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  1. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_X_host.npy +3 -0
  2. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_gen.py +8 -0
  3. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_meta_host.json +3 -0
  4. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_train.py +36 -0
  5. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/forest-m12-95512-20260511_125345.csv +3 -0
  6. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/forestdiffusion_model.joblib +3 -0
  7. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/gen_20260511_125345.log +3 -0
  8. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/input_snapshot.json +3 -0
  9. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/models_fd/model.joblib +3 -0
  10. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/public_gate/normalized_schema_snapshot.json +3 -0
  11. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/public_gate/public_gate_report.json +3 -0
  12. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/public_gate/staged_input_manifest.json +3 -0
  13. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/run_config.json +3 -0
  14. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/runtime_result.json +3 -0
  15. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/forestdiffusion/adapter_report.json +3 -0
  16. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/forestdiffusion/adapter_transforms_applied.json +3 -0
  17. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/forestdiffusion/model_input_manifest.json +3 -0
  18. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/public/staged_features.json +3 -0
  19. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/public/test.csv +3 -0
  20. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/public/train.csv +3 -0
  21. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/staged/public/val.csv +3 -0
  22. syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/train_20260511_124709.log +3 -0
syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_X_host.npy ADDED
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+ version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_gen.py ADDED
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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/m12/forestdiffusion/forest-m12-20260511_124708/forestdiffusion_model.joblib')
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+ # generate:batch_size 为样本数
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+ arr = m.generate(batch_size=int(95512))
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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/m12/forestdiffusion/forest-m12-20260511_124708/forest-m12-95512-20260511_125345.csv', index=False)
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+ print("saved", len(df))
syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_meta_host.json ADDED
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+ version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/_fd_train.py ADDED
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+
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+ import os, shutil, json
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+ with open('/tmp/pgrep', 'w') as _f:
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+ _f.write("#!/usr/bin/env python3\n")
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+ _f.write("import subprocess, sys\n")
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+ _f.write("ppid = sys.argv[-1]\n")
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+ _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n")
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+ _f.write("print(out, end='')\n")
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+ os.chmod('/tmp/pgrep', 0o755)
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+ os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '')
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+ shutil.copy(r'/work/output-Benchmark-trainonly-v1/m12/forestdiffusion/forest-m12-20260511_124708/_fd_X_host.npy', '/tmp/fd_X.npy')
12
+ with open(r'/work/output-Benchmark-trainonly-v1/m12/forestdiffusion/forest-m12-20260511_124708/_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=2 "
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+ f"n_estimators=1 duplicate_K=1 n_jobs=1 "
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+ f"max_depth=2 xgb_verbosity=0 xgb_nthread=1",
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+ flush=True,
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+ )
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+ m = ForestDiffusionModel(
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+ X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1,
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+ model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes,
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+ verbosity=0, nthread=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/m12/forestdiffusion/forest-m12-20260511_124708/forestdiffusion_model.joblib')
syntheticSuccess/m12/forestdiffusion/forest-m12-20260511_124708/forest-m12-95512-20260511_125345.csv ADDED
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