diff --git a/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_generate.py b/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c4338f88e2215dfd796ab3b232fd3d09805baeaf --- /dev/null +++ b/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n9/arf/arf-n9-20260325_095114/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=8794) +syn.to_csv("/work/output-SpecializedModels/n9/arf/arf-n9-20260325_095114/arf-n9-8794-20260330_070327.csv", index=False) +print(f"[ARF] Generated 8794 rows -> /work/output-SpecializedModels/n9/arf/arf-n9-20260325_095114/arf-n9-8794-20260330_070327.csv") diff --git a/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_train.py b/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dff55261e3c4815e01c245193a2ffc65a21decce --- /dev/null +++ b/syntheticSuccess/n9/arf/arf-n9-20260325_095114/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n9/arf/arf-n9-20260325_095114/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/n9/arf/arf-n9-20260325_095114/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n9/arf/arf-n9-20260325_095114/arf_model.pkl") diff --git a/syntheticSuccess/n9/arf/arf-n9-20260325_095114/arf-n9-1000-20260325_095951.csv 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diff --git a/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_bayesnet_generate.py b/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..251fa9a15e7f21d7b8570585e8b73f2d172df5af --- /dev/null +++ b/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_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/n9/bayesnet/bayesnet-n9-20260321_084158/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=8794).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/n9/bayesnet/bayesnet-n9-20260321_084158/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/n9/bayesnet/bayesnet-n9-20260321_084158/bayesnet-n9-8794-20260330_070334.csv", index=False) +print(f"[BayesNet] Generated 8794 rows -> /work/output-SpecializedModels/n9/bayesnet/bayesnet-n9-20260321_084158/bayesnet-n9-8794-20260330_070334.csv") diff --git a/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_bayesnet_train.py b/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fc38031977b9a2167dc3cd6b666a064c329d871b --- /dev/null +++ b/syntheticSuccess/n9/bayesnet/bayesnet-n9-20260321_084158/_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/n9/bayesnet/bayesnet-n9-20260321_084158/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/n9/bayesnet/bayesnet-n9-20260321_084158/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/n9/bayesnet/bayesnet-n9-20260321_084158/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n9/bayesnet/bayesnet-n9-20260321_084158/bayesnet_model.pkl") diff --git 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100644 index 0000000000000000000000000000000000000000..e9e75670f4236e1f78bb0b764470e809292e294b --- /dev/null +++ b/syntheticSuccess/n9/realtabformer/rtf-n9-20260331_082817/train_20260331_082817.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b76424caaa1563be7c12a2c40e0d169ffa8d77bd69b993f95d3517d81b729fc8 +size 1953941 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_tabddpm_sample.py b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..5e22f72ade2417a443af0338e7c2e0c421e3ff53 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_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 8794 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_074644.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/n9/tabddpm/tabddpm-n9-20260321_160857/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/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/n9/tabddpm/tabddpm-n9-20260321_160857/tabddpm-n9-8794-20260425_074644.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/tabddpm-n9-8794-20260425_074644.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_tabddpm_train.py b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c86cd6ad412a94402f456d46261dad62e9e99a --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/_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/n9/tabddpm/tabddpm-n9-20260321_160857/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config.toml b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..2678d9c510ecc9101e0504c2db9da6a712c5bac3 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/output" +real_data_path = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 10 +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/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260424_210831.toml b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260424_210831.toml new file mode 100644 index 0000000000000000000000000000000000000000..3957ac678833e33a7cf7c7a5303c6a6722479117 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260424_210831.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/output" +real_data_path = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 10 +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 = 8794 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_032337.toml b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_032337.toml new file mode 100644 index 0000000000000000000000000000000000000000..3957ac678833e33a7cf7c7a5303c6a6722479117 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_032337.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/output" +real_data_path = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 10 +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 = 8794 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_074644.toml b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_074644.toml new file mode 100644 index 0000000000000000000000000000000000000000..3957ac678833e33a7cf7c7a5303c6a6722479117 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/config_sample_20260425_074644.