\\\" AS REAL))) OVER (PARTITION BY \\\"gender\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\", \\\"city\\\"\\nORDER BY \\\"share_within_group\\\" DESC\\nLIMIT 15;\",\"notes\":\"Uses the planned Within-Group Share of Total template with group_col=\\\"gender\\\", item_col=\\\"city\\\", and casts TEXT \\\"city_development_index\\\" to REAL for SQLite-compatible summation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14766,"cached_input_tokens":12032,"output_tokens":1203,"reasoning_output_tokens":1034}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d1c04559691dbcfc2f650277f7ab2d7dbe238911 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"gender\", \"city\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"city_development_index\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"city_development_index\" AS REAL))) OVER (PARTITION BY \"gender\") AS \"share_within_group\"\nFROM \"m9\"\nGROUP BY \"gender\", \"city\"\nORDER BY \"share_within_group\" DESC\nLIMIT 15;","notes":"Uses the planned Within-Group Share of Total template with group_col=\"gender\", item_col=\"city\", and casts TEXT \"city_development_index\" to REAL for SQLite-compatible summation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f05afdd3be04aa2c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7f47cad149ee2a88514cd58aa151cc0d196fbb30 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9399, "bytes_utf8": 9399, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 675, "bytes_utf8": 675, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14664, "cached_input_tokens": 13696, "output_tokens": 541, "reasoning_output_tokens": 365}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fd653a0c3671a083eaf2a16ea3ddaac4b8b1d0b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f0d28c375aa5e7d8", + "api_calls": 0, + "input_tokens": 14664, + "cached_input_tokens": 13696, + "output_tokens": 541, + "total_tokens": 15205, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11532.15, + "sql_execution_elapsed_ms_total": 36.4, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8524ea7824a24e4446a0bc6413799bd66886ff2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:07:35.629177+00:00", + "ended_at": "2026-05-19T16:07:47.161358+00:00", + "elapsed_ms": 11532.15, + "prompt_metrics": { + "chars": 9399, + "bytes_utf8": 9399, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1049, + "bytes_utf8": 1049, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 675, + "bytes_utf8": 675, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14664, + "cached_input_tokens": 13696, + "output_tokens": 541, + "reasoning_output_tokens": 365 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..50c9af84c567d30658f20161da5fd05dcd9ffdb0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=enrolled_university, measure_col=enrollee_id.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "enrolled_university", + "measure_col": "enrollee_id", + "top_k": 15, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f797916b091ae81a0c02d5ecab67ed7921022c3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fe-1cc1-7473-b9de-21de17c8c44c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"enrolled_university\\\",\\n AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"enrolled_university\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"enrolled_university\\\" IS NOT NULL\\n AND \\\"enrolled_university\\\" <> ''\\n AND \\\"enrollee_id\\\" IS NOT NULL\\n AND \\\"enrollee_id\\\" <> ''\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the required Window Partition Average template with group_col=\\\"enrolled_university\\\" and measure_col=\\\"enrollee_id\\\". Applied filtered_stable_view-style filtering to exclude null/empty group and measure values, and cast the text identifier to REAL so AVG is SQLite-compatible.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14664,"cached_input_tokens":13696,"output_tokens":541,"reasoning_output_tokens":365}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..baaa57663064319cfe94f61099aa14eda0bc301d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"enrolled_university\",\n AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"enrolled_university\") AS avg_measure\nFROM \"m9\"\nWHERE \"enrolled_university\" IS NOT NULL\n AND \"enrolled_university\" <> ''\n AND \"enrollee_id\" IS NOT NULL\n AND \"enrollee_id\" <> ''\nORDER BY avg_measure DESC;","notes":"Used the required Window Partition Average template with group_col=\"enrolled_university\" and measure_col=\"enrollee_id\". Applied filtered_stable_view-style filtering to exclude null/empty group and measure values, and cast the text identifier to REAL so AVG is SQLite-compatible."