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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 | """Search score-level fusions for one dynamic notebook-style split."""
from __future__ import annotations
import argparse
import importlib.util
from pathlib import Path
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_train_module(path: Path):
spec = importlib.util.spec_from_file_location("train_val_lgcn_ensemble", 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))
return float(f[i]), float(t[i] if i < len(t) else 0.5), float(roc_auc_score(y, s))
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 main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
parser.add_argument("--split-seed", type=int, required=True)
parser.add_argument("--train-frac", type=float, default=0.9)
parser.add_argument("--top-k", type=int, default=24)
parser.add_argument("--random-iters", type=int, default=15000)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
root = args.package_root
tv = load_train_module(root / "code" / "train_val_lgcn_ensemble.py")
_, val_pairs = tv.make_notebook_style_split(root, args.split_seed, args.train_frac)
labels = val_pairs["label"].to_numpy(np.int8)
split_dir = root / "validation_runs" / f"dynamic_seed{args.split_seed}"
score_files = []
score_files.extend(split_dir.glob("dyn*/scores/val_*.npy"))
score_files.extend(split_dir.glob("feature_fusion/val_*.npy"))
score_files.extend(split_dir.glob("score_modes/*.npy"))
score_files = sorted(set(score_files))
names, cols = [], []
for path in score_files:
x = np.load(path).astype(np.float32)
if len(x) != len(labels) or np.std(x) < 1e-8:
continue
names.append(str(path.relative_to(split_dir)))
cols.append(x)
if not cols:
raise SystemExit(f"no compatible scores under {split_dir}")
X = np.vstack(cols).T
rows = []
for j, name in enumerate(names):
f1, th, auc = best_f1(labels, X[:, j])
rows.append({"method": "single", "name": name, "n": 1, "f1": f1, "threshold": th, "auc": auc})
single = pd.DataFrame(rows).sort_values("f1", ascending=False)
top_idx = [names.index(n) for n in single["name"].head(min(args.top_k, len(names)))]
for method, transform in [("all_rank_mean", rank01), ("all_z_mean", zscore)]:
S = np.vstack([transform(X[:, j]) for j in range(X.shape[1])]).T
f1, th, auc = best_f1(labels, S.mean(axis=1))
rows.append({"method": method, "name": "all", "n": X.shape[1], "f1": f1, "threshold": th, "auc": auc})
rng = np.random.default_rng(args.seed)
for space_name, transform in [("rank", rank01), ("z", zscore)]:
S = np.vstack([transform(X[:, j]) for j in top_idx]).T
best = None
for _ in range(args.random_iters):
w = rng.dirichlet(rng.uniform(0.4, 4.0, size=len(top_idx)))
scores = S @ w
f1, th, auc = best_f1(labels, scores)
if best is None or f1 > best["f1"]:
best = {"f1": f1, "threshold": th, "auc": auc, "weights": w}
assert best is not None
rows.append(
{
"method": f"random_{space_name}_top{len(top_idx)}",
"name": ",".join(names[i] for i in top_idx),
"n": len(top_idx),
"f1": best["f1"],
"threshold": best["threshold"],
"auc": best["auc"],
}
)
np.save(split_dir / f"fusion_weights_{space_name}.npy", best["weights"])
for k in [8, 12, min(20, len(top_idx))]:
sub_idx = top_idx[:k]
S = np.vstack([rank01(X[:, j]) for j in sub_idx]).T
oof = np.zeros(len(labels), dtype=np.float32)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=args.seed)
for tr, va in skf.split(S, labels):
clf = LogisticRegression(C=0.2, max_iter=1000, solver="lbfgs")
clf.fit(S[tr], labels[tr])
oof[va] = clf.predict_proba(S[va])[:, 1]
f1, th, auc = best_f1(labels, oof)
rows.append(
{
"method": f"logistic_oof_rank_top{k}",
"name": ",".join(names[i] for i in sub_idx),
"n": k,
"f1": f1,
"threshold": th,
"auc": auc,
}
)
result = pd.DataFrame(rows).sort_values("f1", ascending=False)
out = split_dir / "dynamic_fusion_results.csv"
result.to_csv(out, index=False)
print(f"loaded {X.shape[1]} score columns")
print(result.head(50).to_string(index=False))
if __name__ == "__main__":
main()
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