from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from mla.naive_bayes import NaiveBayesClassifier


def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(
        n_samples=1000,
        n_features=10,
        n_informative=10,
        random_state=1111,
        n_classes=2,
        class_sep=2.5,
        n_redundant=0,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.1, random_state=1111
    )

    model = NaiveBayesClassifier()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)[:, 1]

    print("classification accuracy", roc_auc_score(y_test, predictions))


if __name__ == "__main__":
    classification()
