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  1. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py +23 -0
  2. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py +30 -0
  3. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/gen_20260429_032133.log +3 -0
  4. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/input_snapshot.json +3 -0
  5. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/models_300epochs/train_20260429_031938.log +3 -0
  6. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt +3 -0
  7. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/public_gate/normalized_schema_snapshot.json +3 -0
  8. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/public_gate/public_gate_report.json +3 -0
  9. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/public_gate/staged_input_manifest.json +3 -0
  10. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/runtime_result.json +3 -0
  11. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/public/staged_features.json +3 -0
  12. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/public/test.csv +3 -0
  13. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/public/train.csv +3 -0
  14. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/public/val.csv +3 -0
  15. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/tvae/adapter_report.json +3 -0
  16. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/tvae/adapter_transforms_applied.json +3 -0
  17. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/staged/tvae/model_input_manifest.json +3 -0
  18. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv +3 -0
  19. syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json +3 -0
syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py ADDED
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+ import os, sys
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+ sys.path.insert(0, "/work")
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+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
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+ apply_parallel_cap_from_env()
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+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
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+ apply_ctgan_inverse_fix()
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+ import pandas as pd
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+ from ctgan.synthesizers.tvae import TVAE
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+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
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+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
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+ os.environ.setdefault("MKL_NUM_THREADS", "8")
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+ model = TVAE.load("/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt")
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+ total = 9864
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+ chunk = min(50000, total) if total > 50000 else total
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+ parts = []
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+ left = total
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+ while left > 0:
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+ take = min(chunk, left)
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+ parts.append(model.sample(take))
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+ left -= take
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+ samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
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+ samples.to_csv("/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv", index=False)
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+ print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv")
syntheticSuccess/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import json, os, sys
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+ sys.path.insert(0, "/work")
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+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
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+ apply_parallel_cap_from_env()
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+ import pandas as pd
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+ from ctgan.data import read_csv
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+ from ctgan.synthesizers.tvae import TVAE
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+
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+ # Keep transform stage parallelism bounded for stability on shared host.
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+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
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+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
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+ os.environ.setdefault("MKL_NUM_THREADS", "8")
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+ _nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
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+ if _nj:
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+ print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
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+ else:
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+ print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
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+ print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
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+
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+ csv_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/staged/public/train.csv"
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+ meta_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json"
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+ save_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt"
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+ epochs = 300
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+
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+ data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
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+ print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
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+ model = TVAE(epochs=epochs, batch_size=500)
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+ model.fit(data, discrete_columns)
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+ model.save(save_path)
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+ print(f"[TVAE] Model saved -> {save_path}")
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