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Resume SynthData0523 main/c7 batch 3

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  1. .gitattributes +39 -0
  2. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/gen_20260429_032437.log +3 -0
  3. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/input_snapshot.json +3 -0
  4. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt +3 -0
  5. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/train_20260429_032054.log +3 -0
  6. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/public_gate/normalized_schema_snapshot.json +3 -0
  7. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json +3 -0
  8. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/public_gate/staged_input_manifest.json +3 -0
  9. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/runtime_result.json +3 -0
  10. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_report.json +3 -0
  11. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_transforms_applied.json +3 -0
  12. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/model_input_manifest.json +3 -0
  13. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json +3 -0
  14. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv +3 -0
  15. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv +3 -0
  16. SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv +3 -0
  17. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy +3 -0
  18. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/_fd_gen.py +8 -0
  19. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json +3 -0
  20. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/_fd_train.py +28 -0
  21. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv +3 -0
  22. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib +3 -0
  23. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/gen_20260430_054452.log +3 -0
  24. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/input_snapshot.json +3 -0
  25. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/models_fd/model.joblib +3 -0
  26. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/normalized_schema_snapshot.json +3 -0
  27. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json +3 -0
  28. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/staged_input_manifest.json +3 -0
  29. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/runtime_result.json +3 -0
  30. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_report.json +3 -0
  31. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_transforms_applied.json +3 -0
  32. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/model_input_manifest.json +3 -0
  33. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json +3 -0
  34. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv +3 -0
  35. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv +3 -0
  36. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv +3 -0
  37. SynthData0523/main/c7/forestdiffusion/forest-c7-20260429_210000/train_20260429_210000.log +3 -0
  38. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy +3 -0
  39. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_gen.py +8 -0
  40. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json +1 -0
  41. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_train.py +28 -0
  42. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv +3 -0
  43. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib +3 -0
  44. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/gen_20260430_060359.log +3 -0
  45. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/input_snapshot.json +36 -0
  46. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/models_fd/model.joblib +3 -0
  47. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/public_gate/normalized_schema_snapshot.json +183 -0
  48. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/public_gate/public_gate_report.json +37 -0
  49. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/public_gate/staged_input_manifest.json +188 -0
  50. SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/runtime_result.json +15 -0
.gitattributes CHANGED
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@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+
2
+ import joblib, pandas as pd
3
+ m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib')
4
+ # generate：batch_size 为样本数
5
+ arr = m.generate(batch_size=int(10368))
6
+ df = pd.DataFrame(arr, columns=meta["column_names"])
7
+ df.to_csv(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv', index=False)
8
+ print("saved", len(df))
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@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import shutil, json
3
+ shutil.copy(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy', '/tmp/fd_X.npy')
4
+ with open(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json') as f:
5
+ open('/tmp/fd_meta.json','w').write(f.read())
6
+
7
+ import numpy as np, joblib, json, os
8
+ from ForestDiffusion import ForestDiffusionModel
9
+ X = np.load("/tmp/fd_X.npy")
10
+ with open("/tmp/fd_meta.json") as f:
11
+ meta = json.load(f)
12
+ cat_indexes = meta["cat_indexes"]
13
+ print(
14
+ "[ForestDiffusion] train config: "
15
+ f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 "
16
+ f"n_estimators=100 duplicate_K=20 n_jobs=2 "
17
+ f"xgb_verbosity=1",
18
+ flush=True,
19
+ )
20
+ m = ForestDiffusionModel(
21
+ X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
22
+ model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
23
+ verbosity=1,
24
+ )
25
+ joblib.dump((m, meta), "/tmp/fd_model.joblib")
26
+ print("ForestDiffusion train OK")
27
+
28
+ shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib')
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1
+
2
+ import joblib, pandas as pd
3
+ m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib')
4
+ # generate：batch_size 为样本数
5
+ arr = m.generate(batch_size=int(10368))
6
+ df = pd.DataFrame(arr, columns=meta["column_names"])
7
+ df.to_csv(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv', index=False)
8
+ print("saved", len(df))
SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"column_names": ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5, 6, 7]}
SynthData0523/main/c7/forestdiffusion/forest-c7-20260430_060233/_fd_train.py ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import shutil, json
3
+ shutil.copy(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy', '/tmp/fd_X.npy')
4
+ with open(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json') as f:
5
+ open('/tmp/fd_meta.json','w').write(f.read())
6
+
7
+ import numpy as np, joblib, json, os
8
+ from ForestDiffusion import ForestDiffusionModel
9
+ X = np.load("/tmp/fd_X.npy")
10
+ with open("/tmp/fd_meta.json") as f:
11
+ meta = json.load(f)
12
+ cat_indexes = meta["cat_indexes"]
13
+ print(
14
+ "[ForestDiffusion] train config: "
15
+ f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 "
16
+ f"n_estimators=100 duplicate_K=20 n_jobs=2 "
17
+ f"xgb_verbosity=1",
18
+ flush=True,
19
+ )
20
+ m = ForestDiffusionModel(
21
+ X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
22
+ model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
23
+ verbosity=1,
24
+ )
25
+ joblib.dump((m, meta), "/tmp/fd_model.joblib")
26
+ print("ForestDiffusion train OK")
27
+
28
+ shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib')
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+ {
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+ "model": "forestdiffusion",
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