Resume SynthData0523 main/c7 batch 6

.gitattributes

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 SynthData0523/main/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/X_cat_val.npy filter=lfs diff=lfs merge=lfs -text
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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/_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 10368 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.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/c7/tabddpm/tabddpm-c7-20260422_211246"
+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/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/_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/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+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

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+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 = 10368
+batch_size = 1000
+seed = 0

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/data/X_cat_train.npy

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/data/info.json

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+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 8,
+  "train_size": 10368,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7
+  ],
+  "target_col_idx": [
+    8
+  ],
+  "column_names": [
+    "parents",
+    "has_nurs",
+    "form",
+    "children",
+    "housing",
+    "finance",
+    "social",
+    "health",
+    "class"
+  ],
+  "num_classes": 5
+}

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/data/y_train.npy

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/input_snapshot.json

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+{
+  "dataset_id": "c7",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv",
+      "exists": true,
+      "size": 857718,
+      "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv",
+      "exists": true,
+      "size": 107489,
+      "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv",
+      "exists": true,
+      "size": 107327,
+      "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json",
+      "exists": true,
+      "size": 4014,
+      "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 4759,
+      "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd"
+    }
+  }
+}

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/output/X_cat_unnorm.npy

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/output/config.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
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+
+[diffusion_params]
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+gaussian_loss_type = "mse"
+
+[train.main]
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+
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+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
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+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "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": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "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": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/public_gate/public_gate_report.json"
+}

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260422_211246/train_20260422_211247.log

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SynthData0523/main/c7/tabddpm/tabddpm-c7-20260429_052039/_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 10368 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/config_sample_20260429_052217_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/c7/tabddpm/tabddpm-c7-20260429_052039/data/info.json"
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/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/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c7/tabddpm/tabddpm-c7-20260429_052039/_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/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

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