Resume SynthData0523 main/n7 batch 2

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SynthData0523/main/n7/tabpfgen/n7-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/n7/staged/public/train.csv")
+target_col = "Family"
+
+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 5756 rows via generate_classification")
+X_syn, y_syn = gen.generate_classification(X, y, n_samples=5756)
+
+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/n7/tabpfgen-n7-5756-20260422_070322.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n7/tabpfgen-n7-5756-20260422_070322.csv")

SynthData0523/main/n7/tabpfgen/n7-migrated-20260422_183752/gen_20260422_070322.log

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SynthData0523/main/n7/tabpfgen/n7-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json

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SynthData0523/main/n7/tabpfgen/n7-migrated-20260422_183752/staged/tabpfgen/model_input_manifest.json

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SynthData0523/main/n7/tabpfgen/n7-migrated-20260422_183752/tabpfgen-n7-5756-20260422_070322.csv

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SynthData0523/main/n7/tvae/tvae-n7-20260328_053145/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(5756)
+samples.to_csv("/work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/tvae-n7-5756-20260330_070253.csv", index=False)
+print(f"[TVAE] Generated 5756 rows -> /work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/tvae-n7-5756-20260330_070253.csv")

SynthData0523/main/n7/tvae/tvae-n7-20260328_053145/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/n7/tvae/tvae-n7-20260328_053145/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

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