Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=5516)
+syn.to_csv("/work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/arf-c16-5516-20260318_002710.csv", index=False)
+print(f"[ARF] Generated 5516 rows -> /work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/arf-c16-5516-20260318_002710.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/DatasetNew/c16/c16-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/c16/arf/arf-c16-20260318_002212/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/gen_20260318_002710.log

@@ -0,0 +1,30 @@
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+Traceback (most recent call last):
+  File "/work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/_arf_generate.py", line 4, in <module>
+    syn = model.forge(n=5516)
+  File "/usr/local/lib/python3.10/site-packages/arfpy/arf.py", line 346, in forge
+    data_new.isetitem(j, scipy.stats.truncnorm(a =(myclip_a - myloc) / myscale,b = (myclip_b - myloc) / myscale, loc = myloc , scale = myscale ).rvs(size = n))
+  File "/usr/local/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py", line 491, in rvs
+    return self.dist.rvs(*self.args, **kwds)
+  File "/usr/local/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py", line 1055, in rvs
+    raise ValueError(message)
+ValueError: Domain error in arguments. The `scale` parameter must be positive for all distributions, and many distributions have restrictions on shape parameters. Please see the `scipy.stats.truncnorm` documentation for details.

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/train_20260318_002212.log

@@ -0,0 +1,7 @@
+[ARF] Training on 5516 rows, 13 cols
+Initial accuracy is 0.909173313995649
+Iteration number 1 reached accuracy of 0.7390319071791153.
+Iteration number 2 reached accuracy of 0.6618020304568528.
+Iteration number 3 reached accuracy of 0.6328861493836113.
+Iteration number 4 reached accuracy of 0.6442168237853517.
+[ARF] Model saved -> /work/output-SpecializedModels/c16/arf/arf-c16-20260318_002212/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(5516)
+with open("/work/output-SpecializedModels/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/gen_20260422_060318.log

@@ -0,0 +1,23 @@
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+[ARF] Generated 5516 rows (requested 5516) -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c16",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "EYE",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/staged_input_manifest.json

@@ -0,0 +1,275 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "run_id": "arf-c16-20260422_055912",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/arf/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/arf/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/arf/model_input_manifest.json

@@ -0,0 +1,277 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json

@@ -0,0 +1,67 @@
+[
+  {
+    "feature_name": "page_id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "name",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "urlslug",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "ID",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIGN",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "EYE",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "HAIR",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "SEX",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "GSM",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIVE",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "APPEARANCES",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "FIRST APPEARANCE",
+    "data_type": "timestamp",
+    "is_target": false
+  },
+  {
+    "feature_name": "YEAR",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/train_20260422_055912.log

@@ -0,0 +1,6 @@
+[ARF] Training on 5516 rows, 13 cols
+Initial accuracy is 0.9218636693255983
+Iteration number 1 reached accuracy of 0.735859318346628.
+Iteration number 2 reached accuracy of 0.6398658448150834.
+Iteration number 3 reached accuracy of 0.6560913705583756.
+[ARF] Model saved -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_002724/_bayesnet_train.py

@@ -0,0 +1,62 @@
+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/DatasetNew/c16/c16-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/c16/bayesnet/bayesnet-c16-20260318_002724/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/c16/bayesnet/bayesnet-c16-20260318_002724/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_002724/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_002724/train_20260318_002724.log

@@ -0,0 +1,59 @@
+[2026-03-17T16:27:51.632344+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T16:27:51.643319+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Training on 5516 rows, 13 cols
+Traceback (most recent call last):
+  File "/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_002724/_bayesnet_train.py", line 58, in <module>
+    plugin.fit(loader)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/plugin.py", line 254, in fit
+    output = self._fit(X, *args, **kwargs)
+  File "/pip_libs/synthcity/plugins/generic/plugin_bayesian_network.py", line 168, in _fit
+    self.encoder.fit(df)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/tabular_encoder.py", line 161, in fit
+    column_transform_info = self._fit_feature(raw_data[name], ftype)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/tabular_encoder.py", line 128, in _fit_feature
+    encoder.fit(feature)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/feature_encoder.py", line 68, in fit
+    output = self._fit(input, **kwargs)._transform(input)
+  File "/pip_libs/synthcity/plugins/core/models/feature_encoder.py", line 208, in _fit
+    self.model.fit(x)
+  File "/pip_libs/sklearn/mixture/_base.py", line 182, in fit
+    self.fit_predict(X, y)
+  File "/pip_libs/sklearn/base.py", line 1365, in wrapper
+    return fit_method(estimator, *args, **kwargs)
+  File "/pip_libs/sklearn/mixture/_base.py", line 213, in fit_predict
+    X = validate_data(self, X, dtype=[np.float64, np.float32], ensure_min_samples=2)
+  File "/pip_libs/sklearn/utils/validation.py", line 2954, in validate_data
+    out = check_array(X, input_name="X", **check_params)
+  File "/pip_libs/sklearn/utils/validation.py", line 1105, in check_array
+    _assert_all_finite(
+  File "/pip_libs/sklearn/utils/validation.py", line 120, in _assert_all_finite
+    _assert_all_finite_element_wise(
+  File "/pip_libs/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise
+    raise ValueError(msg_err)
+ValueError: Input X contains NaN.
+BayesianGaussianMixture does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_043911/_bayesnet_train.py

