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f28d994 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """Score-level meta stack over all cached high-value predictors.
This is intentionally cheap: it reuses OOF validation scores and cached test
scores instead of retraining GNN / random-walk embeddings.
"""
from __future__ import annotations
import argparse
import importlib.util
from pathlib import Path
import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
def load_module(name: str, path: Path):
spec = importlib.util.spec_from_file_location(name, path)
module = importlib.util.module_from_spec(spec)
assert spec.loader is not None
spec.loader.exec_module(module)
return module
def best_f1(y: np.ndarray, s: np.ndarray):
p, r, t = precision_recall_curve(y, s)
f = 2 * p * r / (p + r + 1e-12)
i = int(np.argmax(f))
th = float(t[i]) if i < len(t) else 0.5
return float(f[i]), float(t[i] if i < len(t) else 0.5), float(roc_auc_score(y, s)), float(p[i]), float(r[i])
def prf(y: np.ndarray, pred: np.ndarray):
tp = int(((pred == 1) & (y == 1)).sum())
fp = int(((pred == 1) & (y == 0)).sum())
fn = int(((pred == 0) & (y == 1)).sum())
p = tp / (tp + fp + 1e-12)
r = tp / (tp + fn + 1e-12)
f = 2 * p * r / (p + r + 1e-12)
return p, r, f, tp, fp, fn
def rank01(x: np.ndarray) -> np.ndarray:
order = np.argsort(x, kind="mergesort")
out = np.empty(len(x), dtype=np.float32)
out[order] = np.linspace(0, 1, len(x), dtype=np.float32)
return out
def zscore(x: np.ndarray) -> np.ndarray:
return ((x - x.mean()) / (x.std() + 1e-8)).astype(np.float32)
def author_rank01(pairs: np.ndarray, score: np.ndarray) -> np.ndarray:
out = np.zeros(len(score), dtype=np.float32)
df = pd.DataFrame({"idx": np.arange(len(score)), "author": pairs[:, 0], "score": score})
for _, g in df.groupby("author", sort=False):
idx = g["idx"].to_numpy()
order = np.argsort(g["score"].to_numpy(), kind="mergesort")
vals = np.linspace(0, 1, len(idx), dtype=np.float32) if len(idx) > 1 else np.array([1.0], dtype=np.float32)
out[idx[order]] = vals
return out
def add_score_block(pairs: np.ndarray, score: np.ndarray) -> np.ndarray:
return np.column_stack([score.astype(np.float32), zscore(score), rank01(score), author_rank01(pairs, score)]).astype(np.float32)
def read_txt(path: Path) -> list[list[int]]:
return [list(map(int, line.strip().split())) for line in path.open()]
def load_sources(root: Path, split_seed: int, n_val: int, n_test: int) -> list[tuple[str, np.ndarray, np.ndarray]]:
base = root / "validation_runs" / f"dynamic_seed{split_seed}"
candidates: list[tuple[str, Path, Path]] = [
("post95_lgb", base / "post95_ablation/ensemble_lgcn_oof.npy", base / "post95_submission/test_post95_ens_pred.npy"),
("post95_xgb", base / "post95_ablation/xgboost_76feat_oof.npy", base / "post95_xgboost_submission/test_post95_xgb_pred.npy"),
("content_mf", base / "extra_score_sources/bpr_mf_stack_oof.npy", base / "extra_bprmf_submission/test_post95_content_mf_lgb_pred.npy"),
("content_rich", base / "content_rich/rich_content_stack_oof.npy", base / "content_rich_submission/test_content_rich_mf_lgb_pred.npy"),
("n2v_dw_anchor", base / "node2vec_deepwalk/node2vec_stack_oof.npy", base / "node2vec_deepwalk_submission/test_content_mf_deepwalk_node2vec_lgb_pred.npy"),
]
rw_dir = base / "randomwalk_systematic"
rw_test_dir = base / "randomwalk_ensemble_submission"
for p in sorted(rw_dir.glob("*_oof.npy")):
stem = p.stem
test = rw_test_dir / f"test_{stem[:-4] if stem.endswith('_oof') else stem}_pred.npy"
if not test.exists():
test = rw_test_dir / f"test_{stem.replace('_oof', '')}_pred.npy"
if test.exists():
candidates.append((stem.replace("_oof", ""), p, test))
out = []
seen = set()
for name, vp, tp in candidates:
if name in seen or not vp.exists() or not tp.exists():
continue
v = np.load(vp).astype(np.float32)
t = np.load(tp).astype(np.float32)
if len(v) == n_val and len(t) == n_test and np.std(v) > 1e-8:
out.append((name, v, t))
seen.add(name)
return out
def fit_oof_predict(X: np.ndarray, y: np.ndarray, X_test: np.ndarray, kind: str, seed: int, n_splits: int):
oof = np.zeros(len(y), dtype=np.float32)
test = np.zeros(X_test.shape[0], dtype=np.float32)
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)
models = []
for fold, (tr, va) in enumerate(skf.split(X, y), start=1):
if kind == "logreg":
clf = LogisticRegression(C=0.5, max_iter=1000, solver="lbfgs")
else:
clf = lgb.LGBMClassifier(
n_estimators=900,
learning_rate=0.025,
num_leaves=15 if kind == "lgb_small" else 31,
subsample=0.9,
