src/deep_baseline.py

"""
Lightweight deep baseline using sklearn MLPClassifier.
Serves as a stronger nonlinear baseline when full EEGNet stack is unavailable.
"""
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score

from src.preprocess import build_dataset_from_folder


def main():
    X0, y0 = build_dataset_from_folder("data/relaxed", label=0)
    X1, y1 = build_dataset_from_folder("data/focused", label=1)
    X = np.vstack([X0, X1])
    y = np.concatenate([y0, y1])

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )

    model = Pipeline([
        ("scaler", StandardScaler()),
        ("mlp", MLPClassifier(
            hidden_layer_sizes=(128, 64),
            activation="relu",
            alpha=1e-4,
            batch_size=64,
            learning_rate_init=1e-3,
            max_iter=200,
            random_state=42,
            early_stopping=True,
        ))
    ])

    model.fit(X_train, y_train)
    pred = model.predict(X_test)
    proba = model.predict_proba(X_test)[:, 1]

    metrics = {
        "model": "MLP",
        "accuracy": float(accuracy_score(y_test, pred)),
        "f1": float(f1_score(y_test, pred)),
        "auc": float(roc_auc_score(y_test, proba)),
    }

    out = Path("outputs")
    out.mkdir(exist_ok=True)
    pd.DataFrame([metrics]).to_csv(out / "deep_baseline_results.csv", index=False)
    print(metrics)


if __name__ == "__main__":
    main()