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a/SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log b/SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log new file mode 100644 index 0000000000000000000000000000000000000000..e9d006129b48b223ae8dffa15e87645b214c8b34 --- /dev/null +++ b/SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00a81fea0758acec86f7d70be97f369c00eb01c7291ae5cbc1c52605b4c3662d +size 855 diff --git a/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_gen.py b/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..d46c65f756f81dc8f4633c2855ef2b0a3a70caab --- /dev/null +++ b/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/c5/tabdiff/tabdiff-c5-20260420_055925/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(6732)), +]) +# test() 写入 tabdiff/result////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/c5/tabdiff/tabdiff-c5-20260420_055925/tabdiff-c5-6732-20260420_060648.csv") diff --git a/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_train.py b/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5ad094b099a852dc511cf98376c58ac32e8340f9 --- /dev/null +++ b/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/c5/tabdiff/tabdiff-c5-20260420_055925/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", +]) diff --git a/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/input_snapshot.json b/SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c380c873407ba5894e4a9d702ca862e198017c19 --- /dev/null +++ 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0000000000000000000000000000000000000000..07e63b13dcc5dba8b7e477a117e04d32184e5a27 --- /dev/null +++ b/SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/_tabpfgen_generate.py @@ -0,0 +1,131 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +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)") + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(6732) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +n_sgld_steps = max(1, int(os.environ.get("TABPFGEN_N_SGLD_STEPS", "1000"))) +sgld_step_size = float(os.environ.get("TABPFGEN_SGLD_STEP_SIZE", "0.01")) +sgld_noise_scale = float(os.environ.get("TABPFGEN_SGLD_NOISE_SCALE", "0.01")) + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=n_sgld_steps, + sgld_step_size=sgld_step_size, + sgld_noise_scale=sgld_noise_scale, + device=device, +) + +print( + f"[TabPFGen] Generating {target_n} rows via generate_classification " + f"(chunk_rows={chunk_rows}, device={device}, " + f"n_sgld_steps={n_sgld_steps}, sgld_step_size={sgld_step_size}, " + f"sgld_noise_scale={sgld_noise_scale})" +) +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + 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] + +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: + 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/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/tabpfgen-c5-6732-20260511_061054.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/tabpfgen-c5-6732-20260511_061054.csv") diff --git a/SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/gen_20260511_061054.log b/SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/gen_20260511_061054.log new file mode 100644 index 0000000000000000000000000000000000000000..7dfbba00875cde94f361524232cf73963b139e89 --- /dev/null +++ b/SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/gen_20260511_061054.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1903405b611216531665a2f28b7f544b5c21b50a521bbdca146f773fad8baa87 +size 7005