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syntheticSuccess/n3/ctgan/ctgan-n3-20260501_005113/_ctgan_generate.py

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+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/models_300epochs/ctgan_300epochs.pt")
+total = 3918
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv")

syntheticSuccess/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv

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