diff --git a/.gitattributes b/.gitattributes index a6efabce7a4f914faffed3201e1af8163fedc374..b7ec82b4050208313100357cc8894014878a4205 100644 --- a/.gitattributes +++ b/.gitattributes @@ -60,3 +60,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text *.csv filter=lfs diff=lfs merge=lfs -text *.log filter=lfs diff=lfs merge=lfs -text +*.toml filter=lfs diff=lfs merge=lfs -text +*.json filter=lfs diff=lfs merge=lfs -text diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_arf_generate.py b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..522544761acd9dbc6c19d6f1b03f0eaa4f3751e9 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(103976) +with open("/work/output-SpecializedModels/c18/arf/arf-c18-20260422_055912/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=n_target) +syn = syn.reset_index(drop=True) +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 = model.forge(n=max(need, 1)).reset_index(drop=True) + if len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] +syn.to_csv("/work/output-SpecializedModels/c18/arf/arf-c18-20260422_055912/arf-c18-103976-20260422_073913.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c18/arf/arf-c18-20260422_055912/arf-c18-103976-20260422_073913.csv") diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_arf_train.py b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e4306dbfcdc10821a022a118d7dcf2b38861d853 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/_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-SpecializedModels/c18/arf/arf-c18-20260422_055912/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-SpecializedModels/c18/arf/arf-c18-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c18/arf/arf-c18-20260422_055912/arf_model.pkl") diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf-c18-103976-20260422_073913.csv b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf-c18-103976-20260422_073913.csv new file mode 100644 index 0000000000000000000000000000000000000000..5740bd9cb2933eb30496cbf485cc026411a1dcab --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf-c18-103976-20260422_073913.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:efd9a936e0a6c2ceb8a2e5b81a75fa2a08274bbff98501c02e20418a0d12993f +size 46496248 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf_model.pkl b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..28dcfad4308cf8c3330a969d9ae3b1df2f292cbd --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5fd1cdc08e24aebcd8a88b5b2389635d905871aa66aae9e6703866890a279836 +size 1193005953 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/gen_20260422_073913.log b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/gen_20260422_073913.log new file mode 100644 index 0000000000000000000000000000000000000000..c812f0dc74a9c5bc09d5622a416f794515515823 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/gen_20260422_073913.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d78d0b88b9b6251fd3d55a1bb085865159dd2b3c120e56b7a045dc53a8bc38c1 +size 3624 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/input_snapshot.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..54f8217155a7dee35e1310c26167bb55ffd11509 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1c10b8cc4ec29f65b5327b5abba22064e170a371bbf0842768a4db88eab0e6d +size 1359 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..81a5a8fe857370f3d498da0a554adaa750601938 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca33a02c28873c5ec5fec52da747d206cd9e461100244e401a7a2e5ef3cba096 +size 8391 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/public_gate_report.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..38ea6f87fe9f756dee62ec3ef3960cfb786cefe0 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/public_gate_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27f1ca3c2a10e936a41ba26048d2f0bc62addbfac55033a559ba761621c8ed22 +size 921 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/staged_input_manifest.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..590ca9272713d67155abb514cc4cb7dad4138a2f --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/public_gate/staged_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5326e8dc8c38920650d69a484d1ad0328b0a37a5ec76dc39dae42cf01b199a49 +size 9142 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/runtime_result.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..539b1a3ceb6910d5eaf8d85c3fd0701cf5d67f34 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/runtime_result.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59011ca16f704b0ef3581e3d556dc196ad7da7332da51aca3f2cc03a750d5428 +size 574 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_report.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2f8f861192177aeb3c09978a820a44621c236a00 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a9b26bdad70e840f578c14cbcf2ba40e38f4124c8f67dda44dd0b15181c55b8 +size 306 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_transforms_applied.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/model_input_manifest.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..79c4b1edd432cf486810b8a277a35f12598a20fd --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/arf/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8435b77ec8bc47a4d7cf5f6a6109ea1763384aaa35cf7f0d4fac64df27be8c6 +size 9324 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/staged_features.json b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..3df50ddea99fb662b436d831f16fbb802620002e --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/staged_features.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:632cd1c583d7dfd3107947bb9a317b8bb4bc567040e03ec88232c59e6f87a161 +size 1350 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/test.csv b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..1c45f428b08f350b9b4baef7768b0162d14b20f2 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9b247fe90d1b3de687afbee0871f768b5b9b41d8974110c1a1a4be0a1581258 +size 5283050 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/train.csv b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..1e7d4ca0d3ef9102ea24035ff0ab56ed80bafe6a --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d36ba8fd95415355fed46a70cd71921339f0b4b8f05320279bef35443ce4cc16 +size 42317177 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/val.csv b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..cd5eb5afe44b68bca4d332dac21af4f65289da18 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6368d0c3f97d7449830710d697fc67e7fb71fa7038bc480892279eb41c153f0b +size 5289617 diff --git a/syntheticSuccess/c18/arf/arf-c18-20260422_055912/train_20260422_055923.log b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/train_20260422_055923.log new file mode 100644 index 0000000000000000000000000000000000000000..66b65e0bdd2d6147841c93f6e0ceac62f78dfef0 --- /dev/null +++ b/syntheticSuccess/c18/arf/arf-c18-20260422_055912/train_20260422_055923.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ab14e38c987b32b095b7647c4be6f6bc305a0c75e29ebc58906f8e65381379e +size 625 diff --git a/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_bayesnet_generate.py b/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f22a8a3f52a23158aea32c9568ea24f065569e8c --- /dev/null +++ b/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_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-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/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(103976) +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-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet-c18-103976-20260422_192851.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet-c18-103976-20260422_192851.csv") diff --git a/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_bayesnet_train.py b/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4378984ba9e1ff6a98d3a7951d2864abf66654ad --- /dev/null +++ b/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/_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-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/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-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/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-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/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": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet_model.pkl") diff --git a/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet-c18-103976-20260422_192851.csv b/syntheticSuccess/c18/bayesnet/bayesnet-c18-20260422_192809/bayesnet-c18-103976-20260422_192851.csv new file mode 100644 index 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