31 lines
836 B
Python
31 lines
836 B
Python
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([[0.0, 1.0, 0.0], [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)
|