TabQueryBench commited on
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72d240b
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1 Parent(s): 8ec72cd

Copy 5090 success runs into syntheticSuccess (batch 5)

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  1. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/_tabbyflow_gen.py +33 -0
  2. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/_tabbyflow_train.py +22 -0
  3. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/input_snapshot.json +3 -0
  4. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/models_tabbyflow/trained.pt +3 -0
  5. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/public_gate/normalized_schema_snapshot.json +3 -0
  6. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/public_gate/public_gate_report.json +3 -0
  7. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/public_gate/staged_input_manifest.json +3 -0
  8. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/runtime_result.json +3 -0
  9. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/public/staged_features.json +3 -0
  10. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/public/test.csv +3 -0
  11. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/public/train.csv +3 -0
  12. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/public/val.csv +3 -0
  13. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/tabbyflow/adapter_report.json +3 -0
  14. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/tabbyflow/adapter_transforms_applied.json +3 -0
  15. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/staged/tabbyflow/model_input_manifest.json +3 -0
  16. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabbyflow-m10-1600-20260423_231458.csv +3 -0
  17. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabbyflow_resolved_ckpt.json +3 -0
  18. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabbyflow_train_meta.json +3 -0
  19. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_cat_test.npy +3 -0
  20. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_cat_train.npy +3 -0
  21. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_cat_val.npy +3 -0
  22. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_num_test.npy +3 -0
  23. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_num_train.npy +3 -0
  24. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/X_num_val.npy +3 -0
  25. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/info.json +3 -0
  26. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/real.csv +3 -0
  27. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/test.csv +3 -0
  28. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/val.csv +3 -0
  29. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/y_test.npy +3 -0
  30. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/y_train.npy +3 -0
  31. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds/y_val.npy +3 -0
  32. syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/train_20260423_231356.log +3 -0
  33. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_X_host.npy +3 -0
  34. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_gen.py +8 -0
  35. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_meta_host.json +3 -0
  36. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_train.py +20 -0
  37. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/forest-m11-304887-20260426_123310.csv +3 -0
  38. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/forestdiffusion_model.joblib +3 -0
  39. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/input_snapshot.json +3 -0
  40. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/models_fd/model.joblib +3 -0
  41. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/public_gate/normalized_schema_snapshot.json +3 -0
  42. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/public_gate/public_gate_report.json +3 -0
  43. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/public_gate/staged_input_manifest.json +3 -0
  44. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/runtime_result.json +3 -0
  45. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/forestdiffusion/adapter_report.json +3 -0
  46. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/forestdiffusion/adapter_transforms_applied.json +3 -0
  47. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/forestdiffusion/model_input_manifest.json +3 -0
  48. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/public/staged_features.json +3 -0
  49. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/public/test.csv +3 -0
  50. syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/staged/public/train.csv +3 -0
syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/_tabbyflow_gen.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os, shutil, subprocess, sys
3
+ root = r"/workspace/ef-vfm"
4
+ name = r"pipeline_ds"
5
+ src = r"/work/output-SpecializedModels/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds"
6
+ dst_data = os.path.join(root, "data", name)
7
+ shutil.rmtree(dst_data, ignore_errors=True)
8
+ shutil.copytree(src, dst_data)
9
+ dst_syn = os.path.join(root, "synthetic", name)
10
+ os.makedirs(dst_syn, exist_ok=True)
11
+ for fn in ("real.csv", "test.csv", "val.csv"):
12
+ shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
13
+ os.chdir(root)
14
+ os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
15
+ subprocess.check_call([
16
+ sys.executable, "main.py",
17
+ "--dataname", name, "--mode", "test", "--gpu", "0",
18
+ "--no_wandb", "--exp_name", r"adapter_tabbyflow-m10-20260423_231356",
19
+ "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_tabbyflow-m10-20260423_231356/model_500.pt",
20
+ "--num_samples_to_generate", str(int(1600)),
21
+ ])
22
+ base = os.path.join(root, "ef_vfm", "result", name, r"adapter_tabbyflow-m10-20260423_231356")
23
+ best = None
24
+ best_t = -1.0
25
+ for r, _, files in os.walk(base):
26
+ if "samples.csv" in files:
27
+ p = os.path.join(r, "samples.csv")
28
+ t = os.path.getmtime(p)
29
+ if t > best_t:
30
+ best_t, best = t, p
31
+ if not best:
32
+ raise SystemExit("tabbyflow: no samples.csv in " + base)
33
+ shutil.copy(best, r"/work/output-SpecializedModels/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabbyflow-m10-1600-20260423_231458.csv")
syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/_tabbyflow_train.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os, shutil, subprocess, sys
3
+ root = r"/workspace/ef-vfm"
4
+ name = r"pipeline_ds"
5
+ src = r"/work/output-SpecializedModels/m10/tabbyflow/tabbyflow-m10-20260423_231356/tabular_bundle/pipeline_ds"
6
+ os.makedirs(os.path.join(root, "data", name), exist_ok=True)
7
+ dst_data = os.path.join(root, "data", name)
8
+ dst_syn = os.path.join(root, "synthetic", name)
9
+ shutil.rmtree(dst_data, ignore_errors=True)
10
+ shutil.copytree(src, dst_data)
11
+ os.makedirs(dst_syn, exist_ok=True)
12
+ for fn in ("real.csv", "test.csv", "val.csv"):
13
+ shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
14
+ os.chdir(root)
15
+ os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
16
+ os.environ["EFVFM_SMOKE_STEPS"] = "500"
17
+ os.environ["EFVFM_ADAPTER_TRAIN"] = "1"
18
+ subprocess.check_call([
19
+ sys.executable, "main.py",
20
+ "--dataname", name, "--mode", "train", "--gpu", "0",
21
+ "--no_wandb", "--exp_name", r"adapter_tabbyflow-m10-20260423_231356",
22
+ ])
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syntheticSuccess/m10/tabbyflow/tabbyflow-m10-20260423_231356/models_tabbyflow/trained.pt ADDED
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+ import joblib, pandas as pd
3
+ m, meta = joblib.load(r'/work/output-SpecializedModels/m11/forestdiffusion/forest-m11-20260426_040649/forestdiffusion_model.joblib')
4
+ # generate:batch_size 为样本数
5
+ arr = m.generate(batch_size=int(304887))
6
+ df = pd.DataFrame(arr, columns=meta["column_names"])
7
+ df.to_csv(r'/work/output-SpecializedModels/m11/forestdiffusion/forest-m11-20260426_040649/forest-m11-304887-20260426_123310.csv', index=False)
8
+ print("saved", len(df))
syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_meta_host.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m11/forestdiffusion/forest-m11-20260426_040649/_fd_train.py ADDED
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1
+
2
+ import shutil, json
3
+ shutil.copy(r'/work/output-SpecializedModels/m11/forestdiffusion/forest-m11-20260426_040649/_fd_X_host.npy', '/tmp/fd_X.npy')
4
+ with open(r'/work/output-SpecializedModels/m11/forestdiffusion/forest-m11-20260426_040649/_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
+ m = ForestDiffusionModel(
14
+ X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
15
+ model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
16
+ )
17
+ joblib.dump((m, meta), "/tmp/fd_model.joblib")
18
+ print("ForestDiffusion train OK")
19
+
20
+ shutil.copy('/tmp/fd_model.joblib', r'/work/output-SpecializedModels/m11/forestdiffusion/forest-m11-20260426_040649/forestdiffusion_model.joblib')
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