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f0d28c375aa5e7d8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c20337d89459cdf2a880ca84fb56d587e6d0cb0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9615, "bytes_utf8": 9615, "lines": 268, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 432, "bytes_utf8": 432, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14744, "cached_input_tokens": 13696, "output_tokens": 636, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d9a4f916282ccaa6f30fe5e019e95e93857bdc8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f416f21fe5fff340", + "api_calls": 0, + "input_tokens": 14744, + "cached_input_tokens": 13696, + "output_tokens": 636, + "total_tokens": 15380, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15934.61, + "sql_execution_elapsed_ms_total": 9.42, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..848c528b6b681420390855ef656a1a2c04024cd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:47.657072+00:00", + "ended_at": "2026-05-19T15:44:03.591715+00:00", + "elapsed_ms": 15934.61, + "prompt_metrics": { + "chars": 9615, + "bytes_utf8": 9615, + "lines": 268, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 799, + "bytes_utf8": 799, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 432, + "bytes_utf8": 432, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14744, + "cached_input_tokens": 13696, + "output_tokens": 636, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..18a6c323124b4dcff52023a384dd0615c7a05c9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,268 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=company_size, group_col_2=company_type.", + "planned_template_id": "tpl_c2_filtered_group_count_2d", + "bindings": { + "group_col": "company_size", + "group_col_2": "company_type", + "predicate_col": "enrollee_id", + "predicate_op": ">=", + "predicate_value": 25169.75, + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..769168f426e0f82098d4c4c2cec95e0dbec66cde --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e8-52c1-7720-9a0f-69467f6d2755"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d.\\nSELECT \\\"company_size\\\", \\\"company_type\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE CAST(\\\"enrollee_id\\\" AS REAL) >= 25169.75\\nGROUP BY \\\"company_size\\\", \\\"company_type\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the required template shape with bound grouping columns. \\\"enrollee_id\\\" is cast to REAL for the numeric predicate because the schema stores it as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14744,"cached_input_tokens":13696,"output_tokens":636,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..70e8380f3675e639dc7e079aeaa46da4d3ae1f34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_c2_filtered_group_count_2d.\nSELECT \"company_size\", \"company_type\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE CAST(\"enrollee_id\" AS REAL) >= 25169.75\nGROUP BY \"company_size\", \"company_type\"\nORDER BY \"row_count\" DESC;","notes":"Uses the required template shape with bound grouping columns. \"enrollee_id\" is cast to REAL for the numeric predicate because the schema stores it as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f416f21fe5fff340/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..70daa7d574a05e13c240d877151cf23cbb2b20b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9186, "bytes_utf8": 9186, "lines": 262, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 303, "bytes_utf8": 303, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14617, "cached_input_tokens": 12032, "output_tokens": 331, "reasoning_output_tokens": 251}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..cbd7c40b25dba1fadfb5cbce9594756790a8d41d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f41dcfcfe45dbbb2", + "api_calls": 0, + "input_tokens": 14617, + "cached_input_tokens": 12032, + "output_tokens": 331, + "total_tokens": 14948, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11799.33, + "sql_execution_elapsed_ms_total": 6.39, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d7430cb77b83d8ed4b546b3ca83d2b39711d079f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:19.344548+00:00", + "ended_at": "2026-05-19T15:32:31.143908+00:00", + "elapsed_ms": 11799.33, + "prompt_metrics": { + "chars": 9186, + "bytes_utf8": 9186, + "lines": 262, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 657, + "bytes_utf8": 657, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 303, + "bytes_utf8": 303, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14617, + "cached_input_tokens": 12032, + "output_tokens": 331, + "reasoning_output_tokens": 251 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a592eb71ffd6053faf1b627b2c2faf544a616331 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,262 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=gender.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "gender", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e70e1bd2d5b2d8da84018de0306e6582781e789 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-d213-74d0-8865-de499634d9ae"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"gender\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped count template with \\\"gender\\\" as the grouping column; counts all rows, including blank gender values if present.