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import numpy as np
import pytest
from sklearn._loss import HalfPoissonLoss
from sklearn.neural_network._base import binary_log_loss, log_loss, poisson_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)
def test_poisson_loss(global_random_seed):
"""Test Poisson loss against well tested HalfPoissonLoss."""
n = 1000
rng = np.random.default_rng(global_random_seed)
y_true = rng.integers(low=0, high=10, size=n).astype(float)
y_raw = rng.standard_normal(n)
y_pred = np.exp(y_raw)
sw = rng.uniform(low=0.1, high=10, size=n)
assert 0 in y_true
loss = poisson_loss(y_true=y_true, y_pred=y_pred, sample_weight=sw)
pl = HalfPoissonLoss()
loss_ref = (
pl(y_true=y_true, raw_prediction=y_raw, sample_weight=sw)
+ pl.constant_to_optimal_zero(y_true=y_true, sample_weight=sw).mean()
/ sw.mean()
)
assert loss == pytest.approx(loss_ref, rel=1e-12)
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