| # 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)) | |
| 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 | |