import matplotlib.pyplot as plt

from sklearn.metrics import roc_curve, auc

def results(y_pred, y_test):

    """

    :param y_pred: the predicted y-values

    :param y_test: the actual y-values

    :return: the number of correct predictions, incorrect predictions, and the percent correct

    """

    num_right = 0

    num_wrong = 0

    for i in range(len(y_pred)):

        if y_pred[i] == y_test[i]:

            num_right += 1

        else:

            num_wrong += 1

    return num_right/(num_right + num_wrong)

def roc_results(y_pred, y_test, model_type):

    fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=1)

    plt.figure(figsize=(8, 6))

    lw = 2

    plt.plot(fpr, tpr,

             lw=lw, label=f'{model_type} (AUC = {round(auc(fpr, tpr), 3)})')

    plt.plot([0, 1], [0, 1], lw=lw, linestyle='--')

    plt.xlim([0.0, 1.0])

    plt.ylim([0.0, 1.05])

    plt.xlabel('False Positive Rate')

    plt.ylabel('True Positive Rate')

    plt.title('ROC Curve')

    plt.legend(loc="lower right")

    plt.show()