from typing import Sequence


def false_positive_rate(y_true: Sequence, y_pred: Sequence) -> float:
    """Calculates the false positive rate binary classification results.
    Assumes that the negative class is -1.
    """
    assert set(y_true).issubset({1, -1})
    assert set(y_pred).issubset({1, -1})

    false_positives = 0
    true_negatives = 0
    for true_label, predicted_label in zip(y_true, y_pred):
        if true_label == -1 and predicted_label == 1:
            false_positives += 1
        elif true_label == predicted_label == -1:
            true_negatives += 1
    try:
        return false_positives / (false_positives + true_negatives)
    except ZeroDivisionError:
        return 0