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Resume SynthData0523 main/c2 batch 2

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  1. .gitattributes +21 -0
  2. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_transforms_applied.json +3 -0
  3. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/model_input_manifest.json +3 -0
  4. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/public/staged_features.json +3 -0
  5. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/public/test.csv +3 -0
  6. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv +3 -0
  7. SynthData0523/main/c2/ctgan/ctgan-c2-20260501_070350/staged/public/val.csv +3 -0
  8. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py +18 -0
  9. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py +17 -0
  10. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv +3 -0
  11. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json +32 -0
  12. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log +3 -0
  13. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json +36 -0
  14. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt +3 -0
  15. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log +3 -0
  16. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json +144 -0
  17. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json +37 -0
  18. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json +149 -0
  19. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/run_config.json +46 -0
  20. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json +27 -0
  21. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json +7 -0
  22. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json +1 -0
  23. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json +151 -0
  24. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json +37 -0
  25. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv +3 -0
  26. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv +3 -0
  27. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv +3 -0
  28. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py +18 -0
  29. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py +17 -0
  30. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv +3 -0
  31. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json +32 -0
  32. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log +3 -0
  33. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json +36 -0
  34. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt +3 -0
  35. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log +3 -0
  36. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json +144 -0
  37. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json +37 -0
  38. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json +149 -0
  39. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/run_config.json +46 -0
  40. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json +27 -0
  41. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json +7 -0
  42. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json +1 -0
  43. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json +151 -0
  44. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json +37 -0
  45. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv +3 -0
  46. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv +3 -0
  47. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv +3 -0
  48. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py +18 -0
  49. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py +17 -0
  50. SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv +3 -0
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SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
4
+ apply_ctgan_inverse_fix()
5
+ import pandas as pd
6
+ from ctgan.synthesizers.ctgan import CTGAN
7
+ model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt")
8
+ total = 1382
9
+ chunk = min(50000, total) if total > 50000 else total
10
+ parts = []
11
+ left = total
12
+ while left > 0:
13
+ take = min(chunk, left)
14
+ parts.append(model.sample(take))
15
+ left -= take
16
+ sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
17
+ sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv", index=False)
18
+ print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv")
SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from ctgan.synthesizers.ctgan import CTGAN
3
+
4
+ data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv")
5
+ discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']
6
+ model = CTGAN(
7
+ embedding_dim=8,
8
+ generator_dim=(16, 16),
9
+ discriminator_dim=(16, 16),
10
+ batch_size=32,
11
+ pac=1,
12
+ epochs=50,
13
+ verbose=True,
14
+ )
15
+ model.fit(data, discrete_columns)
16
+ model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt")
17
+ print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt")
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+ {
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+ "name": "buying",
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+ "type": "categorical"
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+ },
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+ {
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+ "name": "maint",
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+ "type": "categorical"
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+ },
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+ "name": "doors",
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+ "type": "categorical"
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+ },
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+ {
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+ "name": "persons",
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+ "type": "categorical"
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+ },
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+ "name": "lug_boot",
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+ "type": "categorical"
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+ },
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+ "type": "categorical"
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+ },
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+ {
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+ "name": "class",
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+ "type": "categorical"
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+ }
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+ ]
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+ }
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+ "model": "ctgan",
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+ {
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+ "target_column": "class",
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+ "task_type": "classification",
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+ import sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
4
+ apply_ctgan_inverse_fix()
5
+ import pandas as pd
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+ from ctgan.synthesizers.ctgan import CTGAN
7
+ model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt")
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+ total = 1382
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+ chunk = min(50000, total) if total > 50000 else total
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+ parts = []
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+ left = total
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+ while left > 0:
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+ take = min(chunk, left)
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+ parts.append(model.sample(take))
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+ left -= take
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+ sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
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+ print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv")
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@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from ctgan.synthesizers.ctgan import CTGAN
3
+
4
+ data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv")
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+ discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']
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+ model = CTGAN(
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+ embedding_dim=16,
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+ generator_dim=(32, 32),
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+ discriminator_dim=(32, 32),
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+ batch_size=64,
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+ pac=1,
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+ epochs=100,
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+ verbose=True,
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+ )
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+ model.fit(data, discrete_columns)
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+ model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt")
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+ print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt")
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+ {
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+ "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt"
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+ }
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+ ],
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+ "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json",
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+ "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv",
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+ "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv",
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+ "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv",
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+ }
SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json ADDED
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+ [
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SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv ADDED
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SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
4
+ apply_ctgan_inverse_fix()
5
+ import pandas as pd
6
+ from ctgan.synthesizers.ctgan import CTGAN
7
+ model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt")
8
+ total = 1382
9
+ chunk = min(50000, total) if total > 50000 else total
10
+ parts = []
11
+ left = total
12
+ while left > 0:
13
+ take = min(chunk, left)
14
+ parts.append(model.sample(take))
15
+ left -= take
16
+ sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
17
+ sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv", index=False)
18
+ print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv")
SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from ctgan.synthesizers.ctgan import CTGAN
3
+
4
+ data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv")
5
+ discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']
6
+ model = CTGAN(
7
+ embedding_dim=32,
8
+ generator_dim=(64, 64),
9
+ discriminator_dim=(64, 64),
10
+ batch_size=128,
11
+ pac=1,
12
+ epochs=150,
13
+ verbose=True,
14
+ )
15
+ model.fit(data, discrete_columns)
16
+ model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt")
17
+ print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt")
SynthData0523/main/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ size 41931