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b/SynthData0523/main/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..2f237cd3aeca01ef451b6cae57cf0beb232bd996 --- /dev/null +++ b/SynthData0523/main/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/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_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(6400)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +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-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/tabbyflow-n6-6400-20260429_033823.csv") diff --git a/SynthData0523/main/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py b/SynthData0523/main/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fab5bc3ce3e88239d9f54eeffac04e64fe9418d8 --- /dev/null +++ b/SynthData0523/main/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/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", + 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sha256:2abd54789951964148a9da786dbe67842a5a2a85236d669d6bd00a636cf9facf +size 373184 diff --git a/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_sample_r0.py b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..5d3a36d1dbdabbd27854afaaa6867e941bff28b8 --- /dev/null +++ b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_sample_r0.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 6400 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config_sample_20260429_052109_r0.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-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/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-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/tabddpm-n6-6400-20260429_052109.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/tabddpm-n6-6400-20260429_052109.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_train.py b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..03a88ed2945f1661f4e5483b339d21755b4ea092 --- /dev/null +++ b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/_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-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml 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sha256:81f2520f27fe8a866aecfa58aaeef2b6b6201d6d2e3bcef9ed01d229ca2dc17d +size 1054983 diff --git a/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/train_20260429_052038.log b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/train_20260429_052038.log new file mode 100644 index 0000000000000000000000000000000000000000..c7312dbd4e5f46ba26b30e0e9e4893cbb76fa833 --- /dev/null +++ b/SynthData0523/main/n6/tabddpm/tabddpm-n6-20260429_052038/train_20260429_052038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21c37f6a1a0f20f1be0cc8a5c92d98655d87cd48206c29deb90c05d35105611f +size 1066 diff --git a/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..1908fdbb327fcc5da55f1c86215db91bc4f31217 --- /dev/null +++ b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n6" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabular_bundle/pipeline_n6" +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_n6/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(6400)), +]) +# 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-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabdiff-n6-6400-20260429_042528.csv") diff --git a/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aff1ad2b168d6a006f405c30b75b96ac640db953 --- /dev/null +++ b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n6" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabular_bundle/pipeline_n6" +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/n6/tabdiff/tabdiff-n6-20260429_041029/gen_20260429_042528.log b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/gen_20260429_042528.log new file mode 100644 index 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b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/train_20260429_041029.log new file mode 100644 index 0000000000000000000000000000000000000000..054fabc8014f7cc36901535ac28095a352f26d75 --- /dev/null +++ b/SynthData0523/main/n6/tabdiff/tabdiff-n6-20260429_041029/train_20260429_041029.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:858a827540ba84d0e10911a5a911e4c50b57c41117336b42dbdc3590b52b3da7 +size 389062 diff --git a/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py b/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b55df02efa0425b202deef4aa28542251d2888fe --- /dev/null +++ b/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +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/n6/tabpfgen/tabpfgen-n6-20260429_060202/staged/public/train.csv") +target_col = "y" + +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 +target_n = int(6400) + +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 + +# 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=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 + +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/n6/tabpfgen/tabpfgen-n6-20260429_060202/tabpfgen-n6-6400-20260429_064819.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n6/tabpfgen/tabpfgen-n6-20260429_060202/tabpfgen-n6-6400-20260429_064819.csv") diff --git a/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log b/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log new file mode 100644 index 0000000000000000000000000000000000000000..593fc2b9bc06af4f5ae64828565285a3d252a13e --- /dev/null +++ b/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..4f424cf7ba6bd4d64c6468624f77ba685c256276 --- /dev/null +++ b/SynthData0523/main/n6/tabpfgen/tabpfgen-n6-20260429_060202/train_20260429_060202.