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  1. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/_ctgan_generate.py +18 -0
  2. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/_ctgan_train.py +17 -0
  3. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/ctgan-c18-103976-20260512_083614.csv +3 -0
  4. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/ctgan_metadata.json +3 -0
  5. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/ctgan_train_prepared.csv +3 -0
  6. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/gen_20260512_083614.log +3 -0
  7. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/input_snapshot.json +3 -0
  8. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/models_50epochs/ctgan_50epochs.pt +3 -0
  9. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/models_50epochs/train_20260511_052110.log +3 -0
  10. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/public_gate/normalized_schema_snapshot.json +3 -0
  11. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/public_gate/public_gate_report.json +3 -0
  12. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/public_gate/staged_input_manifest.json +3 -0
  13. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/run_config.json +3 -0
  14. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/runtime_result.json +3 -0
  15. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/adapter_report.json +3 -0
  16. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/adapter_transforms_applied.json +3 -0
  17. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/model_input_manifest.json +3 -0
  18. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/public/staged_features.json +3 -0
  19. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/public/test.csv +3 -0
  20. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/public/train.csv +3 -0
  21. syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/staged/public/val.csv +3 -0
syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/_ctgan_generate.py ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import sys
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+ sys.path.insert(0, "/work")
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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.ctgan import CTGAN
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+ model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/models_50epochs/ctgan_50epochs.pt")
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+ total = 103976
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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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+ sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
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+ sampled.to_csv("/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/ctgan-c18-103976-20260512_083614.csv", index=False)
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+ print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/ctgan-c18-103976-20260512_083614.csv")
syntheticSuccess/c18/ctgan/ctgan-c18-20260511_052106/_ctgan_train.py ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import pandas as pd
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+ from ctgan.synthesizers.ctgan import CTGAN
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+
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+ data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/ctgan_train_prepared.csv")
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+ discrete_columns = ['country', 'description', 'designation', 'province', 'region_1', 'region_2', 'taster_name', 'taster_twitter_handle', 'title', 'variety', 'winery']
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+ model = CTGAN(
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+ embedding_dim=4,
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+ generator_dim=(8, 8),
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+ discriminator_dim=(8, 8),
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+ batch_size=4,
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+ pac=1,
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+ epochs=50,
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+ verbose=True,
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+ )
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+ model.fit(data, discrete_columns)
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+ model.save("/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/models_50epochs/ctgan_50epochs.pt")
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+ print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c18/ctgan/ctgan-c18-20260511_052106/models_50epochs/ctgan_50epochs.pt")
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