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34 lines
1.2 KiB
34 lines
1.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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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import numpy as np
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from numpy.testing import assert_array_equal
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from sklearn.linear_model import LinearRegression
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from sklearn.neighbors import KNeighborsClassifier
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from miplearn.classifiers.sklearn import ScikitLearnClassifier, ScikitLearnRegressor
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def test_constant_prediction():
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x_train = np.array([[0.0, 1.0], [1.0, 0.0]])
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y_train = np.array([[True, False], [True, False]])
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clf = ScikitLearnClassifier(KNeighborsClassifier(n_neighbors=1))
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clf.fit(x_train, y_train)
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proba = clf.predict_proba(x_train)
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assert_array_equal(
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proba,
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np.array([[1.0, 0.0], [1.0, 0.0]]),
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)
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def test_regressor():
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x_train = np.array([[0.0, 1.0], [1.0, 4.0], [2.0, 2.0]])
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y_train = np.array([[1.0], [5.0], [4.0]])
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x_test = np.array([[4.0, 4.0], [0.0, 0.0]])
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clf = ScikitLearnRegressor(LinearRegression())
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clf.fit(x_train, y_train)
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y_test_actual = clf.predict(x_test)
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y_test_expected = np.array([[8.0], [0.0]])
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assert_array_equal(np.round(y_test_actual, 2), y_test_expected)
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