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14617,"cached_input_tokens":12032,"output_tokens":331,"reasoning_output_tokens":251}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9d8727530b8dec948a97e45bbaccb357c4e592d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"gender\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nGROUP BY \"gender\"\nORDER BY \"row_count\" DESC;","notes":"Uses the grouped count template with \"gender\" as the grouping column; counts all rows, including blank gender values if present."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f41dcfcfe45dbbb2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bfc5431504f7c73fbcb8d6f74161e99bb9aa3364 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9736, "bytes_utf8": 9736, "lines": 266, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 852, "bytes_utf8": 852, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14768, "cached_input_tokens": 12032, "output_tokens": 740, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..aa4a0849253f87b22033847f23e6ad5c99bf10a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f6d95d112f27cb53", + "api_calls": 0, + "input_tokens": 14768, + "cached_input_tokens": 12032, + "output_tokens": 740, + "total_tokens": 15508, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16669.34, + "sql_execution_elapsed_ms_total": 34.52, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0853da84983630e91293aec140f47f7da2abe3f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:36:24.921607+00:00", + "ended_at": "2026-05-19T15:36:41.590978+00:00", + "elapsed_ms": 16669.34, + "prompt_metrics": { + "chars": 9736, + "bytes_utf8": 9736, + "lines": 266, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1238, + "bytes_utf8": 1238, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 852, + "bytes_utf8": 852, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14768, + "cached_input_tokens": 12032, + "output_tokens": 740, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b23a43c29e13f3b68ced1af532d4ab912bcb295 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,266 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=education_level, measure_col=city_development_index.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "education_level", + "measure_col": "city_development_index", + "item_col": "training_hours", + "top_k": 18, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c484e464d4b0fee6908223b3d698145965ffd48 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-9162-7d20-9c5a-7885d2daad85"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"education_level\\\",\\n \\\"training_hours\\\",\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"city_development_index\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"city_development_index\\\" AS REAL))) OVER (PARTITION BY \\\"education_level\\\") AS share_within_group\\nFROM \\\"m9\\\"\\nWHERE \\\"education_level\\\" <> ''\\n AND \\\"training_hours\\\" <> ''\\n AND \\\"city_development_index\\\" <> ''\\nGROUP BY \\\"education_level\\\", \\\"training_hours\\\"\\nORDER BY share_within_group DESC\\nLIMIT 18;\",\"notes\":\"Uses the required Within-Group Share of Total template with group_col=education_level, item_col=training_hours, and measure_col=city_development_index. CAST to REAL is needed because city_development_index is stored as TEXT, and blank values are excluded to avoid distorting sums.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14768,"cached_input_tokens":12032,"output_tokens":740,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8edf98f569f47c5cda350776685d964e08bd91fc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"education_level\",\n \"training_hours\",\n SUM(CAST(\"city_development_index\" AS REAL)) AS total_measure,\n SUM(CAST(\"city_development_index\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"city_development_index\" AS REAL))) OVER (PARTITION BY \"education_level\") AS share_within_group\nFROM \"m9\"\nWHERE \"education_level\" <> ''\n AND \"training_hours\" <> ''\n AND \"city_development_index\" <> ''\nGROUP BY \"education_level\", \"training_hours\"\nORDER BY share_within_group DESC\nLIMIT 18;","notes":"Uses the required Within-Group Share of Total template with group_col=education_level, item_col=training_hours, and measure_col=city_development_index. CAST to REAL is needed because city_development_index is stored as TEXT, and blank values are excluded to avoid distorting sums."