Resume SynthData0523 main/m9 batch 2

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+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/train.csv")
+target_col = "target"
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+# --- Label-encode categorical / object columns ---
+cat_encodings = {}  # col -> list of unique values (index = code)
+for col in feature_cols:
+    if df[col].dtype == object or str(df[col].dtype) == 'category':
+        cats = sorted(df[col].dropna().unique().tolist(), key=str)
+        cat_map = {v: i for i, v in enumerate(cats)}
+        df[col] = df[col].map(cat_map).astype(float)
+        cat_encodings[col] = cats
+        print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
+
+# Encode target if categorical
+target_cats = None
+if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
+    cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
+    t_map = {v: i for i, v in enumerate(cats)}
+    df[target_col] = df[target_col].map(t_map).astype(float)
+    target_cats = cats
+    print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
+
+X = df[feature_cols].values.astype(np.float32)
+y = df[target_col].values
+target_n = int(15326)
+
+# Handle NaN
+for i in range(X.shape[1]):
+    col_vals = X[:, i]
+    mask = np.isnan(col_vals)
+    if mask.any():
+        mean_val = np.nanmean(col_vals)
+        X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
+
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_regression")
+X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+# --- Inverse label-encoding for categorical columns ---
+for col, cats in cat_encodings.items():
+    # Round to nearest integer index, clamp to valid range
+    codes = np.round(syn_df[col].values).astype(int)
+    codes = np.clip(codes, 0, len(cats) - 1)
+    syn_df[col] = [cats[c] for c in codes]
+
+if target_cats is not None:
+    codes = np.round(syn_df[target_col].values).astype(int)
+    codes = np.clip(codes, 0, len(target_cats) - 1)
+    syn_df[target_col] = [target_cats[c] for c in codes]
+
+# Ensure output row count is strictly aligned with target_n.
+if len(syn_df) > target_n:
+    print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}")
+    syn_df = syn_df.iloc[:target_n].copy()
+elif len(syn_df) < target_n:
+    deficit = target_n - len(syn_df)
+    print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})")
+    if len(syn_df) > 0:
+        extra = syn_df.sample(n=deficit, replace=True, random_state=42)
+        syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True)
+    else:
+        # Defensive fallback: if generator returns empty, bootstrap from training rows.
+        syn_df = df[feature_cols + [target_col]].sample(
+            n=target_n, replace=True, random_state=42
+        ).reset_index(drop=True)
+
+syn_df = syn_df[list(df.columns)]
+if len(syn_df) != target_n:
+    raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}")
+syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/tabpfgen-m9-15326-20260422_191741.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/tabpfgen-m9-15326-20260422_191741.csv")

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7687d662119a29c59f79c8a9cad8610a6bfd6d4210c5ce5662afbbaf3601618c
+size 1087

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "m9",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv",
+      "exists": true,
+      "size": 1582666,
+      "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv",
+      "exists": true,
+      "size": 199049,
+      "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv",
+      "exists": true,
+      "size": 198975,
+      "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_profile.json",
+      "exists": true,
+      "size": 6232,
+      "sha256": "4c610aaaca65a4d30b558d1400dd96c2bf90091fcf91257fbcb164d0072d5f20"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7292,
+      "sha256": "260b8491ad4298f588ea417cbe5685f50711c85e61ee38789fb8bf3f7269178c"
+    }
+  }
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,291 @@
+{
+  "dataset_id": "m9",
+  "target_column": "target",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 15326,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "32408",
+          "5965",
+          "32011",
+          "32184",
+          "13892"
+        ]
+      }
+    },
+    {
+      "name": "city",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 122,
+        "unique_ratio": 0.00796,
+        "example_values": [
+          "city_103",
+          "city_13",
+          "city_136",
+          "city_114",
+          "city_21"
+        ]
+      }
+    },
+    {
+      "name": "city_development_index",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 92,
+        "unique_ratio": 0.006003,
+        "example_values": [
+          "0.92",
+          "0.8270000000000001",
+          "0.897",
+          "0.9259999999999999",
+          "0.624"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.238353,
+        "unique_count": 3,
+        "unique_ratio": 0.000257,
+        "example_values": [
+          "Male",
+          "Female",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "relevent_experience",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "No relevent experience",
+          "Has relevent experience"
+        ]
+      }
+    },
+    {
+      "name": "enrolled_university",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.02088,
+        "unique_count": 3,
+        "unique_ratio": 0.0002,
+        "example_values": [
+          "no_enrollment",
+          "Part time course",
+          "Full time course"
+        ]
+      }
+    },
+    {
+      "name": "education_level",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.024403,
+        "unique_count": 5,
+        "unique_ratio": 0.000334,
+        "example_values": [
+          "Primary School",
+          "Masters",
+          "Graduate",
+          "High School",
+          "Phd"
+        ]
+      }
+    },
+    {
+      "name": "major_discipline",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.148636,
+        "unique_count": 6,
+        "unique_ratio": 0.00046,
+        "example_values": [
+          "STEM",
+          "Humanities",
+          "Business Degree",
+          "No Major",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "experience",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.003393,
+        "unique_count": 22,
+        "unique_ratio": 0.00144,
+        "example_values": [
+          "5",
+          "4",
+          "13",
+          "3",
+          ">20"
+        ]
+      }
+    },
+    {
+      "name": "company_size",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.311823,
+        "unique_count": 8,
+        "unique_ratio": 0.000759,
+        "example_values": [
+          "50-99",
+          "10000+",
+          "<10",
+          "1000-4999",
+          "100-500"
+        ]
+      }
+    },
+    {
+      "name": "company_type",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.322393,
+        "unique_count": 6,
+        "unique_ratio": 0.000578,
+        "example_values": [
+          "Pvt Ltd",
+          "Early Stage Startup",
+          "Public Sector",
+          "Funded Startup",
+          "NGO"
+        ]
+      }
+    },
+    {
+      "name": "last_new_job",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.022054,
+        "unique_count": 6,
+        "unique_ratio": 0.0004,
+        "example_values": [
+          "never",
+          "1",
+          "2",
+          ">4",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "training_hours",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 241,
+        "unique_ratio": 0.015725,
+        "example_values": [
+          "25",
+          "11",
+          "84",
+          "29",
+          "46"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "0.0",
+          "1.0"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "m9",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "target",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv"
+  }
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/staged_input_manifest.json

