scripts/train_baseline.py

from __future__ import annotations

import argparse
import json
from pathlib import Path

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler


def main() -> int:
    parser = argparse.ArgumentParser(description="Train a simple UFO-MNIST baseline model.")
    parser.add_argument("--dataset", type=Path, default=Path("data/ufo_mnist_v1/ufo_mnist_28x28.npz"))
    parser.add_argument("--output", type=Path, default=Path("data/ufo_mnist_v1/baseline_metrics.json"))
    parser.add_argument("--seed", type=int, default=1337)
    args = parser.parse_args()

    data = np.load(args.dataset)
    class_names = [str(name) for name in data["class_names"]]
    train_x = data["train_images"].reshape(data["train_images"].shape[0], -1).astype(np.float32) / 255.0
    test_x = data["test_images"].reshape(data["test_images"].shape[0], -1).astype(np.float32) / 255.0
    train_y = data["train_labels"].astype(int)
    test_y = data["test_labels"].astype(int)

    model = make_pipeline(
        StandardScaler(),
        LogisticRegression(
            C=1.0,
            max_iter=2000,
            random_state=args.seed,
            solver="lbfgs",
        ),
    )
    model.fit(train_x, train_y)
    pred = model.predict(test_x)

    report = classification_report(test_y, pred, target_names=class_names, output_dict=True, zero_division=0)
    metrics = {
        "model": "StandardScaler + multinomial LogisticRegression",
        "dataset": str(args.dataset),
        "seed": args.seed,
        "train_examples": int(train_x.shape[0]),
        "test_examples": int(test_x.shape[0]),
        "accuracy": float(accuracy_score(test_y, pred)),
        "macro_f1": float(f1_score(test_y, pred, average="macro")),
        "weighted_f1": float(f1_score(test_y, pred, average="weighted")),
        "classification_report": report,
        "confusion_matrix": confusion_matrix(test_y, pred).tolist(),
        "class_names": class_names,
    }

    args.output.parent.mkdir(parents=True, exist_ok=True)
    args.output.write_text(json.dumps(metrics, indent=2, sort_keys=True) + "\n", encoding="utf-8")

    print(f"model: {metrics['model']}")
    print(f"train_examples: {metrics['train_examples']}")
    print(f"test_examples: {metrics['test_examples']}")
    print(f"accuracy: {metrics['accuracy']:.4f}")
    print(f"macro_f1: {metrics['macro_f1']:.4f}")
    print(f"weighted_f1: {metrics['weighted_f1']:.4f}")
    print(f"wrote: {args.output}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())