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f6d95d112f27cb53/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..174d476b19d69bb2b1351913968b6053782afb9d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9480, "bytes_utf8": 9480, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 2003, "bytes_utf8": 2003, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14684, "cached_input_tokens": 13696, "output_tokens": 3923, "reasoning_output_tokens": 3311}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..43e3d7dca6aebbc893500a14258e75f68ef583d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f78aaa1f5f51d498", + "api_calls": 0, + "input_tokens": 14684, + "cached_input_tokens": 13696, + "output_tokens": 3923, + "total_tokens": 18607, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 58741.04, + "sql_execution_elapsed_ms_total": 73.83, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6efedac5a2cbaa225a7ad1b1ce148cdc057eaf21 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:57:35.522399+00:00", + "ended_at": "2026-05-19T15:58:34.263477+00:00", + "elapsed_ms": 58741.04, + "prompt_metrics": { + "chars": 9480, + "bytes_utf8": 9480, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2647, + "bytes_utf8": 2647, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 2003, + "bytes_utf8": 2003, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14684, + "cached_input_tokens": 13696, + "output_tokens": 3923, + "reasoning_output_tokens": 3311 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f868eb3d127cd8ceab1f7ef3151bf6d744fe2f00 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=company_type, measure_col=training_hours.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "company_type", + "measure_col": "training_hours", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 88.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..98bad92726e8ecbe672b2f1e2e4ef2fa0f8d931e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-f4c0-7551-ac4c-641542bbb549"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n CAST(\\\"training_hours\\\" AS REAL) AS \\\"training_hours_num\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"company_type\\\" IS NOT NULL\\n AND \\\"company_type\\\" <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND \\\"training_hours\\\" <> ''\\n),\\n\\\"ranked\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"training_hours_num\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"company_type\\\"\\n ORDER BY \\\"training_hours_num\\\"\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"company_type\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"base\\\"\\n),\\n\\\"positioned\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"training_hours_num\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n CAST((95 * \\\"cnt\\\" + 5) / 100 AS INTEGER) AS \\\"lower_idx\\\",\\n CAST((95 * \\\"cnt\\\" + 5) / 100 AS INTEGER) + CASE WHEN ((95 * \\\"cnt\\\" + 5) % 100) > 0 THEN 1 ELSE 0 END AS \\\"upper_idx\\\",\\n ((95 * \\\"cnt\\\" + 5) % 100) AS \\\"frac_num\\\"\\n FROM \\\"ranked\\\"\\n),\\n\\\"interpolated\\\" AS (\\n SELECT\\n \\\"company_type\\\",\\n \\\"cnt\\\",\\n \\\"lower_idx\\\",\\n \\\"upper_idx\\\",\\n \\\"frac_num\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"lower_idx\\\" THEN \\\"training_hours_num\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN \\\"rn\\\" = \\\"upper_idx\\\" THEN \\\"training_hours_num\\\" END) AS \\\"upper_val\\\"\\n FROM \\\"positioned\\\"\\n GROUP BY\\n \\\"company_type\\\",\\n \\\"cnt\\\",\\n \\\"lower_idx\\\",\\n \\\"upper_idx\\\",\\n \\\"frac_num\\\"\\n)\\nSELECT\\n \\\"company_type\\\",\\n CASE\\n WHEN \\\"frac_num\\\" = 0 THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + (\\\"frac_num\\\" / 100.0) * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"interpolated\\\"\\nWHERE \\\"cnt\\\" >= 5\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite-compatible rewrite of Grouped Percentile Point using window functions and linear interpolation to emulate PERCENTILE_CONT(0.95) over CAST(\\\"training_hours\\\" AS REAL), grouped by non-empty \\\"company_type\\\" values and filtered to groups with at least 5 rows.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14684,"cached_input_tokens":13696,"output_tokens":3923,"reasoning_output_tokens":3311}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a22006f6ca064f8d5985ce4198423316534c2f7e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n SELECT\n \"company_type\",\n CAST(\"training_hours\" AS REAL) AS \"training_hours_num\"\n FROM \"m9\"\n WHERE \"company_type\" IS NOT NULL\n AND \"company_type\" <> ''\n AND \"training_hours\" IS NOT NULL\n AND \"training_hours\" <> ''\n),\n\"ranked\" AS (\n SELECT\n \"company_type\",\n \"training_hours_num\",\n ROW_NUMBER() OVER (\n PARTITION BY \"company_type\"\n ORDER BY \"training_hours_num\"\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"company_type\"\n ) AS \"cnt\"\n FROM \"base\"\n),\n\"positioned\" AS (\n SELECT\n \"company_type\",\n \"training_hours_num\",\n \"rn\",\n \"cnt\",\n CAST((95 * \"cnt\" + 5) / 100 AS INTEGER) AS \"lower_idx\",\n CAST((95 * \"cnt\" + 5) / 100 AS INTEGER) + CASE WHEN ((95 * \"cnt\" + 5) % 100) > 0 THEN 1 ELSE 0 END AS \"upper_idx\",\n ((95 * \"cnt\" + 5) % 100) AS \"frac_num\"\n FROM \"ranked\"\n),\n\"interpolated\" AS (\n SELECT\n \"company_type\",\n \"cnt\",\n \"lower_idx\",\n \"upper_idx\",\n \"frac_num\",\n MAX(CASE WHEN \"rn\" = \"lower_idx\" THEN \"training_hours_num\" END) AS \"lower_val\",\n MAX(CASE WHEN \"rn\" = \"upper_idx\" THEN \"training_hours_num\" END) AS \"upper_val\"\n FROM \"positioned\"\n GROUP BY\n \"company_type\",\n \"cnt\",\n \"lower_idx\",\n \"upper_idx\",\n \"frac_num\"\n)\nSELECT\n \"company_type\",\n CASE\n WHEN \"frac_num\" = 0 THEN \"lower_val\"\n ELSE \"lower_val\" + (\"frac_num\" / 100.0) * (\"upper_val\" - \"lower_val\")\n END AS \"percentile_measure\"\nFROM \"interpolated\"\nWHERE \"cnt\" >= 5\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite-compatible rewrite of Grouped Percentile Point using window functions and linear interpolation to emulate PERCENTILE_CONT(0.95) over CAST(\"training_hours\" AS REAL), grouped by non-empty \"company_type\" values and filtered to groups with at least 5 rows."