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e80a1cd11cf3475fda3886f822166c60419b46ff3e9a940b60da4ad8b9c8ce2 +size 595 diff --git a/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..75373f24f10188cb46905d2b754baae28714e5f7 --- /dev/null +++ b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136" +dataname = "tabsyn_n6" +output_csv = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136/tabsyn-n6-6400-20260429_060149.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 6400 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..08044c22b6890ca9db858fbf146b52b583c7e5e9 --- /dev/null +++ b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136" +dataname = "tabsyn_n6" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4b30720bcfa50b549dc5cd917882157c1d9aa60b --- /dev/null +++ b/SynthData0523/main/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfbe32acad77c018301c459c8995e77626255566f0e6b962feddffd7b38c148d +size 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diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_tvae_generate.py b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..99e92e04f19c5157fa4f98f2bf068f54ef1133cf --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n6/tvae/tvae-n6-20260320_072129/models_300epochs/tvae_300epochs.pt") +samples = model.sample(6400) +samples.to_csv("/work/output-SpecializedModels/n6/tvae/tvae-n6-20260320_072129/tvae-n6-6400-20260330_070145.csv", index=False) +print(f"[TVAE] Generated 6400 rows -> /work/output-SpecializedModels/n6/tvae/tvae-n6-20260320_072129/tvae-n6-6400-20260330_070145.csv") diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_tvae_train.py b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2fe493d494b2d4e411ad21ad79dff456f4a1e95 --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/_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/DatasetNew/n6/n6-train.csv" +meta_path = "/work/output-SpecializedModels/n6/tvae/tvae-n6-20260320_072129/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n6/tvae/tvae-n6-20260320_072129/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}") diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/gen_20260320_072435.log 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sha256:ea38a565c480e4f48476c1deff5037b6a5cffea30580b43867418e38fd7c8bf1 +size 48688 diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae-n6-6400-20260330_070145.csv b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae-n6-6400-20260330_070145.csv new file mode 100644 index 0000000000000000000000000000000000000000..47bacf8221b713728d0d37c606775f42f1439805 --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae-n6-6400-20260330_070145.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6e50310025b23761cc73fcb51d29b20474b6e11d175b37d2962c47d6a2c52c6 +size 308910 diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae_metadata.json b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7cdebbf77c20cba8a8d4c8afc0980437d1b54231 --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260320_072129/tvae_metadata.json @@ -0,0 +1,72 @@ +{ + "columns": [ + { + "name": "X1", + "type": "continuous" + }, + { + "name": "X2", + "type": "continuous" + }, + { + "name": "X3", + "type": "continuous" + }, + { + "name": "X4", + "type": "continuous" + }, + { + "name": "X5", + "type": "continuous" + }, + { + "name": "X6", + "type": "continuous" + }, + { + "name": "X7", + "type": "continuous" + }, + { + "name": "X8", + "type": "continuous" + }, + { + "name": "X9", + "type": "continuous" + }, + { + "name": "X10", + "type": "continuous" + }, + { + "name": "X11", + "type": "continuous" + }, + { + "name": "X12", + "type": "continuous" + }, + { + "name": "X13", + "type": "continuous" + }, + { + "name": "X14", + "type": "continuous" + }, + { + "name": "X15", + "type": "continuous" + }, + { + "name": "X16", + "type": "continuous" + }, + { + "name": "y", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..13d46b511588db3010384ffa8edc99b6969f0641 --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/models_300epochs/tvae_300epochs.pt") +total = 6400 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae-n6-6400-20260429_032109.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae-n6-6400-20260429_032109.csv") diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a0ba5846da17d7ab5065a35f8b6e78c234c97ed1 --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/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}") diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/gen_20260429_032109.log b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/gen_20260429_032109.log new file mode 100644 index 0000000000000000000000000000000000000000..bb405adc4fc20da4bf6420b05ff352fa25f12b0b --- /dev/null +++ b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/gen_20260429_032109.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5195b5d01c3f8c8a54b50e37de818f5982c69cd76589c2e35784e98a689bdba5 +size 405 diff --git a/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/input_snapshot.json b/SynthData0523/main/n6/tvae/tvae-n6-20260429_031938/input_snapshot.json new file mode 100644 index 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