Add n11 hyper parameter tuning results

Hyper ParameterTuning/n11/README.md

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+# n11 Hyper Parameter Tuning
+
+This folder stores RF-guided hyperparameter tuning outputs for n11.
+
+Structure:
+- `<model>/runs/`: copied training/generation run directories referenced by phase1/phase2 score tables.
+- `<model>/_rf_tuning/`: tuning manifests, score tables, reports, and final recommendations.
+
+Models currently included:
+- tvae
+- tabsyn
+- tabdiff
+- ctgan

Hyper ParameterTuning/n11/ctgan/_rf_tuning/final_recommendation.json

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Hyper ParameterTuning/n11/ctgan/_rf_tuning/phase1_manifest.json

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Hyper ParameterTuning/n11/ctgan/_rf_tuning/phase1_scores.csv

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Hyper ParameterTuning/n11/ctgan/_rf_tuning/phase2_manifest.json

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Hyper ParameterTuning/n11/ctgan/_rf_tuning/phase2_report.csv

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/_ctgan_generate.py

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+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/models_100epochs/ctgan_100epochs.pt")
+total = 15215
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/ctgan-n11-15215-20260520_235141.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/ctgan-n11-15215-20260520_235141.csv")

Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/_ctgan_train.py

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+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+
+data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/staged/public/train.csv")
+discrete_columns = ['g']
+model = CTGAN(
+    embedding_dim=128,
+    generator_dim=(256, 256),
+    discriminator_dim=(256, 256),
+    batch_size=128,
+    pac=2,
+    epochs=100,
+    verbose=True,
+)
+model.fit(data, discrete_columns)
+model.save("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/models_100epochs/ctgan_100epochs.pt")
+print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_234320/models_100epochs/ctgan_100epochs.pt")

Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/bo_combo.json

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/ctgan_metadata.json

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/gen_20260520_235141.log

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/models_100epochs/ctgan_100epochs.pt

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_234320/staged/public/val.csv

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Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_235258/_ctgan_generate.py

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+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/models_100epochs/ctgan_100epochs.pt")
+total = 15215
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/ctgan-n11-15215-20260521_000102.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/ctgan-n11-15215-20260521_000102.csv")

Hyper ParameterTuning/n11/ctgan/runs/ctgan-n11-20260520_235258/_ctgan_train.py

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+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+
+data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/staged/public/train.csv")
+discrete_columns = ['g']
+model = CTGAN(
+    embedding_dim=128,
+    generator_dim=(32, 32),
+    discriminator_dim=(32, 32),
+    batch_size=128,
+    pac=2,
+    epochs=100,
+    verbose=True,
+)
+model.fit(data, discrete_columns)
+model.save("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/models_100epochs/ctgan_100epochs.pt")
+print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260520_235258/models_100epochs/ctgan_100epochs.pt")

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