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Resume SynthData0523 main/n1 batch 2

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  1. .gitattributes +107 -0
  2. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/staged/tabbyflow/model_input_manifest.json +3 -0
  3. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabbyflow-n1-3680-20260510_205755.csv +3 -0
  4. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabbyflow_train_meta.json +3 -0
  5. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_cat_test.npy +3 -0
  6. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_cat_train.npy +3 -0
  7. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_cat_val.npy +3 -0
  8. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_num_test.npy +3 -0
  9. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_num_train.npy +3 -0
  10. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/X_num_val.npy +3 -0
  11. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/info.json +3 -0
  12. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/real.csv +3 -0
  13. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/staged_features.json +3 -0
  14. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/test.csv +3 -0
  15. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/train.csv +3 -0
  16. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/val.csv +3 -0
  17. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/y_test.npy +3 -0
  18. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/y_train.npy +3 -0
  19. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/tabular_bundle/pipeline_n1/y_val.npy +3 -0
  20. SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/train_20260510_205654.log +3 -0
  21. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_sample.py +67 -0
  22. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_train.py +32 -0
  23. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml +39 -0
  24. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml +39 -0
  25. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/data/X_cat_train.npy +3 -0
  26. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/data/X_num_train.npy +3 -0
  27. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/data/info.json +132 -0
  28. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/data/y_train.npy +3 -0
  29. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/gen_20260424_034040.log +3 -0
  30. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/input_snapshot.json +36 -0
  31. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/X_cat_train.npy +3 -0
  32. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/X_cat_unnorm.npy +3 -0
  33. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/X_num_train.npy +3 -0
  34. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/X_num_unnorm.npy +3 -0
  35. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/config.toml +39 -0
  36. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/info.json +132 -0
  37. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/loss.csv +3 -0
  38. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/model.pt +3 -0
  39. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/model_ema.pt +3 -0
  40. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/output/y_train.npy +3 -0
  41. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/public_gate/normalized_schema_snapshot.json +1222 -0
  42. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/public_gate/public_gate_report.json +37 -0
  43. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/public_gate/staged_input_manifest.json +1227 -0
  44. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/runtime_result.json +15 -0
  45. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/public/staged_features.json +292 -0
  46. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/public/test.csv +3 -0
  47. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/public/train.csv +3 -0
  48. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/public/val.csv +3 -0
  49. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/tabddpm/adapter_report.json +7 -0
  50. SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/staged/tabddpm/adapter_transforms_applied.json +1 -0
.gitattributes CHANGED
@@ -10558,3 +10558,110 @@ SynthData0523/main/n1/tabbyflow/tabbyflow-n1-20260510_205653/staged/public/train
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SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_sample.py ADDED
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+ 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 3680 rows")
25
+ ret = subprocess.run(
26
+ [sys.executable, wrapper, "scripts/pipeline.py",
27
+ "--config", "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.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
36
+ work_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725"
37
+ info_path = os.path.join(work_dir, "data", "info.json")
38
+ with open(info_path) as f:
39
+ info = json.load(f)
40
+
41
+ output_dir = os.path.join(work_dir, "output")
42
+ col_names = info.get("column_names", [])
43
+
44
+ parts = []
45
+ x_num_path = os.path.join(output_dir, "X_num_train.npy")
46
+ x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
47
+ y_path = os.path.join(output_dir, "y_train.npy")
48
+
49
+ if os.path.exists(x_num_path):
50
+ parts.append(np.load(x_num_path, allow_pickle=True))
51
+ if os.path.exists(x_cat_path):
52
+ parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
53
+ if os.path.exists(y_path):
54
+ y = np.load(y_path, allow_pickle=True)
55
+ parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
56
+
57
+ if parts:
58
+ combined = np.concatenate(parts, axis=1)
59
+ if col_names and len(col_names) == combined.shape[1]:
60
+ df = pd.DataFrame(combined, columns=col_names)
61
+ else:
62
+ df = pd.DataFrame(combined)
63
+ df.to_csv("/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv", index=False)
64
+ print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv")
65
+ else:
66
+ print("[TabDDPM] WARNING: No output .npy files found")
67
+ sys.exit(1)
SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/_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-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml")
23
+ ret = subprocess.run(
24
+ [sys.executable, wrapper, "scripts/pipeline.py",
25
+ "--config", "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/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")
SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed = 0
2
+ parent_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/output"
3
+ real_data_path = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/data"
4
+ model_type = "mlp"
5
+ num_numerical_features = 54
6
+ device = "cuda:0"
7
+
8
+ [model_params]
9
+ d_in = 57
10
+ num_classes = 2
11
+ is_y_cond = true
12
+
13
+ [model_params.rtdl_params]
14
+ d_layers = [256, 256]
15
+ dropout = 0.0
16
+
17
+ [diffusion_params]
18
+ num_timesteps = 1000
19
+ gaussian_loss_type = "mse"
20
+
21
+ [train.main]
22
+ steps = 5000
23
+ lr = 0.001
24
+ weight_decay = 0.0
25
+ batch_size = 256
26
+
27
+ [train.T]
28
+ seed = 0
29
+ normalization = "quantile"
30
+ num_nan_policy = "__none__"
31
+ cat_nan_policy = "__none__"
32
+ cat_min_frequency = "__none__"
33
+ cat_encoding = "__none__"
34
+ y_policy = "default"
35
+
36
+ [sample]
37
+ num_samples = 1000
38
+ batch_size = 1000
39
+ seed = 0
SynthData0523/main/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed = 0
2
+ parent_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/output"
3
+ real_data_path = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/data"
4
+ model_type = "mlp"
5
+ num_numerical_features = 54
6
+ device = "cuda:0"
7
+
8
+ [model_params]
9
+ d_in = 57
10
+ num_classes = 2
11
+ is_y_cond = true
12
+
13
+ [model_params.rtdl_params]
14
+ d_layers = [256, 256]
15
+ dropout = 0.0
16
+
17
+ [diffusion_params]
18
+ num_timesteps = 1000
19
+ gaussian_loss_type = "mse"
20
+
21
+ [train.main]
22
+ steps = 5000
23
+ lr = 0.001
24
+ weight_decay = 0.0
25
+ batch_size = 256
26
+
27
+ [train.T]
28
+ seed = 0
29
+ normalization = "quantile"
30
+ num_nan_policy = "__none__"
31
+ cat_nan_policy = "__none__"
32
+ cat_min_frequency = "__none__"
33
+ cat_encoding = "__none__"
34
+ y_policy = "default"
35
+
36
+ [sample]
37
+ num_samples = 3680
38
+ batch_size = 1000
39
+ seed = 0
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