diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_generate.py b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..30e563931feb607ac927cfb7ec5981977a8971bb --- /dev/null +++ b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=1600) +syn.to_csv("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf-m10-1600-20260330_065838.csv", index=False) +print(f"[ARF] Generated 1600 rows -> /work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf-m10-1600-20260330_065838.csv") diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_train.py b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..96c6744992d5177c9debf16ed91ffa5513c9d96b --- /dev/null +++ b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/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/m10/arf/arf-m10-20260325_034752/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf_model.pkl") diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/arf-m10-1000-20260325_034828.csv 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d112b674dd456a5bdc2c17ad698087f2df416abb02166d3a29a5873900ca5c0 +size 12392 diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/train.csv b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..9e8d03429b530f020698bd6ea7f4d95ffe614067 --- /dev/null +++ b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de150b4643e0b79637165cc914e9fcfb6de70087020ecc8330be516c425aa579 +size 97926 diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/val.csv b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..f49ab3dff7a74beab94f4d6ffd5258b4b7d101c8 --- /dev/null +++ b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cc0732d8711d262fd1bc7e0157e67872bcc08b0ac05ae2c75bd2773560e29fd +size 12423 diff --git a/syntheticSuccess/m10/arf/arf-m10-20260325_034752/train_20260325_034752.log b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/train_20260325_034752.log new file mode 100644 index 0000000000000000000000000000000000000000..9245eae3bed0fad2c1f96ac1e70cbd90e46b43c1 --- /dev/null +++ b/syntheticSuccess/m10/arf/arf-m10-20260325_034752/train_20260325_034752.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd37ad7fc99686c357032b1fedf7fd040e3e1129015a5859a050b02528b8fdba +size 215 diff --git a/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_generate.py b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..50dd4aeb28bb2d71fc7e1822fd60ebc584b30ab9 --- /dev/null +++ b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_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/m10/bayesnet/bayesnet-m10-20260321_080304/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/m10/bayesnet/bayesnet-m10-20260321_080304/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/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet-m10-1600-20260330_065844.csv", index=False) +print(f"[BayesNet] Generated 1600 rows -> /work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet-m10-1600-20260330_065844.csv") diff --git a/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_train.py b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ecfefb0255d876dff561e47095f9510b705b2883 --- /dev/null +++ b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_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/m10/bayesnet/bayesnet-m10-20260321_080304/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/m10/bayesnet/bayesnet-m10-20260321_080304/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/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl") diff --git a/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet-m10-1000-20260321_080356.csv 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+{} \ No newline at end of file diff --git a/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260321_080356.log b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260321_080356.log new file mode 100644 index 0000000000000000000000000000000000000000..10a0131807f1fa18a04e8b9b20955cf4ee78da3d --- /dev/null +++ b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260321_080356.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cdcb8c41755b369871da850c1e83bee0ad1613f370e24b7f873c8313e494865b +size 235 diff --git a/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260330_065844.log b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260330_065844.log new file mode 100644 index 0000000000000000000000000000000000000000..d3e946c798af56efbec73aa683852537035092c7 --- /dev/null +++ b/syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/gen_20260330_065844.log @@ -0,0 +1,3 @@ +version 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/realtabformer/rtf-m10-20260328_112649/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/realtabformer/rtf-m10-20260328_112649/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/realtabformer/rtf-m10-20260328_112649/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m10/realtabformer/rtf-m10-20260328_112649/train_20260328_112650.log b/syntheticSuccess/m10/realtabformer/rtf-m10-20260328_112649/train_20260328_112650.log new file mode 100644 index 0000000000000000000000000000000000000000..578bd2a8acbdd8f849fa85745138975207c1d2ce --- /dev/null +++ b/syntheticSuccess/m10/realtabformer/rtf-m10-20260328_112649/train_20260328_112650.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3983dad2166430a5c296f78a45debebb7ae11b15950280d1ff5228873f33cecc +size 368057 diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_tabddpm_sample.py b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..cc6cd87742657f898f5d6ec8b90f503db497447e --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_tabddpm_sample.py @@ -0,0 +1,67 @@ +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/m10/tabddpm/tabddpm-m10-20260424_033725/config_sample_20260424_034154.