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141 lines
5.7 KiB
141 lines
5.7 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 import DynamicLazyConstraintsComponent, LearningSolver, InternalSolver
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from miplearn.classifiers import Classifier
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from miplearn.tests import get_test_pyomo_instances
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from numpy.linalg import norm
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E = 0.1
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def test_lazy_fit():
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instances, models = get_test_pyomo_instances()
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instances[0].found_violated_lazy_constraints = ["a", "b"]
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instances[1].found_violated_lazy_constraints = ["b", "c"]
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classifier = Mock(spec=Classifier)
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component = DynamicLazyConstraintsComponent(classifier=classifier)
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component.fit(instances)
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# Should create one classifier for each violation
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assert "a" in component.classifiers
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assert "b" in component.classifiers
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assert "c" in component.classifiers
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# Should provide correct x_train to each classifier
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expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
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actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
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actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
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assert norm(expected_x_train_a - actual_x_train_a) < E
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assert norm(expected_x_train_b - actual_x_train_b) < E
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assert norm(expected_x_train_c - actual_x_train_c) < E
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# Should provide correct y_train to each classifier
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expected_y_train_a = np.array([1.0, 0.0])
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expected_y_train_b = np.array([1.0, 1.0])
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expected_y_train_c = np.array([0.0, 1.0])
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actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
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actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
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actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
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assert norm(expected_y_train_a - actual_y_train_a) < E
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assert norm(expected_y_train_b - actual_y_train_b) < E
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assert norm(expected_y_train_c - actual_y_train_c) < E
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def test_lazy_before():
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instances, models = get_test_pyomo_instances()
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instances[0].build_lazy_constraint = Mock(return_value="c1")
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solver = LearningSolver()
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solver.internal_solver = Mock(spec=InternalSolver)
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component = DynamicLazyConstraintsComponent(threshold=0.10)
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component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
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component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
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component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
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component.before_solve(solver, instances[0], models[0])
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# Should ask classifier likelihood of each constraint being violated
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expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
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expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
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actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
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actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
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assert norm(expected_x_test_a - actual_x_test_a) < E
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assert norm(expected_x_test_b - actual_x_test_b) < E
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# Should ask instance to generate cut for constraints whose likelihood
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# of being violated exceeds the threshold
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instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
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# Should ask internal solver to add generated constraint
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solver.internal_solver.add_constraint.assert_called_once_with("c1")
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def test_lazy_evaluate():
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instances, models = get_test_pyomo_instances()
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component = DynamicLazyConstraintsComponent()
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component.classifiers = {
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"a": Mock(spec=Classifier),
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"b": Mock(spec=Classifier),
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"c": Mock(spec=Classifier),
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}
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component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
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component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
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component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
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instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
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instances[1].found_violated_lazy_constraints = ["b", "d"]
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assert component.evaluate(instances) == {
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0: {
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"Accuracy": 0.75,
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"F1 score": 0.8,
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"Precision": 1.0,
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"Recall": 2 / 3.0,
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"Predicted positive": 2,
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"Predicted negative": 2,
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"Condition positive": 3,
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"Condition negative": 1,
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"False negative": 1,
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"False positive": 0,
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"True negative": 1,
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"True positive": 2,
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"Predicted positive (%)": 50.0,
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"Predicted negative (%)": 50.0,
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"Condition positive (%)": 75.0,
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"Condition negative (%)": 25.0,
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"False negative (%)": 25.0,
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"False positive (%)": 0,
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"True negative (%)": 25.0,
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"True positive (%)": 50.0,
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},
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1: {
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"Accuracy": 0.5,
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"F1 score": 0.5,
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"Precision": 0.5,
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"Recall": 0.5,
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"Predicted positive": 2,
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"Predicted negative": 2,
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"Condition positive": 2,
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"Condition negative": 2,
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"False negative": 1,
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"False positive": 1,
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"True negative": 1,
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"True positive": 1,
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"Predicted positive (%)": 50.0,
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"Predicted negative (%)": 50.0,
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"Condition positive (%)": 50.0,
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"Condition negative (%)": 50.0,
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"False negative (%)": 25.0,
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"False positive (%)": 25.0,
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"True negative (%)": 25.0,
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"True positive (%)": 25.0,
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},
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}
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