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39 lines
1.2 KiB
39 lines
1.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from unittest.mock import Mock
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import numpy as np
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from miplearn.classifiers import Classifier
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from miplearn.classifiers.threshold import MinPrecisionThreshold
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def test_threshold_dynamic():
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clf = Mock(spec=Classifier)
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clf.predict_proba = Mock(
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return_value=np.array(
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[
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[0.10, 0.90],
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[0.10, 0.90],
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[0.20, 0.80],
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[0.30, 0.70],
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]
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)
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)
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x_train = np.array([0, 1, 2, 3])
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y_train = np.array([1, 1, 0, 0])
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threshold = MinPrecisionThreshold(min_precision=1.0)
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assert threshold.find(clf, x_train, y_train) == 0.90
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threshold = MinPrecisionThreshold(min_precision=0.65)
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assert threshold.find(clf, x_train, y_train) == 0.80
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threshold = MinPrecisionThreshold(min_precision=0.50)
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assert threshold.find(clf, x_train, y_train) == 0.70
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threshold = MinPrecisionThreshold(min_precision=0.00)
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assert threshold.find(clf, x_train, y_train) == 0.70
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