Datasets:

TabQueryBench2026
/

server

+import joblib, pandas as pd
+m, meta = joblib.load(r'/work/output-SpecializedModels/m10/forestdiffusion/forest-m10-20260425_200135/forestdiffusion_model.joblib')
+# generate：batch_size 为样本数
+arr = m.generate(batch_size=int(1600))
+df = pd.DataFrame(arr, columns=meta["column_names"])
+df.to_csv(r'/work/output-SpecializedModels/m10/forestdiffusion/forest-m10-20260425_200135/forest-m10-1600-20260426_101122.csv', index=False)
+print("saved", len(df))

5090-Success/m10/forestdiffusion/forest-m10-20260425_200135/_fd_meta_host.json ADDED Viewed

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+size 288

5090-Success/m10/forestdiffusion/forest-m10-20260425_200135/_fd_train.py ADDED Viewed

+import shutil, json
+shutil.copy(r'/work/output-SpecializedModels/m10/forestdiffusion/forest-m10-20260425_200135/_fd_X_host.npy', '/tmp/fd_X.npy')
+with open(r'/work/output-SpecializedModels/m10/forestdiffusion/forest-m10-20260425_200135/_fd_meta_host.json') as f:
+    open('/tmp/fd_meta.json','w').write(f.read())
+import numpy as np, joblib, json, os
+from ForestDiffusion import ForestDiffusionModel
+X = np.load("/tmp/fd_X.npy")
+with open("/tmp/fd_meta.json") as f:
+    meta = json.load(f)
+cat_indexes = meta["cat_indexes"]
+m = ForestDiffusionModel(
+    X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
+    model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
+)
+joblib.dump((m, meta), "/tmp/fd_model.joblib")
+print("ForestDiffusion train OK")
+shutil.copy('/tmp/fd_model.joblib', r'/work/output-SpecializedModels/m10/forestdiffusion/forest-m10-20260425_200135/forestdiffusion_model.joblib')

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