Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c20",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+      "exists": true,
+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv",
+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_profile.json",
+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/output"
+real_data_path = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/data"
+model_type = "mlp"
+num_numerical_features = 6
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 3
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/data/info.json

@@ -0,0 +1,29 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 6,
+  "n_cat_features": 0,
+  "train_size": 35855,
+  "num_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "cat_col_idx": [],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "white_piece0_strength",
+    "white_piece0_file",
+    "white_piece0_rank",
+    "black_piece0_strength",
+    "black_piece0_file",
+    "black_piece0_rank",
+    "class"
+  ],
+  "num_classes": 3
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/train_20260319_072624.log

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+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+[0]
+6
+{'d_in': 6, 'num_classes': 3, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+Traceback (most recent call last):
+  File "/workspace/tabddpm/code/_compat_run.py", line 6, in <module>
+    runpy.run_path(sys.argv[0], run_name='__main__')
+  File "<frozen runpy>", line 291, in run_path
+  File "<frozen runpy>", line 98, in _run_module_code
+  File "<frozen runpy>", line 88, in _run_code
+  File "scripts/pipeline.py", line 112, in <module>
+    main()
+  File "scripts/pipeline.py", line 48, in main
+    train(
+  File "/workspace/tabddpm/code/scripts/train.py", line 154, in train
+    trainer.run_loop()
+  File "/workspace/tabddpm/code/scripts/train.py", line 53, in run_loop
+    x, out_dict = next(self.train_iter)
+                  ^^^^^^^^^^^^^^^^^^^^^
+  File "/workspace/tabddpm/code/lib/data.py", line 605, in prepare_fast_dataloader
+    y = torch.from_numpy(D.y[split])
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
+[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_072621/config.toml

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/output"
+real_data_path = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/data"
+model_type = "mlp"
+num_numerical_features = 6
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 3
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/train_20260319_224929.log

@@ -0,0 +1,26 @@
+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+Traceback (most recent call last):
+  File "/workspace/tabddpm/code/_compat_run.py", line 6, in <module>
+    runpy.run_path(sys.argv[0], run_name='__main__')
+[0]
+6
+{'d_in': 6, 'num_classes': 3, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+  File "<frozen runpy>", line 291, in run_path
+  File "<frozen runpy>", line 98, in _run_module_code
+  File "<frozen runpy>", line 88, in _run_code
+  File "scripts/pipeline.py", line 112, in <module>
+    main()
+  File "scripts/pipeline.py", line 48, in main
+    train(
+  File "/workspace/tabddpm/code/scripts/train.py", line 154, in train
+    trainer.run_loop()
+  File "/workspace/tabddpm/code/scripts/train.py", line 53, in run_loop
+    x, out_dict = next(self.train_iter)
+                  ^^^^^^^^^^^^^^^^^^^^^
+  File "/workspace/tabddpm/code/lib/data.py", line 605, in prepare_fast_dataloader
+    y = torch.from_numpy(D.y[split])
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
+[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260319_224926/config.toml

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/_tabddpm_sample.py

@@ -0,0 +1,67 @@
+import os, sys, subprocess, json
+import numpy as np
+import pandas as pd
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Reuse the compat wrapper (patches collections.Sequence for skorch)
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+if not os.path.exists(wrapper):
+    with open(wrapper, "w") as f:
+        f.write(
+            "import collections, collections.abc\n"
+            "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+            "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+            "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+            "import sys, runpy\n"
+            "sys.argv = sys.argv[1:]\n"
+            "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+        )
+
+print(f"[TabDDPM] Sampling 1000 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/config.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV
+work_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520"
+info_path = os.path.join(work_dir, "data", "info.json")
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = os.path.join(work_dir, "output")
+col_names = info.get("column_names", [])
+
+parts = []
+x_num_path = os.path.join(output_dir, "X_num_train.npy")
+x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
+y_path = os.path.join(output_dir, "y_train.npy")
+
+if os.path.exists(x_num_path):
+    parts.append(np.load(x_num_path, allow_pickle=True))
+if os.path.exists(x_cat_path):
+    parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
+if os.path.exists(y_path):
+    y = np.load(y_path, allow_pickle=True)
+    parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
+
+if parts:
+    combined = np.concatenate(parts, axis=1)
+    if col_names and len(col_names) == combined.shape[1]:
+        df = pd.DataFrame(combined, columns=col_names)
+    else:
+        df = pd.DataFrame(combined)
+    df.to_csv("/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/tabddpm-c20-1000-20260321_150712.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/tabddpm-c20-1000-20260321_150712.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/_tabddpm_train.py

