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Resume SynthData0523 main/c18 batch 2

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  1. .gitattributes +48 -0
  2. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/run_config.json +3 -0
  3. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/runtime_result.json +3 -0
  4. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/adapter_report.json +3 -0
  5. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/adapter_transforms_applied.json +3 -0
  6. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/model_input_manifest.json +3 -0
  7. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/public/staged_features.json +3 -0
  8. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/public/test.csv +3 -0
  9. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/public/train.csv +3 -0
  10. SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/public/val.csv +3 -0
  11. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/_fd_X_host.npy +3 -0
  12. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/_fd_gen.py +8 -0
  13. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/_fd_meta_host.json +3 -0
  14. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/_fd_train.py +36 -0
  15. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/forest-c18-103976-20260511_003121.csv +3 -0
  16. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/forestdiffusion_model.joblib +3 -0
  17. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/gen_20260511_003121.log +3 -0
  18. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/input_snapshot.json +3 -0
  19. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/models_fd/model.joblib +3 -0
  20. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/public_gate/normalized_schema_snapshot.json +3 -0
  21. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/public_gate/public_gate_report.json +3 -0
  22. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/public_gate/staged_input_manifest.json +3 -0
  23. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/run_config.json +3 -0
  24. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/runtime_result.json +3 -0
  25. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/forestdiffusion/adapter_report.json +3 -0
  26. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/forestdiffusion/adapter_transforms_applied.json +3 -0
  27. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/forestdiffusion/model_input_manifest.json +3 -0
  28. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/public/staged_features.json +3 -0
  29. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/public/test.csv +3 -0
  30. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/public/train.csv +3 -0
  31. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/staged/public/val.csv +3 -0
  32. SynthData0523/main/c18/forestdiffusion/forest-c18-20260510_220505/train_20260510_220508.log +3 -0
  33. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/gen_20260425_084947.log +3 -0
  34. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/input_snapshot.json +3 -0
  35. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/models_100epochs/id000017770781809035876352/rtf_config.json +3 -0
  36. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/models_100epochs/id000017770781809035876352/rtf_model.pt +3 -0
  37. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/public_gate/normalized_schema_snapshot.json +3 -0
  38. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/public_gate/public_gate_report.json +3 -0
  39. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/public_gate/staged_input_manifest.json +3 -0
  40. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/realtabformer_features.json +3 -0
  41. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf-c18-103976-20260425_084947.csv +3 -0
  42. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/config.json +3 -0
  43. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/generation_config.json +3 -0
  44. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/model.safetensors +3 -0
  45. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/optimizer.pt +3 -0
  46. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/rng_state.pth +3 -0
  47. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/scaler.pt +3 -0
  48. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/scheduler.pt +3 -0
  49. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/trainer_state.json +3 -0
  50. SynthData0523/main/c18/realtabformer/rtf-c18-20260424_175433/rtf_checkpoints/checkpoint-324500/training_args.bin +3 -0
.gitattributes CHANGED
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+ SynthData0523/main/c18/ctgan/ctgan-c18-20260511_052106/staged/ctgan/adapter_report.json filter=lfs diff=lfs merge=lfs -text
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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/c18/forestdiffusion/forest-c18-20260510_220505/forestdiffusion_model.joblib')
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+ # generate:batch_size 为样本数
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+ arr = m.generate(batch_size=int(103976))
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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/c18/forestdiffusion/forest-c18-20260510_220505/forest-c18-103976-20260511_003121.csv', index=False)
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+ print("saved", len(df))
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@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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("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/c18/forestdiffusion/forest-c18-20260510_220505/_fd_X_host.npy', '/tmp/fd_X.npy')
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+ with open(r'/work/output-Benchmark-trainonly-v1/c18/forestdiffusion/forest-c18-20260510_220505/_fd_meta_host.json') as f:
13
+ open('/tmp/fd_meta.json','w').write(f.read())
14
+
15
+ import numpy as np, joblib, json, os
16
+ from ForestDiffusion import ForestDiffusionModel
17
+ X = np.load("/tmp/fd_X.npy")
18
+ with open("/tmp/fd_meta.json") as f:
19
+ meta = json.load(f)
20
+ cat_indexes = meta["cat_indexes"]
21
+ print(
22
+ "[ForestDiffusion] train config: "
23
+ f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 "
24
+ f"n_estimators=1 duplicate_K=1 n_jobs=1 "
25
+ f"max_depth=2 xgb_verbosity=0 xgb_nthread=1",
26
+ flush=True,
27
+ )
28
+ m = ForestDiffusionModel(
29
+ X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1,
30
+ model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes,
31
+ verbosity=0, nthread=1,
32
+ )
33
+ joblib.dump((m, meta), "/tmp/fd_model.joblib")
34
+ print("ForestDiffusion train OK")
35
+
36
+ shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c18/forestdiffusion/forest-c18-20260510_220505/forestdiffusion_model.joblib')
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