Resume SynthData0523 main/c2 batch 18

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py

@@ -0,0 +1,66 @@
+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 1382 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir）
+info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json"
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/_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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml

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+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 4
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 200
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 2000
+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 = 256
+seed = 0

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml

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+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 4
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 200
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 2000
+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 = 1382
+batch_size = 256
+seed = 0

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json

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+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 6,
+  "train_size": 1382,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "buying",
+    "maint",
+    "doors",
+    "persons",
+    "lug_boot",
+    "safety",
+    "class"
+  ],
+  "num_classes": 4
+}

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+{
+  "dataset_id": "c2",
+  "model": "tabddpm",
+  "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"
+    }
+  }
+}

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml

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+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 4
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 200
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 2000
+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 = 1382
+batch_size = 256
+seed = 0

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json

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+      "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/tabddpm/tabddpm-c2-20260504_181103/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/public_gate_report.json"
+}

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv

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+size 41650

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log

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+size 766

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py

@@ -0,0 +1,66 @@
+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 1382 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir）
+info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json"
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/_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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 4
+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 = 256
+seed = 0

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 6
+num_classes = 4
+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 = 1382
+batch_size = 256
+seed = 0

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 66464

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json

@@ -0,0 +1,29 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 6,
+  "train_size": 1382,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5
+  ],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "buying",
+    "maint",
+    "doors",
+    "persons",
+    "lug_boot",
+    "safety",
+    "class"
+  ],
+  "num_classes": 4
+}

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4
+size 11184