| """ |
| Script: train_baseline.py |
| |
| Trains and saves the naive baseline recommenders: |
| 1. GlobalPopularityRecommender |
| 2. CategoryPopularityRecommender |
| |
| Outputs: |
| models/naive_baseline.pkl (GlobalPopularity) |
| models/naive_category.pkl (CategoryPopularity) |
| |
| Usage: |
| python scripts/train_baseline.py |
| python scripts/train_baseline.py --debug |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) |
|
|
| import pandas as pd |
|
|
| from src.config import MODELS_DIR, ensure_dirs, get_config |
| from src.models.naive import CategoryPopularityRecommender, GlobalPopularityRecommender |
| from src.utils.logging import get_logger |
|
|
| logger = get_logger(__name__) |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description="Train naive baseline recommenders.") |
| parser.add_argument("--debug", action="store_true") |
| return parser.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| cfg = get_config(debug=args.debug) |
| ensure_dirs(cfg) |
|
|
| logger.info("Loading training data…") |
| train_df = pd.read_parquet(cfg.data.train_path) |
| item_features = pd.read_parquet(cfg.data.item_features_path) |
| user_features = pd.read_parquet(cfg.data.user_features_path) |
|
|
| logger.info("Training GlobalPopularityRecommender…") |
| global_model = GlobalPopularityRecommender() |
| global_model.fit(train_df) |
| global_model.save(MODELS_DIR / "naive_baseline.pkl") |
|
|
| logger.info("Training CategoryPopularityRecommender…") |
| cat_model = CategoryPopularityRecommender() |
| cat_model.fit( |
| train_df, |
| item_features, |
| user_features, |
| positive_threshold=cfg.data.positive_rating_threshold, |
| ) |
| cat_model.save(MODELS_DIR / "naive_category.pkl") |
|
|
| logger.info("Naive baselines saved to %s", MODELS_DIR) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
