Resume SynthData0523 main/c2 batch 4

.gitattributes

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SynthData0523/main/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_transforms_applied.json

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

SynthData0523/main/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json

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+{
+  "dataset_id": "c2",
+  "model": "ctgan",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json"
+}

SynthData0523/main/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py

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+
+import joblib, pandas as pd
+m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib')
+# generate：batch_size 为样本数
+arr = m.generate(batch_size=int(1382))
+df = pd.DataFrame(arr, columns=meta["column_names"])
+df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv', index=False)
+print("saved", len(df))

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json

@@ -0,0 +1 @@
+{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py

@@ -0,0 +1,28 @@
+
+import shutil, json
+shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy', '/tmp/fd_X.npy')
+with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json') as f:
+    open('/tmp/fd_meta.json','w').write(f.read())
+
+import numpy as np, joblib, json, os
+from ForestDiffusion import ForestDiffusionModel
+X = np.load("/tmp/fd_X.npy")
+with open("/tmp/fd_meta.json") as f:
+    meta = json.load(f)
+cat_indexes = meta["cat_indexes"]
+print(
+    "[ForestDiffusion] train config: "
+    f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 "
+    f"n_estimators=100 duplicate_K=20 n_jobs=2 "
+    f"xgb_verbosity=1",
+    flush=True,
+)
+m = ForestDiffusionModel(
+    X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
+    model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
+    verbosity=1,
+)
+joblib.dump((m, meta), "/tmp/fd_model.joblib")
+print("ForestDiffusion train OK")
+
+shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib')

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json

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+{
+  "dataset_id": "c2",
+  "model": "forestdiffusion",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json

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+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "role": "feature",
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+      "impute_strategy": "mode",
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "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": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json

@@ -0,0 +1,149 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
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+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
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+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
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+        "unique_ratio": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/runtime_result.json

@@ -0,0 +1,27 @@
+{
+  "dataset_id": "c2",
+  "model": "forestdiffusion",
+  "run_id": "forest-c2-20260501_180312",
+  "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-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib"
+  },
+  "timings": {
+    "train": {
+      "started_at": "2026-05-01T18:03:12",
+      "ended_at": "2026-05-01T18:05:07",
+      "duration_sec": 115.491
+    },
+    "generate": {
+      "started_at": "2026-05-01T18:05:07",
+      "ended_at": "2026-05-01T18:05:11",
+      "duration_sec": 3.318
+    }
+  }
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/forestdiffusion/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-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/forestdiffusion/model_input_manifest.json"
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/forestdiffusion/adapter_transforms_applied.json

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

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/forestdiffusion/model_input_manifest.json

@@ -0,0 +1,151 @@
+{
+  "dataset_id": "c2",
+  "model": "forestdiffusion",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
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+        "example_values": [
+          "2",
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+        ]
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+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json"
+}

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/test.csv

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/train.csv

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SynthData0523/main/c2/forestdiffusion/forest-c2-20260501_180312/train_20260501_180312.log

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SynthData0523/main/c2/goggle/goggle-c2-20260414_034344/_goggle_gen.py

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+
+import os, torch, pandas as pd
+from goggle.model.Goggle import Goggle
+os.environ["PYTHONPATH"] = "/workspace/GOGGLE/src:" + os.environ.get("PYTHONPATH", "")
+d = torch.load(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_034344/goggle_state.pt", map_location="cuda")
+meta = d["meta"]
+g = Goggle(
+    meta["input_dim"], 64, 2, True, 64, 2, 0.1, "gcn",
+    None, None, "cuda",
+).cuda()
+g.load_state_dict(d["state_dict"], strict=False)
+g.eval()
+with torch.no_grad():
+    z = g.sample(int(200)).cpu().numpy()
+df = pd.DataFrame(z, columns=meta["columns"])
+df.to_csv(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_034344/goggle-c2-200-20260414_034520.csv", index=False)

SynthData0523/main/c2/goggle/goggle-c2-20260414_034344/_goggle_meta.json

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SynthData0523/main/c2/goggle/goggle-c2-20260414_034344/_goggle_train.py

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+
+import json, os, torch, pandas as pd
+os.environ["PYTHONPATH"] = "/workspace/GOGGLE/src:" + os.environ.get("PYTHONPATH", "")
+os.chdir("/work")
+from goggle.GoggleModel import GoggleModel
+with open(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_034344/_goggle_meta.json") as f:
+    meta = json.load(f)
+df = pd.read_csv(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_034344/_goggle_train.csv")
+m = GoggleModel(
+    "pipe", input_dim=meta["input_dim"], encoder_dim=64, decoder_dim=64,
+    encoder_l=2, decoder_l=2, decoder_arch="gcn", device="cuda",
+    epochs=2, batch_size=128, patience=min(20, 2), logging_epoch=max(1, 2//5),
+)
+m.fit(df)
+torch.save({"state_dict": m.model.state_dict(), "meta": meta}, r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_034344/goggle_state.pt")
+print("GOGGLE train OK")

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