diff --git a/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_generate.py b/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f234a16701bb8b9af33b6d55283681a759a799db --- /dev/null +++ b/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=7857) +syn.to_csv("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf-n15-7857-20260330_070701.csv", index=False) +print(f"[ARF] Generated 7857 rows -> /work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf-n15-7857-20260330_070701.csv") diff --git a/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_train.py b/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..180b8fb7a6410b0f5396d2da99639733860a666e --- /dev/null +++ b/syntheticSuccess/n15/arf/arf-n15-20260326_084758/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/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/n15/arf/arf-n15-20260326_084758/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n15/arf/arf-n15-20260326_084758/arf_model.pkl") diff --git a/syntheticSuccess/n15/arf/arf-n15-20260326_084758/arf-n15-1000-20260326_090652.csv 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sha256:9772d058260b724e2ace5d462f093adf48ef174b62e615eccc623a5a2e4b3082 +size 563 diff --git a/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_generate.py b/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..71745bec139eae66fe6404ac686e0410a194dc3c --- /dev/null +++ b/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_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/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=7857).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/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/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet-n15-7857-20260330_070726.csv", index=False) +print(f"[BayesNet] Generated 7857 rows -> /work/output-SpecializedModels/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet-n15-7857-20260330_070726.csv") diff --git a/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_train.py b/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..657606c72fe42a23140fa2d5b48420cdc8ae4c52 --- /dev/null +++ b/syntheticSuccess/n15/bayesnet/bayesnet-n15-20260321_090444/_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/n15/bayesnet/bayesnet-n15-20260321_090444/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/n15/bayesnet/bayesnet-n15-20260321_090444/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/n15/bayesnet/bayesnet-n15-20260321_090444/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] 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CTGAN.load("/work/output-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/models_300epochs/ctgan_300epochs.pt") +total = 7857 +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 +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv") \ No newline at end of file diff --git a/syntheticSuccess/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv b/syntheticSuccess/n15/ctgan/ctgan-n15-20260328_054854/ctgan-n15-7857-20260420_031238.csv new file mode 100644 index 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sha256:cec46647dc60a503f07f525a3a67a7cf822fdf9b06cc421b8b008df747951f33 +size 1778883 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_sample.py b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..fa8cd60477973b86510cf3fe7ba7f18f0c538508 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_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 7857 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.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/n15/tabddpm/tabddpm-n15-20260321_161922/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/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/n15/tabddpm/tabddpm-n15-20260321_161922/tabddpm-n15-7857-20260425_075524.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/tabddpm-n15-7857-20260425_075524.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_train.py b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e9c5b07506a67a6080eb6163cd78251a32d3aa --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/_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/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e632960a401498205d631bc4f41b5f17349bc78c --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +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/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260424_211539.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +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 = 7857 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_033100.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +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 = 7857 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba6fb8d977b5f7732a27bacc05c92cae3c5cfb0d --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/config_sample_20260425_075524.