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+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Ensure data symlink exists
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+print(f"[TabSyn] Sampling 2551 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/_tabsyn_train.py

@@ -0,0 +1,62 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446"
+dataname = "tabsyn_c3"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Symlink data dir into TabSyn data/
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+_te = None
+if _te is not None:
+    env["TABSYN_VAE_EPOCHS"] = str(_te)
+    env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2))
+
+# Data preprocessing is done on the host side (_prepare_data_dir)
+# which creates .npy files, train/test CSVs, and info.json
+
+# Step 1: Train VAE (produces latent embeddings)
+print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "vae",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] VAE training failed")
+    sys.exit(ret.returncode)
+
+# Step 2: Train diffusion model on latent space
+print(f"[TabSyn] Step 2/2: Training diffusion model")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "tabsyn",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] Diffusion training failed")
+    sys.exit(ret.returncode)
+print("[TabSyn] Training complete (VAE + Diffusion)")

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/X_cat_test.npy

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+oid sha256:fcceec7eda6e86ff9799b933ebfaaa423aca45ad851421202b7942e900a6bb75
+size 2688

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/X_cat_train.npy

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+size 23080

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/X_num_test.npy

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+version https://git-lfs.github.com/spec/v1
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+size 1408

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/X_num_train.npy

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+size 11604

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/info.json

@@ -0,0 +1,57 @@
+{
+  "name": "tabsyn_c3",
+  "task_type": "multiclass",
+  "n_num_features": 1,
+  "n_cat_features": 1,
+  "train_size": 2869,
+  "num_col_idx": [
+    1
+  ],
+  "cat_col_idx": [
+    2
+  ],
+  "target_col_idx": [
+    0
+  ],
+  "column_names": [
+    "EI",
+    "ATRINS-DONOR-521",
+    "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG"
+  ],
+  "train_num": 2869,
+  "test_num": 320,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_c3/train.csv",
+  "test_path": null,
+  "idx_mapping": {
+    "0": 2,
+    "1": 0,
+    "2": 1
+  },
+  "inverse_idx_mapping": {
+    "2": 0,
+    "0": 1,
+    "1": 2
+  },
+  "idx_name_mapping": {
+    "0": "EI",
+    "1": "ATRINS-DONOR-521",
+    "2": "CCAGCTGCATCACAGGAGGCCAGCGAGCAGGTCTGTTCCAAGGGCCTTCGAGCCAGTCTG"
+  },
+  "n_classes": 3,
+  "metadata": {
+    "columns": {
+      "1": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "2": {
+        "sdtype": "categorical"
+      },
+      "0": {
+        "sdtype": "categorical"
+      }
+    }
+  }
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/test.csv

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+version https://git-lfs.github.com/spec/v1
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+size 2084

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/train.csv

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+size 32169

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/y_test.npy

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+size 2688

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/data/tabsyn_c3/y_train.npy

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+size 23080

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/gen_20260421_004207.log

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+size 665

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c3",
+  "model": "tabsyn",
+  "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"
+    }
+  }
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/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"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/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"
+  }
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/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/tabsyn/tabsyn-c3-20260420_233446/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "EI",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.001176,
+        "example_values": [
+          "EI",
+          "IE",
+          "N"
+        ]
+      }
+    },
+    {
+      "name": "ATRINS-DONOR-521",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
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+      "impute_strategy": "keep_raw",
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+        "unique_count": 2541,
+        "unique_ratio": 0.99608,
+        "example_values": [
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+          "HUMMHCD8A-ACCEPTOR-733",
+          "HUMGCB1-ACCEPTOR-6344",
+          "HUMNEBB-NEG-241",
+          "HUMATP1A2-NEG-2101"
+        ]
+      }
+    },
+    {
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+      "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"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c3",
+  "model": "tabsyn",
+  "run_id": "tabsyn-c3-20260420_233446",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/tabsyn-c3-2551-20260421_004207.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446"
+  }
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/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
+  }
+]

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SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/staged/tabsyn/adapter_report.json

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+{
+  "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/tabsyn/tabsyn-c3-20260420_233446/staged/tabsyn/model_input_manifest.json"
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/staged/tabsyn/adapter_transforms_applied.json

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+[]

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/staged/tabsyn/model_input_manifest.json

@@ -0,0 +1,75 @@
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+  "dataset_id": "c3",
+  "model": "tabsyn",
+  "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"
+        ]
+      }
+    },
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+      "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/tabsyn/tabsyn-c3-20260420_233446/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c3/tabsyn/tabsyn-c3-20260420_233446/public_gate/public_gate_report.json"
+}

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/synthetic/tabsyn_c3/real.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8318611a30eea3817eab5e2a68bc0c6b4dad8b9ff813c663c4432a9cefcfc014
+size 32169

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/synthetic/tabsyn_c3/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e7236117a72795aa9a0bae6cac4f022ecdd249168315f9a2f989f668bc35b4a
+size 2084

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/tabsyn-c3-2551-20260421_004207.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:786774393ca3f60bca374014012d21ee43516d4e23e89c058a7f52eaa2295354
+size 41931

SynthData0523/main/c3/tabsyn/tabsyn-c3-20260420_233446/train_20260420_233446.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7118dbf6f6edb958b4d00586435a08939d60e39489392c498232ecade098edf8
+size 2531016

SynthData0523/main/c3/tvae/tvae-c3-20260322_205426/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(2551)
+samples.to_csv("/work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/tvae-c3-2551-20260330_065244.csv", index=False)
+print(f"[TVAE] Generated 2551 rows -> /work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/tvae-c3-2551-20260330_065244.csv")

SynthData0523/main/c3/tvae/tvae-c3-20260322_205426/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/c3/tvae/tvae-c3-20260322_205426/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

SynthData0523/main/c3/tvae/tvae-c3-20260322_205426/gen_20260322_205517.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:322026ca861f2521165912a7bb2ac73444ecf2eb1cc804173a74b2ab34955de6
+size 126

SynthData0523/main/c3/tvae/tvae-c3-20260322_205426/gen_20260330_065244.log

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+size 126

SynthData0523/main/c3/tvae/tvae-c3-20260322_205426/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c3",
+  "model": "tvae",
+  "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"
+    }
+  }
+}