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Resume SynthData0523 main/m6 batch 2

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  1. .gitattributes +99 -0
  2. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/public_gate/staged_input_manifest.json +3 -0
  3. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/runtime_result.json +3 -0
  4. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/public/staged_features.json +3 -0
  5. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/public/test.csv +3 -0
  6. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/public/train.csv +3 -0
  7. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/public/val.csv +3 -0
  8. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/tabdiff/adapter_report.json +3 -0
  9. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/tabdiff/adapter_transforms_applied.json +3 -0
  10. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/staged/tabdiff/model_input_manifest.json +3 -0
  11. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabdiff-m6-9864-20260429_043402.csv +3 -0
  12. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabdiff_train_meta.json +3 -0
  13. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_cat_test.npy +3 -0
  14. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_cat_train.npy +3 -0
  15. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_cat_val.npy +3 -0
  16. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_num_test.npy +3 -0
  17. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_num_train.npy +3 -0
  18. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/X_num_val.npy +3 -0
  19. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/info.json +3 -0
  20. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/real.csv +3 -0
  21. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/test.csv +3 -0
  22. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/val.csv +3 -0
  23. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_test.npy +3 -0
  24. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_train.npy +3 -0
  25. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_val.npy +3 -0
  26. SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/train_20260429_042701.log +3 -0
  27. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py +68 -0
  28. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log +3 -0
  29. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json +36 -0
  30. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json +377 -0
  31. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/public_gate_report.json +37 -0
  32. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/staged_input_manifest.json +382 -0
  33. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/runner.log +3 -0
  34. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/runtime_result.json +14 -0
  35. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/public/staged_features.json +92 -0
  36. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/public/test.csv +3 -0
  37. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/public/train.csv +3 -0
  38. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/public/val.csv +3 -0
  39. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/tabpfgen/adapter_report.json +7 -0
  40. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/tabpfgen/adapter_transforms_applied.json +1 -0
  41. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/staged/tabpfgen/model_input_manifest.json +384 -0
  42. SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv +3 -0
  43. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py +100 -0
  44. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log +3 -0
  45. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json +3 -0
  46. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/normalized_schema_snapshot.json +3 -0
  47. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/public_gate_report.json +3 -0
  48. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/staged_input_manifest.json +3 -0
  49. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/runtime_result.json +3 -0
  50. SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/staged_features.json +3 -0
.gitattributes CHANGED
@@ -9256,3 +9256,102 @@ SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/input_snapshot.json fil
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+ import numpy as np
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+ import pandas as pd
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+ import json
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+ from tabpfgen import TabPFGen
5
+
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+ df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv")
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+ target_col = "VisitorType"
8
+
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+ feature_cols = [c for c in df.columns if c != target_col]
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+
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+ # --- Label-encode categorical / object columns ---
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+ cat_encodings = {} # col -> list of unique values (index = code)
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+ for col in feature_cols:
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+ if df[col].dtype == object or str(df[col].dtype) == 'category':
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+ cats = sorted(df[col].dropna().unique().tolist(), key=str)
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+ cat_map = {v: i for i, v in enumerate(cats)}
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+ df[col] = df[col].map(cat_map).astype(float)
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+ cat_encodings[col] = cats
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+ print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
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+
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+ # Encode target if categorical
22
+ target_cats = None
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+ if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
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+ cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
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+ t_map = {v: i for i, v in enumerate(cats)}
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+ df[target_col] = df[target_col].map(t_map).astype(float)
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+ target_cats = cats
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+ print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
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+
30
+ X = df[feature_cols].values.astype(np.float32)
31
+ y = df[target_col].values
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+
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+ # Handle NaN
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+ for i in range(X.shape[1]):
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+ col_vals = X[:, i]
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+ mask = np.isnan(col_vals)
