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b/SynthData0523/main/c6/tabdiff/tabdiff-c6-20260420_062412/train_20260420_062412.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8125d83cd3faa4b001d648631e71236dfeb4f3c2121c350cebbcf4b387a52428 +size 368323 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/_tabpfgen_generate.py b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..81c455fceb9b5f12ce687ca301ed5ec568530e91 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv") +target_col = "Type of Answer" + +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(7636) + +# 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_classification") +X_syn, y_syn = gen.generate_classification(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/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv") diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/gen_20260422_200031.log b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/gen_20260422_200031.log new file mode 100644 index 0000000000000000000000000000000000000000..f75d2152745a77546ea955907aeb513f6affeec0 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/gen_20260422_200031.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d65d5950faf566e7ef8d39c6ddadf94d7bbf941e054206d7a551feb4955217e +size 755 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/input_snapshot.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0c7e627ff39f71f89cfcfe7d5c87dd7ae0e7a960 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c6", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "exists": true, + "size": 849500, + "sha256": "7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "exists": true, + "size": 108137, + "sha256": "9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv", + "exists": true, + "size": 107696, + "sha256": "d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_profile.json", + "exists": true, + "size": 4145, + "sha256": "70c4d3f4f544b9bff7543f502136d9b1403d8589ad5ef0a9695842d8ef9d5185" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_contract_v1.json", + "exists": true, + "size": 4740, + "sha256": "602750e8159221cf97836d44d530098411b5f2cd6fc47c06776171da79d06593" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..62de672c6242a8fe33594746bc19ea183e203ce7 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,169 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "columns": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/public_gate_report.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..68d5e4a7e5533d3434a96c463bb1367c9d25256e --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c6", + "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": "Type of Answer", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/staged_input_manifest.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7b25db05fad1406ffa89a01909d8f790dc89033d --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/staged_input_manifest.json @@ -0,0 +1,174 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/runtime_result.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..242c9893fef89843aaf62292deaf39590371a33c --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c6", + "model": "tabpfgen", + "run_id": "tabpfgen-c6-20260422_200030", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..5523fb7a16718db5c4122f8381d3cff51e337c45 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json @@ -0,0 +1,42 @@ +[ + { + "feature_name": "Student ID", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Student Country", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Question ID", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Type of Answer", + "data_type": "binary", + "is_target": true + }, + { + "feature_name": "Question Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Topic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Subtopic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Keywords", + "data_type": "categorical", + "is_target": false + } +] \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/test.csv b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..221052bae020346f46e1c71d36fce7eb347ed62d --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f +size 107696 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..28f63ae8128dbf03647f9cda7458db51d98581f5 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272 +size 849500 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/val.csv b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..400c35bc93bd5a660ae20c7574c7bea7bc24035f --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe +size 108137 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_report.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d235ea9404787f536c0b249809f9182c3e8af065 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/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/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_transforms_applied.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/model_input_manifest.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9d1e41dc7fa8118b59dec275dde6e9950156a412 --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,176 @@ +{ + "dataset_id": "c6", + "model": "tabpfgen", + "target_column": "Type of Answer", + "task_type": "classification", + "column_schema": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, 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"missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv new file mode 100644 index 0000000000000000000000000000000000000000..021959e873ef57e4492b9bdea06695154cbf308c --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen-c6-7636-20260422_200031.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8963481e4274e605ae74329f65a056375b21220d0504731be311b0f07d54bd79 +size 937173 diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen_meta.json b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..2447cf3a09e60e72f96bbd1be477198ae1d3847d --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/tabpfgen_meta.json @@ -0,0 +1,8 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/public/staged_features.json", + "target_col": "Type of Answer", + "is_classification": true, + "n_rows": 7636, + "n_cols": 8 +} \ No newline at end of file diff --git a/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/train_20260422_200031.log b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/train_20260422_200031.log new file mode 100644 index 0000000000000000000000000000000000000000..b93c930ddc815ba46d1bb6eb19596459b61beaab --- /dev/null +++ b/SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/train_20260422_200031.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1ed7933d5831d5ec40f319c21e4ce5dafafa2166700dd37d1a5a31442854fa5 +size 186 diff --git a/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_sample.py b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..552062cc758748896d2a6feacbdb76f140c7d4a8 --- /dev/null +++ b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c6/tabsyn/tabsyn-c6-20260420_233446" +dataname = "tabsyn_c6" +output_csv = "/work/output-SpecializedModels/c6/tabsyn/tabsyn-c6-20260420_233446/tabsyn-c6-7636-20260421_005324.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 7636 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}") diff --git a/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_train.py b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..840e9d935cf98bf91297aa2cd27dcc1f16c15f6f --- /dev/null +++ b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c6/tabsyn/tabsyn-c6-20260420_233446" +dataname = "tabsyn_c6" +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)") diff --git a/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/data/tabsyn_c6/X_cat_test.npy b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/data/tabsyn_c6/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c137c9333d828b98fe4e827908550abeb1d04cf5 --- /dev/null +++ b/SynthData0523/main/c6/tabsyn/tabsyn-c6-20260420_233446/data/tabsyn_c6/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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