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV +work_dir = "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725" +info_path = os.path.join(work_dir, "data", "info.json") +with open(info_path) as f: + info = json.load(f) + +output_dir = os.path.join(work_dir, "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/m10/tabddpm/tabddpm-m10-20260424_033725/tabddpm-m10-1600-20260424_034154.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/tabddpm-m10-1600-20260424_034154.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_tabddpm_train.py b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..18bfb2c931a7a9c04f544e13f417952c803e25ee --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/_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/m10/tabddpm/tabddpm-m10-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/config.toml b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..799389655516a97b5ccd090acc2bbcd00e02353a --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 20 +num_classes = 0 +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/m10/tabddpm/tabddpm-m10-20260424_033725/config_sample_20260424_034154.toml b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/config_sample_20260424_034154.toml new file mode 100644 index 0000000000000000000000000000000000000000..c3a20842794a3ad7ccdfbd70f5b8bfe7c5dd2e33 --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/config_sample_20260424_034154.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m10/tabddpm/tabddpm-m10-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 20 +num_classes = 0 +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/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_cat_train.npy b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b77e20e3dfdb15817a16c7aea98911bddbd7c5ed --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:255aa1664e1ca43dc97e8f0c35078c241a07403b16a120f28b09e057cb7b0fa7 +size 76928 diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_num_train.npy b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..96dbc059f6f531a4d72c15b1d6e22f94ba4821d9 --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b56d40cd4b61a0256457bd350e08cbe081449c6674d88986211ad4ea5e93ff8 +size 89728 diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/info.json b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..68d326183c4251b4f54dbecf7f3857c2d62bcfae --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/data/info.json @@ -0,0 +1,57 @@ +{ + "name": "benchmark_dataset", + "task_type": "regression", + "n_num_features": 14, + "n_cat_features": 6, + "train_size": 1600, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, 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sha256:65fe3c4873e09d20789594d06a9e19777a390503cda60f7542111be40f043b96 +size 174180 diff --git a/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/train_20260424_033725.log b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/train_20260424_033725.log new file mode 100644 index 0000000000000000000000000000000000000000..6fba4fe03033181b1c92a67df8f723e3cfd60d0f --- /dev/null +++ b/syntheticSuccess/m10/tabddpm/tabddpm-m10-20260424_033725/train_20260424_033725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23a8a9687704ede5bf349c73b750f4a2150eb9f8939e8e6481abfbfbab46c2ed +size 8898 diff --git a/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/_tabpfgen_generate.py b/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..df82ac2f8bc81cd62a93ea6fc8436b95d2c302f8 --- /dev/null +++ b/syntheticSuccess/m10/tabpfgen/m10-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/m10/staged/public/train.csv") +target_col = "int_memory" + +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/m10/tabpfgen-m10-1600-20260422_070320.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m10/tabpfgen-m10-1600-20260422_070320.csv") diff --git a/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/gen_20260422_070320.log b/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/gen_20260422_070320.log new file mode 100644 index 0000000000000000000000000000000000000000..ec65892af9469834019398cffdf0d96d335e7535 --- /dev/null +++ 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a/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/tabpfgen-m10-1600-20260422_070320.csv b/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/tabpfgen-m10-1600-20260422_070320.csv new file mode 100644 index 0000000000000000000000000000000000000000..6dda7418e76e4a37f7e9d79d6a3cbe5557fde4e2 --- /dev/null +++ b/syntheticSuccess/m10/tabpfgen/m10-migrated-20260422_183752/tabpfgen-m10-1600-20260422_070320.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c92c5ecbf6d28ba7726512f4602e83fba9e4e32e1a1d46ed85a1ae7794fb23fa +size 363504 diff --git a/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_sample.py b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..ad7d96bf43a410627c0ccbdd202ca93a5f8c5fc7 --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m10/tabsyn/tabsyn-m10-20260421_021428" +dataname = "tabsyn_m10" +output_csv = "/work/output-SpecializedModels/m10/tabsyn/tabsyn-m10-20260421_021428/tabsyn-m10-1600-20260421_031134.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/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_train.py b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b0d1e5b30418cf911dfcfa3f424d11626d23c56d --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m10/tabsyn/tabsyn-m10-20260421_021428" +dataname = "tabsyn_m10" +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") +_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/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/data/tabsyn_m10/X_cat_test.npy b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/data/tabsyn_m10/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7d6a677930a23868e57a07f5fe0c7af27bc7d491 --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/data/tabsyn_m10/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/tabsyn/tabsyn-m10-20260421_021428/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/real.csv b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..119ae05326f32eefb41c6e8d3b754f2dfbe8f5d3 --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50d8e1bcfb8999abf0212428929bf582554e436e452eef9bc680c23bceb3066e +size 110180 diff --git a/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/test.csv b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ff791c5483466d091be4c24bb7578988089a7eb --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/synthetic/tabsyn_m10/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cfbf342f5357760914d0f03e6943d6aa88ba2b218ddc06f374b730a290805f25 +size 12392 diff --git a/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/tabsyn-m10-1600-20260421_031134.csv b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/tabsyn-m10-1600-20260421_031134.csv new file mode 100644 index 0000000000000000000000000000000000000000..cd0039a64cd1ef531750f451b9a95c545d81f668 --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/tabsyn-m10-1600-20260421_031134.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:633f3b793d91c4b7a80f62ea51ff771e1a904301757cd11f9f06b27e5b52b7b9 +size 214928 diff --git a/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/train_20260421_021428.log b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/train_20260421_021428.log new file mode 100644 index 0000000000000000000000000000000000000000..88ed4de30ea0b2ac9d27a8c57b4828749b722b38 --- /dev/null +++ b/syntheticSuccess/m10/tabsyn/tabsyn-m10-20260421_021428/train_20260421_021428.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0ccd3c4f29e1bdaee5c26db5564a7f000b5613d2cdc3c6aebac00cd7b1635b0 +size 2445493 diff --git a/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_tvae_generate.py b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..47e32879a6c5f1a7d54975c59f3a4da26e1cf117 --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m10/tvae/tvae-m10-20260328_052616/models_300epochs/tvae_300epochs.pt") +samples = model.sample(1600) +samples.to_csv("/work/output-SpecializedModels/m10/tvae/tvae-m10-20260328_052616/tvae-m10-1600-20260330_065830.csv", index=False) +print(f"[TVAE] Generated 1600 rows -> /work/output-SpecializedModels/m10/tvae/tvae-m10-20260328_052616/tvae-m10-1600-20260330_065830.csv") diff --git a/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_tvae_train.py b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..78f6405ac5e9fe86671c8e3c0b5c8c3c19e1227a --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/_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/m10/tvae/tvae-m10-20260328_052616/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m10/tvae/tvae-m10-20260328_052616/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m10/tvae/tvae-m10-20260328_052616/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/m10/tvae/tvae-m10-20260328_052616/gen_20260328_053025.log b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/gen_20260328_053025.log new file mode 100644 index 0000000000000000000000000000000000000000..5329047e03ab3b70bb756192f541699a8afbafcd --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/gen_20260328_053025.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cba2e2ee48153cac22479ef7c4c5d3e9e3cd41e950d266038e82e0d42189bd3 +size 129 diff --git a/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/gen_20260330_065830.log b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/gen_20260330_065830.log new file mode 100644 index 0000000000000000000000000000000000000000..e96fe2a1254781dbbe6825bbff2e6b00df1dee54 --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/gen_20260330_065830.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f932c2de378f6ebb37ff6c154887a8577292fa041d1ed2ef7186e8e3c85480e +size 129 diff --git a/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/input_snapshot.json b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..d6f7b1aec0d2e7c407735a1953de5713f3d8439b --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m10", + "model": "tvae", + "inputs": { + "train_csv": { + "path": 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b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..90de28296734176807318c488517fd1b4be31823 --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f19a402adf885a5208d333f34a508159ee56505b570471cd283841776aee0460 +size 861676 diff --git a/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e19e392a6194d099623be567420356ea70d43783 --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,429 @@ +{ + "dataset_id": "m10", + "target_column": "int_memory", + "task_type": "regression", + 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0000000000000000000000000000000000000000..fd363da3cda810a83c82e712cb31f57a694910bc --- /dev/null +++ b/syntheticSuccess/m10/tvae/tvae-m10-20260328_052616/tvae_metadata.json @@ -0,0 +1,88 @@ +{ + "columns": [ + { + "name": "battery_power", + "type": "continuous" + }, + { + "name": "blue", + "type": "categorical" + }, + { + "name": "clock_speed", + "type": "continuous" + }, + { + "name": "dual_sim", + "type": "categorical" + }, + { + "name": "fc", + "type": "continuous" + }, + { + "name": "four_g", + "type": "categorical" + }, + { + "name": "int_memory", + "type": "continuous" + }, + { + "name": "m_dep", + "type": "continuous" + }, + { + "name": "mobile_wt", + "type": "continuous" + }, + { + "name": "n_cores", + "type": "continuous" + }, + { + "name": "pc", + "type": "continuous" + }, + { + "name": "px_height", + "type": "continuous" + }, + { + "name": "px_width", + "type": "continuous" + }, + { + "name": "ram", + "type": "continuous" + }, + { + "name": "sc_h", + "type": "continuous" + }, + { + "name": "sc_w", + "type": "continuous" + }, + { + "name": "talk_time", + "type": "continuous" + }, + { + "name": "three_g", + "type": "categorical" + }, + { + "name": "touch_screen", + "type": "categorical" + }, + { + "name": "wifi", + "type": "categorical" + }, + { + "name": "price_range", + "type": "continuous" + } + ] +} \ No newline at end of file