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+ if mask.any():
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+ mean_val = np.nanmean(col_vals)
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+ X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
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+
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+ gen = TabPFGen(
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+ n_sgld_steps=1000,
43
+ sgld_step_size=0.01,
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+ sgld_noise_scale=0.01,
45
+ device="auto",
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+ )
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+
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+ print(f"[TabPFGen] Generating 9864 rows via generate_classification")
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+ X_syn, y_syn = gen.generate_classification(X, y, n_samples=9864)
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+
51
+ syn_df = pd.DataFrame(X_syn, columns=feature_cols)
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+ syn_df[target_col] = y_syn
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+
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+ # --- Inverse label-encoding for categorical columns ---
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+ for col, cats in cat_encodings.items():
56
+ # Round to nearest integer index, clamp to valid range
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+ codes = np.round(syn_df[col].values).astype(int)
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+ codes = np.clip(codes, 0, len(cats) - 1)
59
+ syn_df[col] = [cats[c] for c in codes]
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+
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+ if target_cats is not None:
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+ codes = np.round(syn_df[target_col].values).astype(int)
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+ codes = np.clip(codes, 0, len(target_cats) - 1)
64
+ syn_df[target_col] = [target_cats[c] for c in codes]
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+
66
+ syn_df = syn_df[list(df.columns)]
67
+ syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv", index=False)
68
+ print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv")
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+ {
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+ "model": "tabpfgen",
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+ }
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+ {
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+ "dataset_id": "m6",
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+ "task_type": "classification",
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+ "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/staged_input_manifest.json",
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+ "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv",
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+ "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/val.csv",
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+ "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/test.csv",
382
+ "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/staged_features.json",
383
+ "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/public_gate_report.json"
384
+ }
SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:2e051e11efa0bbca0495b445e9ee238b6804b24b3de4d39549a023d3d14ef7d5
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SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import numpy as np
3
+ import pandas as pd
4
+ import json
5
+ from tabpfgen import TabPFGen
6
+
7
+ df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/train.csv")
8
+ target_col = "VisitorType"
9
+
10
+ target_missing = df[target_col].isna()
11
+ if target_missing.any():
12
+ dropped = int(target_missing.sum())
13
+ df = df.loc[~target_missing].copy()
14
+ print(
15
+ f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
16
+ )
17
+ if df.empty:
18
+ raise ValueError(
19
+ f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
20
+ )
21
+
22
+ feature_cols = [c for c in df.columns if c != target_col]
23
+
24
+ cat_encodings = {}
25
+ for col in feature_cols:
26
+ if df[col].dtype == object or str(df[col].dtype) == 'category':
27
+ cats = sorted(df[col].dropna().unique().tolist(), key=str)
28
+ cat_map = {v: i for i, v in enumerate(cats)}
29
+ df[col] = df[col].map(cat_map).astype(float)
30
+ cat_encodings[col] = cats
31
+ print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
32
+
33
+ target_cats = None
34
+ if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
35
+ cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
36
+ t_map = {v: i for i, v in enumerate(cats)}
37
+ df[target_col] = df[target_col].map(t_map).astype(float)
38
+ target_cats = cats
39
+ print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
40
+
41
+ X = df[feature_cols].values.astype(np.float32)
42
+ y = df[target_col].values
43
+ target_n = int(9864)
44
+
45
+ for i in range(X.shape[1]):
46
+ col_vals = X[:, i]
47
+ mask = np.isnan(col_vals)
48
+ if mask.any():
49
+ mean_val = np.nanmean(col_vals)
50
+ X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
51
+
52
+ # TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
53
+ # （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
54
+ gen = TabPFGen(
55
+ n_sgld_steps=1000,
56
+ sgld_step_size=0.01,
57
+ sgld_noise_scale=0.01,
58
+ device="auto",
59
+ )
60
+
61
+ print(f"[TabPFGen] Generating {target_n} rows via generate_classification")
62
+ X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n)
63
+
64
+ syn_df = pd.DataFrame(X_syn, columns=feature_cols)
65
+ syn_df[target_col] = y_syn
66
+
67
+ for col, cats in cat_encodings.items():
68
+ codes = np.round(syn_df[col].values).astype(int)
69
+ codes = np.clip(codes, 0, len(cats) - 1)
70
+ syn_df[col] = [cats[c] for c in codes]
71
+
72
+ if target_cats is not None:
73
+ codes = np.round(syn_df[target_col].values).astype(int)
74
+ codes = np.clip(codes, 0, len(target_cats) - 1)
75
+ syn_df[target_col] = [target_cats[c] for c in codes]
76
+
77
+ if len(syn_df) > target_n:
78
+ print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}")
79
+ syn_df = syn_df.iloc[:target_n].copy()
80
+ elif len(syn_df) < target_n:
81
+ deficit = target_n - len(syn_df)
82
+ print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})")
83
+ if len(syn_df) > 0:
84
+ extra = syn_df.sample(n=deficit, replace=True, random_state=42)
85
+ syn_df = pd.concat(
86
+ [syn_df.reset_index(drop=True), extra.reset_index(drop=True)],
87
+ ignore_index=True,
88
+ )
89
+ else:
90
+ syn_df = df[feature_cols + [target_col]].sample(
91
+ n=target_n, replace=True, random_state=42
92
+ ).reset_index(drop=True)
93
+
94
+ syn_df = syn_df[list(df.columns)]
95
+ if len(syn_df) != target_n:
96
+ raise RuntimeError(
97
+ f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}"
98
+ )
99
+ syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv", index=False)
100
+ print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv")
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