@@ -0,0 +1,62 @@
+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/DatasetNew/c16/c16-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/c16/bayesnet/bayesnet-c16-20260318_043911/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/c16/bayesnet/bayesnet-c16-20260318_043911/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_043911/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_043911/train_20260318_043911.log

@@ -0,0 +1,59 @@
+[2026-03-17T20:39:37.047879+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T20:39:37.058976+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Training on 5516 rows, 13 cols
+Traceback (most recent call last):
+  File "/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260318_043911/_bayesnet_train.py", line 58, in <module>
+    plugin.fit(loader)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/plugin.py", line 254, in fit
+    output = self._fit(X, *args, **kwargs)
+  File "/pip_libs/synthcity/plugins/generic/plugin_bayesian_network.py", line 168, in _fit
+    self.encoder.fit(df)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/tabular_encoder.py", line 161, in fit
+    column_transform_info = self._fit_feature(raw_data[name], ftype)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/tabular_encoder.py", line 128, in _fit_feature
+    encoder.fit(feature)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 56, in wrapper_function
+    return vd.call(*args, **kwargs)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 151, in call
+    return self.execute(m)
+  File "/pip_libs/pydantic/deprecated/decorator.py", line 227, in execute
+    return self.raw_function(**d, **var_kwargs)
+  File "/pip_libs/synthcity/plugins/core/models/feature_encoder.py", line 68, in fit
+    output = self._fit(input, **kwargs)._transform(input)
+  File "/pip_libs/synthcity/plugins/core/models/feature_encoder.py", line 208, in _fit
+    self.model.fit(x)
+  File "/pip_libs/sklearn/mixture/_base.py", line 182, in fit
+    self.fit_predict(X, y)
+  File "/pip_libs/sklearn/base.py", line 1365, in wrapper
+    return fit_method(estimator, *args, **kwargs)
+  File "/pip_libs/sklearn/mixture/_base.py", line 213, in fit_predict
+    X = validate_data(self, X, dtype=[np.float64, np.float32], ensure_min_samples=2)
+  File "/pip_libs/sklearn/utils/validation.py", line 2954, in validate_data
+    out = check_array(X, input_name="X", **check_params)
+  File "/pip_libs/sklearn/utils/validation.py", line 1105, in check_array
+    _assert_all_finite(
+  File "/pip_libs/sklearn/utils/validation.py", line 120, in _assert_all_finite
+    _assert_all_finite_element_wise(
+  File "/pip_libs/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise
+    raise ValueError(msg_err)
+ValueError: Input X contains NaN.
+BayesianGaussianMixture does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/_bayesnet_generate.py

@@ -0,0 +1,75 @@
+
+import pickle
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.sampling import BayesianModelSampling
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+with open("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/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 {}
+
+sampler = BayesianModelSampling(network)
+raw = sampler.forward_sample(size=5516, show_progress=False)
+
+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
+
+final.to_csv("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv", index=False)
+print(f"[BayesNet] Generated 5516 rows -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/_bayesnet_train.py

@@ -0,0 +1,93 @@
+
+import json
+import pickle
+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)
+
+with open("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/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/c16/bayesnet/bayesnet-c16-20260419_073440/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/c16/bayesnet/bayesnet-c16-20260419_073440/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)
+max_bins = 10
+
+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)")
+
+dag = TreeSearch(enc).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/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_coltypes.json

@@ -0,0 +1,57 @@
+{
+  "columns": [
+    {
+      "name": "page_id",
+      "type": "continuous"
+    },
+    {
+      "name": "name",
+      "type": "categorical"
+    },
+    {
+      "name": "urlslug",
+      "type": "categorical"
+    },
+    {
+      "name": "ID",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIGN",
+      "type": "categorical"
+    },
+    {
+      "name": "EYE",
+      "type": "categorical"
+    },
+    {
+      "name": "HAIR",
+      "type": "categorical"
+    },
+    {
+      "name": "SEX",
+      "type": "categorical"
+    },
+    {
+      "name": "GSM",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIVE",
+      "type": "categorical"
+    },
+    {
+      "name": "APPEARANCES",
+      "type": "continuous"
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "type": "categorical"
+    },
+    {
+      "name": "YEAR",
+      "type": "continuous"
+    }
+  ],
+  "integer_columns": []
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/gen_20260419_073509.log