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/output" +real_data_path = "/work/output-SpecializedModels/n9/tabddpm/tabddpm-n9-20260321_160857/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 10 +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 = 8794 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_test.npy b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..00d98a3191d0930abdf1a44dec9bfaed7de5fb4c --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5eab9516ecff27932251cde94fe173ef0f77e59c846c36f4aea2d1942980edce +size 70464 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_train.npy b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..636efa6ceac30a0236927aedc6ef0c0bb1a79efd --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c1d44c64882653114c790dc5f3ebf710f9435469b196fc0408488f33f15fef8 +size 562944 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_val.npy b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..830515d781b0f34066cd691d077b9f767752ecf8 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87803d0debb266a57200d66fd4f60552716d764eb3763d44e04eb83e6e516ae5 +size 70464 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/info.json b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..11aa378a8ae933754293351c52759daeb941c31d --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/info.json @@ -0,0 +1,49 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 16, + "n_cat_features": 0, + "train_size": 8794, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15 + ], + "cat_col_idx": [], + "target_col_idx": [ + 16 + ], + "column_names": [ + "feature_1", + "feature_2", + "feature_3", + "feature_4", + "feature_5", + "feature_6", + "feature_7", + "feature_8", + "feature_9", + "feature_10", + "feature_11", + "feature_12", + "feature_13", + "feature_14", + "feature_15", + "feature_16", + "class" + ], + "num_classes": 10 +} \ No newline at end of file diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/y_test.npy b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..871541b7bbd9be9126969b9036657a6a6860f08c --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/data/y_test.npy @@ -0,0 +1,3 @@ +version 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0000000000000000000000000000000000000000..da1d184d008884e5bf182c9d8df9989d2a569145 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/gen_20260425_074644.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54fb159c068759ba8d64ab2f64eab097febe307280fc3c0f41ce242587d2ec5b +size 189230 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/input_snapshot.json b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31b5c7a11e282dd7fd5b6db9aaeb1c6a5c9ca3d0 --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n9", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n9/n9-train.csv", + "exists": true, + "size": 440661, + "sha256": "5ecc0202bc3746d01a2a2868cb29a2450251fe27224a9f4f76fec0cde010fbdd" + }, 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https://git-lfs.github.com/spec/v1 +oid sha256:ccda2d3c49ece694a4663b9779b8963d81a6f4f779e1b79bc97faa52e3f8233e +size 1285719 diff --git a/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/train_20260321_160858.log b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/train_20260321_160858.log new file mode 100644 index 0000000000000000000000000000000000000000..aa05336f8876a20e23131eb390816ecf9fda8f4b --- /dev/null +++ b/syntheticSuccess/n9/tabddpm/tabddpm-n9-20260321_160857/train_20260321_160858.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9d8909d7414b08e79f05db7190d08728bb96d331f625e2d4e4566d19b558dc6 +size 1123 diff --git a/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/_tabpfgen_generate.py b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..099fd8bf2acf1e2389475d552f7548c296d6a1ff --- /dev/null +++ b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n9/staged/public/train.csv") +target_col = "class" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +# Encode target if categorical +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(8794) + +# Handle NaN +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +# Ensure output row count is strictly aligned with target_n. +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True) + else: + # Defensive fallback: if generator returns empty, bootstrap from training rows. + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}") +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n9/tabpfgen-n9-8794-20260422_191741.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n9/tabpfgen-n9-8794-20260422_191741.csv") diff --git a/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/gen_20260422_191741.log b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/gen_20260422_191741.log new file mode 100644 index 0000000000000000000000000000000000000000..0af785846ef35bc2f9447e6ca208bad5ce302cf9 --- /dev/null +++ b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/gen_20260422_191741.log 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a/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/tabpfgen-n9-8794-20260422_191741.csv b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/tabpfgen-n9-8794-20260422_191741.csv new file mode 100644 index 0000000000000000000000000000000000000000..efc603e90a1f8fc400d7bc9287877af134078f92 --- /dev/null +++ b/syntheticSuccess/n9/tabpfgen/n9-migrated-20260422_193053/tabpfgen-n9-8794-20260422_191741.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa0f9cadc9f40ba9b74f9301cbb02dba87d2e146116513e8840e9a4bd232f0cb +size 1532403 diff --git a/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_generate.py b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c48c4c9a85afd2791df4f7a3ebc2e23423a14e53 --- /dev/null +++ b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/models_300epochs/tvae_300epochs.pt") +samples = model.sample(8794) +samples.to_csv("/work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/tvae-n9-8794-20260330_070320.csv", index=False) +print(f"[TVAE] Generated 8794 rows -> /work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/tvae-n9-8794-20260330_070320.csv") diff --git a/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_train.py b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0f765e9d2913557d03292f7fdc1ea8af7a135bd8 --- /dev/null +++ b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n9/tvae/tvae-n9-20260328_053408/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/n9/tvae/tvae-n9-20260328_053408/gen_20260328_054231.log b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/gen_20260328_054231.log new file mode 100644 index 0000000000000000000000000000000000000000..f38380ddba6326aa8a714b73f160ecce9c5eb54b --- /dev/null +++ b/syntheticSuccess/n9/tvae/tvae-n9-20260328_053408/gen_20260328_054231.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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