| """ |
| Script: train_classical.py |
| |
| Trains the LightGBM reranker on the full feature set and saves the model. |
| |
| Outputs: |
| models/lgbm_model.pkl |
| data/outputs/figures/feature_importance.png |
| |
| Usage: |
| python scripts/train_classical.py |
| python scripts/train_classical.py --debug |
| python scripts/train_classical.py --feature-set id_metadata # ablation tier |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import pickle |
| import sys |
| from pathlib import Path |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
|
|
| from src.config import FIGURES_DIR, MODELS_DIR, PROCESSED_DIR, ensure_dirs, get_config |
| from src.features.builder import FEATURE_SETS, build_feature_matrix |
| from src.models.classical import LightGBMReranker |
| from src.utils.logging import get_logger |
|
|
| logger = get_logger(__name__) |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description="Train LightGBM reranker.") |
| parser.add_argument("--debug", action="store_true") |
| parser.add_argument( |
| "--feature-set", |
| type=str, |
| default="id_metadata_text_history", |
| choices=FEATURE_SETS, |
| help="Feature tier to use for training.", |
| ) |
| parser.add_argument( |
| "--output-name", |
| type=str, |
| default="lgbm_model", |
| help="Stem of the output .pkl filename (in models/).", |
| ) |
| return parser.parse_args() |
|
|
|
|
| def plot_feature_importance(model: LightGBMReranker, output_path: Path) -> None: |
| """Save a horizontal bar chart of feature importances.""" |
| imp = model.feature_importance() |
| top = imp.head(20) |
|
|
| fig, ax = plt.subplots(figsize=(9, 6)) |
| ax.barh(top["feature"][::-1], top["importance"][::-1], color="#3b82f6") |
| ax.set_xlabel("Importance (Gain)") |
| ax.set_title("LightGBM Feature Importance (Top 20)") |
| plt.tight_layout() |
| output_path.parent.mkdir(parents=True, exist_ok=True) |
| fig.savefig(output_path, dpi=150) |
| plt.close(fig) |
| logger.info("Feature importance plot saved → %s", output_path) |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| cfg = get_config(debug=args.debug) |
| ensure_dirs(cfg) |
|
|
| logger.info("Loading pre-built feature matrices…") |
| |
| if cfg.data.train_features_path.exists(): |
| train_feat = pd.read_parquet(cfg.data.train_features_path) |
| val_feat = pd.read_parquet(cfg.data.val_features_path) |
| else: |
| logger.warning("Pre-built features not found. Loading raw splits and rebuilding…") |
| train_df = pd.read_parquet(cfg.data.train_path) |
| val_df = pd.read_parquet(cfg.data.val_path) |
| user_features = pd.read_parquet(cfg.data.user_features_path) |
| item_features = pd.read_parquet(cfg.data.item_features_path) |
|
|
| artifacts_path = PROCESSED_DIR / "feature_artifacts.pkl" |
| with open(artifacts_path, "rb") as f: |
| artifacts = pickle.load(f) |
|
|
| user_profiles_path = PROCESSED_DIR / "user_profiles.parquet" |
| if user_profiles_path.exists(): |
| user_profiles = pd.read_parquet(user_profiles_path) |
| else: |
| from src.features.text_features import build_user_text_profiles |
| user_profiles = build_user_text_profiles(train_df, item_features) |
|
|
| train_feat, _ = build_feature_matrix( |
| train_df, user_features, item_features, user_profiles, |
| artifacts["vectorizer"], feature_set=args.feature_set, |
| ) |
| val_feat, _ = build_feature_matrix( |
| val_df, user_features, item_features, user_profiles, |
| artifacts["vectorizer"], feature_set=args.feature_set, |
| ) |
|
|
| |
| exclude = {"label", "user_idx", "item_idx", "user_id", "item_id", "rating", "timestamp"} |
| feature_cols = [c for c in train_feat.columns if c not in exclude] |
|
|
| logger.info("Training LightGBM on %d features, %d rows…", len(feature_cols), len(train_feat)) |
| model = LightGBMReranker( |
| n_estimators=cfg.classical.n_estimators, |
| learning_rate=cfg.classical.learning_rate, |
| num_leaves=cfg.classical.num_leaves, |
| max_depth=cfg.classical.max_depth, |
| min_child_samples=cfg.classical.min_child_samples, |
| subsample=cfg.classical.subsample, |
| colsample_bytree=cfg.classical.colsample_bytree, |
| reg_alpha=cfg.classical.reg_alpha, |
| reg_lambda=cfg.classical.reg_lambda, |
| n_jobs=cfg.classical.n_jobs, |
| random_state=cfg.data.random_seed, |
| early_stopping_rounds=cfg.classical.early_stopping_rounds, |
| ) |
| model.fit(train_feat, val_feat, feature_cols) |
|
|
| |
| model_path = MODELS_DIR / f"{args.output_name}.pkl" |
| model.save(model_path) |
|
|
| |
| plot_feature_importance(model, FIGURES_DIR / "feature_importance.png") |
|
|
| logger.info("LightGBM training complete.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