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f78aaa1f5f51d498/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7da4e0479b101a872c2d726b7a43874f0c19d543 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9566, "bytes_utf8": 9566, "lines": 267, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 365, "bytes_utf8": 365, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14710, "cached_input_tokens": 12032, "output_tokens": 343, "reasoning_output_tokens": 241}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..42017e8c8586386c3d80258ddb0ef0722f12075d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_f8506904d051199f", + "api_calls": 0, + "input_tokens": 14710, + "cached_input_tokens": 12032, + "output_tokens": 343, + "total_tokens": 15053, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8818.31, + "sql_execution_elapsed_ms_total": 9.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..33d363a84322ea2019d5d8365f1dd0ba796bf140 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:41.411098+00:00", + "ended_at": "2026-05-19T16:00:50.229446+00:00", + "elapsed_ms": 8818.31, + "prompt_metrics": { + "chars": 9566, + "bytes_utf8": 9566, + "lines": 267, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 720, + "bytes_utf8": 720, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 365, + "bytes_utf8": 365, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14710, + "cached_input_tokens": 12032, + "output_tokens": 343, + "reasoning_output_tokens": 241 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9fe9bc271c11c9a0ee597aa7e6debfdf240b3f75 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,267 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=education_level, condition_col=major_discipline.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "education_level", + "condition_col": "major_discipline", + "condition_value": "STEM", + "positive_value": "STEM", + "negative_value": "", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.92, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b7a15b390b49de613b14ffbfc4c27004ca8a505 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-cac2-7f41-a84e-d29e932a8531"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"education_level\\\",\\n AVG(CASE WHEN \\\"major_discipline\\\" = 'STEM' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"education_level\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Computes the within-group proportion of rows with major_discipline = 'STEM' for each education_level.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14710,"cached_input_tokens":12032,"output_tokens":343,"reasoning_output_tokens":241}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..73563f5dd64cf8b2c4e5429bb4cca22e50472f31 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"education_level\",\n AVG(CASE WHEN \"major_discipline\" = 'STEM' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m9\"\nGROUP BY \"education_level\"\nORDER BY \"condition_rate\" DESC;","notes":"Computes the within-group proportion of rows with major_discipline = 'STEM' for each education_level."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_f8506904d051199f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6f173c9efd403f86c825e23bd4bd6d87af57b9e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9852, "bytes_utf8": 9852, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 825, "bytes_utf8": 825, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14786, "cached_input_tokens": 13696, "output_tokens": 728, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..857a81be25af10baf65db10d3a336ad44554a933 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_fef72908b4f36fae", + "api_calls": 0, + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 728, + "total_tokens": 15514, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13699.55, + "sql_execution_elapsed_ms_total": 16.6, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3557bc4f3d0d93a50bf9eddf4ce51137e4a8df83 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:11.009739+00:00", + "ended_at": "2026-05-19T15:49:24.709329+00:00", + "elapsed_ms": 13699.55, + "prompt_metrics": { + "chars": 9852, + "bytes_utf8": 9852, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1206, + "bytes_utf8": 1206, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 825, + "bytes_utf8": 825, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14786, + "cached_input_tokens": 13696, + "output_tokens": 728, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ee7c453754f7ba7b841b6eb1aecec7c1ad452fc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=experience, measure_col=enrollee_id.