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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 | """Evaluate saved validation checkpoints with multiple scoring functions."""
from __future__ import annotations
import argparse
import importlib.util
import re
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from sklearn.metrics import precision_recall_curve, roc_auc_score
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(labels: np.ndarray, scores: np.ndarray):
precision, recall, thresholds = precision_recall_curve(labels, scores)
f1s = 2 * precision * recall / (precision + recall + 1e-12)
idx = int(np.argmax(f1s))
threshold = float(thresholds[idx]) if idx < len(thresholds) else 0.5
return float(f1s[idx]), threshold, float(roc_auc_score(labels, scores))
def infer_layers(path: Path, state: dict) -> int:
if "layer_weight" in state:
return int(state["layer_weight"].shape[0] - 1)
text = f"{path.parent.parent.name}_{path.name}"
match = re.search(r"_l(\d+)d", text)
if match:
return int(match.group(1))
match = re.search(r"L(\d+)", text)
if match:
return int(match.group(1))
return 4
@torch.no_grad()
def score_model(module, model, data, pairs: np.ndarray, mode: str, batch_size: int) -> np.ndarray:
model.eval()
z = model.encode(data)
author = z["author"].detach().cpu().numpy()
paper = z["paper"].detach().cpu().numpy()
scores = []
for start in range(0, len(pairs), batch_size):
batch = pairs[start : start + batch_size]
a = author[batch[:, 0]]
p = paper[batch[:, 1]]
if mode == "dot":
s = np.sum(a * p, axis=1)
elif mode == "cos":
s = module.cos_sim(a, p)
elif mode == "neg_l2":
s = -np.sum((a - p) ** 2, axis=1)
else:
raise ValueError(mode)
scores.append(s.astype(np.float32))
return np.concatenate(scores)
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, default=None)
parser.add_argument("--train-frac", type=float, default=0.9)
parser.add_argument("--run-glob", default="dyn*")
parser.add_argument("--device", default="cuda:0" if torch.cuda.is_available() else "cpu")
parser.add_argument("--batch-size", type=int, default=65536)
args = parser.parse_args()
root = args.package_root
module = load_train_module(root / "code" / "train_val_lgcn_ensemble.py")
if args.split_seed is None:
split_name = "notebook_seed0"
split_dir = root / "splits" / split_name
parts = module.build_parts(root, split_dir, 79937)
else:
split_name = f"dynamic_seed{args.split_seed}"
parts = module.build_parts(root, None, 79937, split_seed=args.split_seed, train_frac=args.train_frac)
data_cache = {}
val_pairs = parts["val_pairs"][["source", "target"]].to_numpy(dtype=np.int64)
labels = parts["val_pairs"]["label"].to_numpy(dtype=np.int8)
out_dir = root / "validation_runs" / split_name / "score_modes"
out_dir.mkdir(parents=True, exist_ok=True)
rows = []
checkpoint_paths = sorted((root / "validation_runs" / split_name).glob(f"{args.run_glob}/checkpoints/*.pt"))
for path in checkpoint_paths:
state = torch.load(path, map_location=args.device)
embed_dim = state["author_emb.weight"].shape[1]
variant = "learnw" if "learnw" in path.name else "vanilla"
layers = infer_layers(path, state)
run_name = path.parent.parent.name
use_citation = "no_cite" not in run_name and "author_paper_only" not in run_name
use_coauthor = "no_coauthor" not in run_name and "author_paper_only" not in run_name
data_key = (use_citation, use_coauthor)
if data_key not in data_cache:
data_cache[data_key] = module.build_data(
parts,
6611,
79937,
torch.device(args.device),
use_citation=use_citation,
use_coauthor=use_coauthor,
)
data = data_cache[data_key]
model_cls = module.LearnableWeightLightGCN if variant == "learnw" else module.LightGCN
model = model_cls(6611, parts["paper_feat_aug"].shape[1], embed_dim, layers).to(torch.device(args.device))
model.load_state_dict(state)
stem = f"{path.parent.parent.name}_{path.stem}"
for mode in ["cos", "dot", "neg_l2"]:
scores = score_model(module, model, data, val_pairs, mode, args.batch_size)
np.save(out_dir / f"{stem}_{mode}.npy", scores)
f1, th, auc = best_f1(labels, scores)
rows.append(
{
"run": path.parent.parent.name,
"checkpoint": path.name,
"variant": variant,
"dim": embed_dim,
"mode": mode,
"f1": f1,
"threshold": th,
"auc": auc,
}
)
print(f"{stem} {mode}: f1={f1:.6f} th={th:.6f} auc={auc:.6f}")
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
df = pd.DataFrame(rows).sort_values("f1", ascending=False)
df.to_csv(out_dir / "score_mode_results.csv", index=False)
print("\nTop results:")
print(df.head(30).to_string(index=False))
if __name__ == "__main__":
main()
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