| """Generate a submission for one systematic random-walk model.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import importlib.util |
| import sys |
| from pathlib import Path |
|
|
| import lightgbm as lgb |
| import numpy as np |
| import pandas as pd |
| from gensim.models import Word2Vec |
|
|
|
|
| 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 |
| sys.modules[name] = module |
| spec.loader.exec_module(module) |
| return module |
|
|
|
|
| def make_subs(root: Path, out_dir: Path, version: str, score: np.ndarray, ratios: list[float], thresholds: list[float]) -> None: |
| known = np.load(root / "cached_scores" / "test_known_mask.npy").astype(bool) |
| current_path = root / "validation_runs" / "dynamic_seed202" / "node2vec_deepwalk_submission" / "submission_content_mf_deepwalk_node2vec_lgb_th0.480000.csv" |
| current = pd.read_csv(current_path)["Predicted"].to_numpy(np.int8) if current_path.exists() else None |
| rows = [] |
| 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_{version}_r{ratio:.3f}.csv" |
| pd.DataFrame({"Index": np.arange(len(pred), dtype=np.int64), "Predicted": pred}).to_csv(path, index=False) |
| changed = int((pred != current).sum()) if current is not None else -1 |
| rows.append({"path": str(path), "rule": f"r{ratio:.3f}", "positive_ratio": float(pred.mean()), "changed_vs_current": changed}) |
| print(path, int(pred.sum()), float(pred.mean()), "changed", changed) |
| for th in thresholds: |
| pred = (score >= th).astype(np.int8) |
| pred[known] = 1 |
| path = out_dir / f"submission_{version}_th{th:.6f}.csv" |
| pd.DataFrame({"Index": np.arange(len(pred), dtype=np.int64), "Predicted": pred}).to_csv(path, index=False) |
| changed = int((pred != current).sum()) if current is not None else -1 |
| rows.append({"path": str(path), "rule": f"th{th:.6f}", "positive_ratio": float(pred.mean()), "changed_vs_current": changed}) |
| print(path, int(pred.sum()), float(pred.mean()), "changed", changed) |
| pd.DataFrame(rows).to_csv(out_dir / f"{version}_submission_summary.csv", index=False) |
|
|
|
|
| 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("--version-name", required=True) |
| parser.add_argument("--threshold", type=float, default=0.5134975910186768) |
| parser.add_argument("--ratios", nargs="*", type=float, default=[0.498, 0.499, 0.500, 0.501, 0.502]) |
| 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") |
| rw = load_module("rw", root / "code" / "randomwalk_systematic_ablation.py") |
|
|
| sys_dir = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "randomwalk_systematic" |
| out_dir = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "randomwalk_ensemble_submission" |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| 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) |
| main = np.load(args.main_val_score_file).astype(np.float32) |
| builder = stack.ExplicitGraphFeatures(root, train_refs) |
| Xh = builder.transform(pairs) |
| X_val = np.column_stack( |
| [ |
| stack.add_rank_features(pairs, main), |
| Xh, |
| post.negative_evidence_features(Xh, main), |
| gen.topk_content_similarity_fast(root, pairs, 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 = extra.content_mean_score(root, pairs, builder) |
| mf = np.load(root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "extra_score_sources" / "val_mf_bpr_s202_d256.npy").astype(np.float32) |
| Xc, _ = extra.score_to_features(content, "content_mean_cos", pairs) |
| Xm, _ = extra.score_to_features(mf, "mf_bpr", pairs) |
| X_val = np.column_stack([X_val, Xc, Xm]).astype(np.float32) |
|
|
| cfg = next(c for c in rw.small_configs() + rw.graph_configs() + rw.extra_configs() if c.version_name == args.version_name) |
| model = Word2Vec.load(str(sys_dir / "models" / f"{args.version_name}.model")) |
| block, _ = rw.pair_feature_block(model, pairs, cfg, root, args.split_seed, train_refs) |
| X_val = np.column_stack([X_val, block]).astype(np.float32) |
| 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", |
| n_jobs=8, |
| verbose=-1, |
| random_state=202, |
| ) |
| print("fit", X_val.shape) |
| 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 = np.load(root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "extra_bprmf_submission" / "test_mf_bpr_dynamic_s202_d256_e220.npy").astype(np.float32) |
| 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) |
| test_block, _ = rw.pair_feature_block(model, test_pairs, cfg, root, args.split_seed, full_refs) |
| X_test = np.column_stack([X_test, test_block]).astype(np.float32) |
| print("predict", X_test.shape) |
| score = clf.predict_proba(X_test)[:, 1].astype(np.float32) |
| np.save(out_dir / f"test_{args.version_name}_pred.npy", score) |
| make_subs(root, out_dir, args.version_name, score, args.ratios, [args.threshold, 0.48, 0.50]) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
