Add syntheticSuccess c8

syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=8844)
+syn.to_csv("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv", index=False)
+print(f"[ARF] Generated 8844 rows -> /work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv")

syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf_model.pkl")

syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-1000-20260321_065504.csv

@@ -0,0 +1,3 @@
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+oid sha256:f5a4495f80333e74642bf880fc4d9bcc2bc374d67231a435d84f9c80046de974
+size 293128

syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv

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+size 2613474

syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf_model.pkl

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+size 79043840

syntheticSuccess/c8/arf/arf-c8-20260321_064711/gen_20260321_065504.log

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+size 438

syntheticSuccess/c8/arf/arf-c8-20260321_064711/gen_20260330_065322.log

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syntheticSuccess/c8/arf/arf-c8-20260321_064711/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c8",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-train.csv",
+      "exists": true,
+      "size": 640080,
+      "sha256": "b79676a58294f2aad042d087ff1dcfc051164efcf602109c878dd1b722a610fc"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-val.csv",
+      "exists": true,
+      "size": 80592,
+      "sha256": "47f84e81fc7ae28ac74afb86ed4a196ca5f4a5b6de2decf3a0fea52e9020c008"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-test.csv",
+      "exists": true,
+      "size": 80387,
+      "sha256": "3f9f79d3671796c3b632f50938278cd9fe2a692c1849e80d5cd5680cd3d561ca"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c8/c8-dataset_profile.json",
+      "exists": true,
+      "size": 12087,
+      "sha256": "1dae703bc78790b4f9ab775d4aff0c39a375e8f7854b0d8783d1afd8e2e99e95"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c8/c8-dataset_contract_v1.json",
+      "exists": true,
+      "size": 13516,
+      "sha256": "6ae07dba7e830a4eea5110098aa6554dac7e956097b742e39f0cd27386c58789"
+    }
+  }
+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,573 @@
+{
+  "dataset_id": "c8",
+  "target_column": "having_At_Symbol",
+  "task_type": "classification",
+  "columns": [
+    {
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+      "role": "feature",
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+      "impute_strategy": "median",
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+      "role": "feature",
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+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c8",
+  "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": "having_At_Symbol",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-test.csv"
+  }
+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/staged_input_manifest.json

@@ -0,0 +1,578 @@
+{
+  "dataset_id": "c8",
+  "target_column": "having_At_Symbol",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/train.csv",
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syntheticSuccess/c8/arf/arf-c8-20260321_064711/runtime_result.json

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+{
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+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/arf/adapter_report.json

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+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/arf/adapter_transforms_applied.json

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syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/arf/model_input_manifest.json

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+      "name": "HTTPS_token",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "example_values": [
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+        ]
+      }
+    },
+    {
+      "name": "Request_URL",
+      "role": "feature",
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+      "nullable": false,
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+      "parse_format": null,
+      "impute_strategy": "median",
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+      "role": "feature",
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+      "nullable": false,
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+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "example_values": [
+          "0",
+          "-1",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "Links_in_tags",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000339,
+        "example_values": [
+          "-1",
+          "1",
+          "0"
+        ]
+      }
+    },
+    {
+      "name": "SFH",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000339,
+        "example_values": [
+          "-1",
+          "1",
+          "0"
+        ]
+      }
+    },
+    {
+      "name": "Submitting_to_email",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Abnormal_URL",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Redirect",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "0"
+        ]
+      }
+    },
+    {
+      "name": "on_mouseover",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "RightClick",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "popUpWidnow",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Iframe",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "age_of_domain",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "-1",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "DNSRecord",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "web_traffic",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000339,
+        "example_values": [
+          "1",
+          "0",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Page_Rank",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "-1",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "Google_Index",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Links_pointing_to_page",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000339,
+        "example_values": [
+          "0",
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Statistical_report",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "1",
+          "-1"
+        ]
+      }
+    },
+    {
+      "name": "Result",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000226,
+        "example_values": [
+          "-1",
+          "1"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json"
+}

syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "having_IP_Address",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "URL_Length",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Shortining_Service",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "having_At_Symbol",
+    "data_type": "continuous",
+    "is_target": true
+  },
+  {
+    "feature_name": "double_slash_redirecting",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Prefix_Suffix",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "having_Sub_Domain",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "SSLfinal_State",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Domain_registeration_length",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Favicon",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "port",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "HTTPS_token",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Request_URL",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "URL_of_Anchor",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Links_in_tags",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "SFH",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Submitting_to_email",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Abnormal_URL",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Redirect",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "on_mouseover",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "RightClick",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "popUpWidnow",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Iframe",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "age_of_domain",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "DNSRecord",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "web_traffic",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Page_Rank",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Google_Index",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Links_pointing_to_page",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Statistical_report",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Result",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/test.csv

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+size 79486

syntheticSuccess/c8/arf/arf-c8-20260321_064711/train_20260321_064712.log

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=8844).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv", index=False)
+print(f"[BayesNet] Generated 8844 rows -> /work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv")

syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_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/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/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/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl")

syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-1000-20260321_062026.csv

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syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl

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syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/const_cols.json

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+{}

syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260321_062026.log

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syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/staged/bayesnet/adapter_report.json

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