diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_generate.py b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..90dc9f0c9bd734ca23125dd20adfed29c8b31b8f --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/c4/arf/arf-c4-20260321_060712/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=2557) +syn.to_csv("/work/output-SpecializedModels/c4/arf/arf-c4-20260321_060712/arf-c4-2557-20260330_065255.csv", index=False) +print(f"[ARF] Generated 2557 rows -> /work/output-SpecializedModels/c4/arf/arf-c4-20260321_060712/arf-c4-2557-20260330_065255.csv") diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_train.py b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..de66e0ddf719eac5cee2d5044a2b78d08f3d6d80 --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/c4/arf/arf-c4-20260321_060712/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/c4/arf/arf-c4-20260321_060712/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c4/arf/arf-c4-20260321_060712/arf_model.pkl") diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-1000-20260321_061017.csv b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-1000-20260321_061017.csv new file mode 100644 index 0000000000000000000000000000000000000000..a59a7d13d0298c17686f29a83b84ab158e5e2999 --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-1000-20260321_061017.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b17d358c8fcbaa433b26b12710e619e8688ee16ff495267ab8b3641f7335593 +size 77146 diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-2557-20260330_065255.csv b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-2557-20260330_065255.csv new file mode 100644 index 0000000000000000000000000000000000000000..a70623d8b56a5300a8858384acb20cf29ba3a5ec --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf-c4-2557-20260330_065255.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1adf4f4ae03060a686377fe9c6bc676919d914899c008755f45551f70cb90453 +size 197000 diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf_model.pkl b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..52bca137fa641a602ec3459a8ff02ba93e43b84b --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:816f0fad792569f049afb98197f2bdce21c43882ef4e3ab857f007452ebd9770 +size 28941457 diff --git a/syntheticSuccess/c4/arf/arf-c4-20260321_060712/gen_20260321_061017.log b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/gen_20260321_061017.log new file mode 100644 index 0000000000000000000000000000000000000000..267b5ee1925a408d113ee2a2e87062c46bfca092 --- /dev/null +++ b/syntheticSuccess/c4/arf/arf-c4-20260321_060712/gen_20260321_061017.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd98534cfd29eb592a2300fa60032e03f0995e7e6184b7a50b3414b48ed4c731 +size 11814 diff --git 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0000000000000000000000000000000000000000..9a2315361628bb242e8b991b46485073bd2cf4e7 --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/_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/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=2557).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/c4/bayesnet/bayesnet-c4-20260321_060849/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/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-2557-20260330_065256.csv", index=False) +print(f"[BayesNet] Generated 2557 rows -> /work/output-SpecializedModels/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-2557-20260330_065256.csv") diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/_bayesnet_train.py b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5ee1616fd4f177f76399777b559b39d73d1cee18 --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/_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/c4/bayesnet/bayesnet-c4-20260321_060849/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/c4/bayesnet/bayesnet-c4-20260321_060849/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/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl") diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-1000-20260321_060932.csv b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-1000-20260321_060932.csv new file mode 100644 index 0000000000000000000000000000000000000000..80f4037a41d860931ac3be40024165b3e184a6b6 --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-1000-20260321_060932.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5055aeebf8252ccc4062fffa7ed13a5c38f264c9c0fc0abd868a9a285057d899 +size 77172 diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-2557-20260330_065256.csv b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-2557-20260330_065256.csv new file mode 100644 index 0000000000000000000000000000000000000000..b0d78d0bde545b384143e3eaa4abbd23c40a2dd2 --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet-c4-2557-20260330_065256.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea71a0c9deb6a91b577ffbad48378862194bf63ad6808838e5fd60437cb23d86 +size 197012 diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..96591bfe76a87a8736552e6de77c91199d771af7 --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8648b82274d740e0c8678925bb67fffff54738926a36229dbd371e2fe59abca6 +size 1068255 diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/const_cols.json b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git 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+ } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/public_gate/public_gate_report.json b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a014357bde8d7bd6bb925a2b91381ca954b18e5a --- /dev/null +++ b/syntheticSuccess/c4/bayesnet/bayesnet-c4-20260321_060849/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": 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+ } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/realtabformer/rtf-c4-20260328_055837/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c4/realtabformer/rtf-c4-20260328_055837/train_20260328_055837.log b/syntheticSuccess/c4/realtabformer/rtf-c4-20260328_055837/train_20260328_055837.log new file mode 100644 index 0000000000000000000000000000000000000000..14915b466ddde79c316b852f0e5d84e1d29fedb7 --- /dev/null +++ b/syntheticSuccess/c4/realtabformer/rtf-c4-20260328_055837/train_20260328_055837.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c39f248377c816580f65e7348ee51a3c133d257103650078bb32c7311504364 +size 580648 diff --git a/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_tabddpm_sample.py b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..5990eaced8fd325c52f5714e800591ddb2fe3254 --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_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 2557 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/config_sample_20260422_210909.