Resume SynthData0523 main/m5 batch 2

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SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/_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/m5/tabpfgen/tabpfgen-m5-20260422_200335/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
+target_n = int(3539)
+
+# 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/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv")

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+{
+  "dataset_id": "m5",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-train.csv",
+      "exists": true,
+      "size": 422717,
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+    },
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+      "sha256": "b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-test.csv",
+      "exists": true,
+      "size": 53943,
+      "sha256": "696cfc46d2e611ee56a5419f4496b758f643f49f3386d4181296783760117c8c"
+    },
+    "profile_json": {
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+      "exists": true,
+      "size": 14974,
+      "sha256": "6ca9a300883081c4197534dd44e5e37df852ef129b5c06666629d8dd8270af0d"
+    },
+    "contract_json": {
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+    }
+  }
+}

SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/public_gate/normalized_schema_snapshot.json

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+{
+  "dataset_id": "m5",
+  "target_column": "Target",
+  "task_type": "classification",
+  "columns": [
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SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
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+}

SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/public_gate/staged_input_manifest.json

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+      "name": "Target",
+      "role": "target",
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+      "impute_strategy": "mode",
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/public_gate/public_gate_report.json"
+}

SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b7761ee900660d92e40b3fe973a7cb09959540b650c7578dcb27e930e0d755b
+size 753897

SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json

@@ -0,0 +1,8 @@
+{
+  "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/train.csv",
+  "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/staged_features.json",
+  "target_col": "Target",
+  "is_classification": true,
+  "n_rows": 3539,
+  "n_cols": 37
+}

SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/train_20260422_200336.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1f3a133d48c190c52620cfea2ad6e85f890c3d304d020ea20e15a052dcb1c82
+size 186

SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648"
+dataname = "tabsyn_m5"
+output_csv = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.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 3539 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/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py

@@ -0,0 +1,62 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648"
+dataname = "tabsyn_m5"
+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")
+_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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+size 28480

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+size 446000

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+{
+  "name": "tabsyn_m5",
+  "task_type": "multiclass",
+  "n_num_features": 28,
+  "n_cat_features": 8,
+  "train_size": 3981,
+  "num_col_idx": [
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+  ],
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+    4,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    20
+  ],
+  "target_col_idx": [
+    36
+  ],
+  "column_names": [
+    "Marital status",
+    "Application mode",
+    "Application order",
+    "Course",
+    "Daytime/evening attendance",
+    "Previous qualification",
+    "Previous qualification (grade)",
+    "Nacionality",
+    "Mother's qualification",
+    "Father's qualification",
+    "Mother's occupation",
+    "Father's occupation",
+    "Admission grade",
+    "Displaced",
+    "Educational special needs",
+    "Debtor",
+    "Tuition fees up to date",
+    "Gender",
+    "Scholarship holder",
+    "Age at enrollment",
+    "International",
+    "Curricular units 1st sem (credited)",
+    "Curricular units 1st sem (enrolled)",
+    "Curricular units 1st sem (evaluations)",
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+    "Curricular units 2nd sem (approved)",
+    "Curricular units 2nd sem (grade)",
+    "Curricular units 2nd sem (without evaluations)",
+    "Unemployment rate",
+    "Inflation rate",
+    "GDP",
+    "Target"
+  ],
+  "train_num": 3981,
+  "test_num": 443,
+  "header": 0,
+  "file_type": "csv",
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SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/data/tabsyn_m5/test.csv

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SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/public_gate/public_gate_report.json

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SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/public_gate/staged_input_manifest.json

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