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+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/config.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/output"
+real_data_path = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/data"
+model_type = "mlp"
+num_numerical_features = 6
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 3
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/gen_20260321_150712.log

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+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+mlp
+Sample timestep  999
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+Discrete cols: [0, 1, 2, 3, 4, 5]
+Num shape:  (1000, 6)
+Elapsed time: 0:00:11
+[TabDDPM] Sampling 1000 rows
+[TabDDPM] Saved 1000 rows -> /work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/tabddpm-c20-1000-20260321_150712.csv

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c20",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+      "exists": true,
+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv",
+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_profile.json",
+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c20",
+  "model": "tabddpm",
+  "run_id": "tabddpm-c20-20260321_150520",
+  "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/c20/tabddpm/tabddpm-c20-20260321_150520/tabddpm-c20-1000-20260321_150712.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/train_20260321_150520.log

@@ -0,0 +1,19 @@
+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+[0]
+6
+{'d_in': 6, 'num_classes': 3, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+Step 500/5000 MLoss: 0.0 GLoss: 0.5155 Sum: 0.5155
+Step 1000/5000 MLoss: 0.0 GLoss: 0.4573 Sum: 0.4573
+Step 1500/5000 MLoss: 0.0 GLoss: 0.4333 Sum: 0.4333
+Step 2000/5000 MLoss: 0.0 GLoss: 0.4251 Sum: 0.4251
+Step 2500/5000 MLoss: 0.0 GLoss: 0.4261 Sum: 0.4261
+Step 3000/5000 MLoss: 0.0 GLoss: 0.4159 Sum: 0.4159
+Step 3500/5000 MLoss: 0.0 GLoss: 0.4096 Sum: 0.4096
+Step 4000/5000 MLoss: 0.0 GLoss: 0.4023 Sum: 0.4023
+Step 4500/5000 MLoss: 0.0 GLoss: 0.3999 Sum: 0.3999
+Step 5000/5000 MLoss: 0.0 GLoss: 0.3928 Sum: 0.3928
+Elapsed time: 0:01:38
+[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260321_150520/config.toml
+[TabDDPM] Training complete

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/_tabddpm_sample.py

@@ -0,0 +1,67 @@
+import os, sys, subprocess, json
+import numpy as np
+import pandas as pd
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Reuse the compat wrapper (patches collections.Sequence for skorch)
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+if not os.path.exists(wrapper):
+    with open(wrapper, "w") as f:
+        f.write(
+            "import collections, collections.abc\n"
+            "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+            "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+            "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+            "import sys, runpy\n"
+            "sys.argv = sys.argv[1:]\n"
+            "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+        )
+
+print(f"[TabDDPM] Sampling 35855 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config_sample_20260424_033931.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV
+work_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725"
+info_path = os.path.join(work_dir, "data", "info.json")
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = os.path.join(work_dir, "output")
+col_names = info.get("column_names", [])
+
+parts = []
+x_num_path = os.path.join(output_dir, "X_num_train.npy")
+x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
+y_path = os.path.join(output_dir, "y_train.npy")
+
+if os.path.exists(x_num_path):
+    parts.append(np.load(x_num_path, allow_pickle=True))
+if os.path.exists(x_cat_path):
+    parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
+if os.path.exists(y_path):
+    y = np.load(y_path, allow_pickle=True)
+    parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
+
+if parts:
+    combined = np.concatenate(parts, axis=1)
+    if col_names and len(col_names) == combined.shape[1]:
+        df = pd.DataFrame(combined, columns=col_names)
+    else:
+        df = pd.DataFrame(combined)
+    df.to_csv("/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/tabddpm-c20-35855-20260424_033931.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/tabddpm-c20-35855-20260424_033931.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/output"
+real_data_path = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/data"
+model_type = "mlp"
+num_numerical_features = 6
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 3
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config_sample_20260424_033931.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/output"
+real_data_path = "/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/data"
+model_type = "mlp"
+num_numerical_features = 6
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 3
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 35855
+batch_size = 1000
+seed = 0

