"""Generate post95 submissions with content-mean and BPR-MF score features.""" 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 import torch import torch.nn.functional as F 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 train_mf_test_scores(extra, root: Path, train_refs: pd.DataFrame, test_pairs: np.ndarray, out_dir: Path, device: str, seed: int, dim: int, epochs: int) -> np.ndarray: out_path = out_dir / f"test_mf_bpr_dynamic_s{seed}_d{dim}_e{epochs}.npy" if out_path.exists(): return np.load(out_path) torch.manual_seed(seed) np.random.seed(seed) rng = np.random.default_rng(seed) train = train_refs[["source", "target"]].to_numpy(np.int64) train_set = set(map(tuple, train.tolist())) model = extra.MF(6611, 79937, dim).to(torch.device(device)) opt = torch.optim.AdamW(model.parameters(), lr=0.01, weight_decay=1e-6) train_t = torch.as_tensor(train, dtype=torch.long, device=device) batch_size = 65536 for ep in range(epochs): idx = torch.randint(0, train_t.size(0), (batch_size,), device=device) pos = train_t[idx] neg_np = np.empty((batch_size, 2), dtype=np.int64) authors = pos[:, 0].detach().cpu().numpy() filled = 0 while filled < batch_size: papers = rng.integers(0, 79937, size=batch_size - filled) for a, p in zip(authors[filled:], papers): if (int(a), int(p)) not in train_set: neg_np[filled] = (a, p) filled += 1 if filled >= batch_size: break neg = torch.as_tensor(neg_np, dtype=torch.long, device=device) loss = -F.logsigmoid(model.score(pos) - model.score(neg)).mean() opt.zero_grad() loss.backward() opt.step() if (ep + 1) % 20 == 0: print(f"mf-test epoch={ep+1:03d} loss={loss.item():.4f}") test_t = torch.as_tensor(test_pairs, dtype=torch.long, device=device) scores = [] with torch.no_grad(): for st in range(0, len(test_pairs), 131072): scores.append(model.score(test_t[st : st + 131072]).detach().cpu().numpy()) scores = np.concatenate(scores).astype(np.float32) np.save(out_path, scores) return scores def make_subs(root: Path, out_dir: Path, score: np.ndarray, ratios: list[float]) -> None: known = np.load(root / "cached_scores" / "test_known_mask.npy").astype(bool) for ratio in ratios: pred = np.zeros(len(score), dtype=np.int8) pred[np.argsort(score)[-int(round(len(score) * ratio)):]] = 1 pred[known] = 1 path = out_dir / f"submission_post95_content_mf_lgb_r{ratio:.3f}.csv" pd.DataFrame({"Index": np.arange(len(pred), dtype=np.int64), "Predicted": pred}).to_csv(path, index=False) print(path, int(pred.sum()), float(pred.mean())) pred = (score >= 0.5).astype(np.int8) pred[known] = 1 path = out_dir / "submission_post95_content_mf_lgb_score_ge0.500.csv" pd.DataFrame({"Index": np.arange(len(pred), dtype=np.int64), "Predicted": pred}).to_csv(path, index=False) print(path, int(pred.sum()), float(pred.mean())) 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=202) parser.add_argument("--main-val-score-file", type=Path, required=True) parser.add_argument("--device", default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--seed", type=int, default=202) parser.add_argument("--mf-dim", type=int, default=256) parser.add_argument("--mf-epochs", type=int, default=220) parser.add_argument("--ratios", nargs="*", type=float, default=[0.498, 0.500, 0.502, 0.504, 0.505]) args = parser.parse_args() root = args.package_root stack = load_module("stack", root / "code" / "stack_rank_calibration.py") lgcn = load_module("lgcn", root / "code" / "train_val_lgcn_ensemble.py") post = load_module("post", root / "code" / "post95_ablation.py") gen = load_module("gen", root / "code" / "generate_post95_submission.py") extra = load_module("extra", root / "code" / "extra_score_sources_ablation.py") out_dir = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "extra_bprmf_submission" out_dir.mkdir(parents=True, exist_ok=True) train_refs, val_pairs = lgcn.make_notebook_style_split(root, args.split_seed, 0.9) val_pairs_arr = val_pairs[["source", "target"]].to_numpy(np.int64) y = val_pairs["label"].to_numpy(np.int8) main_val = np.load(args.main_val_score_file).astype(np.float32) val_builder = stack.ExplicitGraphFeatures(root, train_refs) Xh = val_builder.transform(val_pairs_arr) X_val = np.column_stack( [ stack.add_rank_features(val_pairs_arr, main_val), Xh, post.negative_evidence_features(Xh, main_val), gen.topk_content_similarity_fast(root, val_pairs_arr, val_builder), ] ).astype(np.float32) selected = [Path(x.strip()) for x in (root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "post95_submission" / "selected_variant_val_scores.txt").read_text().splitlines() if x.strip()] X_val = np.column_stack([X_val, gen.variant_feature_matrix(post, [np.load(p).astype(np.float32) for p in selected])]).astype(np.float32) content_val = extra.content_mean_score(root, val_pairs_arr, val_builder) mf_val = np.load(root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "extra_score_sources" / f"val_mf_bpr_s{args.seed}_d{args.mf_dim}.npy").astype(np.float32) Xc, _ = extra.score_to_features(content_val, "content_mean_cos", val_pairs_arr) Xm, _ = extra.score_to_features(mf_val, "mf_bpr", val_pairs_arr) X_val = np.column_stack([X_val, Xc, Xm]).astype(np.float32) print("fit LightGBM", X_val.shape) clf = lgb.LGBMClassifier( n_estimators=1200, learning_rate=0.025, num_leaves=31, subsample=0.9, colsample_bytree=0.9, reg_lambda=5.0, min_child_samples=80, objective="binary", verbose=-1, random_state=args.seed, ) clf.fit(X_val, y) test_pairs = np.array(gen.read_txt(root / "data_and_docs" / "bipartite_test_ann.txt"), dtype=np.int64) main_test = np.load(root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "post95_test_scores" / "dyn202_l2d512_bpr_bigbatch_more" / "scores" / "test_vanilla_ensemble_mean.npy").astype(np.float32) full_refs = pd.DataFrame(gen.read_txt(root / "data_and_docs" / "bipartite_train_ann.txt"), columns=["source", "target"]) test_builder = stack.ExplicitGraphFeatures(root, full_refs) Xht = test_builder.transform(test_pairs) X_test = np.column_stack( [ stack.add_rank_features(test_pairs, main_test), Xht, post.negative_evidence_features(Xht, main_test), gen.topk_content_similarity_fast(root, test_pairs, test_builder), ] ).astype(np.float32) test_scores = [] for p in selected: rel = p.resolve().relative_to(root / "validation_runs" / f"dynamic_seed{args.split_seed}") tp = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "post95_test_scores" / rel.parent / rel.name.replace("val_", "test_", 1) test_scores.append(np.load(tp).astype(np.float32)) X_test = np.column_stack([X_test, gen.variant_feature_matrix(post, test_scores)]).astype(np.float32) content_test = extra.content_mean_score(root, test_pairs, test_builder) mf_test = train_mf_test_scores(extra, root, train_refs, test_pairs, out_dir, args.device, args.seed, args.mf_dim, args.mf_epochs) Xct, _ = extra.score_to_features(content_test, "content_mean_cos", test_pairs) Xmt, _ = extra.score_to_features(mf_test, "mf_bpr", test_pairs) X_test = np.column_stack([X_test, Xct, Xmt]).astype(np.float32) print("predict", X_test.shape) pred_score = clf.predict_proba(X_test)[:, 1].astype(np.float32) np.save(out_dir / "test_post95_content_mf_lgb_pred.npy", pred_score) make_subs(root, out_dir, pred_score, args.ratios) if __name__ == "__main__": main()