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "experience", + "measure_col": "enrollee_id", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 22283.62, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6c33ac159867383a3b3ed27d454f53179202c4cd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-41f5-75a1-a93a-176bd4cd5304"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT\\n \\\"experience\\\",\\n SUM(CAST(\\\"enrollee_id\\\" AS REAL)) AS group_value\\n FROM \\\"m9\\\"\\n WHERE COALESCE(\\\"experience\\\", '') <> ''\\n AND COALESCE(\\\"enrollee_id\\\", '') <> ''\\n GROUP BY \\\"experience\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT\\n g.\\\"experience\\\",\\n g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\\\"experience\\\" and measure_col=\\\"enrollee_id\\\", casting the text identifier to REAL for SQLite aggregation and filtering out empty group/measure values for a stable grouped view.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14786,"cached_input_tokens":13696,"output_tokens":728,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..21d15b617123f04e312baa58e16026a9203a5cbb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT\n \"experience\",\n SUM(CAST(\"enrollee_id\" AS REAL)) AS group_value\n FROM \"m9\"\n WHERE COALESCE(\"experience\", '') <> ''\n AND COALESCE(\"enrollee_id\", '') <> ''\n GROUP BY \"experience\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT\n g.\"experience\",\n g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\"experience\" and measure_col=\"enrollee_id\", casting the text identifier to REAL for SQLite aggregation and filtering out empty group/measure values for a stable grouped view."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_fef72908b4f36fae/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..05c3f9149ddd70cde5ea9a7dddf1aa099d02a231 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9865, "bytes_utf8": 9865, "lines": 264, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 963, "bytes_utf8": 963, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14784, "cached_input_tokens": 13696, "output_tokens": 960, "reasoning_output_tokens": 694}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..60de101a05b32f131daf2a349bf44fa217d8fdba --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "m9", + "model": "v2-cli:codex", + "run_id": "v2q_m9_ff38c86f32a8a957", + "api_calls": 0, + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 960, + "total_tokens": 15744, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16041.86, + "sql_execution_elapsed_ms_total": 18.64, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0ed87812a7339422d20a5291a4d2ded1736d5c73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:48:38.159786+00:00", + "ended_at": "2026-05-19T15:48:54.201686+00:00", + "elapsed_ms": 16041.86, + "prompt_metrics": { + "chars": 9865, + "bytes_utf8": 9865, + "lines": 264, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1407, + "bytes_utf8": 1407, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 963, + "bytes_utf8": 963, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 14784, + "cached_input_tokens": 13696, + "output_tokens": 960, + "reasoning_output_tokens": 694 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..194f0e06b9f460b43e5af54da360d33888cd7789 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,264 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: m9 +- dataset_name: Hr Analytics Job Change Of Data Scientists +- table_name: m9 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one tabular observation with 13 feature columns and target `target`. +- task_type: classification +- target_column: target +- main_row_count: 19158 +- important_fields: +- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id. +- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city. +- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index. +- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender. +- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience. +- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university. +- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level. +- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline. +- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience. +- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size. +- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type. +- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job. +- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours. +- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target. +- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']] +- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level'] +- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists + +SQLite schema snapshot: +{ + "table_name": "m9", + "quoted_table_name": "\"m9\"", + "row_count": 19158, + "columns": [ + { + "name": "enrollee_id", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "city_development_index", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "gender", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "relevent_experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "enrolled_university", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "education_level", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "major_discipline", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "experience", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_size", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "company_type", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "last_new_job", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "training_hours", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "target", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "enrollee_id": "8949", + "city": "city_103", + "city_development_index": "0.92", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "", + "company_type": "", + "last_new_job": "1", + "training_hours": "36", + "target": "1.0" + }, + { + "enrollee_id": "29725", + "city": "city_40", + "city_development_index": "0.7759999999999999", + "gender": "Male", + "relevent_experience": "No relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "15", + "company_size": "50-99", + "company_type": "Pvt Ltd", + "last_new_job": ">4", + "training_hours": "47", + "target": "0.0" + }, + { + "enrollee_id": "11561", + "city": "city_21", + "city_development_index": "0.624", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "Full time course", + "education_level": "Graduate", + "major_discipline": "STEM", + "experience": "5", + "company_size": "", + "company_type": "", + "last_new_job": "never", + "training_hours": "83", + "target": "0.0" + }, + { + "enrollee_id": "33241", + "city": "city_115", + "city_development_index": "0.789", + "gender": "", + "relevent_experience": "No relevent experience", + "enrolled_university": "", + "education_level": "Graduate", + "major_discipline": "Business Degree", + "experience": "<1", + "company_size": "", + "company_type": "Pvt Ltd", + "last_new_job": "never", + "training_hours": "52", + "target": "1.0" + }, + { + "enrollee_id": "666", + "city": "city_162", + "city_development_index": "0.767", + "gender": "Male", + "relevent_experience": "Has relevent experience", + "enrolled_university": "no_enrollment", + "education_level": "Masters", + "major_discipline": "STEM", + "experience": ">20", + "company_size": "50-99", + "company_type": "Funded Startup", + "last_new_job": "4", + "training_hours": "8", + "target": "0.0" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "m9", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=major_discipline, measure_col=training_hours.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "major_discipline", + "measure_col": "training_hours", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 70.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..68f4f0ef9d12d38da20a0757a456a243e40c4bf2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ec-c1a5-7fc0-8653-d2cb2f8c62b8"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"major_discipline\\\",\\n SUM(CAST(\\\"training_hours\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"m9\\\"\\n WHERE \\\"major_discipline\\\" IS NOT NULL\\n AND \\\"major_discipline\\\" <> ''\\n AND \\\"training_hours\\\" IS NOT NULL\\n AND \\\"training_hours\\\" <> ''\\n GROUP BY \\\"major_discipline\\\"\\n),\\n\\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT\\n g.\\\"major_discipline\\\",\\n g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the requested Relative-to-Total Extreme Threshold template with group_col=\\\"major_discipline\\\" and measure_col=\\\"training_hours\\\". CAST(\\\"training_hours\\\" AS REAL) is used because the schema stores it as TEXT, and blank values are filtered out for both grouped and measured fields.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":14784,"cached_input_tokens":13696,"output_tokens":960,"reasoning_output_tokens":694}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b66e79dff84b67dfaf6568a66d3f017298ae2105 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT\n \"major_discipline\",\n SUM(CAST(\"training_hours\" AS REAL)) AS \"group_value\"\n FROM \"m9\"\n WHERE \"major_discipline\" IS NOT NULL\n AND \"major_discipline\" <> ''\n AND \"training_hours\" IS NOT NULL\n AND \"training_hours\" <> ''\n GROUP BY \"major_discipline\"\n),\n\"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT\n g.\"major_discipline\",\n g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Uses the requested Relative-to-Total Extreme Threshold template with group_col=\"major_discipline\" and measure_col=\"training_hours\". CAST(\"training_hours\" AS REAL) is used because the schema stores it as TEXT, and blank values are filtered out for both grouped and measured fields."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_ff38c86f32a8a957/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391