Add syntheticSuccess n20

syntheticSuccess/n20/arf/arf-n20-20260328_032219/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=7654)
+syn.to_csv("/work/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/arf-n20-7654-20260330_071030.csv", index=False)
+print(f"[ARF] Generated 7654 rows -> /work/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/arf-n20-7654-20260330_071030.csv")

syntheticSuccess/n20/arf/arf-n20-20260328_032219/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/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/n20/arf/arf-n20-20260328_032219/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/arf_model.pkl")

syntheticSuccess/n20/arf/arf-n20-20260328_032219/arf-n20-1000-20260328_032306.csv

@@ -0,0 +1,3 @@
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+oid sha256:9115a67bf2c85222af3e2bc694ae34d3974e4e0763cccb0792f83d07cab9d347
+size 91725

syntheticSuccess/n20/arf/arf-n20-20260328_032219/arf-n20-7654-20260330_071030.csv

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+size 702241

syntheticSuccess/n20/arf/arf-n20-20260328_032219/arf_model.pkl

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+oid sha256:b5fa0ff691d7bd32f7033d8e4d23a55fe7916c4902eaaebd36e823f2257341ad
+size 10876939

syntheticSuccess/n20/arf/arf-n20-20260328_032219/gen_20260328_032306.log

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+size 441

syntheticSuccess/n20/arf/arf-n20-20260328_032219/gen_20260330_071030.log

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+size 441

syntheticSuccess/n20/arf/arf-n20-20260328_032219/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "n20",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+      "exists": true,
+      "size": 291758,
+      "sha256": "8768b9641a081ce32375b2739c6d0a6cdf66c1b03268c7db5f71ffd0019c97e2"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+      "exists": true,
+      "size": 36657,
+      "sha256": "c2dae02f854856ba824310ecaca5c73a72a3302753396aa681c82b69d65b174c"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv",
+      "exists": true,
+      "size": 36232,
+      "sha256": "82c48522e82ea4c5e4c407ec9239e82558dcff1df413f3d0c382e77fa041bc88"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_profile.json",
+      "exists": true,
+      "size": 2675,
+      "sha256": "7e7d747ab11beae1e58278dc8d6ae08d7ae6f14fe93f77a4fc1e6e589df21df4"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3047,
+      "sha256": "20f513ccbc9f09d5286bc8998a5772f42b78b40aa1ed60b079fa73edf9c78eec"
+    }
+  }
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,112 @@
+{
+  "dataset_id": "n20",
+  "target_column": "PE",
+  "task_type": "regression",
+  "columns": [
+    {
+      "name": "AT",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2635,
+        "unique_ratio": 0.344264,
+        "example_values": [
+          "29.64",
+          "10.63",
+          "27.09",
+          "24.2",
+          "15.31"
+        ]
+      }
+    },
+    {
+      "name": "V",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 625,
+        "unique_ratio": 0.081657,
+        "example_values": [
+          "67.790000000000006",
+          "37.5",
+          "59.15",
+          "57.85",
+          "52.75"
+        ]
+      }
+    },
+    {
+      "name": "AP",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2365,
+        "unique_ratio": 0.308989,
+        "example_values": [
+          "1009.99",
+          "1008.93",
+          "1013.02",
+          "1013.05",
+          "1025.21"
+        ]
+      }
+    },
+    {
+      "name": "RH",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4096,
+        "unique_ratio": 0.535145,
+        "example_values": [
+          "51.23",
+          "98.14",
+          "55.18",
+          "70.290000000000006",
+          "55.22"
+        ]
+      }
+    },
+    {
+      "name": "PE",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4351,
+        "unique_ratio": 0.568461,
+        "example_values": [
+          "440.74",
+          "474.81",
+          "438.9",
+          "446.87",
+          "460.77"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "n20",
+  "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": "PE",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv"
+  }
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/public_gate/staged_input_manifest.json

@@ -0,0 +1,117 @@
+{
+  "dataset_id": "n20",
+  "target_column": "PE",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "AT",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2635,
+        "unique_ratio": 0.344264,
+        "example_values": [
+          "29.64",
+          "10.63",
+          "27.09",
+          "24.2",
+          "15.31"
+        ]
+      }
+    },
+    {
+      "name": "V",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 625,
+        "unique_ratio": 0.081657,
+        "example_values": [
+          "67.790000000000006",
+          "37.5",
+          "59.15",
+          "57.85",
+          "52.75"
+        ]
+      }
+    },
+    {
+      "name": "AP",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2365,
+        "unique_ratio": 0.308989,
+        "example_values": [
+          "1009.99",
+          "1008.93",
+          "1013.02",
+          "1013.05",
+          "1025.21"
+        ]
+      }
+    },
+    {
+      "name": "RH",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4096,
+        "unique_ratio": 0.535145,
+        "example_values": [
+          "51.23",
+          "98.14",
+          "55.18",
+          "70.290000000000006",
+          "55.22"
+        ]
+      }
+    },
+    {
+      "name": "PE",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4351,
+        "unique_ratio": 0.568461,
+        "example_values": [
+          "440.74",
+          "474.81",
+          "438.9",
+          "446.87",
+          "460.77"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "n20",
+  "model": "arf",
+  "run_id": "arf-n20-20260328_032219",
+  "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/n20/arf/arf-n20-20260328_032219/arf-n20-7654-20260330_071030.csv"
+  }
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/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/n20/arf/arf-n20-20260328_032219/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/arf/model_input_manifest.json

