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+SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/staged/tvae/adapter_transforms_applied.json filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/staged/tvae/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/tvae-n10-1000-20260328_104438.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/tvae-n10-10888-20260330_070355.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/tvae_metadata.json filter=lfs diff=lfs merge=lfs -text diff --git a/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_generate.py b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..35cf5dd505b3910545b5b6d4b1b25cb6bcebdcba --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=10888) +syn.to_csv("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf-n10-10888-20260330_070413.csv", index=False) +print(f"[ARF] Generated 10888 rows -> /work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf-n10-10888-20260330_070413.csv") diff --git a/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_train.py b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1fe42c916387f57835959cfffaef4ba422abacc9 --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/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/n10/arf/arf-n10-20260325_095958/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n10/arf/arf-n10-20260325_095958/arf_model.pkl") diff --git a/SynthData0523/main/n10/arf/arf-n10-20260325_095958/arf-n10-1000-20260325_101108.csv b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/arf-n10-1000-20260325_101108.csv new file mode 100644 index 0000000000000000000000000000000000000000..1e8b52ab6bca6d05356d659826747cf361be405b --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/arf-n10-1000-20260325_101108.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:41b95c761c6d3260e4d2866364567a9258401fc4bd1ec42393c683e68fcd85b4 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0000000000000000000000000000000000000000..90e6e0252dad22e8f286734580ec1c999b8293cf --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d264cc48e17ff398df7edeb6819e5f66b3dbf14935184253bf21ac0cbb1d84e +size 3036991 diff --git a/SynthData0523/main/n10/arf/arf-n10-20260325_095958/staged/public/val.csv b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..933f64ac2f7ad15b50d3fbbea8fc861f9beec864 --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:158fe1f6049fb53cca70e222d76ff7901731586869da103df04b314ad2f195e1 +size 379595 diff --git a/SynthData0523/main/n10/arf/arf-n10-20260325_095958/train_20260325_095959.log b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/train_20260325_095959.log new file mode 100644 index 0000000000000000000000000000000000000000..602596c6d90324f9f9947ca2cea4ae4f4b7b0de9 --- /dev/null +++ b/SynthData0523/main/n10/arf/arf-n10-20260325_095958/train_20260325_095959.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15cc0432eca43479405cd4c93839ee99e027e94883db094169f983f4b4ae73ef +size 467 diff --git a/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_generate.py b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bea00df16d8124963ae1f82fc3c97586afa79259 --- /dev/null +++ b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_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/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=10888).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/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/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-10888-20260330_070414.csv", index=False) +print(f"[BayesNet] Generated 10888 rows -> /work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-10888-20260330_070414.csv") diff --git a/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_train.py b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c0be361145d4e818dd11350677a9c8ec48de3c9b --- /dev/null +++ b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/_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/n10/bayesnet/bayesnet-n10-20260321_084344/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/n10/bayesnet/bayesnet-n10-20260321_084344/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/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet_model.pkl") diff --git a/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-1000-20260321_084907.csv b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-1000-20260321_084907.csv new file mode 100644 index 0000000000000000000000000000000000000000..081b5f7efeed568e02994043e3a40e892711e333 --- /dev/null +++ b/SynthData0523/main/n10/bayesnet/bayesnet-n10-20260321_084344/bayesnet-n10-1000-20260321_084907.csv @@ -0,0 +1,3 @@ +version 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b/SynthData0523/main/n10/ctgan/ctgan-n10-20260328_053637/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..90e6e0252dad22e8f286734580ec1c999b8293cf --- /dev/null +++ b/SynthData0523/main/n10/ctgan/ctgan-n10-20260328_053637/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d264cc48e17ff398df7edeb6819e5f66b3dbf14935184253bf21ac0cbb1d84e +size 3036991 diff --git a/SynthData0523/main/n10/ctgan/ctgan-n10-20260328_053637/staged/public/val.csv b/SynthData0523/main/n10/ctgan/ctgan-n10-20260328_053637/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..933f64ac2f7ad15b50d3fbbea8fc861f9beec864 --- /dev/null +++ b/SynthData0523/main/n10/ctgan/ctgan-n10-20260328_053637/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:158fe1f6049fb53cca70e222d76ff7901731586869da103df04b314ad2f195e1 +size 379595 diff --git a/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_X_host.npy b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..4512a9ced006055e662b62593fc864edcb9107b2 --- /dev/null +++ b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3947eeef27a6612459d1d353a47ae22f8e0515ff5fa222136469de2a9acc6a96 +size 740512 diff --git a/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_gen.py b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..acbb9003e0262dab983bd21c5cb052572cccbff9 --- /dev/null +++ b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-SpecializedModels/n10/forestdiffusion/forest-n10-20260430_052420/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(10888)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-SpecializedModels/n10/forestdiffusion/forest-n10-20260430_052420/forest-n10-10888-20260430_132532.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_meta_host.json b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..fd3f0b9f99f06a783fa7f027ce8db15b7c85ba04 --- /dev/null +++ b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea2867b5fc46f0a7890cd7137b9bd657ee50d042c3d09fe8f2a0365a4db62693 +size 282 diff --git a/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_train.py b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..75273182a1d760c921f285610702768633147f48 --- /dev/null +++ b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/_fd_train.py @@ -0,0 +1,20 @@ + +import shutil, json +shutil.copy(r'/work/output-SpecializedModels/n10/forestdiffusion/forest-n10-20260430_052420/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-SpecializedModels/n10/forestdiffusion/forest-n10-20260430_052420/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-SpecializedModels/n10/forestdiffusion/forest-n10-20260430_052420/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/forest-n10-10888-20260430_132532.csv b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/forest-n10-10888-20260430_132532.csv new file mode 100644 index 0000000000000000000000000000000000000000..edb27d0d3b915ef762f41cafa69b25c533a9f4db --- /dev/null +++ b/SynthData0523/main/n10/forestdiffusion/forest-n10-20260430_052420/forest-n10-10888-20260430_132532.