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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()
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