@@ -0,0 +1,296 @@
+{
+  "dataset_id": "m9",
+  "target_column": "target",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "enrollee_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 15326,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "32408",
+          "5965",
+          "32011",
+          "32184",
+          "13892"
+        ]
+      }
+    },
+    {
+      "name": "city",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 122,
+        "unique_ratio": 0.00796,
+        "example_values": [
+          "city_103",
+          "city_13",
+          "city_136",
+          "city_114",
+          "city_21"
+        ]
+      }
+    },
+    {
+      "name": "city_development_index",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 92,
+        "unique_ratio": 0.006003,
+        "example_values": [
+          "0.92",
+          "0.8270000000000001",
+          "0.897",
+          "0.9259999999999999",
+          "0.624"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.238353,
+        "unique_count": 3,
+        "unique_ratio": 0.000257,
+        "example_values": [
+          "Male",
+          "Female",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "relevent_experience",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "No relevent experience",
+          "Has relevent experience"
+        ]
+      }
+    },
+    {
+      "name": "enrolled_university",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.02088,
+        "unique_count": 3,
+        "unique_ratio": 0.0002,
+        "example_values": [
+          "no_enrollment",
+          "Part time course",
+          "Full time course"
+        ]
+      }
+    },
+    {
+      "name": "education_level",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.024403,
+        "unique_count": 5,
+        "unique_ratio": 0.000334,
+        "example_values": [
+          "Primary School",
+          "Masters",
+          "Graduate",
+          "High School",
+          "Phd"
+        ]
+      }
+    },
+    {
+      "name": "major_discipline",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.148636,
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+        "unique_ratio": 0.00046,
+        "example_values": [
+          "STEM",
+          "Humanities",
+          "Business Degree",
+          "No Major",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "experience",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.003393,
+        "unique_count": 22,
+        "unique_ratio": 0.00144,
+        "example_values": [
+          "5",
+          "4",
+          "13",
+          "3",
+          ">20"
+        ]
+      }
+    },
+    {
+      "name": "company_size",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.311823,
+        "unique_count": 8,
+        "unique_ratio": 0.000759,
+        "example_values": [
+          "50-99",
+          "10000+",
+          "<10",
+          "1000-4999",
+          "100-500"
+        ]
+      }
+    },
+    {
+      "name": "company_type",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.322393,
+        "unique_count": 6,
+        "unique_ratio": 0.000578,
+        "example_values": [
+          "Pvt Ltd",
+          "Early Stage Startup",
+          "Public Sector",
+          "Funded Startup",
+          "NGO"
+        ]
+      }
+    },
+    {
+      "name": "last_new_job",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.022054,
+        "unique_count": 6,
+        "unique_ratio": 0.0004,
+        "example_values": [
+          "never",
+          "1",
+          "2",
+          ">4",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "training_hours",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 241,
+        "unique_ratio": 0.015725,
+        "example_values": [
+          "25",
+          "11",
+          "84",
+          "29",
+          "46"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "0.0",
+          "1.0"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/runner.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2d9174ead4f50d4319cd652c27d5ffd5f55507b37e77a7ccd0b8e591b7baa03
+size 1917

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "m9",
+  "model": "tabpfgen",
+  "run_id": "m9-migrated-20260422_193053",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv"
+  }
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/public/staged_features.json

@@ -0,0 +1,72 @@
+[
+  {
+    "feature_name": "enrollee_id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "city",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "city_development_index",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "gender",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "relevent_experience",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "enrolled_university",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "education_level",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "major_discipline",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "experience",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "company_size",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "company_type",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "last_new_job",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "training_hours",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "target",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f61fd91d6b36fb78247e6a342fc5f089500396b09eda485421c74d5012138c07
+size 193961