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/c4/tabddpm/tabddpm-c4-20260422_210001" +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/c4/tabddpm/tabddpm-c4-20260422_210001/tabddpm-c4-2557-20260422_210909.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/tabddpm-c4-2557-20260422_210909.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_tabddpm_train.py b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..63183fd6c8ca883968c196cad0c700906903faf4 --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/_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/c4/tabddpm/tabddpm-c4-20260422_210001/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/config.toml b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1df7888423833bdb3098ed342e82c515b1796c3b --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/output" +real_data_path = "/work/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 36 +num_classes = 2 +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/c4/tabddpm/tabddpm-c4-20260422_210001/config_sample_20260422_210909.toml b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/config_sample_20260422_210909.toml new file mode 100644 index 0000000000000000000000000000000000000000..35c11574ebe20530ce81b4a7d56b7d8482c81117 --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/config_sample_20260422_210909.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabddpm/tabddpm-c4-20260422_210001/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/tabddpm-c4-2557-20260422_210909.csv b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/tabddpm-c4-2557-20260422_210909.csv new file mode 100644 index 0000000000000000000000000000000000000000..bf53dcee9f5070e941593bbc39cd8d7c405a6747 --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/tabddpm-c4-2557-20260422_210909.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c55d962610e1b10b3d5bb12cb6138303a4a3544a65a0347f7f2f5ff7a116c5e +size 378658 diff --git a/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/train_20260422_210001.log b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/train_20260422_210001.log new file mode 100644 index 0000000000000000000000000000000000000000..8c34815e9fb205ce21528e4f24151f0df94c1d91 --- /dev/null +++ b/syntheticSuccess/c4/tabddpm/tabddpm-c4-20260422_210001/train_20260422_210001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe54627796289f4a001c4ee041d00584b4b603a73cce021dc8a4d14eed4b8ef1 +size 863 diff --git a/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/_tabpfgen_generate.py b/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc1d94ed627e2964ce3ff74dd5149465664b7bf --- /dev/null +++ b/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/_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/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/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(2557) + +# 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_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- 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/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/tabpfgen-c4-2557-20260422_200031.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/tabpfgen-c4-2557-20260422_200031.csv") diff --git a/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/gen_20260422_200031.log b/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/gen_20260422_200031.log new file mode 100644 index 0000000000000000000000000000000000000000..bcbe0c0338edc77be0ac305e1aab98bbe9ccc367 --- /dev/null +++ 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a/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/train_20260422_200031.log b/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/train_20260422_200031.log new file mode 100644 index 0000000000000000000000000000000000000000..bb040c1a1864055ec7bd9a6f452f1ed026e2e6a5 --- /dev/null +++ b/syntheticSuccess/c4/tabpfgen/tabpfgen-c4-20260422_200030/train_20260422_200031.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43a00f579aab94c4e08f555ffed8358d65155087c010460c8f31dc0a35139f2d +size 186 diff --git a/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_tvae_generate.py b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a376cfed9abcf4a6829caca17e9b0d4d5b28adbf --- /dev/null +++ b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/c4/tvae/tvae-c4-20260321_061952/models_300epochs/tvae_300epochs.pt") +samples = model.sample(2557) +samples.to_csv("/work/output-SpecializedModels/c4/tvae/tvae-c4-20260321_061952/tvae-c4-2557-20260330_065247.csv", index=False) +print(f"[TVAE] Generated 2557 rows -> /work/output-SpecializedModels/c4/tvae/tvae-c4-20260321_061952/tvae-c4-2557-20260330_065247.csv") diff --git a/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_tvae_train.py b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c11bf49c46af924836862a78f13418420fd9997e --- /dev/null +++ b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/_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/c4/tvae/tvae-c4-20260321_061952/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/c4/tvae/tvae-c4-20260321_061952/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/c4/tvae/tvae-c4-20260321_061952/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/c4/tvae/tvae-c4-20260321_061952/gen_20260321_062111.log b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/gen_20260321_062111.log new file mode 100644 index 0000000000000000000000000000000000000000..dbb719fc828b1783ce8158b20995a82e1eeffadc --- /dev/null +++ b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/gen_20260321_062111.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd3893d37bc414cabc76d668e56a8303f143e8a0cca3d2409554a621a3c44f36 +size 126 diff --git a/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/gen_20260330_065247.log b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/gen_20260330_065247.log new file mode 100644 index 0000000000000000000000000000000000000000..03c810f4093eb0793239e0132463ceed9c94fb58 --- /dev/null +++ b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/gen_20260330_065247.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4acd016b15dc1e2b020c122cc9a45134d18a5f32d82fdbe9ca703cab0da4d5f0 +size 126 diff --git a/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/input_snapshot.json b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1deb87a4b960ae47fff6db536d921494aaa7df99 --- /dev/null +++ b/syntheticSuccess/c4/tvae/tvae-c4-20260321_061952/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c4", + "model": 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