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fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
import pytest
from fairlearn.postprocessing import ThresholdOptimizer
from fairlearn.postprocessing._threshold_optimizer import _SUPPORTED_CONSTRAINTS
from fairlearn.postprocessing._constants import _MATPLOTLIB_IMPORT_ERROR_MESSAGE
class FakePredictor:
def predict(self, X):
return np.random.random(len(X))
@pytest.mark.parametrize("constraints", _SUPPORTED_CONSTRAINTS)
def test_no_matplotlib(constraints):
n_samples = 50
n_features = 50
n_sensitive_feature_values = 2
n_classes = 2
threshold_optimizer = ThresholdOptimizer(unconstrained_predictor=FakePredictor(),
constraints=constraints,
plot=True)
with pytest.raises(RuntimeError) as exc:
threshold_optimizer.fit(X=np.random.random((n_samples, n_features)),
y=np.random.randint(n_classes, size=n_samples),
sensitive_features=np.random.randint(n_sensitive_feature_values,
size=n_samples))
assert str(exc.value) == _MATPLOTLIB_IMPORT_ERROR_MESSAGE
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