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  1. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.py +66 -0
  2. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py +32 -0
  3. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml +3 -0
  4. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml +3 -0
  5. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_cat_train.npy +3 -0
  6. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_num_train.npy +3 -0
  7. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json +3 -0
  8. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/data/y_train.npy +3 -0
  9. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/gen_20260501_000111_r0.log +3 -0
  10. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/input_snapshot.json +3 -0
  11. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/X_cat_train.npy +3 -0
  12. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/X_cat_unnorm.npy +3 -0
  13. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/X_num_train.npy +3 -0
  14. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/X_num_unnorm.npy +3 -0
  15. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/config.toml +3 -0
  16. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/info.json +3 -0
  17. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/loss.csv +3 -0
  18. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/model.pt +3 -0
  19. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/model_ema.pt +3 -0
  20. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/output/y_train.npy +3 -0
  21. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/public_gate/normalized_schema_snapshot.json +3 -0
  22. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/public_gate/public_gate_report.json +3 -0
  23. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/public_gate/staged_input_manifest.json +3 -0
  24. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/runtime_result.json +3 -0
  25. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/public/staged_features.json +3 -0
  26. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/public/test.csv +3 -0
  27. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/public/train.csv +3 -0
  28. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/public/val.csv +3 -0
  29. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/tabddpm/adapter_report.json +3 -0
  30. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/tabddpm/adapter_transforms_applied.json +3 -0
  31. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/staged/tabddpm/model_input_manifest.json +3 -0
  32. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv +3 -0
  33. syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/train_20260501_000003.log +3 -0
syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.py ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys, subprocess, json
2
+ import numpy as np
3
+ import pandas as pd
4
+
5
+ tabddpm_root = "/workspace/tabddpm/code"
6
+ assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
7
+ env = os.environ.copy()
8
+ env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
9
+
10
+ # Reuse the compat wrapper (patches collections.Sequence for skorch)
11
+ wrapper = os.path.join(tabddpm_root, "_compat_run.py")
12
+ if not os.path.exists(wrapper):
13
+ with open(wrapper, "w") as f:
14
+ f.write(
15
+ "import collections, collections.abc\n"
16
+ "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
17
+ "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
18
+ " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
19
+ "import sys, runpy\n"
20
+ "sys.argv = sys.argv[1:]\n"
21
+ "runpy.run_path(sys.argv[0], run_name='__main__')\n"
22
+ )
23
+
24
+ print(f"[TabDDPM] Sampling 36168 rows")
25
+ ret = subprocess.run(
26
+ [sys.executable, wrapper, "scripts/pipeline.py",
27
+ "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml",
28
+ "--sample"],
29
+ cwd=tabddpm_root,
30
+ env=env
31
+ )
32
+ if ret.returncode != 0:
33
+ sys.exit(ret.returncode)
34
+
35
+ # 将 .npy 输出转为 CSV(npy 在 TabDDPM 的 parent_dir,即 npy_dir)
36
+ info_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json"
37
+ with open(info_path) as f:
38
+ info = json.load(f)
39
+
40
+ output_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/output"
41
+ col_names = info.get("column_names", [])
42
+
43
+ parts = []
44
+ x_num_path = os.path.join(output_dir, "X_num_train.npy")
45
+ x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
46
+ y_path = os.path.join(output_dir, "y_train.npy")
47
+
48
+ if os.path.exists(x_num_path):
49
+ parts.append(np.load(x_num_path, allow_pickle=True))
50
+ if os.path.exists(x_cat_path):
51
+ parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
52
+ if os.path.exists(y_path):
53
+ y = np.load(y_path, allow_pickle=True)
54
+ parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
55
+
56
+ if parts:
57
+ combined = np.concatenate(parts, axis=1)
58
+ if col_names and len(col_names) == combined.shape[1]:
59
+ df = pd.DataFrame(combined, columns=col_names)
60
+ else:
61
+ df = pd.DataFrame(combined)
62
+ df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv", index=False)
63
+ print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv")
64
+ else:
65
+ print("[TabDDPM] WARNING: No output .npy files found")
66
+ sys.exit(1)
syntheticSuccess/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys, subprocess
2
+
3
+ tabddpm_root = "/workspace/tabddpm/code"
4
+ assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
5
+ env = os.environ.copy()
6
+ env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
7
+
8
+ # Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
9
+ # before running pipeline.py - needed because skorch uses old API
10
+ wrapper = os.path.join(tabddpm_root, "_compat_run.py")
11
+ with open(wrapper, "w") as f:
12
+ f.write(
13
+ "import collections, collections.abc\n"
14
+ "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
15
+ "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
16
+ " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
17
+ "import sys, runpy\n"
18
+ "sys.argv = sys.argv[1:]\n"
19
+ "runpy.run_path(sys.argv[0], run_name='__main__')\n"
20
+ )
21
+
22
+ print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml")
23
+ ret = subprocess.run(
24
+ [sys.executable, wrapper, "scripts/pipeline.py",
25
+ "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml",
26
+ "--train"],
27
+ cwd=tabddpm_root,
28
+ env=env
29
+ )
30
+ if ret.returncode != 0:
31
+ sys.exit(ret.returncode)
32
+ print("[TabDDPM] Training complete")
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