colsample_bytree=0.85,
reg_lambda=10.0,
min_child_samples=120,
objective="binary",
verbose=-1,
random_state=seed + fold,
)
clf.fit(X[tr], y[tr])
oof[va] = clf.predict_proba(X[va])[:, 1].astype(np.float32)
test += clf.predict_proba(X_test)[:, 1].astype(np.float32) / n_splits
models.append(clf)
return oof, test, models
def write_sub(path: Path, score: np.ndarray, known: np.ndarray, anchor: np.ndarray, *, th: float | None = None, ratio: float | None = None):
if th is not None:
pred = (score >= th).astype(np.int8)
else:
pred = np.zeros(len(score), dtype=np.int8)
pred[np.argsort(score, kind="mergesort")[-int(round(len(score) * float(ratio))):]] = 1
pred[known] = 1
pd.DataFrame({"Index": np.arange(len(pred), dtype=np.int64), "Predicted": pred}).to_csv(path, index=False)
return float(pred.mean()), int((pred != anchor).sum())
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
ap.add_argument("--split-seed", type=int, default=202)
ap.add_argument("--seed", type=int, default=202)
ap.add_argument("--n-splits", type=int, default=5)
args = ap.parse_args()
root = args.package_root.resolve()
lgcn = load_module("lgcn", root / "code/train_val_lgcn_ensemble.py")
train_refs, val_pairs = lgcn.make_notebook_style_split(root, args.split_seed, 0.9)
pairs = val_pairs[["source", "target"]].to_numpy(np.int64)
y = val_pairs["label"].to_numpy(np.int8)
test_pairs = np.array(read_txt(root / "data_and_docs/bipartite_test_ann.txt"), dtype=np.int64)
known = np.load(root / "cached_scores/test_known_mask.npy").astype(bool)
anchor_path = root / "validation_runs/dynamic_seed202/node2vec_deepwalk_submission/submission_content_mf_deepwalk_node2vec_lgb_th0.480000.csv"
anchor = pd.read_csv(anchor_path)["Predicted"].to_numpy(np.int8)
sources = load_sources(root, args.split_seed, len(y), len(test_pairs))
print("sources", len(sources))
for name, v, _ in sources:
f, th, auc, p, r = best_f1(y, v)
print(f"{name:90s} f1={f:.6f} th={th:.6f} auc={auc:.6f}")
blocks = []
test_blocks = []
names = []
raw_val = []
raw_test = []
for name, v, t in sources:
blocks.append(add_score_block(pairs, v))
test_blocks.append(add_score_block(test_pairs, t))
names.extend([name, name + "_z", name + "_rank", name + "_author_rank"])
raw_val.append(rank01(v))
raw_test.append(rank01(t))
R = np.vstack(raw_val)
Rt = np.vstack(raw_test)
summary = np.column_stack([R.mean(axis=0), R.std(axis=0), R.max(axis=0), R.min(axis=0), (R >= 0.5).sum(axis=0), (R >= 0.9).sum(axis=0)]).astype(np.float32)
summary_t = np.column_stack([Rt.mean(axis=0), Rt.std(axis=0), Rt.max(axis=0), Rt.min(axis=0), (Rt >= 0.5).sum(axis=0), (Rt >= 0.9).sum(axis=0)]).astype(np.float32)
X = np.column_stack([*blocks, summary]).astype(np.float32)
Xt = np.column_stack([*test_blocks, summary_t]).astype(np.float32)
print("X", X.shape, "Xt", Xt.shape)
out = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "score_level_meta_stack"
sub_dir = out / "submissions"
out.mkdir(parents=True, exist_ok=True)
sub_dir.mkdir(parents=True, exist_ok=True)
pd.Series(names + ["rank_mean", "rank_std", "rank_max", "rank_min", "vote_ge_05", "vote_ge_09"]).to_csv(out / "feature_names.csv", index=False)
rows = []
for kind in ["logreg", "lgb_small", "lgb"]:
oof, test_score, _ = fit_oof_predict(X, y, Xt, kind, args.seed + len(rows) * 31, args.n_splits)
np.save(out / f"{kind}_oof.npy", oof)
np.save(out / f"{kind}_test_pred.npy", test_score)
f, th, auc, p, r = best_f1(y, oof)
pred = (oof >= th).astype(np.int8)
_, _, _, tp, fp, fn = prf(y, pred)
for rule_name, kwargs in [
(f"{kind}_valbest_th", {"th": th}),
(f"{kind}_r_valratio", {"ratio": float(pred.mean())}),
(f"{kind}_r0500", {"ratio": 0.500}),
(f"{kind}_r0499", {"ratio": 0.499}),
(f"{kind}_r0501", {"ratio": 0.501}),
]:
path = sub_dir / f"submission_{rule_name}.csv"
pos_ratio, changed = write_sub(path, test_score, known, anchor, **kwargs)
rows.append(
{
"experiment": rule_name,
"model": kind,
"validation_f1": f,
"threshold": th,
"auc": auc,
"precision": p,
"recall": r,
"val_pred_ratio": float(pred.mean()),
"tp": tp,
"fp": fp,
"fn": fn,
"test_positive_ratio": pos_ratio,
"changed_vs_anchor": changed,
"public_submission_path": str(path),
}
)
pd.DataFrame(rows).sort_values(["validation_f1", "changed_vs_anchor"], ascending=[False, True]).to_csv(out / "summary.csv", index=False)
print(pd.DataFrame(rows).sort_values(["validation_f1", "changed_vs_anchor"], ascending=[False, True]).to_string(index=False))
if __name__ == "__main__":
main()
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