@@ -0,0 +1,119 @@
+{
+  "dataset_id": "n20",
+  "model": "arf",
+  "target_column": "PE",
+  "task_type": "regression",
+  "column_schema": [
+    {
+      "name": "AT",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2635,
+        "unique_ratio": 0.344264,
+        "example_values": [
+          "29.64",
+          "10.63",
+          "27.09",
+          "24.2",
+          "15.31"
+        ]
+      }
+    },
+    {
+      "name": "V",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 625,
+        "unique_ratio": 0.081657,
+        "example_values": [
+          "67.790000000000006",
+          "37.5",
+          "59.15",
+          "57.85",
+          "52.75"
+        ]
+      }
+    },
+    {
+      "name": "AP",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2365,
+        "unique_ratio": 0.308989,
+        "example_values": [
+          "1009.99",
+          "1008.93",
+          "1013.02",
+          "1013.05",
+          "1025.21"
+        ]
+      }
+    },
+    {
+      "name": "RH",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4096,
+        "unique_ratio": 0.535145,
+        "example_values": [
+          "51.23",
+          "98.14",
+          "55.18",
+          "70.290000000000006",
+          "55.22"
+        ]
+      }
+    },
+    {
+      "name": "PE",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4351,
+        "unique_ratio": 0.568461,
+        "example_values": [
+          "440.74",
+          "474.81",
+          "438.9",
+          "446.87",
+          "460.77"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/arf/arf-n20-20260328_032219/public_gate/public_gate_report.json"
+}

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/public/staged_features.json

@@ -0,0 +1,27 @@
+[
+  {
+    "feature_name": "AT",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "V",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "AP",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "RH",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "PE",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/public/test.csv

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+oid sha256:5db4ad3456ef2c15e4c6e8e3e2405eb0d506997097ae5af3de903a5fe5608cce
+size 31623

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/public/train.csv

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+oid sha256:e1a8d2910fbc1f78f2a58a208938e8c565d222cccaa0f516fcfd93a0d46db489
+size 252576

syntheticSuccess/n20/arf/arf-n20-20260328_032219/staged/public/val.csv

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+oid sha256:7b80a697c0aa16609cc20a3c7365f6fc3de47345dbd60d4a31b2012b7388af44
+size 31620

syntheticSuccess/n20/arf/arf-n20-20260328_032219/train_20260328_032219.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef21113f0aafc1c13d328460317c63b9316bbe23e0f88054c0a9b71a0276d040
+size 232

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/_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/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=7654).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/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/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet-n20-7654-20260330_071037.csv", index=False)
+print(f"[BayesNet] Generated 7654 rows -> /work/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet-n20-7654-20260330_071037.csv")

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/_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/n20/bayesnet/bayesnet-n20-20260321_091217/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/n20/bayesnet/bayesnet-n20-20260321_091217/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/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet_model.pkl")

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet-n20-1000-20260321_091312.csv

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet-n20-7654-20260330_071037.csv

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet_model.pkl

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/const_cols.json

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/gen_20260321_091312.log

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/input_snapshot.json

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+{
+  "dataset_id": "n20",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+      "exists": true,
+      "size": 291758,
+      "sha256": "8768b9641a081ce32375b2739c6d0a6cdf66c1b03268c7db5f71ffd0019c97e2"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+      "exists": true,
+      "size": 36657,
+      "sha256": "c2dae02f854856ba824310ecaca5c73a72a3302753396aa681c82b69d65b174c"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv",
+      "exists": true,
+      "size": 36232,
+      "sha256": "82c48522e82ea4c5e4c407ec9239e82558dcff1df413f3d0c382e77fa041bc88"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_profile.json",
+      "exists": true,
+      "size": 2675,
+      "sha256": "7e7d747ab11beae1e58278dc8d6ae08d7ae6f14fe93f77a4fc1e6e589df21df4"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3047,
+      "sha256": "20f513ccbc9f09d5286bc8998a5772f42b78b40aa1ed60b079fa73edf9c78eec"
+    }
+  }
+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/public_gate/normalized_schema_snapshot.json

