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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)
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