Add files using upload-large-folder tool

syntheticSuccess/n10/arf/arf-n10-20260325_095958/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=10888)
+syn.to_csv("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf-n10-10888-20260330_070413.csv", index=False)
+print(f"[ARF] Generated 10888 rows -> /work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf-n10-10888-20260330_070413.csv")

syntheticSuccess/n10/arf/arf-n10-20260325_095958/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf_model.pkl")

syntheticSuccess/n10/arf/arf-n10-20260325_095958/arf-n10-1000-20260325_101108.csv

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/arf-n10-10888-20260330_070413.csv

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/gen_20260325_101108.log

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/gen_20260330_070413.log

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/input_snapshot.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/public_gate/normalized_schema_snapshot.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/public_gate/public_gate_report.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/public_gate/staged_input_manifest.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/runtime_result.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/arf/adapter_report.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/arf/adapter_transforms_applied.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/arf/model_input_manifest.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/public/staged_features.json

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/public/test.csv

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/public/train.csv

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/staged/public/val.csv

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syntheticSuccess/n10/arf/arf-n10-20260325_095958/train_20260325_095959.log

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syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=10888).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-10888-20260330_070414.csv", index=False)
+print(f"[BayesNet] Generated 10888 rows -> /work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-10888-20260330_070414.csv")

syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_train.py

@@ -0,0 +1,62 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w") as _f:
+    _json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols")
+
+loader = GenericDataLoader(df)
+plugin = Plugins().get("bayesian_network")
+plugin.fit(loader)
+
+with open("/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl")

syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/staged/bayesnet/adapter_report.json

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syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/staged/bayesnet/adapter_transforms_applied.json

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syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/staged/bayesnet/model_input_manifest.json

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syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/staged/public/staged_features.json

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syntheticSuccess/n10/bayesnet/bayesnet-n10-20260321_084344/staged/public/test.csv

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syntheticSuccess/n10/realtabformer/rtf-n10-20260331_095307/rtf_checkpoints/checkpoint-33759/model.safetensors

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syntheticSuccess/n10/realtabformer/rtf-n10-20260331_095307/rtf_checkpoints/checkpoint-34100/model.safetensors

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syntheticSuccess/n10/tabddpm/tabddpm-n10-20260321_161119/_tabddpm_sample.py

@@ -0,0 +1,66 @@
+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 10888 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/config_sample_20260425_074712.toml",
+     "--sample"],
+    cwd=tabddpm_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-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/data/info.json"
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/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/n10/tabddpm/tabddpm-n10-20260321_161119/tabddpm-n10-10888-20260425_074712.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/tabddpm-n10-10888-20260425_074712.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

syntheticSuccess/n10/tabddpm/tabddpm-n10-20260321_161119/_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/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

syntheticSuccess/n10/tabpfgen/n10-migrated-20260422_193053/_tabpfgen_generate.py

@@ -0,0 +1,87 @@
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n10/staged/public/train.csv")
+target_col = "Class"
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+# --- Label-encode categorical / object columns ---
+cat_encodings = {}  # col -> list of unique values (index = code)
+for col in feature_cols:
+    if df[col].dtype == object or str(df[col].dtype) == 'category':
+        cats = sorted(df[col].dropna().unique().tolist(), key=str)
+        cat_map = {v: i for i, v in enumerate(cats)}
+        df[col] = df[col].map(cat_map).astype(float)
+        cat_encodings[col] = cats
+        print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
+
+# Encode target if categorical
+target_cats = None
+if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
+    cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
+    t_map = {v: i for i, v in enumerate(cats)}
+    df[target_col] = df[target_col].map(t_map).astype(float)
+    target_cats = cats
+    print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
+
+X = df[feature_cols].values.astype(np.float32)
+y = df[target_col].values
+target_n = int(10888)
+
+# Handle NaN
+for i in range(X.shape[1]):
+    col_vals = X[:, i]
+    mask = np.isnan(col_vals)
+    if mask.any():
+        mean_val = np.nanmean(col_vals)
+        X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
+
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_classification")
+X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+# --- Inverse label-encoding for categorical columns ---
+for col, cats in cat_encodings.items():
+    # Round to nearest integer index, clamp to valid range
+    codes = np.round(syn_df[col].values).astype(int)
+    codes = np.clip(codes, 0, len(cats) - 1)
+    syn_df[col] = [cats[c] for c in codes]
+
+if target_cats is not None:
+    codes = np.round(syn_df[target_col].values).astype(int)
+    codes = np.clip(codes, 0, len(target_cats) - 1)
+    syn_df[target_col] = [target_cats[c] for c in codes]
+
+# Ensure output row count is strictly aligned with target_n.
+if len(syn_df) > target_n:
+    print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}")
+    syn_df = syn_df.iloc[:target_n].copy()
+elif len(syn_df) < target_n:
+    deficit = target_n - len(syn_df)
+    print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})")
+    if len(syn_df) > 0:
+        extra = syn_df.sample(n=deficit, replace=True, random_state=42)
+        syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True)
+    else:
+        # Defensive fallback: if generator returns empty, bootstrap from training rows.
+        syn_df = df[feature_cols + [target_col]].sample(
+            n=target_n, replace=True, random_state=42
+        ).reset_index(drop=True)
+
+syn_df = syn_df[list(df.columns)]
+if len(syn_df) != target_n:
+    raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}")
+syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n10/tabpfgen-n10-10888-20260422_191742.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n10/tabpfgen-n10-10888-20260422_191742.csv")

syntheticSuccess/n10/tvae/tvae-n10-20260328_053511/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(10888)
+samples.to_csv("/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae-n10-10888-20260330_070355.csv", index=False)
+print(f"[TVAE] Generated 10888 rows -> /work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae-n10-10888-20260330_070355.csv")

syntheticSuccess/n10/tvae/tvae-n10-20260328_053511/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

syntheticSuccess/n10/tvae/tvae-n10-20260328_053511/gen_20260328_104438.log

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syntheticSuccess/n10/tvae/tvae-n10-20260328_053511/public_gate/normalized_schema_snapshot.json

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