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c20",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+      "exists": true,
+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv",
+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_profile.json",
+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c20",
+  "model": "tabddpm",
+  "run_id": "tabddpm-c20-20260424_033725",
+  "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/c20/tabddpm/tabddpm-c20-20260424_033725/tabddpm-c20-35855-20260424_033931.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/train_20260424_033725.log

@@ -0,0 +1,17 @@
+[0]
+6
+{'d_in': 6, 'num_classes': 3, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+Step 500/5000 MLoss: 0.0 GLoss: 0.5155 Sum: 0.5155
+Step 1000/5000 MLoss: 0.0 GLoss: 0.4573 Sum: 0.4573
+Step 1500/5000 MLoss: 0.0 GLoss: 0.4333 Sum: 0.4333
+Step 2000/5000 MLoss: 0.0 GLoss: 0.4251 Sum: 0.4251
+Step 2500/5000 MLoss: 0.0 GLoss: 0.4261 Sum: 0.4261
+Step 3000/5000 MLoss: 0.0 GLoss: 0.4159 Sum: 0.4159
+Step 3500/5000 MLoss: 0.0 GLoss: 0.4096 Sum: 0.4096
+Step 4000/5000 MLoss: 0.0 GLoss: 0.4023 Sum: 0.4023
+Step 4500/5000 MLoss: 0.0 GLoss: 0.3999 Sum: 0.3999
+Step 5000/5000 MLoss: 0.0 GLoss: 0.3928 Sum: 0.3928
+Elapsed time: 0:01:57
+[TabDDPM] Training, config=/work/output-SpecializedModels/c20/tabddpm/tabddpm-c20-20260424_033725/config.toml
+[TabDDPM] Training complete

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260421_023648/data/tabsyn_c20/info.json

@@ -0,0 +1,93 @@
+{
+  "name": "tabsyn_c20",
+  "task_type": "multiclass",
+  "n_num_features": 6,
+  "n_cat_features": 0,
+  "train_size": 40336,
+  "num_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "cat_col_idx": [],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "white_piece0_strength",
+    "white_piece0_file",
+    "white_piece0_rank",
+    "black_piece0_strength",
+    "black_piece0_file",
+    "black_piece0_rank",
+    "class"
+  ],
+  "train_num": 40336,
+  "test_num": 4483,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_c20/train.csv",
+  "test_path": null,
+  "idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "inverse_idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "idx_name_mapping": {
+    "0": "white_piece0_strength",
+    "1": "white_piece0_file",
+    "2": "white_piece0_rank",
+    "3": "black_piece0_strength",
+    "4": "black_piece0_file",
+    "5": "black_piece0_rank",
+    "6": "class"
+  },
+  "n_classes": 3,
+  "metadata": {
+    "columns": {
+      "0": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "1": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "2": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "3": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "4": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "5": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "6": {
+        "sdtype": "categorical"
+      }
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260421_023648/data/tabsyn_c20/test.csv

@@ -0,0 +1,4484 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/data/tabsyn_c20/info.json

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SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/public_gate/normalized_schema_snapshot.json

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SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/public_gate/public_gate_report.json

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+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/public_gate/staged_input_manifest.json

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+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/public_gate/public_gate_report.json",
+  "column_schema": [
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SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/public/staged_features.json

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+[
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+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
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+  {
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+  {
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+  {
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+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

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SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/tabsyn/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/c20/tabsyn/tabsyn-c20-20260426_203054/staged/tabsyn/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/tabsyn/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c20/tabsyn/tabsyn-c20-20260426_203054/staged/tabsyn/model_input_manifest.json

@@ -0,0 +1,159 @@
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+}