Resume SynthData0523 main/n12 batch 2

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SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/_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/n12/tabpfgen/tabpfgen-n12-20260321_065425/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
+
+# 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 1000 rows via generate_regression")
+X_syn, y_syn = gen.generate_regression(X, y, n_samples=1000)
+
+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]
+
+syn_df = syn_df[list(df.columns)]
+syn_df.to_csv("/work/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/tabpfgen-n12-1000-20260321_065426.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/tabpfgen-n12-1000-20260321_065426.csv")

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+{
+  "dataset_id": "n12",
+  "model": "tabpfgen",
+  "inputs": {
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n12/n12-train.csv",
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+    }
+  }
+}

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+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/public_gate/public_gate_report.json

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+{
+  "dataset_id": "n12",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
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+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
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+      "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/n12/n12-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n12/n12-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n12/n12-test.csv"
+  }
+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/public_gate/staged_input_manifest.json

@@ -0,0 +1,93 @@
+{
+  "dataset_id": "n12",
+  "target_column": "target",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/public_gate/public_gate_report.json",
+  "column_schema": [
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+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "n12",
+  "model": "tabpfgen",
+  "run_id": "tabpfgen-n12-20260321_065425",
+  "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/n12/tabpfgen/tabpfgen-n12-20260321_065425/tabpfgen-n12-1000-20260321_065426.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425"
+  }
+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/staged_features.json

@@ -0,0 +1,22 @@
+[
+  {
+    "feature_name": "feature_1",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "feature_2",
+    "data_type": "continuous",
+    "is_target": false
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+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "target",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/test.csv

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SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/train.csv

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SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/val.csv

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+size 315663

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/tabpfgen/adapter_report.json

@@ -0,0 +1,7 @@
+{
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+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/tabpfgen/model_input_manifest.json"
+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/tabpfgen/adapter_transforms_applied.json

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

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/tabpfgen/model_input_manifest.json

@@ -0,0 +1,95 @@
+{
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/public_gate/public_gate_report.json"
+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/tabpfgen-n12-1000-20260321_065426.csv

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+size 31325

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/tabpfgen_meta.json

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+{
+  "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/train.csv",
+  "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n12/tabpfgen/tabpfgen-n12-20260321_065425/staged/public/staged_features.json",
+  "target_col": "target",
+  "is_classification": false,
+  "n_rows": 196045,
+  "n_cols": 4
+}

SynthData0523/main/n12/tabpfgen/tabpfgen-n12-20260321_065425/train_20260321_065426.log

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SynthData0523/main/n12/tvae/tvae-n12-20260320_072508/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/n12/tvae/tvae-n12-20260320_072508/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(196045)
+samples.to_csv("/work/output-SpecializedModels/n12/tvae/tvae-n12-20260320_072508/tvae-n12-196045-20260330_070555.csv", index=False)
+print(f"[TVAE] Generated 196045 rows -> /work/output-SpecializedModels/n12/tvae/tvae-n12-20260320_072508/tvae-n12-196045-20260330_070555.csv")

SynthData0523/main/n12/tvae/tvae-n12-20260320_072508/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/DatasetNew/n12/n12-train.csv"
+meta_path = "/work/output-SpecializedModels/n12/tvae/tvae-n12-20260320_072508/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/n12/tvae/tvae-n12-20260320_072508/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

SynthData0523/main/n12/tvae/tvae-n12-20260320_072508/gen_20260320_080436.log

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+{
+  "dataset_id": "n12",
+  "model": "tvae",
+  "inputs": {
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+    },
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+    },
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+      "size": 2302,
+      "sha256": "d4fe8e20de077a2855d2af5dd1e73126e04bf2a0741e28ff80368983f126319b"
+    },
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+      "sha256": "856639afabf103741f49f84eac0546ba3f4f3d2258fa3016263682d164e78a7a"
+    }
+  }
+}

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SynthData0523/main/n12/tvae/tvae-n12-20260320_072508/public_gate/public_gate_report.json

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+{
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+  "status": "pass",
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+      "status": "pass"
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+      "status": "pass"
+    },
+    {
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+      "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/n12/n12-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n12/n12-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n12/n12-test.csv"
+  }
+}