Add syntheticSuccess c3

syntheticSuccess/c3/arf/arf-c3-20260321_060642/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=2551)
+syn.to_csv("/work/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/arf-c3-2551-20260330_065245.csv", index=False)
+print(f"[ARF] Generated 2551 rows -> /work/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/arf-c3-2551-20260330_065245.csv")

syntheticSuccess/c3/arf/arf-c3-20260321_060642/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/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/c3/arf/arf-c3-20260321_060642/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/arf_model.pkl")

syntheticSuccess/c3/arf/arf-c3-20260321_060642/arf-c3-1000-20260321_060705.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd6feeac5fc8ee5ba53b9fab2fa83e64fb1a51515c93b117742d8717fb3bba38
+size 82553

syntheticSuccess/c3/arf/arf-c3-20260321_060642/arf-c3-2551-20260330_065245.csv

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+oid sha256:49657dd521742fb83de82074f557a1386bc4c564796907e24402395338de2be9
+size 210860

syntheticSuccess/c3/arf/arf-c3-20260321_060642/arf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0c6d377b3ec6366c3db6126615eef83f073bad8f1a97545a0f9d1b152cfab86
+size 11138547

syntheticSuccess/c3/arf/arf-c3-20260321_060642/gen_20260321_060705.log

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+oid sha256:b94428688e0d87a4e79348ce8c44a65dd4f9f2a937e0228319039cefed0f8da2
+size 1070

syntheticSuccess/c3/arf/arf-c3-20260321_060642/gen_20260330_065245.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee66115d5a180d771ace2ac108942833e8db9e3599e775534d52f6476b13169e
+size 1070

syntheticSuccess/c3/arf/arf-c3-20260321_060642/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c3",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-train.csv",
+      "exists": true,
+      "size": 213326,
+      "sha256": "a1ee97b37e9abc68953c8bb4c99a5f8535c68b121c60de73d3e23fd4beb516f3"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-val.csv",
+      "exists": true,
+      "size": 26805,
+      "sha256": "5f384eded9999ef705f6b10ae0d20ce981a1b92339d451e7feb3946e95fdcbd9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-test.csv",
+      "exists": true,
+      "size": 26871,
+      "sha256": "3f0c932764d3321b9bc1ae15932af108b80382f4c89248839c02f48e1d08ffad"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c3/c3-dataset_profile.json",
+      "exists": true,
+      "size": 2635,
+      "sha256": "dfb01e28765bec4ac04cdb273987b23581194b35638d6450dac66f4525614335"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c3/c3-dataset_contract_v1.json",
+      "exists": true,
+      "size": 2422,
+      "sha256": "a408b4f800a67fc20e9bc37e847c54de7ed6f608b93d440c3933dead213acfca"
+    }
+  }
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,68 @@
+{
+  "dataset_id": "c3",
+  "target_column": "EI",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "EI",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.001176,
+        "example_values": [
+          "EI",
+          "IE",
+          "N"
+        ]
+      }
+    },
+    {
+      "name": "ATRINS-DONOR-521",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2541,
+        "unique_ratio": 0.99608,
+        "example_values": [
+          "HUMH19-DONOR-2562",
+          "HUMMHCD8A-ACCEPTOR-733",
+          "HUMGCB1-ACCEPTOR-6344",
+          "HUMNEBB-NEG-241",
+          "HUMATP1A2-NEG-2101"
+        ]
+      }
+    },
+    {
+      "name": "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2426,
+        "unique_ratio": 0.951,
+        "example_values": [
+          "TCTGGGCTCCCAGAACCCACAACATGAAAGGTGAGGGNCTTCCTGCCACACTTGGGGTGG",
+          "TTGCTCAGCCTCGCTGCTTGCCTCCTGCAGACGCCGCCAGGCCGAGCCAGTTCCGGGTGT",
+          "GGGACTCTGACCATCTGTTCCCACATTCAGCAAGTTCATTCCTGAGGGCTCCCAGAGAGT",
+          "GCCAACTACAGGAACGTGATCCATACCTACAACATGCTTCCTGATGCCATGAGCTTTGAA",
+          "GGGAGTCCTAGCCTGAGAGGCTGGGGGTCCATTTTGAGGTTAGAGAGGGGCAGTAGAGCA"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c3",
+  "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": "EI",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-test.csv"
+  }
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/public_gate/staged_input_manifest.json

