Resume SynthData0523 main/c6 batch 18

.gitattributes

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 SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/tuned_models/mlp/insurance_cv.json filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/tuned_models/mlp/king_cv.json filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/tuned_models/mlp/miniboone_cv.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/tuned_models/mlp/wilt_cv.json filter=lfs diff=lfs merge=lfs -text
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SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/tuned_models/mlp/wilt_cv.json

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SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_sample_r0.py

@@ -0,0 +1,75 @@
+import os, sys, subprocess, json
+import numpy as np
+import pandas as pd
+
+tabddpm_root = "/workspace/tabddpm/code"
+runtime_root = "/work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+
+if not os.path.exists(runtime_root):
+    def _ignore(_, names):
+        skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"}
+        return [n for n in names if n in skip or n.endswith(".pyc")]
+    import shutil
+    shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore)
+
+env = os.environ.copy()
+env["PYTHONPATH"] = runtime_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Reuse the compat wrapper (patches collections.Sequence for skorch)
+wrapper = os.path.join(runtime_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 7636 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/config_sample_20260510_222509_r0.toml",
+     "--sample"],
+    cwd=runtime_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/c6/tabddpm/tabddpm-c6-20260510_222430/data/info.json"
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = "/work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/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/c6/tabddpm/tabddpm-c6-20260510_222430/tabddpm-c6-7636-20260510_222509.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/tabddpm-c6-7636-20260510_222509.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_train.py

@@ -0,0 +1,42 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+runtime_root = "/work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+
+def _ignore(_, names):
+    skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"}
+    return [n for n in names if n in skip or n.endswith(".pyc")]
+
+import shutil
+shutil.rmtree(runtime_root, ignore_errors=True)
+shutil.copytree(tabddpm_root, runtime_root, ignore=_ignore)
+
+env = os.environ.copy()
+env["PYTHONPATH"] = runtime_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(runtime_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/c6/tabddpm/tabddpm-c6-20260510_222430/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c6/tabddpm/tabddpm-c6-20260510_222430/config.toml",
+     "--train"],
+    cwd=runtime_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

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SynthData0523/main/c6/tabdiff/tabdiff-c6-20260420_062412/_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/c6/tabdiff/tabdiff-c6-20260420_062412/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(7636)),
+])
+# 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/c6/tabdiff/tabdiff-c6-20260420_062412/tabdiff-c6-7636-20260420_063023.csv")

SynthData0523/main/c6/tabdiff/tabdiff-c6-20260420_062412/_tabdiff_train.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c6/tabdiff/tabdiff-c6-20260420_062412/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", "")
+os.environ["TABDIFF_SMOKE_STEPS"] = "500"
+os.environ["TABDIFF_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+])

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