Add syntheticSuccess m5

syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(3539)
+with open("/work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=n_target)
+syn = syn.reset_index(drop=True)
+if len(syn) > n_target:
+    syn = syn.iloc[:n_target]
+elif len(syn) < n_target:
+    parts = [syn]
+    tries = 0
+    while sum(len(p) for p in parts) < n_target and tries < 64:
+        tries += 1
+        need = n_target - sum(len(p) for p in parts)
+        chunk = model.forge(n=max(need, 1)).reset_index(drop=True)
+        if len(chunk) == 0:
+            break
+        parts.append(chunk)
+    syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+syn.to_csv("/work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv")

syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_train.py

@@ -0,0 +1,37 @@
+import pickle
+import numpy as np
+import pandas as pd
+from arfpy import arf
+
+def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame:
+    """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。"""
+    df = df.replace([np.inf, -np.inf], np.nan)
+    df = df.dropna(axis=1, how="all")
+    for col in df.select_dtypes(include=[np.number]).columns:
+        med = df[col].median()
+        if pd.isna(med):
+            med = 0.0
+        df[col] = df[col].fillna(med)
+        nu = int(df[col].nunique(dropna=True))
+        if nu <= 1:
+            continue
+        lo, hi = df[col].quantile(0.001), df[col].quantile(0.999)
+        if pd.notna(lo) and pd.notna(hi) and lo < hi:
+            df[col] = df[col].clip(lo, hi)
+    return df
+
+df = pd.read_csv("/work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/staged/public/train.csv")
+df = _sanitize_for_arf(df)
+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/m5/arf/arf-m5-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf_model.pkl")

syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv

@@ -0,0 +1,3 @@
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+size 1912738

syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf_model.pkl

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+oid sha256:48297851fbf4b5cb9749048d291da9bcc117578ebf36ce6ec610ee5a15389a2b
+size 51471366

syntheticSuccess/m5/arf/arf-m5-20260422_055912/gen_20260422_060829.log

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+oid sha256:43abd61e2dad5483d73dcd3fe330bca0780011a3c44d171edebc8d9b892ed7fb
+size 455

syntheticSuccess/m5/arf/arf-m5-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "m5",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-train.csv",
+      "exists": true,
+      "size": 422717,
+      "sha256": "012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-val.csv",
+      "exists": true,
+      "size": 53889,
+      "sha256": "b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-test.csv",
+      "exists": true,
+      "size": 53943,
+      "sha256": "696cfc46d2e611ee56a5419f4496b758f643f49f3386d4181296783760117c8c"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m5/m5-dataset_profile.json",
+      "exists": true,
+      "size": 14974,
+      "sha256": "6ca9a300883081c4197534dd44e5e37df852ef129b5c06666629d8dd8270af0d"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m5/m5-dataset_contract_v1.json",
+      "exists": true,
+      "size": 17696,
+      "sha256": "d5ce8aae5a21071b4e1af75dcdf7fa3118c7b487163ad0c8244ecc33d08d7c89"
+    }
+  }
+}

syntheticSuccess/m5/arf/arf-m5-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,758 @@
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+  "target_column": "Target",
+  "task_type": "classification",
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syntheticSuccess/m5/arf/arf-m5-20260422_055912/public_gate/public_gate_report.json

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syntheticSuccess/m5/arf/arf-m5-20260422_055912/public_gate/staged_input_manifest.json

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syntheticSuccess/m5/arf/arf-m5-20260422_055912/runtime_result.json

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syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/arf/adapter_report.json

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syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/arf/adapter_transforms_applied.json

