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112 lines
3.7 KiB
112 lines
3.7 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.components.primal import PrimalSolutionComponent
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from .. import get_test_pyomo_instances
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def test_predict():
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instances, models = get_test_pyomo_instances()
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comp = PrimalSolutionComponent()
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comp.fit(instances)
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solution = comp.predict(instances[0])
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assert "x" in solution
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assert 0 in solution["x"]
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assert 1 in solution["x"]
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assert 2 in solution["x"]
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assert 3 in solution["x"]
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def test_evaluate():
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instances, models = get_test_pyomo_instances()
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clf_zero = Mock(spec=Classifier)
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clf_zero.predict_proba = Mock(
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return_value=np.array(
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[
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[0.0, 1.0], # x[0]
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[0.0, 1.0], # x[1]
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[1.0, 0.0], # x[2]
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[1.0, 0.0], # x[3]
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]
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)
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)
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clf_one = Mock(spec=Classifier)
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clf_one.predict_proba = Mock(
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return_value=np.array(
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[
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[1.0, 0.0], # x[0] instances[0]
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[1.0, 0.0], # x[1] instances[0]
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[0.0, 1.0], # x[2] instances[0]
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[1.0, 0.0], # x[3] instances[0]
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]
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)
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)
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comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
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comp.fit(instances[:1])
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assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
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assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
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ev = comp.evaluate(instances[:1])
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assert ev == {
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"Fix one": {
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0: {
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"Accuracy": 0.5,
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"Condition negative": 1,
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"Condition negative (%)": 25.0,
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"Condition positive": 3,
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"Condition positive (%)": 75.0,
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"F1 score": 0.5,
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"False negative": 2,
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"False negative (%)": 50.0,
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"False positive": 0,
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"False positive (%)": 0.0,
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"Precision": 1.0,
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"Predicted negative": 3,
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"Predicted negative (%)": 75.0,
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"Predicted positive": 1,
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"Predicted positive (%)": 25.0,
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"Recall": 0.3333333333333333,
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"True negative": 1,
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"True negative (%)": 25.0,
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"True positive": 1,
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"True positive (%)": 25.0,
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}
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},
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"Fix zero": {
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0: {
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"Accuracy": 0.75,
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"Condition negative": 3,
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"Condition negative (%)": 75.0,
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"Condition positive": 1,
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"Condition positive (%)": 25.0,
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"F1 score": 0.6666666666666666,
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"False negative": 0,
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"False negative (%)": 0.0,
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"False positive": 1,
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"False positive (%)": 25.0,
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"Precision": 0.5,
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"Predicted negative": 2,
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"Predicted negative (%)": 50.0,
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"Predicted positive": 2,
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"Predicted positive (%)": 50.0,
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"Recall": 1.0,
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"True negative": 2,
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"True negative (%)": 50.0,
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"True positive": 1,
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"True positive (%)": 25.0,
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}
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},
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}
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def test_primal_parallel_fit():
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instances, models = get_test_pyomo_instances()
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comp = PrimalSolutionComponent()
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comp.fit(instances, n_jobs=2)
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assert len(comp.classifiers) == 2
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