diff --git a/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_generate.py b/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bbfa81ae3add27722b0b2174684cf87a09bffd2 --- /dev/null +++ b/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n14/arf/arf-n14-20260326_084240/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=1600) +syn.to_csv("/work/output-SpecializedModels/n14/arf/arf-n14-20260326_084240/arf-n14-1600-20260330_070631.csv", index=False) +print(f"[ARF] Generated 1600 rows -> /work/output-SpecializedModels/n14/arf/arf-n14-20260326_084240/arf-n14-1600-20260330_070631.csv") diff --git a/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_train.py b/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3f613512a141633791f622af290767963b253746 --- /dev/null +++ b/syntheticSuccess/n14/arf/arf-n14-20260326_084240/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n14/arf/arf-n14-20260326_084240/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/n14/arf/arf-n14-20260326_084240/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n14/arf/arf-n14-20260326_084240/arf_model.pkl") diff --git a/syntheticSuccess/n14/arf/arf-n14-20260326_084240/arf-n14-1000-20260326_084749.csv 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sha256:58e0b3391cc6afc9b9f2363b47794d758e1db21219dcaf83ae7d5977cb3dfbf0 +size 462 diff --git a/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_bayesnet_generate.py b/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e88b93427ce87cc6ad1eb33db9323c030e464cc1 --- /dev/null +++ b/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_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/n14/bayesnet/bayesnet-n14-20260321_090237/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=1600).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/n14/bayesnet/bayesnet-n14-20260321_090237/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/n14/bayesnet/bayesnet-n14-20260321_090237/bayesnet-n14-1600-20260330_070633.csv", index=False) +print(f"[BayesNet] Generated 1600 rows -> /work/output-SpecializedModels/n14/bayesnet/bayesnet-n14-20260321_090237/bayesnet-n14-1600-20260330_070633.csv") diff --git a/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_bayesnet_train.py b/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9a0c88dc7c314ba4aa2a263ff8780ab505517797 --- /dev/null +++ b/syntheticSuccess/n14/bayesnet/bayesnet-n14-20260321_090237/_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/n14/bayesnet/bayesnet-n14-20260321_090237/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/n14/bayesnet/bayesnet-n14-20260321_090237/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/n14/bayesnet/bayesnet-n14-20260321_090237/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] 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b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..b7580f1f92c2df9f8a6f48bfdffaac0936ec5081 --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/_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 1600 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_075513.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/n14/tabddpm/tabddpm-n14-20260328_053124/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/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/n14/tabddpm/tabddpm-n14-20260328_053124/tabddpm-n14-1600-20260425_075513.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/tabddpm-n14-1600-20260425_075513.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/_tabddpm_train.py b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4273962bad6bce8a794de83c13e9130052c14b20 --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/_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/n14/tabddpm/tabddpm-n14-20260328_053124/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config.toml b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..53f894958ad7ed5b5a930fe77acf2b311241b887 --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/output" +real_data_path = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/data" +model_type = "mlp" +num_numerical_features = 51 +device = "cuda:0" + +[model_params] +d_in = 51 +num_classes = 6 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260424_211530.toml b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260424_211530.toml new file mode 100644 index 0000000000000000000000000000000000000000..07dc595dae58211acefba43b89f128e9955d720b --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260424_211530.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/output" +real_data_path = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/data" +model_type = "mlp" +num_numerical_features = 51 +device = "cuda:0" + +[model_params] +d_in = 51 +num_classes = 6 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1600 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_033049.toml b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_033049.toml new file mode 100644 index 0000000000000000000000000000000000000000..07dc595dae58211acefba43b89f128e9955d720b --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_033049.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/output" +real_data_path = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/data" +model_type = "mlp" +num_numerical_features = 51 +device = "cuda:0" + +[model_params] +d_in = 51 +num_classes = 6 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1600 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_075513.toml b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_075513.toml new file mode 100644 index 0000000000000000000000000000000000000000..07dc595dae58211acefba43b89f128e9955d720b --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/config_sample_20260425_075513.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/output" +real_data_path = "/work/output-SpecializedModels/n14/tabddpm/tabddpm-n14-20260328_053124/data" +model_type = "mlp" +num_numerical_features = 51 +device = "cuda:0" + +[model_params] +d_in = 51 +num_classes = 6 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1600 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/data/X_num_test.npy b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/data/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..81990b77089bc2d38e25e94d7fda5a6d46bf8c70 --- /dev/null +++ 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b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:046788c7fdd87f7c5b9721eed3ec718d114452d6fbc88c2a7ce8dce6a3a87a6d +size 652928 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/X_num_unnorm.npy b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d38b10711079b8285478da3f1a12e30a9d53fec --- /dev/null +++ b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b78f7d603ca74229a08d08499810762d241aa90de43e6cf2d6dd5b4dd650bc1 +size 652928 diff --git a/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/config.toml b/syntheticSuccess/n14/tabddpm/tabddpm-n14-20260328_053124/output/config.toml new file mode 100644 index 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https://git-lfs.github.com/spec/v1 +oid sha256:a237c81591d9d712c0391e57c88f91a1ff0bbf28262658f4ac0282674bcca67c +size 1124 diff --git a/syntheticSuccess/n14/tabpfgen/n14-migrated-20260422_183752/_tabpfgen_generate.py b/syntheticSuccess/n14/tabpfgen/n14-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6ef15d4b368ced8c823102a92cdd80e80f5623a2 --- /dev/null +++ b/syntheticSuccess/n14/tabpfgen/n14-migrated-20260422_183752/_tabpfgen_generate.py @@ -0,0 +1,68 @@ +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_070318/n14/staged/public/train.csv") +target_col = "target" + +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 + +# 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 1600 rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=1600) + +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] + +syn_df = syn_df[list(df.columns)] +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n14/tabpfgen-n14-1600-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} 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sha256:fd5095e5fc1af7893fb41b2570e60be05f0aee6f1cf76ec0a147273b26e41696 +size 950581 diff --git a/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_sample.py b/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..754fcb88c3935a32978ed860705fe93bc9d500f2 --- /dev/null +++ b/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120" +dataname = "tabsyn_n14" +output_csv = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120/tabsyn-n14-1600-20260427_025138.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 1600 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/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_train.py b/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d0babe4d897b12c9af3bd86aad56015fe02c4e --- /dev/null +++ b/syntheticSuccess/n14/tabsyn/tabsyn-n14-20260427_025120/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n14/tabsyn/tabsyn-n14-20260427_025120" +dataname = "tabsyn_n14" +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") +_te = 1 +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 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