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+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 0.002826,
+        "example_values": [
+          "16.2",
+          "9.4",
+          "13.9",
+          "10.8",
+          "15.5"
+        ]
+      }
+    },
+    {
+      "name": "Inflation rate",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.002543,
+        "example_values": [
+          "0.3",
+          "-0.8",
+          "-0.3",
+          "1.4",
+          "2.8"
+        ]
+      }
+    },
+    {
+      "name": "GDP",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 0.002826,
+        "example_values": [
+          "-0.92",
+          "-3.12",
+          "0.79",
+          "1.74",
+          "-4.06"
+        ]
+      }
+    },
+    {
+      "name": "Target",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000848,
+        "example_values": [
+          "Dropout",
+          "Graduate",
+          "Enrolled"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/public_gate/public_gate_report.json"
+}

syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/staged_features.json

@@ -0,0 +1,187 @@
+[
+  {
+    "feature_name": "Marital status",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Application mode",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Application order",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Course",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Daytime/evening attendance",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Previous qualification",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Previous qualification (grade)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Nacionality",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Mother's qualification",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Father's qualification",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Mother's occupation",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Father's occupation",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Admission grade",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Displaced",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Educational special needs",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Debtor",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Tuition fees up to date",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Gender",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Scholarship holder",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Age at enrollment",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "International",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (credited)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (enrolled)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (evaluations)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (approved)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (grade)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 1st sem (without evaluations)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (credited)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (enrolled)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (evaluations)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (approved)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (grade)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Curricular units 2nd sem (without evaluations)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Unemployment rate",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Inflation rate",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "GDP",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Target",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:696cfc46d2e611ee56a5419f4496b758f643f49f3386d4181296783760117c8c
+size 53943

syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f
+size 422717

syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/val.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557
+size 53889

syntheticSuccess/m5/arf/arf-m5-20260422_055912/train_20260422_055912.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6083e73e5532ece05305d1fe94141798440d70d9770d2b6d037c0d04f76efdd1
+size 467

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_generate.py

@@ -0,0 +1,104 @@
+
+import pickle
+import subprocess
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.sampling import BayesianModelSampling
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl", "rb") as f:
+    bundle = pickle.load(f)
+
+network = bundle["network"]
+inverse = bundle["inverse"]
+cols = bundle["column_order"]
+integer_columns = set(bundle.get("integer_columns") or [])
+full_order = bundle.get("full_column_order") or cols
+const_cols = bundle.get("const_cols") or {}
+
+num_rows = int(3539)
+sampler = BayesianModelSampling(network)
+raw = sampler.forward_sample(size=num_rows, show_progress=False)
+raw = raw.reset_index(drop=True)
+if len(raw) > num_rows:
+    raw = raw.iloc[:num_rows]
+_tries = 0
+while len(raw) < num_rows and _tries < 64:
+    _tries += 1
+    nextra = min(10000, num_rows - len(raw))
+    more = sampler.forward_sample(size=max(nextra, 1), show_progress=False)
+    more = more.reset_index(drop=True)
+    if len(more) == 0:
+        break
+    raw = pd.concat([raw, more], ignore_index=True)
+    if len(raw) > num_rows:
+        raw = raw.iloc[:num_rows]
+
+out = pd.DataFrame(index=raw.index)
+rng = np.random.default_rng()
+
+for c in cols:
+    if c in inverse["categorical"]:
+        levels = inverse["categorical"][c]
+        idx = raw[c].astype(int).to_numpy()
+        idx = np.clip(idx, 0, max(0, len(levels) - 1))
+        out[c] = [levels[i] for i in idx]
+    else:
+        edges = np.asarray(inverse["continuous"][c], dtype=float)
+        if edges.size < 2:
+            out[c] = 0.0
+        else:
+            nbin = edges.size - 1
+            res = []
+            for k in raw[c].astype(int).to_numpy():
+                k = int(k)
+                if k < 0:
+                    k = 0
+                if k >= nbin:
+                    k = nbin - 1
+                lo, hi = float(edges[k]), float(edges[k + 1])
+                if hi < lo:
+                    lo, hi = hi, lo
+                v = rng.uniform(lo, hi)
+                if c in integer_columns:
+                    v = int(round(v))
+                res.append(v)
+            out[c] = res
+
+final = pd.DataFrame(index=out.index)
+for c in full_order:
+    if c in const_cols:
+        final[c] = const_cols[c]
+    elif c in out.columns:
+        final[c] = out[c]
+
+dtypes = bundle.get("original_dtypes") or {}
+for c, dts in dtypes.items():
+    if c not in final.columns:
+        continue
+    try:
+        if "int" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64")
+        elif "float" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce")
+    except Exception:
+        pass
+
+if len(final) != num_rows:
+    final = final.iloc[:num_rows].copy()
+final.to_csv("/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv")

