Resume SynthData0523 main/n14 batch 2

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SynthData0523/main/n14/tabpfgen/n14-migrated-20260422_183752/_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_070318/n14/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 1600 rows via generate_regression")
+X_syn, y_syn = gen.generate_regression(X, y, n_samples=1600)
+
+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/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/tabpfgen-n14-1600-20260422_070321.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/tabpfgen-n14-1600-20260422_070321.csv")

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+{
+  "dataset_id": "n14",
+  "model": "tabpfgen",
+  "inputs": {
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n14/n14-train.csv",
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+    }
+  }
+}

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SynthData0523/main/n14/tabpfgen/n14-migrated-20260422_183752/public_gate/public_gate_report.json

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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/public_gate/public_gate_report.json"
+}

SynthData0523/main/n14/tabpfgen/n14-migrated-20260422_183752/tabpfgen-n14-1600-20260422_070321.csv

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+oid sha256:ef50fc22d772d70355c8be99e1aa8e09e7bd9dc6e8ebfe239aabb8219e53cbcf
+size 920619

SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120"
+dataname = "tabsyn_n14"
+output_csv = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120/tabsyn-n14-1600-20260427_025138.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 1600 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/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_train.py

@@ -0,0 +1,63 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120"
+dataname = "tabsyn_n14"
+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")
+_te = 1
+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/n14/tabsyn/tabsyn-n14-20260427_025120/data/tabsyn_n14/X_cat_test.npy

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+size 128

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+size 128

SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/data/tabsyn_n14/X_num_test.npy

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+size 326528

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+  "n_cat_features": 0,
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+  "cat_col_idx": [],
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+  ],
+  "column_names": [
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+    "feature_3",
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SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/public_gate/public_gate_report.json

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SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/public_gate/staged_input_manifest.json

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SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/runtime_result.json

@@ -0,0 +1,15 @@
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+}

SynthData0523/main/n14/tabsyn/tabsyn-n14-20260427_025120/staged/public/staged_features.json

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