Add syntheticSuccess n15

syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=7857)
+syn.to_csv("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf-n15-7857-20260330_070701.csv", index=False)
+print(f"[ARF] Generated 7857 rows -> /work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf-n15-7857-20260330_070701.csv")

syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf_model.pkl")

syntheticSuccess/n15/arf/arf-n15-20260326_084758/arf-n15-1000-20260326_090652.csv

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+size 921264

syntheticSuccess/n15/arf/arf-n15-20260326_084758/arf-n15-7857-20260330_070701.csv

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syntheticSuccess/n15/arf/arf-n15-20260326_084758/arf_model.pkl

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syntheticSuccess/n15/arf/arf-n15-20260326_084758/gen_20260326_090652.log

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syntheticSuccess/n15/arf/arf-n15-20260326_084758/input_snapshot.json

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+{
+  "dataset_id": "n15",
+  "model": "arf",
+  "inputs": {
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n15/n15-train.csv",
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+}

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syntheticSuccess/n15/arf/arf-n15-20260326_084758/public_gate/public_gate_report.json

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syntheticSuccess/n15/arf/arf-n15-20260326_084758/public_gate/staged_input_manifest.json

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syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_generate.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=7857).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet-n15-7857-20260330_070726.csv", index=False)
+print(f"[BayesNet] Generated 7857 rows -> /work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet-n15-7857-20260330_070726.csv")

syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_train.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w") as _f:
+    _json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols")
+
+loader = GenericDataLoader(df)
+plugin = Plugins().get("bayesian_network")
+plugin.fit(loader)
+
+with open("/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl")

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+{
+  "dataset_id": "n15",
+  "model": "bayesnet",
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+    },
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+      "exists": true,
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+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n15/n15-dataset_contract_v1.json",
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+    }
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+}

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syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/public_gate/public_gate_report.json

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syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/public_gate/staged_input_manifest.json

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syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/staged/bayesnet/adapter_report.json

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syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/staged/public/staged_features.json

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syntheticSuccess/n15/ctgan/ctgan-n15-20260328_054854/_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-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/models_300epochs/ctgan_300epochs.pt")
+total = 7857
+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-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv")

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