31 lines
836 B
Python
31 lines
836 B
Python
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import pytest
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import numpy as np
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from sklearn.neural_network._base import binary_log_loss
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from sklearn.neural_network._base import log_loss
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def test_binary_log_loss_1_prob_finite():
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# y_proba is equal to one should result in a finite logloss
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y_true = np.array([[0, 0, 1]]).T
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y_prob = np.array([[0.9, 1.0, 1.0]]).T
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loss = binary_log_loss(y_true, y_prob)
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assert np.isfinite(loss)
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@pytest.mark.parametrize(
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"y_true, y_prob",
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[
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(
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np.array([[1, 0, 0], [0, 1, 0]]),
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np.array([[0.0, 1.0, 0.0], [0.9, 0.05, 0.05]]),
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),
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(np.array([[0, 0, 1]]).T, np.array([[0.9, 1.0, 1.0]]).T),
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],
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)
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def test_log_loss_1_prob_finite(y_true, y_prob):
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# y_proba is equal to 1 should result in a finite logloss
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loss = log_loss(y_true, y_prob)
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assert np.isfinite(loss)
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