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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py

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+
+import joblib, pandas as pd
+m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib')
+# generate：batch_size 为样本数
+arr = m.generate(batch_size=int(36168))
+df = pd.DataFrame(arr, columns=meta["column_names"])
+df.to_csv(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv', index=False)
+print("saved", len(df))

syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py

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+
+import os, shutil, json
+with open('/tmp/pgrep', 'w') as _f:
+    _f.write("#!/usr/bin/env python3\n")
+    _f.write("import subprocess, sys\n")
+    _f.write("ppid = sys.argv[-1]\n")
+    _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n")
+    _f.write("print(out, end='')\n")
+os.chmod('/tmp/pgrep', 0o755)
+os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '')
+shutil.copy(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy', '/tmp/fd_X.npy')
+with open(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_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"]
+print(
+    "[ForestDiffusion] train config: "
+    f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 "
+    f"n_estimators=1 duplicate_K=1 n_jobs=1 "
+    f"max_depth=2 xgb_verbosity=0 xgb_nthread=1",
+    flush=True,
+)
+m = ForestDiffusionModel(
+    X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1,
+    model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes,
+    verbosity=0, nthread=1,
+)
+joblib.dump((m, meta), "/tmp/fd_model.joblib")
+print("ForestDiffusion train OK")
+
+shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib')

syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/gen_20260506_045105.log

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/input_snapshot.json

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/models_fd/model.joblib

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/public_gate/normalized_schema_snapshot.json

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syntheticSuccess/m8/forestdiffusion/forest-m8-20260506_045103/public_gate/staged_input_manifest.json

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