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41 lines
1.3 KiB
41 lines
1.3 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 numpy.linalg import norm
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from sklearn.svm import SVC
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from miplearn import AdaptiveClassifier, ScikitLearnClassifier
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from miplearn.classifiers.adaptive import CandidateClassifierSpecs
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from tests.classifiers import _build_circle_training_data
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def test_adaptive() -> None:
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clf = AdaptiveClassifier(
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candidates={
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"linear": CandidateClassifierSpecs(
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classifier=ScikitLearnClassifier(
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SVC(
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probability=True,
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random_state=42,
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)
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)
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),
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"poly": CandidateClassifierSpecs(
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classifier=ScikitLearnClassifier(
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SVC(
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probability=True,
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kernel="poly",
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degree=2,
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random_state=42,
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)
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)
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),
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}
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)
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x_train, y_train = _build_circle_training_data()
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clf.fit(x_train, y_train)
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proba = clf.predict_proba(x_train)
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y_pred = (proba[:, 1] > 0.5).astype(float)
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assert norm(y_train[:, 1] - y_pred) < 0.1
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