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_train.py

@@ -0,0 +1,118 @@
+
+import json
+import pickle
+import subprocess
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.estimators import TreeSearch
+from pgmpy.models import DiscreteBayesianNetwork
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+    colmeta = json.load(_f)
+integer_columns = set(colmeta.get("integer_columns") or [])
+
+df = pd.read_csv("/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+full_column_order = list(df.columns)
+
+const_cols = {}
+for col in list(df.columns):
+    if df[col].nunique(dropna=True) <= 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}'")
+
+const_path = "/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w", encoding="utf-8") as _f:
+    json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+inverse = {"categorical": {}, "continuous": {}}
+enc = pd.DataFrame(index=df.index)
+_n_samples = len(df)
+_n_plan = sum(
+    1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns
+)
+max_bins = 10
+if _n_plan > 35 or _n_samples > 200000:
+    max_bins = 5
+if _n_plan > 55:
+    max_bins = 4
+print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})")
+
+for entry in colmeta["columns"]:
+    name = entry["name"]
+    if name not in df.columns:
+        continue
+    kind = entry["type"]
+    s = df[name]
+    if kind == "categorical":
+        uniques = sorted(s.dropna().unique(), key=lambda x: str(x))
+        mapping = {str(v): i for i, v in enumerate(uniques)}
+        inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+        enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+    else:
+        s_num = pd.to_numeric(s, errors="coerce")
+        nu = int(s_num.nunique(dropna=True))
+        q = min(max_bins, max(2, nu))
+        if nu < 2:
+            enc[name] = np.zeros(len(s_num), dtype=int)
+            lo, hi = float(s_num.min()), float(s_num.max())
+            inverse["continuous"][name] = [lo, hi]
+        else:
+            try:
+                _, bins = pd.qcut(
+                    s_num, q=q, retbins=True, duplicates="drop"
+                )
+            except Exception:
+                med = float(s_num.median())
+                s2 = s_num.fillna(med)
+                _, bins = pd.qcut(
+                    s2, q=min(q, 3), retbins=True, duplicates="drop"
+                )
+            bins = np.asarray(bins, dtype=float)
+            lab = pd.cut(
+                s_num, bins=bins, labels=False, include_lowest=True
+            )
+            enc[name] = lab.fillna(0).astype(int)
+            inverse["continuous"][name] = bins.tolist()
+
+print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+enc_struct = enc
+if len(enc) > 25000:
+    enc_struct = enc.sample(n=25000, random_state=0, replace=False)
+    print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})")
+dag = TreeSearch(enc_struct).estimate(show_progress=False)
+for col in enc.columns:
+    if col not in dag.nodes():
+        dag.add_node(col)
+        print(f"[BayesNet] Added isolated node to DAG: {col}")
+network = DiscreteBayesianNetwork(dag)
+network.fit(enc)
+
+bundle = {
+    "network": network,
+    "inverse": inverse,
+    "column_order": list(enc.columns),
+    "full_column_order": full_column_order,
+    "integer_columns": list(integer_columns),
+    "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+    "const_cols": const_cols,
+}
+with open("/work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl")

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2dfacf8926d94c75ec9a9a12bc7ed2c1afcb522fc337e28672f47911a63b92e3
+size 1919273