@@ -0,0 +1,73 @@
+{
+  "dataset_id": "c3",
+  "target_column": "EI",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "EI",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.001176,
+        "example_values": [
+          "EI",
+          "IE",
+          "N"
+        ]
+      }
+    },
+    {
+      "name": "ATRINS-DONOR-521",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2541,
+        "unique_ratio": 0.99608,
+        "example_values": [
+          "HUMH19-DONOR-2562",
+          "HUMMHCD8A-ACCEPTOR-733",
+          "HUMGCB1-ACCEPTOR-6344",
+          "HUMNEBB-NEG-241",
+          "HUMATP1A2-NEG-2101"
+        ]
+      }
+    },
+    {
+      "name": "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2426,
+        "unique_ratio": 0.951,
+        "example_values": [
+          "TCTGGGCTCCCAGAACCCACAACATGAAAGGTGAGGGNCTTCCTGCCACACTTGGGGTGG",
+          "TTGCTCAGCCTCGCTGCTTGCCTCCTGCAGACGCCGCCAGGCCGAGCCAGTTCCGGGTGT",
+          "GGGACTCTGACCATCTGTTCCCACATTCAGCAAGTTCATTCCTGAGGGCTCCCAGAGAGT",
+          "GCCAACTACAGGAACGTGATCCATACCTACAACATGCTTCCTGATGCCATGAGCTTTGAA",
+          "GGGAGTCCTAGCCTGAGAGGCTGGGGGTCCATTTTGAGGTTAGAGAGGGGCAGTAGAGCA"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c3",
+  "model": "arf",
+  "run_id": "arf-c3-20260321_060642",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/arf-c3-2551-20260330_065245.csv"
+  }
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/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/c3/arf/arf-c3-20260321_060642/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/arf/model_input_manifest.json

@@ -0,0 +1,75 @@
+{
+  "dataset_id": "c3",
+  "model": "arf",
+  "target_column": "EI",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "EI",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.001176,
+        "example_values": [
+          "EI",
+          "IE",
+          "N"
+        ]
+      }
+    },
+    {
+      "name": "ATRINS-DONOR-521",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2541,
+        "unique_ratio": 0.99608,
+        "example_values": [
+          "HUMH19-DONOR-2562",
+          "HUMMHCD8A-ACCEPTOR-733",
+          "HUMGCB1-ACCEPTOR-6344",
+          "HUMNEBB-NEG-241",
+          "HUMATP1A2-NEG-2101"
+        ]
+      }
+    },
+    {
+      "name": "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2426,
+        "unique_ratio": 0.951,
+        "example_values": [
+          "TCTGGGCTCCCAGAACCCACAACATGAAAGGTGAGGGNCTTCCTGCCACACTTGGGGTGG",
+          "TTGCTCAGCCTCGCTGCTTGCCTCCTGCAGACGCCGCCAGGCCGAGCCAGTTCCGGGTGT",
+          "GGGACTCTGACCATCTGTTCCCACATTCAGCAAGTTCATTCCTGAGGGCTCCCAGAGAGT",
+          "GCCAACTACAGGAACGTGATCCATACCTACAACATGCTTCCTGATGCCATGAGCTTTGAA",
+          "GGGAGTCCTAGCCTGAGAGGCTGGGGGTCCATTTTGAGGTTAGAGAGGGGCAGTAGAGCA"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/arf/arf-c3-20260321_060642/public_gate/public_gate_report.json"
+}

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/public/staged_features.json

@@ -0,0 +1,17 @@
+[
+  {
+    "feature_name": "EI",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "ATRINS-DONOR-521",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG",
+    "data_type": "ID",
+    "is_target": false
+  }
+]

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0c5bad6c59669b0f6ea2a0c953d0bb03ce62ce625357b1b136eabc2de34d99
+size 26550

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4b86543124166673bed177c1bb1e1806d04bbc8aaaa2c545b57c7b538aad82c
+size 210774

syntheticSuccess/c3/arf/arf-c3-20260321_060642/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db595b372b40df454d8ad8152b81c613321b79f80033c020da635825581237ea
+size 26486

syntheticSuccess/c3/arf/arf-c3-20260321_060642/train_20260321_060642.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e790de250602115b85f0e47b9e614ec5a4f20ce9b614abaddd7d27b0320e94fe
+size 443

syntheticSuccess/c3/bayesnet/bayesnet-c3-20260321_060730/_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/c3/bayesnet/bayesnet-c3-20260321_060730/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=2551).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c3/bayesnet/bayesnet-c3-20260321_060730/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/c3/bayesnet/bayesnet-c3-20260321_060730/bayesnet-c3-2551-20260330_065246.csv", index=False)
+print(f"[BayesNet] Generated 2551 rows -> /work/output-SpecializedModels/c3/bayesnet/bayesnet-c3-20260321_060730/bayesnet-c3-2551-20260330_065246.csv")

syntheticSuccess/c3/bayesnet/bayesnet-c3-20260321_060730/_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/c3/bayesnet/bayesnet-c3-20260321_060730/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/c3/bayesnet/bayesnet-c3-20260321_060730/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/c3/bayesnet/bayesnet-c3-20260321_060730/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c3/bayesnet/bayesnet-c3-20260321_060730/bayesnet_model.pkl")

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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c3/c3-test.csv"
+  }
+}

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+  }
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+}

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+}