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  1. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy +3 -0
  2. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py +8 -0
  3. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json +3 -0
  4. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py +28 -0
  5. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv +3 -0
  6. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib +3 -0
  7. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/gen_20260430_054654.log +3 -0
  8. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/input_snapshot.json +3 -0
  9. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib +3 -0
  10. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json +3 -0
  11. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/public_gate_report.json +3 -0
  12. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/staged_input_manifest.json +3 -0
  13. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/runtime_result.json +3 -0
  14. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/forestdiffusion/adapter_report.json +3 -0
  15. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/forestdiffusion/adapter_transforms_applied.json +3 -0
  16. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/forestdiffusion/model_input_manifest.json +3 -0
  17. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/staged_features.json +3 -0
  18. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/test.csv +3 -0
  19. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/train.csv +3 -0
  20. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/val.csv +3 -0
  21. syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/train_20260430_054459.log +3 -0
syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy ADDED
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+ version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
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+
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+ import joblib, pandas as pd
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+ m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib')
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+ # generate:batch_size 为样本数
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+ arr = m.generate(batch_size=int(9864))
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+ df = pd.DataFrame(arr, columns=meta["column_names"])
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+ df.to_csv(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv', index=False)
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+ print("saved", len(df))
syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json ADDED
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+ version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
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+ import shutil, json
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+ shutil.copy(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy', '/tmp/fd_X.npy')
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+ with open(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json') as f:
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+ open('/tmp/fd_meta.json','w').write(f.read())
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+
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+ import numpy as np, joblib, json, os
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+ from ForestDiffusion import ForestDiffusionModel
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+ X = np.load("/tmp/fd_X.npy")
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+ with open("/tmp/fd_meta.json") as f:
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+ meta = json.load(f)
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+ cat_indexes = meta["cat_indexes"]
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+ print(
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+ "[ForestDiffusion] train config: "
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+ f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 "
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+ f"n_estimators=100 duplicate_K=20 n_jobs=2 "
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+ f"xgb_verbosity=1",
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+ flush=True,
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+ )
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+ m = ForestDiffusionModel(
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+ X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
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+ model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
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+ verbosity=1,
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+ )
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+ joblib.dump((m, meta), "/tmp/fd_model.joblib")
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+ print("ForestDiffusion train OK")
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+
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+ shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib')
syntheticSuccess/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv ADDED
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