"""
train_traditional.py
────────────────────
Entry-point: trains TF-IDF + Logistic Regression or Linear SVM.

Usage
─────
    python train_traditional.py                 # Logistic Regression (default)
    python train_traditional.py --model svm     # Linear SVM
    python train_traditional.py --full          # Use all 120 K training samples
    python train_traditional.py --model svm --full
"""
import argparse
import logging

from config import CFG
from data_loader import load_ag_news, get_raw_splits
import traditional_model as tm

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s  %(levelname)-8s  %(message)s",
    datefmt="%H:%M:%S",
)


def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(
        description="Train traditional ML document classifier (TF-IDF + LR or SVM)"
    )
    p.add_argument(
        "--model",
        default="lr",
        choices=["lr", "svm"],
        help="'lr' = Logistic Regression  |  'svm' = Linear SVM  (default: lr)",
    )
    p.add_argument(
        "--full",
        action="store_true",
        help="Disable sample cap; use all 120 K training examples (slower)",
    )
    return p.parse_args()


def main() -> None:
    args = parse_args()

    max_train = None if args.full else CFG.max_train_samples
    max_eval  = None if args.full else CFG.max_eval_samples
    sample_label = "Full dataset" if (args.full or max_train is None) else f"{max_train:,} samples (subset)"

    print(f"\n{'-' * 60}")
    print(f"  Document Classifier  --  Traditional ML Training")
    print(f"  Model   : {args.model.upper()}")
    print(f"  Samples : {sample_label}")
    print(f"{'-' * 60}\n")

    # Load data
    dataset = load_ag_news(max_train=max_train, max_eval=max_eval, max_test=None)
    X_train, y_train, X_val, y_val, X_test, y_test = get_raw_splits(dataset)

    # Train
    pipeline, val_acc = tm.train(
        X_train, y_train,
        X_val,   y_val,
        model_type=args.model,
    )

    # Evaluate on test set
    save_dir = f"outputs/{args.model}"
    results  = tm.evaluate(pipeline, X_test, y_test, save_dir=save_dir)

    # Save the model
    tm.save_model(pipeline, name=args.model)

    print(f"\n  Final test accuracy : {results['accuracy'] * 100:.2f}%")
    print(f"  Model saved to      : saved_models/traditional_{args.model}.joblib\n")


if __name__ == "__main__":
    main()