Resume SynthData0523 main/n3 batch 4

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py

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+import os
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv")
+target_col = "quality"
+
+target_missing = df[target_col].isna()
+if target_missing.any():
+    dropped = int(target_missing.sum())
+    df = df.loc[~target_missing].copy()
+    print(
+        f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
+    )
+if df.empty:
+    raise ValueError(
+        f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
+    )
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+cat_encodings = {}
+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)")
+
+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(3918)
+
+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
+
+# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
+# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
+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
+
+for col, cats in cat_encodings.items():
+    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]
+
+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:
+        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-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv")

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+{
+  "dataset_id": "n3",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv",
+      "exists": true,
+      "size": 211519,
+      "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009"
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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/normalized_schema_snapshot.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/public_gate_report.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/staged_input_manifest.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/runtime_result.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_transforms_applied.json

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/model_input_manifest.json

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+  "model": "tabpfgen",
+  "target_column": "quality",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "fixed acidity",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+        "missing_rate": 0.0,
+        "unique_count": 66,
+        "unique_ratio": 0.016845,
+        "example_values": [
+          "7.1",
+          "8.5",
+          "6.4",
+          "6.8",
+          "7.6"
+        ]
+      }
+    },
+    {
+      "name": "volatile acidity",
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+        "missing_rate": 0.0,
+        "unique_count": 121,
+        "unique_ratio": 0.030883,
+        "example_values": [
+          "0.31",
+          "0.21",
+          "0.24",
+          "0.43",
+          "0.27"
+        ]
+      }
+    },
+    {
+      "name": "citric acid",
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+        "unique_count": 85,
+        "unique_ratio": 0.021695,
+        "example_values": [
+          "0.17",
+          "0.41",
+          "0.31",
+          "0.25",
+          "0.3"
+        ]
+      }
+    },
+    {
+      "name": "residual sugar",
+      "role": "feature",
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+      "nullable": false,
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+          "1",
+          "4.3",
+          "2.8",
+          "13",
+          "12.9"
+        ]
+      }
+    },
+    {
+      "name": "chlorides",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+        "unique_count": 147,
+        "unique_ratio": 0.037519,
+        "example_values": [
+          "0.042",
+          "0.036",
+          "0.038",
+          "0.047",
+          "0.033"
+        ]
+      }
+    },
+    {
+      "name": "free sulfur dioxide",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 120,
+        "unique_ratio": 0.030628,
+        "example_values": [
+          "21",
+          "24",
+          "41",
+          "43",
+          "69"
+        ]
+      }
+    },
+    {
+      "name": "total sulfur dioxide",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 244,
+        "unique_ratio": 0.062277,
+        "example_values": [
+          "144",
+          "99",
+          "114",
+          "132",
+          "160"
+        ]
+      }
+    },
+    {
+      "name": "density",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 839,
+        "unique_ratio": 0.21414,
+        "example_values": [
+          "0.99304",
+          "0.9947",
+          "0.99155",
+          "0.98975",
+          "0.99705"
+        ]
+      }
+    },
+    {
+      "name": "pH",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 100,
+        "unique_ratio": 0.025523,
+        "example_values": [
+          "3.13",
+          "3.18",
+          "3.37",
+          "3.21",
+          "3.16"
+        ]
+      }
+    },
+    {
+      "name": "sulphates",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 78,
+        "unique_ratio": 0.019908,
+        "example_values": [
+          "0.4",
+          "0.53",
+          "0.66",
+          "0.47",
+          "0.5"
+        ]
+      }
+    },
+    {
+      "name": "alcohol",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 97,
+        "unique_ratio": 0.024758,
+        "example_values": [
+          "9.6",
+          "9.7",
+          "11.7",
+          "13.4",
+          "8.8"
+        ]
+      }
+    },
+    {
+      "name": "quality",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7,
+        "unique_ratio": 0.001787,
+        "example_values": [
+          "5",
+          "6",
+          "7",
+          "8",
+          "3"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/public_gate_report.json"
+}

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+{
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+  "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json",
+  "target_col": "quality",
+  "is_classification": true,
+  "task_type": "classification",
+  "n_rows": 3918,
+  "n_cols": 12
+}

SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/train_20260501_005212.log

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/_tabpfgen_generate.py

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+import os
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv")
+target_col = "quality"
+
+target_missing = df[target_col].isna()
+if target_missing.any():
+    dropped = int(target_missing.sum())
+    df = df.loc[~target_missing].copy()
+    print(
+        f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
+    )
+if df.empty:
+    raise ValueError(
+        f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
+    )
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+cat_encodings = {}
+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)")
+
+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
+fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000")))
+if len(X) > fit_rows_cap:
+    rng = np.random.default_rng(42)
+    idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False))
+    X = X[idx]
+    y = y[idx]
+    print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})")
+target_n = int(3918)
+
+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
+
+chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256")))
+device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto"
+
+# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
+# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device=device,
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})")
+x_parts = []
+y_parts = []
+remaining = target_n
+while remaining > 0:
+    take = min(chunk_rows, remaining)
+    X_part, y_part = gen.generate_classification(X, y, n_samples=take)
+    x_parts.append(np.asarray(X_part))
+    y_parts.append(np.asarray(y_part))
+    remaining -= take
+    print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}")
+
+X_syn = np.concatenate(x_parts, axis=0)
+y_syn = np.concatenate(y_parts, axis=0)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+for col, cats in cat_encodings.items():
+    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]
+
+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:
+        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-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv")

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SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py

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+import os
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv")
+target_col = "quality"
+
+target_missing = df[target_col].isna()
+if target_missing.any():
+    dropped = int(target_missing.sum())
+    df = df.loc[~target_missing].copy()
+    print(
+        f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
+    )
+if df.empty:
+    raise ValueError(
+        f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
+    )
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+cat_encodings = {}
+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)")
+
+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
+fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000")))
+if len(X) > fit_rows_cap:
+    rng = np.random.default_rng(42)
+    idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False))
+    X = X[idx]
+    y = y[idx]
+    print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})")
+target_n = int(3918)
+
+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
+
+chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256")))
+device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto"
+
+# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
+# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device=device,
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})")
+x_parts = []
+y_parts = []
+remaining = target_n
+while remaining > 0:
+    take = min(chunk_rows, remaining)
+    X_part, y_part = gen.generate_classification(X, y, n_samples=take)
+    x_parts.append(np.asarray(X_part))
+    y_parts.append(np.asarray(y_part))
+    remaining -= take
+    print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}")
+
+X_syn = np.concatenate(x_parts, axis=0)
+y_syn = np.concatenate(y_parts, axis=0)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+for col, cats in cat_encodings.items():
+    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]
+
+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:
+        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-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv")

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