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+{
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+  "target_column": "PE",
+  "task_type": "regression",
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+      "impute_strategy": "median",
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+          "52.75"
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+          "1013.05",
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "parse_format": null,
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+      "role": "target",
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+    }
+  ]
+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/public_gate/public_gate_report.json

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+{
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+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
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+      "status": "pass"
+    },
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+      "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": "PE",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv"
+  }
+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/public_gate/staged_input_manifest.json

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+{
+  "dataset_id": "n20",
+  "target_column": "PE",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/public_gate/public_gate_report.json",
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+    }
+  ]
+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/runtime_result.json

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+{
+  "dataset_id": "n20",
+  "model": "bayesnet",
+  "run_id": "bayesnet-n20-20260321_091217",
+  "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/n20/bayesnet/bayesnet-n20-20260321_091217/bayesnet-n20-7654-20260330_071037.csv"
+  }
+}

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+  "adapter_ready_status": "pass",
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+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/staged/bayesnet/adapter_transforms_applied.json

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+        ]
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+      "role": "feature",
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n20/bayesnet/bayesnet-n20-20260321_091217/public_gate/public_gate_report.json"
+}

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/staged_features.json

@@ -0,0 +1,27 @@
+[
+  {
+    "feature_name": "AT",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
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+    "data_type": "continuous",
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+    "data_type": "continuous",
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+  }
+]

syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/test.csv

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/staged/public/val.csv

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syntheticSuccess/n20/bayesnet/bayesnet-n20-20260321_091217/train_20260321_091217.log

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/_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/n20/ctgan/ctgan-n20-20260422_031259/models_300epochs/ctgan_300epochs.pt")
+total = 7654
+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/n20/ctgan/ctgan-n20-20260422_031259/ctgan-n20-7654-20260422_031707.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n20/ctgan/ctgan-n20-20260422_031259/ctgan-n20-7654-20260422_031707.csv")

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syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/ctgan_metadata.json

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+{
+  "columns": [
+    {
+      "name": "AT",
+      "type": "continuous"
+    },
+    {
+      "name": "V",
+      "type": "continuous"
+    },
+    {
+      "name": "AP",
+      "type": "continuous"
+    },
+    {
+      "name": "RH",
+      "type": "continuous"
+    },
+    {
+      "name": "PE",
+      "type": "continuous"
+    }
+  ]
+}

syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/gen_20260422_031707.log

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/input_snapshot.json

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+{
+  "dataset_id": "n20",
+  "model": "ctgan",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+      "exists": true,
+      "size": 291758,
+      "sha256": "8768b9641a081ce32375b2739c6d0a6cdf66c1b03268c7db5f71ffd0019c97e2"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+      "exists": true,
+      "size": 36657,
+      "sha256": "c2dae02f854856ba824310ecaca5c73a72a3302753396aa681c82b69d65b174c"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv",
+      "exists": true,
+      "size": 36232,
+      "sha256": "82c48522e82ea4c5e4c407ec9239e82558dcff1df413f3d0c382e77fa041bc88"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_profile.json",
+      "exists": true,
+      "size": 2675,
+      "sha256": "7e7d747ab11beae1e58278dc8d6ae08d7ae6f14fe93f77a4fc1e6e589df21df4"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n20/n20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3047,
+      "sha256": "20f513ccbc9f09d5286bc8998a5772f42b78b40aa1ed60b079fa73edf9c78eec"
+    }
+  }
+}

syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/models_300epochs/ctgan_300epochs.pt

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+version https://git-lfs.github.com/spec/v1
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+size 851427

syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/models_300epochs/train_20260422_031300.log

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+version https://git-lfs.github.com/spec/v1
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+size 1272

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+{
+  "dataset_id": "n20",
+  "target_column": "PE",
+  "task_type": "regression",
+  "columns": [
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 2635,
+        "unique_ratio": 0.344264,
+        "example_values": [
+          "29.64",
+          "10.63",
+          "27.09",
+          "24.2",
+          "15.31"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 625,
+        "unique_ratio": 0.081657,
+        "example_values": [
+          "67.790000000000006",
+          "37.5",
+          "59.15",
+          "57.85",
+          "52.75"
+        ]
+      }
+    },
+    {
+      "name": "AP",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.535145,
+        "example_values": [
+          "51.23",
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+          "55.18",
+          "70.290000000000006",
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+      }
+    },
+    {
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+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4351,
+        "unique_ratio": 0.568461,
+        "example_values": [
+          "440.74",
+          "474.81",
+          "438.9",
+          "446.87",
+          "460.77"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/n20/ctgan/ctgan-n20-20260422_031259/public_gate/public_gate_report.json

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+{
+  "dataset_id": "n20",
+  "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": "PE",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n20/n20-test.csv"
+  }
+}