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_coltypes.json

@@ -0,0 +1,153 @@
+{
+  "columns": [
+    {
+      "name": "Marital status",
+      "type": "continuous"
+    },
+    {
+      "name": "Application mode",
+      "type": "continuous"
+    },
+    {
+      "name": "Application order",
+      "type": "continuous"
+    },
+    {
+      "name": "Course",
+      "type": "continuous"
+    },
+    {
+      "name": "Daytime/evening attendance",
+      "type": "categorical"
+    },
+    {
+      "name": "Previous qualification",
+      "type": "continuous"
+    },
+    {
+      "name": "Previous qualification (grade)",
+      "type": "continuous"
+    },
+    {
+      "name": "Nacionality",
+      "type": "continuous"
+    },
+    {
+      "name": "Mother's qualification",
+      "type": "continuous"
+    },
+    {
+      "name": "Father's qualification",
+      "type": "continuous"
+    },
+    {
+      "name": "Mother's occupation",
+      "type": "continuous"
+    },
+    {
+      "name": "Father's occupation",
+      "type": "continuous"
+    },
+    {
+      "name": "Admission grade",
+      "type": "continuous"
+    },
+    {
+      "name": "Displaced",
+      "type": "categorical"
+    },
+    {
+      "name": "Educational special needs",
+      "type": "categorical"
+    },
+    {
+      "name": "Debtor",
+      "type": "categorical"
+    },
+    {
+      "name": "Tuition fees up to date",
+      "type": "categorical"
+    },
+    {
+      "name": "Gender",
+      "type": "categorical"
+    },
+    {
+      "name": "Scholarship holder",
+      "type": "categorical"
+    },
+    {
+      "name": "Age at enrollment",
+      "type": "continuous"
+    },
+    {
+      "name": "International",
+      "type": "categorical"
+    },
+    {
+      "name": "Curricular units 1st sem (credited)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 1st sem (enrolled)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 1st sem (evaluations)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 1st sem (approved)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 1st sem (grade)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 1st sem (without evaluations)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (credited)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (enrolled)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (evaluations)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (approved)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (grade)",
+      "type": "continuous"
+    },
+    {
+      "name": "Curricular units 2nd sem (without evaluations)",
+      "type": "continuous"
+    },
+    {
+      "name": "Unemployment rate",
+      "type": "continuous"
+    },
+    {
+      "name": "Inflation rate",
+      "type": "continuous"
+    },
+    {
+      "name": "GDP",
+      "type": "continuous"
+    },
+    {
+      "name": "Target",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl

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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/public_gate/public_gate_report.json

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+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-test.csv"
+  }
+}

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/public_gate/staged_input_manifest.json

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+{
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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/runtime_result.json

@@ -0,0 +1,15 @@
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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/staged_features.json

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+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "Application order",
+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "Course",
+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "Daytime/evening attendance",
+    "data_type": "binary",
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+  {
+    "feature_name": "Previous qualification",
+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "Previous qualification (grade)",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Nacionality",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Mother's qualification",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Father's qualification",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Mother's occupation",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Father's occupation",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Admission grade",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Displaced",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Educational special needs",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Debtor",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Tuition fees up to date",
+    "data_type": "binary",
+    "is_target": false
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+    "feature_name": "Gender",
+    "data_type": "binary",
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+    "feature_name": "Scholarship holder",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Age at enrollment",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "International",
+    "data_type": "binary",
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+    "data_type": "continuous",
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+    "data_type": "continuous",
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+  },
+  {
+    "feature_name": "Unemployment rate",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Inflation rate",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "GDP",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Target",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/test.csv

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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/train.csv

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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/val.csv

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syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/train_20260422_060152.log

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syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/_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/m5/ctgan/ctgan-m5-20260422_025941/models_300epochs/ctgan_300epochs.pt")
+total = 3539
+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/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv")

syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv

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+{
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+      "type": "continuous"
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+      "type": "categorical"
+    },
+    {
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+      "type": "categorical"
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+    {
+      "name": "Scholarship holder",
+      "type": "categorical"
+    },
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+    {
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+    {
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+    {
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+    {
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+    {
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+      "type": "continuous"
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+    {
+      "name": "Unemployment rate",
+      "type": "continuous"
+    },
+    {
+      "name": "Inflation rate",
+      "type": "continuous"
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+    {
+      "name": "GDP",
+      "type": "continuous"
+    },
+    {
+      "name": "Target",
+      "type": "categorical"
+    }
+  ]
+}

syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/gen_20260422_030436.log

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+{
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syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/models_300epochs/ctgan_300epochs.pt

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syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/public_gate/public_gate_report.json

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syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/public_gate/staged_input_manifest.json

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syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/runtime_result.json

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+{
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+  "model": "ctgan",
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+  }
+}

syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/staged/ctgan/adapter_report.json

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