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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/staged/public/staged_features.json",
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+}

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SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/_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/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/train.csv")
+target_col = "class"
+
+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(35855)
+
+# 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/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/tabpfgen-c20-35855-20260422_191741.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/tabpfgen-c20-35855-20260422_191741.csv")

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/gen_20260422_191741.log

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+{
+  "dataset_id": "c20",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
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+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
+    "test_csv": {
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+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_profile.json",
+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
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+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json

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+{
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+}

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/public_gate/public_gate_report.json

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+  "status": "pass",
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+      "status": "pass"
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+      "status": "pass"
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+      "status": "pass"
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+      "status": "pass"
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+      "status": "pass"
+    },
+    {
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+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv"
+  }
+}

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/public_gate/staged_input_manifest.json

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+  "target_column": "class",
+  "task_type": "classification",
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+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/public_gate/public_gate_report.json",
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+}

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+  "dataset_id": "c20",
+  "model": "tabpfgen",
+  "run_id": "c20-migrated-20260422_193053",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabpfgen/c20-migrated-20260422_193053/tabpfgen-c20-35855-20260422_191741.csv"
+  }
+}

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+    "feature_name": "black_piece0_rank",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/staged/public/test.csv

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SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/staged/public/val.csv

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SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/staged/tabpfgen/adapter_report.json

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+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/tabpfgen/model_input_manifest.json"
+}

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/staged/tabpfgen/adapter_transforms_applied.json

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+[]

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/staged/tabpfgen/model_input_manifest.json

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+{
+  "dataset_id": "c20",
+  "model": "tabpfgen",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "white_piece0_strength",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000139,
+        "example_values": [
+          "7",
+          "0",
+          "4",
+          "5",
+          "6"
+        ]
+      }
+    },
+    {
+      "name": "white_piece0_file",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7,
+        "unique_ratio": 0.000195,
+        "example_values": [
+          "5",
+          "6",
+          "3",
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "white_piece0_rank",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.000251,
+        "example_values": [
+          "8",
+          "0",
+          "1",
+          "3",
+          "7"
+        ]
+      }
+    },
+    {
+      "name": "black_piece0_strength",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000139,
+        "example_values": [
+          "4",
+          "7",
+          "0",
+          "6",
+          "5"
+        ]
+      }
+    },
+    {
+      "name": "black_piece0_file",
+      "role": "feature",
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+      "impute_strategy": "median",
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+        "unique_ratio": 0.000195,
+        "example_values": [
+          "1",
+          "0",
+          "3",
+          "4",
+          "6"
+        ]
+      }
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+    {
+      "name": "black_piece0_rank",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+        "unique_ratio": 0.000251,
+        "example_values": [
+          "7",
+          "2",
+          "5",
+          "1",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 8.4e-05,
+        "example_values": [
+          "w",
+          "b",
+          "d"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c20/public_gate/public_gate_report.json"
+}

SynthData0523/main/c20/tabpfgen/c20-migrated-20260422_193053/tabpfgen-c20-35855-20260422_191741.csv

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+version https://git-lfs.github.com/spec/v1
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SynthData0523/main/c20/tabsyn/tabsyn-c20-20260512_200819/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c20/tabsyn/tabsyn-c20-20260512_200819"
+dataname = "tabsyn_c20_tabsyn_c20_20260512_200819"
+output_csv = "/work/output-Benchmark-trainonly-v1/c20/tabsyn/tabsyn-c20-20260512_200819/tabsyn-c20-35855-20260512_202151.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 35855 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "0")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "32")
+env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+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/c20/tabsyn/tabsyn-c20-20260512_200819/_tabsyn_train.py

@@ -0,0 +1,69 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c20/tabsyn/tabsyn-c20-20260512_200819"
+dataname = "tabsyn_c20_tabsyn_c20_20260512_200819"
+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", "0")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "32")
+env.setdefault("TABSYN_VAE_NUM_WORKERS", "0")
+env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+# 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)")

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