@@ -0,0 +1,22 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+[BayesNet] Generated 5516 rows -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c16",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "EYE",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/staged_input_manifest.json

@@ -0,0 +1,275 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c16-20260419_073440",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,277 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json

@@ -0,0 +1,67 @@
+[
+  {
+    "feature_name": "page_id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "name",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "urlslug",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "ID",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIGN",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "EYE",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "HAIR",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "SEX",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "GSM",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIVE",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "APPEARANCES",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "FIRST APPEARANCE",
+    "data_type": "timestamp",
+    "is_target": false
+  },
+  {
+    "feature_name": "YEAR",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/train_20260419_073440.log

@@ -0,0 +1,23 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+[BayesNet] Training on 5516 rows, 13 cols (encoded)
+[BayesNet] Model saved -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260318_002244/ctgan_metadata.json

@@ -0,0 +1,56 @@
+{
+  "columns": [
+    {
+      "name": "page_id",
+      "type": "continuous"
+    },
+    {
+      "name": "name",
+      "type": "continuous"
+    },
+    {
+      "name": "urlslug",
+      "type": "continuous"
+    },
+    {
+      "name": "ID",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIGN",
+      "type": "categorical"
+    },
+    {
+      "name": "EYE",
+      "type": "categorical"
+    },
+    {
+      "name": "HAIR",
+      "type": "categorical"
+    },
+    {
+      "name": "SEX",
+      "type": "categorical"
+    },
+    {
+      "name": "GSM",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIVE",
+      "type": "categorical"
+    },
+    {
+      "name": "APPEARANCES",
+      "type": "continuous"
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "type": "categorical"
+    },
+    {
+      "name": "YEAR",
+      "type": "continuous"
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260318_002244/models_300epochs/train_20260318_002244.log

@@ -0,0 +1,16 @@
+Traceback (most recent call last):
+  File "/opt/conda/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/opt/conda/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/tmp/ctgan/ctgan/__main__.py", line 164, in <module>
+    main()
+  File "/tmp/ctgan/ctgan/__main__.py", line 140, in main
+    model.fit(data, discrete_columns)
+  File "/tmp/ctgan/ctgan/synthesizers/base.py", line 52, in wrapper
+    return function(self, *args, **kwargs)
+  File "/tmp/ctgan/ctgan/synthesizers/ctgan.py", line 329, in fit
+    self._validate_null_data(train_data, discrete_columns)
+  File "/tmp/ctgan/ctgan/synthesizers/ctgan.py", line 310, in _validate_null_data
+    raise InvalidDataError(
+ctgan.errors.InvalidDataError: CTGAN does not support null values in the continuous training data. Please remove all null values from your continuous training data.

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/_ctgan_generate.py

@@ -0,0 +1,18 @@
+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/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/ctgan_300epochs.pt")
+total = 5516
+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/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/ctgan_metadata.json

@@ -0,0 +1,56 @@
+{
+  "columns": [
+    {
+      "name": "page_id",
+      "type": "continuous"
+    },
+    {
+      "name": "name",
+      "type": "categorical"
+    },
+    {
+      "name": "urlslug",
+      "type": "categorical"
+    },
+    {
+      "name": "ID",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIGN",
+      "type": "categorical"
+    },
+    {
+      "name": "EYE",
+      "type": "categorical"
+    },
+    {
+      "name": "HAIR",
+      "type": "categorical"
+    },
+    {
+      "name": "SEX",
+      "type": "categorical"
+    },
+    {
+      "name": "GSM",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIVE",
+      "type": "categorical"
+    },
+    {
+      "name": "APPEARANCES",
+      "type": "continuous"
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "type": "categorical"
+    },
+    {
+      "name": "YEAR",
+      "type": "continuous"
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/gen_20260422_031613.log

@@ -0,0 +1,2 @@
+[CTGAN] Generated 5516 rows in 1 chunks -> /work/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv
+[W421 19:16:42.543705632 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "ctgan",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/train_20260422_025942.log

@@ -0,0 +1,16 @@
+/opt/conda/lib/python3.11/site-packages/torch/autograd/graph.py:841: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:270.)
+  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+[W421 19:05:19.544657413 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:20.843577680 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:22.111247030 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:23.445800666 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:24.758455269 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:25.038946814 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:27.372479437 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:28.698154042 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:29.955590526 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:31.239610185 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:32.525339243 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:33.883928945 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:05:35.204156282 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W421 19:16:11.368303707 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

SynthesizePipeline_Archive/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
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+          "Transgender Characters"
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+}