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/output" +real_data_path = "/work/output-SpecializedModels/n15/tabddpm/tabddpm-n15-20260321_161922/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 85 +num_classes = 3 +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 = 7857 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..90c4952db70d0bf30260ca9343b0fa5d8cc98dcf --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e71ac4ca2c52934779fc57e6e63e996f30737a5ec4d7a6fe1a0cd69ccb749181 +size 243912 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3caea3071878e8e38ca12d67bebf2d563f002643 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4fbcd1472bb6d33c0c9a6a3a1768ed9e01d1f3bdf07ca6950663355d8d332e0 +size 1948664 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_val.npy b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b3163a93f477421c11449aca7854c270e35e6165 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_cat_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee9c3d6a1ff1adab8a4850405fe6a1781d7a39870adf76e353997e6cb7435d96 +size 243664 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_num_test.npy b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..d61232f977eee08ec292cd2697af3f480cae3e67 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_num_test.npy @@ -0,0 +1,3 @@ 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b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:379199ab9c0b43e3a39646feaab1606f53c9c41aaf5d6c47057e4268f28f71cd +size 212240 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/info.json b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ab60041aa7a38054e04c3c7b8ee07d0f70f386b0 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/info.json @@ -0,0 +1,188 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 54, + "n_cat_features": 31, + "train_size": 7857, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53 + ], + "cat_col_idx": [ + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84 + ], + "target_col_idx": [ + 85 + ], + "column_names": [ + "MOSTYPE", + "MAANTHUI", + "MGEMOMV", + "MGEMLEEF", + "MOSHOOFD", + "MGODRK", + "MGODPR", + "MGODOV", + "MGODGE", + "MRELGE", + "MRELSA", + "MRELOV", + "MFALLEEN", + "MFGEKIND", + "MFWEKIND", + "MOPLHOOG", + "MOPLMIDD", + "MOPLLAAG", + "MBERHOOG", + "MBERZELF", + "MBERBOER", + "MBERMIDD", + "MBERARBG", + "MBERARBO", + "MSKA", + "MSKB1", + "MSKB2", + "MSKC", + "MSKD", + "MHHUUR", + "MHKOOP", + "MAUT1", + "MAUT2", + "MAUT0", + "MZFONDS", + "MZPART", + "MINKM30", + "MINK3045", + "MINK4575", + "MINK7512", + "MINK123M", + "MINKGEM", + "MKOOPKLA", + "PWAPART", + "PWALAND", + "PPERSAUT", + "PMOTSCO", + "PTRACTOR", + "PBROM", + "PLEVEN", + "PBRAND", + "APERSAUT", + "ALEVEN", + "ABRAND", + "PWABEDR", + "PBESAUT", + "PVRAAUT", + "PAANHANG", + "PWERKT", + "PPERSONG", + "PGEZONG", + "PWAOREG", + "PZEILPL", + "PPLEZIER", + "PFIETS", + "PINBOED", + "PBYSTAND", + "AWAPART", + "AWABEDR", + "AWALAND", + "ABESAUT", + "AMOTSCO", + "AVRAAUT", + "AAANHANG", + "ATRACTOR", + "AWERKT", + "ABROM", + "APERSONG", + "AGEZONG", + "AWAOREG", + "AZEILPL", + "APLEZIER", + "AFIETS", + "AINBOED", + "CARAVAN", + "ABYSTAND" + ], + "num_classes": 3 +} \ No newline at end of file diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/y_test.npy b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8b622c28d03c84175424f79baff670728ea24b6d --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/data/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:a3a0a56c98c8da36e6878d5e930b366d8def656290303d0bff3caef19b797e2b +size 2714323 diff --git a/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/train_20260321_161923.log b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/train_20260321_161923.log new file mode 100644 index 0000000000000000000000000000000000000000..43084141aee218165a69225482b4f86f79b47040 --- /dev/null +++ b/syntheticSuccess/n15/tabddpm/tabddpm-n15-20260321_161922/train_20260321_161923.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:977e0c1b76d86d2ce39d7b519e2aa4a438243e3a802791f2209df041b409ba60 +size 1239 diff --git a/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/_tabpfgen_generate.py b/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..527acc2a474b9175bc3a64a582d431fddce82c5f --- /dev/null +++ b/syntheticSuccess/n15/tabpfgen/n15-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/n15/staged/public/train.csv") +target_col = "ABYSTAND" + +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 7857 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=7857) + +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/n15/tabpfgen-n15-7857-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/tabpfgen-n15-7857-20260422_070321.csv") diff --git a/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/gen_20260422_070321.log b/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/gen_20260422_070321.log new file mode 100644 index 0000000000000000000000000000000000000000..5a2b5f3bae017b69dc3cb7fe7b221d466a18e275 --- /dev/null +++ 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"/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n15/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv b/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv new file mode 100644 index 0000000000000000000000000000000000000000..93da6e7ff37ffed8a8cf3b463f291ec29e1097c8 --- /dev/null +++ b/syntheticSuccess/n15/tabpfgen/n15-migrated-20260422_183752/tabpfgen-n15-7857-20260422_070321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb1d525999ae8eba5a490a20278c58d58c18f1250533667614da2496d88d1444 +size 7989307 diff --git a/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py b/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c6343b43581fc0d8f86b3ca39edfebac74884019 --- /dev/null +++ b/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916" +dataname = "tabsyn_n15" +output_csv = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916/tabsyn-n15-7857-20260426_224314.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 7857 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/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py b/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..70663281b099c19b06ffee7aab55f11aa091491f --- /dev/null +++ b/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n15/tabsyn/tabsyn-n15-20260426_220916" +dataname = "tabsyn_n15" +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 = 1000 +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/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/data/tabsyn_n15/X_cat_test.npy b/syntheticSuccess/n15/tabsyn/tabsyn-n15-20260426_220916/data/tabsyn_n15/X_cat_test.npy new file mode 100644 index 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b/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..05577231c3cc7cd5cc7a251b66aa9059849917e7 --- /dev/null +++ b/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/models_300epochs/tvae_300epochs.pt") +samples = model.sample(7857) +samples.to_csv("/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae-n15-7857-20260330_070643.csv", index=False) +print(f"[TVAE] Generated 7857 rows -> /work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae-n15-7857-20260330_070643.csv") diff --git a/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/_tvae_train.py b/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..998d29b50b5569c1fe9712139c90e2450dc7d5dc --- /dev/null +++ b/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/_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/n15/n15-train.csv" +meta_path = "/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n15/tvae/tvae-n15-20260320_080449/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/syntheticSuccess/n15/tvae/tvae-n15-20260320_080449/gen_20260320_081401.log 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"continuous" + }, + { + "name": "PWAPART", + "type": "continuous" + }, + { + "name": "PWABEDR", + "type": "categorical" + }, + { + "name": "PWALAND", + "type": "continuous" + }, + { + "name": "PPERSAUT", + "type": "continuous" + }, + { + "name": "PBESAUT", + "type": "categorical" + }, + { + "name": "PMOTSCO", + "type": "continuous" + }, + { + "name": "PVRAAUT", + "type": "categorical" + }, + { + "name": "PAANHANG", + "type": "categorical" + }, + { + "name": "PTRACTOR", + "type": "continuous" + }, + { + "name": "PWERKT", + "type": "categorical" + }, + { + "name": "PBROM", + "type": "continuous" + }, + { + "name": "PLEVEN", + "type": "continuous" + }, + { + "name": "PPERSONG", + "type": "categorical" + }, + { + "name": "PGEZONG", + "type": "categorical" + }, + { + "name": "PWAOREG", + "type": "categorical" + }, + { + "name": "PBRAND", + "type": "continuous" + }, + { + "name": "PZEILPL", + "type": "categorical" + }, + { + "name": "PPLEZIER", + "type": "categorical" + }, + { + "name": "PFIETS", + "type": "categorical" + }, + { + "name": "PINBOED", + "type": "categorical" + }, + { + "name": "PBYSTAND", + "type": "categorical" + }, + { + "name": "AWAPART", + "type": "categorical" + }, + { + "name": "AWABEDR", + "type": "categorical" + }, + { + "name": "AWALAND", + "type": "categorical" + }, + { + "name": "APERSAUT", + "type": "continuous" + }, + { + "name": "ABESAUT", + "type": "categorical" + }, + { + "name": "AMOTSCO", + "type": "categorical" + }, + { + "name": "AVRAAUT", + "type": "categorical" + }, + { + "name": "AAANHANG", + "type": "categorical" + }, + { + "name": "ATRACTOR", + "type": "categorical" + }, + { + "name": "AWERKT", + "type": "categorical" + }, + { + "name": "ABROM", + "type": "categorical" + }, + { + "name": "ALEVEN", + "type": "continuous" + }, + { + "name": "APERSONG", + "type": "categorical" + }, + { + "name": "AGEZONG", + "type": "categorical" + }, + { + "name": "AWAOREG", + "type": "categorical" + }, + { + "name": "ABRAND", + "type": "continuous" + }, + { + "name": "AZEILPL", + "type": "categorical" + }, + { + "name": "APLEZIER", + "type": "categorical" + }, + { + "name": "AFIETS", + "type": "categorical" + }, + { + "name": "AINBOED", + "type": "categorical" + }, + { + "name": "ABYSTAND", + "type": "categorical" + }, + { + "name": "CARAVAN", + "type": "categorical" + } + ] +} \ No newline at end of file