Resume SynthData0523 main/c2 batch 22

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SynthData0523/main/c2/tabpfgen/tabpfgen-c2-20260510_071135/_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/c2/tabpfgen/tabpfgen-c2-20260510_071135/staged/public/train.csv")
+target_col = "class"
+
+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(1382)
+
+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"
+
+n_sgld_steps = max(1, int(os.environ.get("TABPFGEN_N_SGLD_STEPS", "1000")))
+sgld_step_size = float(os.environ.get("TABPFGEN_SGLD_STEP_SIZE", "0.01"))
+sgld_noise_scale = float(os.environ.get("TABPFGEN_SGLD_NOISE_SCALE", "0.01"))
+
+# 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=n_sgld_steps,
+    sgld_step_size=sgld_step_size,
+    sgld_noise_scale=sgld_noise_scale,
+    device=device,
+)
+
+print(
+    f"[TabPFGen] Generating {target_n} rows via generate_classification "
+    f"(chunk_rows={chunk_rows}, device={device}, "
+    f"n_sgld_steps={n_sgld_steps}, sgld_step_size={sgld_step_size}, "
+    f"sgld_noise_scale={sgld_noise_scale})"
+)
+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/c2/tabpfgen/tabpfgen-c2-20260510_071135/tabpfgen-c2-1382-20260510_071135.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260510_071135/tabpfgen-c2-1382-20260510_071135.csv")

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+size 41729

SynthData0523/main/c2/tabpfgen/tabpfgen-c2-20260510_071135/tabpfgen_meta.json

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+size 444

SynthData0523/main/c2/tabpfgen/tabpfgen-c2-20260510_071135/train_20260510_071135.log

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+size 595

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336"
+dataname = "tabsyn_c2"
+output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.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 1382 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/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py

@@ -0,0 +1,65 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336"
+dataname = "tabsyn_c2"
+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")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024")
+# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader.
+env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0")
+_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/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_test.npy

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+size 55408

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+size 55408

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+size 5656

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SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/info.json

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+{
+  "name": "tabsyn_c2",
+  "task_type": "multiclass",
+  "n_num_features": 1,
+  "n_cat_features": 5,
+  "train_size": 1382,
+  "num_col_idx": [
+    0
+  ],
+  "cat_col_idx": [
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "buying",
+    "maint",
+    "doors",
+    "persons",
+    "lug_boot",
+    "safety",
+    "class"
+  ],
+  "train_num": 1382,
+  "test_num": 1382,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_c2/train.csv",
+  "test_path": null,
+  "idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "inverse_idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "idx_name_mapping": {
+    "0": "buying",
+    "1": "maint",
+    "2": "doors",
+    "3": "persons",
+    "4": "lug_boot",
+    "5": "safety",
+    "6": "class"
+  },
+  "n_classes": 4,
+  "metadata": {
+    "columns": {
+      "0": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "1": {
+        "sdtype": "categorical"
+      },
+      "2": {
+        "sdtype": "categorical"
+      },
+      "3": {
+        "sdtype": "categorical"
+      },
+      "4": {
+        "sdtype": "categorical"
+      },
+      "5": {
+        "sdtype": "categorical"
+      },
+      "6": {
+        "sdtype": "categorical"
+      }
+    }
+  }
+}

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SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/y_test.npy

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SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/input_snapshot.json

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+{
+  "dataset_id": "c2",
+  "model": "tabsyn",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/normalized_schema_snapshot.json

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+  "target_column": "class",
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+      "role": "feature",
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+      "impute_strategy": "mode",
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+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+          "high"
+        ]
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+      "name": "doors",
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+      "semantic_type": "categorical",
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+      "parse_format": null,
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+        "unique_ratio": 0.002894,
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+          "2",
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+          "3",
+          "4"
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+          "med"
+        ]
+      }
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+      "role": "feature",
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+      "nullable": false,
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+        "example_values": [
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+          "med"
+        ]
+      }
+    },
+    {
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+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "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": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json

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+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
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+      "role": "feature",
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+      "nullable": false,
+      "missing_tokens": [],
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+          "high",
+          "low"
+        ]
+      }
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+      "role": "feature",
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+        ]
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+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "categorical",
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+      "missing_tokens": [],
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+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.002894,
+        "example_values": [
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+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/runtime_result.json

@@ -0,0 +1,27 @@
+{
+  "dataset_id": "c2",
+  "model": "tabsyn",
+  "run_id": "tabsyn-c2-20260501_054336",
+  "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-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336"
+  },
+  "timings": {
+    "train": {
+      "started_at": "2026-05-01T05:43:36",
+      "ended_at": "2026-05-01T06:06:40",
+      "duration_sec": 1383.961
+    },
+    "generate": {
+      "started_at": "2026-05-01T06:06:40",
+      "ended_at": "2026-05-01T06:06:46",
+      "duration_sec": 5.527
+    }
+  }
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv

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