Resume SynthData0523 main/c6 batch 19

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SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/_tabpfgen_generate.py

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+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")

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+{
+  "dataset_id": "c6",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv",
+      "exists": true,
+      "size": 849500,
+      "sha256": "7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272"
+    },
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+      "exists": true,
+      "size": 108137,
+      "sha256": "9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe"
+    },
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+      "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,
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+      "sha256": "602750e8159221cf97836d44d530098411b5f2cd6fc47c06776171da79d06593"
+    }
+  }
+}

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+{
+  "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": {
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+        "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": {
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+        "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": {
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+        "unique_ratio": 0.000262,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "Question Level",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+          "Advanced",
+          "Basic"
+        ]
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+    {
+      "name": "Topic",
+      "role": "feature",
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+      "nullable": false,
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+          "Fundamental Mathematics",
+          "Linear Algebra",
+          "Real Functions of a single variable",
+          "Analytic Geometry"
+        ]
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+          "Vector Spaces",
+          "Limits and Continuity",
+          "Linear Transformations"
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+          "Linear independence,Span,Linear dependence",
+          "Indeterminate forms,Limits",
+          "Range,Kernel"
+        ]
+      }
+    }
+  ]
+}

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"
+  }
+}

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"
+        ]
+      }
+    }
+  ]
+}

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"
+  }
+}

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
+  }
+]

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

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

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

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"
+}

SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/staged/tabpfgen/adapter_transforms_applied.json

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

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,
+      "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"
+        ]
+      }
+    }
+  ],
+  "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"
+}

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

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
+}

SynthData0523/main/c6/tabpfgen/tabpfgen-c6-20260422_200030/train_20260422_200031.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 186

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}")

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)")

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 sha256:abed185fe13007008c1656876f5703e4548c0581ef96ae8155aa1f872ef3e322
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