import pytest
import numpy as np

from sklearn.neural_network._base import binary_log_loss
from sklearn.neural_network._base import log_loss


def test_binary_log_loss_1_prob_finite():
    # y_proba is equal to one should result in a finite logloss
    y_true = np.array([[0, 0, 1]]).T
    y_prob = np.array([[0.9, 1.0, 1.0]]).T

    loss = binary_log_loss(y_true, y_prob)
    assert np.isfinite(loss)


@pytest.mark.parametrize("y_true, y_prob", [
    (np.array([[1, 0, 0], [0, 1, 0]]),
     np.array([[0., 1., 0.], [0.9, 0.05, 0.05]])),
    (np.array([[0, 0, 1]]).T,
     np.array([[0.9, 1.0, 1.0]]).T),
])
def test_log_loss_1_prob_finite(y_true, y_prob):
    # y_proba is equal to 1 should result in a finite logloss
    loss = log_loss(y_true, y_prob)
    assert np.isfinite(loss)