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filter=lfs diff=lfs merge=lfs -text diff --git a/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_generate.py b/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b5ec0e10527ed7e46a9a1e2922de3557739cb157 --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n11/arf/arf-n11-20260325_101116/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=15215) +syn.to_csv("/work/output-SpecializedModels/n11/arf/arf-n11-20260325_101116/arf-n11-15215-20260330_070502.csv", index=False) +print(f"[ARF] Generated 15215 rows -> /work/output-SpecializedModels/n11/arf/arf-n11-20260325_101116/arf-n11-15215-20260330_070502.csv") diff --git a/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_train.py b/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9815a1d4ed587b5a0184e636584047b96e3f44ba --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260325_101116/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n11/arf/arf-n11-20260325_101116/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/n11/arf/arf-n11-20260325_101116/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n11/arf/arf-n11-20260325_101116/arf_model.pkl") diff --git a/SynthData0523/main/n11/arf/arf-n11-20260325_101116/arf-n11-1000-20260325_102123.csv 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0000000000000000000000000000000000000000..2b1904fec133cb0f932f407a49bf7752a57cb918 --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(15215) +c_csv = "/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n11' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv") diff --git a/SynthData0523/main/n11/arf/arf-n11-20260502_020245/_arf_train.py b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4ce16cabc4f6c1831eaa33eac8f957443c73ba48 --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/staged/public/train.csv") +df = _sanitize_for_arf(df) +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-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl") diff --git a/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv new file mode 100644 index 0000000000000000000000000000000000000000..90463a037b8a46ff98d58427157c3414f92c7ac2 --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76d0cfea51042bc9cd99095c47750a816024ecbbcdb76202611fdd96da8693b9 +size 2889646 diff --git a/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf_model.pkl b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..74e633e4811b68e0fa02873d37867f4bd607dfde --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374e44edf25e58ca37eabbef0485c98cecdbdfa9d24da738937f404840bfad44 +size 147784 diff --git a/SynthData0523/main/n11/arf/arf-n11-20260502_020245/train_20260502_020245.log b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/train_20260502_020245.log new file mode 100644 index 0000000000000000000000000000000000000000..cc07d45b1109f8f7c892c5223486ecc30ea38b2d --- /dev/null +++ b/SynthData0523/main/n11/arf/arf-n11-20260502_020245/train_20260502_020245.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65460cc3273fa2b8e89f71a0740c13753c2bd5e22a94257aa11dbad09143eb96 +size 676 diff --git a/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_bayesnet_generate.py b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0d9c39b11f92a38c684645752cc5cd91e45b4e --- /dev/null +++ b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_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/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=15215).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/n11/bayesnet/bayesnet-n11-20260321_085202/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/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet-n11-15215-20260330_070515.csv", index=False) +print(f"[BayesNet] Generated 15215 rows -> /work/output-SpecializedModels/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet-n11-15215-20260330_070515.csv") diff --git a/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_bayesnet_train.py b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dd2b75263f681de02d45aa09335ec78f277f6844 --- /dev/null +++ b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/_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/n11/bayesnet/bayesnet-n11-20260321_085202/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/n11/bayesnet/bayesnet-n11-20260321_085202/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/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet_model.pkl") diff --git a/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260321_085202/bayesnet-n11-1000-20260321_085714.csv 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b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..27cc0bdc5166ab0bf1b358cd3fa54c477cf8727e --- /dev/null +++ b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(15215) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv") diff --git a/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e2b4dc1751fbd47c8b722ca403c82a24ad9c972d --- /dev/null +++ b/SynthData0523/main/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 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}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": 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0000000000000000000000000000000000000000..e0a3b6ab9c53a7f8d97dff3290e643431f9ad023 --- /dev/null +++ b/SynthData0523/main/n11/ctgan/ctgan-n11-20260328_054001/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374e44edf25e58ca37eabbef0485c98cecdbdfa9d24da738937f404840bfad44 +size 147784 diff --git a/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..82700d451beb5cd941aac600c02778e846bfb546 --- /dev/null +++ b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/models_300epochs/ctgan_300epochs.pt") +total = 15215 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv") \ No newline at end of file diff --git a/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv new file mode 100644 index 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100644 index 0000000000000000000000000000000000000000..59e413eb15b50a2ac49cd95bbe57b0d0018ccf95 --- /dev/null +++ b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf4e935f84569b849b662718eaa19be605037d46a5f7391f164728d6d9c3bb50 +size 148017 diff --git a/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc5acd9fe00e8a233592b4da9797300adc83744b --- /dev/null +++ b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ab621ac8239797506ce800c1409422452b8127da93add91dd9e6d63ddeec6f7 +size 1182326 diff --git a/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e0a3b6ab9c53a7f8d97dff3290e643431f9ad023 --- /dev/null +++ b/SynthData0523/main/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374e44edf25e58ca37eabbef0485c98cecdbdfa9d24da738937f404840bfad44 +size 147784 diff --git a/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..271241f78130b5517767b66686f871e64bdd15ee --- /dev/null +++ b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0346bf8583b64028d74f6afb3d6f48466083db517af967cfb868f8f8fcced3ee +size 669588 diff --git a/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..6d1c50a612ab03c3591050beb2931c7d8715e3f9 --- /dev/null +++ b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(15215)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forest-n11-15215-20260503_211948.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..8dd008267725994d28f9bba1b6e520618f0f2b52 --- /dev/null +++ b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2ed8eb6447a805f070ef97028581db180781bab16b408001ec27966d50596db +size 147 diff --git a/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..59fe16770473595dcd58ca3d086209df5fbe1200 --- /dev/null +++ b/SynthData0523/main/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/_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"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +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, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib') 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