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c6b7d7ed75a1d675d959e8fa2d7365eaf9926243cf70ae7d1047a97c3ffc576 +size 3314416 diff --git 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b/SynthData0523/main/n10/realtabformer/rtf-n10-20260331_095307/train_20260331_095308.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9cafe2037f9d1b880ceec6283b3d69d6688f38c9de1a0fc6039fad977011d0ce +size 2442608 diff --git a/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_gen.py b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..387d97b2d7af7c6982cf69ec7eaa65bd2d5393ba --- /dev/null +++ b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/n10/tabbyflow/tabbyflow-n10-20260424_012902/tabular_bundle/pipeline_ds" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_tabbyflow-n10-20260424_012902", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_tabbyflow-n10-20260424_012902/model_500.pt", + "--num_samples_to_generate", str(int(10888)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_tabbyflow-n10-20260424_012902") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-SpecializedModels/n10/tabbyflow/tabbyflow-n10-20260424_012902/tabbyflow-n10-10888-20260424_013059.csv") diff --git a/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_train.py b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8eff3c828ed6fb22c2fa8e9d74e46acbf8ddf605 --- /dev/null +++ b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/n10/tabbyflow/tabbyflow-n10-20260424_012902/tabular_bundle/pipeline_ds" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_tabbyflow-n10-20260424_012902", +]) diff --git a/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/input_snapshot.json b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ac4e1858088817d3d54151f11d1a72645ab5f951 --- /dev/null +++ b/SynthData0523/main/n10/tabbyflow/tabbyflow-n10-20260424_012902/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/_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 10888 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/config_sample_20260425_074712.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/n10/tabddpm/tabddpm-n10-20260321_161119/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/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/n10/tabddpm/tabddpm-n10-20260321_161119/tabddpm-n10-10888-20260425_074712.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/tabddpm-n10-10888-20260425_074712.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/_tabddpm_train.py b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..df7fa97ff9dae4cac0972fe0f467aa349352cd21 --- /dev/null +++ b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/_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/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..92bf7eed60544ea3de561771b7523fc27404202d --- /dev/null +++ b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/config.toml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/train_20260321_161120.log new file mode 100644 index 0000000000000000000000000000000000000000..eefd1dbacd10a932d3a7c8afd3c11bf59864643a --- /dev/null +++ b/SynthData0523/main/n10/tabddpm/tabddpm-n10-20260321_161119/train_20260321_161120.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0985f9c6e5584ac9388816de7ada38989999b1be6420b00b487faaca6bbc26f +size 1124 diff --git a/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_gen.py b/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..30ae2282ad81c1487e04f795dd7c554ecff70a5f --- /dev/null +++ b/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n10" +src = r"/work/output-Benchmark-trainonly-v1/n10/tabdiff/tabdiff-n10-20260501_183727/tabular_bundle/pipeline_n10" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n10/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(10888)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n10/tabdiff/tabdiff-n10-20260501_183727/tabdiff-n10-10888-20260501_184256.csv") diff --git a/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_train.py b/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..66589ec0c1722dd398f4c625d9e368b070772b1d --- /dev/null +++ b/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n10" +src = r"/work/output-Benchmark-trainonly-v1/n10/tabdiff/tabdiff-n10-20260501_183727/tabular_bundle/pipeline_n10" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/gen_20260501_184256.log b/SynthData0523/main/n10/tabdiff/tabdiff-n10-20260501_183727/gen_20260501_184256.log new file mode 100644 index 0000000000000000000000000000000000000000..42aff285ad83426a3280dcd8208cbda44099ebaf --- /dev/null +++ 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b/SynthData0523/main/n10/tabpfgen/n10-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/n10/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(10888) + +# 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/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n10/tabpfgen-n10-10888-20260422_191742.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n10/tabpfgen-n10-10888-20260422_191742.csv") diff --git a/SynthData0523/main/n10/tabpfgen/n10-migrated-20260422_193053/gen_20260422_191742.log b/SynthData0523/main/n10/tabpfgen/n10-migrated-20260422_193053/gen_20260422_191742.log new file mode 100644 index 0000000000000000000000000000000000000000..371e9826007a5b5ba2a2c3f5fbcd514804ddb1c9 --- /dev/null +++ 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0000000000000000000000000000000000000000..786f2ef3e23f20a3ef5d5f924e85d8ab4d8cf3ac --- /dev/null +++ b/SynthData0523/main/n10/tabpfgen/n10-migrated-20260422_193053/tabpfgen-n10-10888-20260422_191742.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63db51cac07fb48126b243c806bdc990d5a70b38213df58716874514ec1e2574 +size 1874512 diff --git a/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_tvae_generate.py b/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4b770c95dec002f84c62caa1bcd2508e03abfd9d --- /dev/null +++ b/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/models_300epochs/tvae_300epochs.pt") +samples = model.sample(10888) +samples.to_csv("/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae-n10-10888-20260330_070355.csv", index=False) +print(f"[TVAE] Generated 10888 rows -> /work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae-n10-10888-20260330_070355.csv") diff --git a/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_tvae_train.py b/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7c4c7b557bd6857e7bc94ca62beffca16a063401 --- /dev/null +++ b/SynthData0523/main/n10/tvae/tvae-n10-20260328_053511/_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/n10/tvae/tvae-n10-20260328_053511/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n10/tvae/tvae-n10-20260328_053511/tvae_metadata.json" +save_path = 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