Copy 5090 success runs into syntheticSuccess (batch 8)

syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/_fd_X_host.npy

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/_fd_gen.py

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+
+import joblib, pandas as pd
+m, meta = joblib.load(r'/work/output-SpecializedModels/n2/forestdiffusion/forest-n2-20260424_075935/forestdiffusion_model.joblib')
+# generate：batch_size 为样本数
+arr = m.generate(batch_size=int(1202))
+df = pd.DataFrame(arr, columns=meta["column_names"])
+df.to_csv(r'/work/output-SpecializedModels/n2/forestdiffusion/forest-n2-20260424_075935/forest-n2-1202-20260424_102412.csv', index=False)
+print("saved", len(df))

syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/_fd_meta_host.json

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/_fd_train.py

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+
+import shutil, json
+shutil.copy(r'/work/output-SpecializedModels/n2/forestdiffusion/forest-n2-20260424_075935/_fd_X_host.npy', '/tmp/fd_X.npy')
+with open(r'/work/output-SpecializedModels/n2/forestdiffusion/forest-n2-20260424_075935/_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/n2/forestdiffusion/forest-n2-20260424_075935/forestdiffusion_model.joblib')

syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/forest-n2-1202-20260424_102412.csv

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/forestdiffusion_model.joblib

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/input_snapshot.json

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/public_gate/normalized_schema_snapshot.json

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syntheticSuccess/n2/forestdiffusion/forest-n2-20260424_075935/train_20260424_075935.log

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syntheticSuccess/n2/tabbyflow/tabbyflow-n2-20260424_004716/_tabbyflow_gen.py

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+
+import os, shutil, subprocess, sys
+root = r"/workspace/ef-vfm"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/n2/tabbyflow/tabbyflow-n2-20260424_004716/tabular_bundle/pipeline_ds"
+dst_data = os.path.join(root, "data", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+shutil.copytree(src, dst_data)
+dst_syn = os.path.join(root, "synthetic", name)
+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(root)
+os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
+subprocess.check_call([
+    sys.executable, "main.py",
+    "--dataname", name, "--mode", "test", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_tabbyflow-n2-20260424_004716",
+    "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_tabbyflow-n2-20260424_004716/model_500.pt",
+    "--num_samples_to_generate", str(int(1202)),
+])
+base = os.path.join(root, "ef_vfm", "result", name, r"adapter_tabbyflow-n2-20260424_004716")
+best = None
+best_t = -1.0
+for r, _, files in os.walk(base):
+    if "samples.csv" in files:
+        p = os.path.join(r, "samples.csv")
+        t = os.path.getmtime(p)
+        if t > best_t:
+            best_t, best = t, p
+if not best:
+    raise SystemExit("tabbyflow: no samples.csv in " + base)
+shutil.copy(best, r"/work/output-SpecializedModels/n2/tabbyflow/tabbyflow-n2-20260424_004716/tabbyflow-n2-1202-20260424_004754.csv")

syntheticSuccess/n2/tabbyflow/tabbyflow-n2-20260424_004716/_tabbyflow_train.py

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+
+import os, shutil, subprocess, sys
+root = r"/workspace/ef-vfm"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/n2/tabbyflow/tabbyflow-n2-20260424_004716/tabular_bundle/pipeline_ds"
+os.makedirs(os.path.join(root, "data", name), exist_ok=True)
+dst_data = os.path.join(root, "data", name)
+dst_syn = os.path.join(root, "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(root)
+os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
+os.environ["EFVFM_SMOKE_STEPS"] = "500"
+os.environ["EFVFM_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "main.py",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_tabbyflow-n2-20260424_004716",
+])

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