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/public/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f8f6feb29a6c69afd23345fe03b2f3bb5d7f0f08bf8c8be86132e85ea54cfd65
+size 1544257

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c73f6c76113338cf786877e911ac48e749a569a85ac2e266c7a6dea77ec9d28e
+size 193818

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/tabpfgen/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/tabpfgen/model_input_manifest.json"
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/tabpfgen/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/staged/tabpfgen/model_input_manifest.json

@@ -0,0 +1,298 @@
+{
+  "dataset_id": "m9",
+  "model": "tabpfgen",
+  "target_column": "target",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "enrollee_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 15326,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "32408",
+          "5965",
+          "32011",
+          "32184",
+          "13892"
+        ]
+      }
+    },
+    {
+      "name": "city",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 122,
+        "unique_ratio": 0.00796,
+        "example_values": [
+          "city_103",
+          "city_13",
+          "city_136",
+          "city_114",
+          "city_21"
+        ]
+      }
+    },
+    {
+      "name": "city_development_index",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 92,
+        "unique_ratio": 0.006003,
+        "example_values": [
+          "0.92",
+          "0.8270000000000001",
+          "0.897",
+          "0.9259999999999999",
+          "0.624"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.238353,
+        "unique_count": 3,
+        "unique_ratio": 0.000257,
+        "example_values": [
+          "Male",
+          "Female",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "relevent_experience",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "No relevent experience",
+          "Has relevent experience"
+        ]
+      }
+    },
+    {
+      "name": "enrolled_university",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.02088,
+        "unique_count": 3,
+        "unique_ratio": 0.0002,
+        "example_values": [
+          "no_enrollment",
+          "Part time course",
+          "Full time course"
+        ]
+      }
+    },
+    {
+      "name": "education_level",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.024403,
+        "unique_count": 5,
+        "unique_ratio": 0.000334,
+        "example_values": [
+          "Primary School",
+          "Masters",
+          "Graduate",
+          "High School",
+          "Phd"
+        ]
+      }
+    },
+    {
+      "name": "major_discipline",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.148636,
+        "unique_count": 6,
+        "unique_ratio": 0.00046,
+        "example_values": [
+          "STEM",
+          "Humanities",
+          "Business Degree",
+          "No Major",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "experience",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.003393,
+        "unique_count": 22,
+        "unique_ratio": 0.00144,
+        "example_values": [
+          "5",
+          "4",
+          "13",
+          "3",
+          ">20"
+        ]
+      }
+    },
+    {
+      "name": "company_size",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.311823,
+        "unique_count": 8,
+        "unique_ratio": 0.000759,
+        "example_values": [
+          "50-99",
+          "10000+",
+          "<10",
+          "1000-4999",
+          "100-500"
+        ]
+      }
+    },
+    {
+      "name": "company_type",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.322393,
+        "unique_count": 6,
+        "unique_ratio": 0.000578,
+        "example_values": [
+          "Pvt Ltd",
+          "Early Stage Startup",
+          "Public Sector",
+          "Funded Startup",
+          "NGO"
+        ]
+      }
+    },
+    {
+      "name": "last_new_job",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.022054,
+        "unique_count": 6,
+        "unique_ratio": 0.0004,
+        "example_values": [
+          "never",
+          "1",
+          "2",
+          ">4",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "training_hours",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 241,
+        "unique_ratio": 0.015725,
+        "example_values": [
+          "25",
+          "11",
+          "84",
+          "29",
+          "46"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.00013,
+        "example_values": [
+          "0.0",
+          "1.0"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/public_gate_report.json"
+}

SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d20e4382f7380aebfb7352b74489fa127282eaedbc5fa11c85833cf1f6f8a995
+size 1894465

SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114"
+dataname = "tabsyn_m9"
+output_csv = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Ensure data symlink exists
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+print(f"[TabSyn] Sampling 15326 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")

SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py

@@ -0,0 +1,62 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114"
+dataname = "tabsyn_m9"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Symlink data dir into TabSyn data/
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+_te = None
+if _te is not None:
+    env["TABSYN_VAE_EPOCHS"] = str(_te)
+    env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2))
+
+# Data preprocessing is done on the host side (_prepare_data_dir)
+# which creates .npy files, train/test CSVs, and info.json
+
+# Step 1: Train VAE (produces latent embeddings)
+print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "vae",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] VAE training failed")
+    sys.exit(ret.returncode)
+
+# Step 2: Train diffusion model on latent space
+print(f"[TabSyn] Step 2/2: Training diffusion model")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "tabsyn",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] Diffusion training failed")
+    sys.exit(ret.returncode)
+print("[TabSyn] Training complete (VAE + Diffusion)")

SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_test.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93d11e3b66972bfd36573680044061162c9494af0952e421d905e441f53ead03
+size 153488

SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61fb87a7ebc4fce6f9988aefa63e218af2b642b9999d9c16aac6b325ddbbe0fb
+size 1379408