Resume SynthData0523 main/c9 batch 3

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SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/_tabddpm_sample.py

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+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 26215 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/config_sample_20260422_212735.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/c9/tabddpm/tabddpm-c9-20260422_212506"
+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/c9/tabddpm/tabddpm-c9-20260422_212506/tabddpm-c9-26215-20260422_212735.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/tabddpm-c9-26215-20260422_212735.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/_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/c9/tabddpm/tabddpm-c9-20260422_212506/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/config.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/output"
+real_data_path = "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/data"
+model_type = "mlp"
+num_numerical_features = 8
+device = "cuda:0"
+
+[model_params]
+d_in = 9
+num_classes = 0
+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/c9/tabddpm/tabddpm-c9-20260422_212506/config_sample_20260422_212735.toml

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+seed = 0
+parent_dir = "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/output"
+real_data_path = "/work/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/data"
+model_type = "mlp"
+num_numerical_features = 8
+device = "cuda:0"
+
+[model_params]
+d_in = 9
+num_classes = 0
+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 = 26215
+batch_size = 1000
+seed = 0

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SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/data/info.json

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+{
+  "name": "benchmark_dataset",
+  "task_type": "regression",
+  "n_num_features": 8,
+  "n_cat_features": 1,
+  "train_size": 26215,
+  "num_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7
+  ],
+  "cat_col_idx": [
+    8
+  ],
+  "target_col_idx": [
+    9
+  ],
+  "column_names": [
+    "RESOURCE",
+    "MGR_ID",
+    "ROLE_ROLLUP_1",
+    "ROLE_ROLLUP_2",
+    "ROLE_DEPTNAME",
+    "ROLE_TITLE",
+    "ROLE_FAMILY_DESC",
+    "ROLE_CODE",
+    "ACTION",
+    "ROLE_FAMILY"
+  ]
+}

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SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/input_snapshot.json

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+{
+  "dataset_id": "c9",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-train.csv",
+      "exists": true,
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+      "sha256": "01557a0b4304c4b5435df0909d16016ec0ba4ce2c3b31a8815f813ef1376b0b9"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-test.csv",
+      "exists": true,
+      "size": 207206,
+      "sha256": "6de1346ef61646cbd096588223a049bf26f2b26229ac65b2734fed139f3c06bb"
+    },
+    "profile_json": {
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+size 203862

SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/staged/tabddpm/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/c9/tabddpm/tabddpm-c9-20260422_212506/staged/tabddpm/model_input_manifest.json"
+}

SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/staged/tabddpm/adapter_transforms_applied.json

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SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/staged/tabddpm/model_input_manifest.json

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+  "model": "tabddpm",
+  "target_column": "ROLE_FAMILY",
+  "task_type": "regression",
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+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 7.6e-05,
+        "example_values": [
+          "1",
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+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 6690,
+        "unique_ratio": 0.255197,
+        "example_values": [
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+    {
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/tabddpm/tabddpm-c9-20260422_212506/public_gate/public_gate_report.json"
+}

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SynthData0523/main/c9/tabddpm/tabddpm-c9-20260422_212506/train_20260422_212507.log

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SynthData0523/main/c9/tabdiff/tabdiff-c9-20260420_073501/_tabdiff_gen.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c9/tabdiff/tabdiff-c9-20260420_073501/tabular_bundle/pipeline_ds"
+dst_data = os.path.join(td, "data", name)
+dst_syn = os.path.join(td, "synthetic", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+shutil.copytree(src, dst_data)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(td)
+os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "")
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "test", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+    "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_ds/adapter_learnable/model_500.pt",
+    "--num_samples_to_generate", str(int(26215)),
+])
+# test() 写入 tabdiff/result/<dataname>/<exp>/<epoch>/samples.csv
+import glob as g
+base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable")
+best = None
+best_t = -1.0
+for root, _, files in os.walk(base):
+    if "samples.csv" in files:
+        p = os.path.join(root, "samples.csv")
+        t = os.path.getmtime(p)
+        if t > best_t:
+            best_t = t
+            best = p
+if not best:
+    raise SystemExit("tabdiff: no samples.csv under " + base)
+shutil.copy(best, r"/work/output-SpecializedModels/c9/tabdiff/tabdiff-c9-20260420_073501/tabdiff-c9-26215-20260420_074317.csv")