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  1. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_gen.py +36 -0
  2. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_train.py +21 -0
  3. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/gen_20260501_005103.log +3 -0
  4. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/input_snapshot.json +3 -0
  5. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/models_tabdiff/trained.pt +3 -0
  6. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/public_gate/normalized_schema_snapshot.json +3 -0
  7. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/public_gate/public_gate_report.json +3 -0
  8. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/public_gate/staged_input_manifest.json +3 -0
  9. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/runtime_result.json +3 -0
  10. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/public/staged_features.json +3 -0
  11. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/public/test.csv +3 -0
  12. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/public/train.csv +3 -0
  13. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/public/val.csv +3 -0
  14. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/tabdiff/adapter_report.json +3 -0
  15. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/tabdiff/adapter_transforms_applied.json +3 -0
  16. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/staged/tabdiff/model_input_manifest.json +3 -0
  17. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabdiff-n3-3918-20260501_005103.csv +3 -0
  18. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabdiff_train_meta.json +3 -0
  19. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_cat_test.npy +3 -0
  20. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_cat_train.npy +3 -0
  21. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_cat_val.npy +3 -0
  22. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_num_test.npy +3 -0
  23. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_num_train.npy +3 -0
  24. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/X_num_val.npy +3 -0
  25. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/info.json +3 -0
  26. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/real.csv +3 -0
  27. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/test.csv +3 -0
  28. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/val.csv +3 -0
  29. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/y_test.npy +3 -0
  30. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/y_train.npy +3 -0
  31. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3/y_val.npy +3 -0
  32. syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/train_20260501_004446.log +3 -0
syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_gen.py ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
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+ import os, shutil, subprocess, sys
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+ td = r"/workspace/TabDiff"
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+ name = r"pipeline_n3"
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+ src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3"
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+ dst_data = os.path.join(td, "data", name)
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+ dst_syn = os.path.join(td, "synthetic", name)
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+ shutil.rmtree(dst_data, ignore_errors=True)
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+ shutil.copytree(src, dst_data)
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+ os.makedirs(dst_syn, exist_ok=True)
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+ for fn in ("real.csv", "test.csv", "val.csv"):
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+ shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
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+ os.chdir(td)
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+ os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "")
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+ subprocess.check_call([
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+ sys.executable, "-m", "tabdiff.main",
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+ "--dataname", name, "--mode", "test", "--gpu", "0",
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+ "--no_wandb", "--exp_name", r"adapter_learnable",
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+ "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt",
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+ "--num_samples_to_generate", str(int(3918)),
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+ ])
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+ # test() 写入 tabdiff/result/<dataname>/<exp>/<epoch>/samples.csv
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+ import glob as g
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+ base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable")
25
+ best = None
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+ best_t = -1.0
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+ for root, _, files in os.walk(base):
28
+ if "samples.csv" in files:
29
+ p = os.path.join(root, "samples.csv")
30
+ t = os.path.getmtime(p)
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+ if t > best_t:
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+ best_t = t
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+ best = p
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+ if not best:
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+ raise SystemExit("tabdiff: no samples.csv under " + base)
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+ shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabdiff-n3-3918-20260501_005103.csv")
syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_train.py ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
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+ import os, shutil, subprocess, sys
3
+ td = r"/workspace/TabDiff"
4
+ name = r"pipeline_n3"
5
+ src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3"
6
+ dst_data = os.path.join(td, "data", name)
7
+ dst_syn = os.path.join(td, "synthetic", name)
8
+ shutil.rmtree(dst_data, ignore_errors=True)
9
+ shutil.copytree(src, dst_data)
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(td)
14
+ os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "")
15
+ os.environ["TABDIFF_SMOKE_STEPS"] = "500"
16
+ os.environ["TABDIFF_ADAPTER_TRAIN"] = "1"
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+ subprocess.check_call([
18
+ sys.executable, "-m", "tabdiff.main",
19
+ "--dataname", name, "--mode", "train", "--gpu", "0",
20
+ "--no_wandb", "--exp_name", r"adapter_learnable",
21
+ ])
syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/gen_20260501_005103.log ADDED
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syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_004446/models_tabdiff/trained.pt ADDED
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