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286 lines
8.2 KiB
286 lines
8.2 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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import numpy as np
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from unittest.mock import Mock, call
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from miplearn import LearningSolver, Instance, InternalSolver
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from miplearn.classifiers import Classifier
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from miplearn.components.relaxation import DropRedundantInequalitiesStep
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def _setup():
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solver = Mock(spec=LearningSolver)
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internal = solver.internal_solver = Mock(spec=InternalSolver)
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internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
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internal.get_inequality_slacks = Mock(
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side_effect=lambda: {
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"c1": 0.5,
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"c2": 0.0,
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"c3": 0.0,
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"c4": 1.4,
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}
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)
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internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
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internal.is_constraint_satisfied = Mock(return_value=False)
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instance = Mock(spec=Instance)
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instance.get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c2": np.array([1.0, 0.0]),
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"c3": np.array([0.5, 0.5]),
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"c4": np.array([1.0]),
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}[cid]
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)
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instance.get_constraint_category = Mock(
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side_effect=lambda cid: {
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"c1": None,
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"c2": "type-a",
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"c3": "type-a",
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"c4": "type-b",
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}[cid]
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)
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classifiers = {
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"type-a": Mock(spec=Classifier),
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"type-b": Mock(spec=Classifier),
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}
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classifiers["type-a"].predict_proba = Mock(
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return_value=np.array(
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[
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[0.20, 0.80],
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[0.05, 0.95],
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]
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)
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)
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classifiers["type-b"].predict_proba = Mock(
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return_value=np.array(
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[
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[0.02, 0.98],
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]
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)
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)
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return solver, internal, instance, classifiers
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def test_drop_redundant():
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solver, internal, instance, classifiers = _setup()
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component = DropRedundantInequalitiesStep()
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component.classifiers = classifiers
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# LearningSolver calls before_solve
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component.before_solve(solver, instance, None)
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# Should query list of constraints
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internal.get_constraint_ids.assert_called_once()
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# Should query category and features for each constraint in the model
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assert instance.get_constraint_category.call_count == 4
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instance.get_constraint_category.assert_has_calls(
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[
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call("c1"),
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call("c2"),
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call("c3"),
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call("c4"),
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]
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)
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# For constraint with non-null categories, should ask for features
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assert instance.get_constraint_features.call_count == 3
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instance.get_constraint_features.assert_has_calls(
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[
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call("c2"),
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call("c3"),
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call("c4"),
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]
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)
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# Should ask ML to predict whether constraint should be removed
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type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
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type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
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np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
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np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
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# Should ask internal solver to remove constraints predicted as redundant
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assert internal.extract_constraint.call_count == 2
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internal.extract_constraint.assert_has_calls(
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[
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call("c3"),
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call("c4"),
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]
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)
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# LearningSolver calls after_solve
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component.after_solve(solver, instance, None, {}, {})
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# Should query slack for all inequalities
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internal.get_inequality_slacks.assert_called_once()
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# Should store constraint slacks in instance object
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assert hasattr(instance, "slacks")
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assert instance.slacks == {
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"c1": 0.5,
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"c2": 0.0,
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"c3": 0.0,
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"c4": 1.4,
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}
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def test_drop_redundant_with_check_dropped():
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solver, internal, instance, classifiers = _setup()
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component = DropRedundantInequalitiesStep(
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check_feasibility=True,
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violation_tolerance=1e-3,
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)
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component.classifiers = classifiers
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# LearningSolver call before_solve
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component.before_solve(solver, instance, None)
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# Assert constraints are extracted
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assert internal.extract_constraint.call_count == 2
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internal.extract_constraint.assert_has_calls(
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[
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call("c3"),
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call("c4"),
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]
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)
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# LearningSolver calls iteration_cb (first time)
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should_repeat = component.iteration_cb(solver, instance, None)
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# Should ask LearningSolver to repeat
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assert should_repeat
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# Should ask solver if removed constraints are satisfied (mock always returns false)
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internal.is_constraint_satisfied.assert_has_calls(
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[
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call("<c3>", 1e-3),
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call("<c4>", 1e-3),
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]
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)
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# Should add constraints back to LP relaxation
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internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
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# LearningSolver calls iteration_cb (second time)
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should_repeat = component.iteration_cb(solver, instance, None)
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assert not should_repeat
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def test_x_y_fit_predict_evaluate():
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instances = [Mock(spec=Instance), Mock(spec=Instance)]
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component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
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component.classifiers = {
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"type-a": Mock(spec=Classifier),
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"type-b": Mock(spec=Classifier),
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}
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component.classifiers["type-a"].predict_proba = Mock(
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return_value=[
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np.array([0.20, 0.80]),
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]
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)
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component.classifiers["type-b"].predict_proba = Mock(
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return_value=np.array(
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[
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[0.50, 0.50],
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[0.05, 0.95],
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]
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)
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)
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# First mock instance
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instances[0].slacks = {
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"c1": 0.00,
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"c2": 0.05,
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"c3": 0.00,
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"c4": 30.0,
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}
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instances[0].get_constraint_category = Mock(
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side_effect=lambda cid: {
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"c1": None,
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"c2": "type-a",
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"c3": "type-a",
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"c4": "type-b",
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}[cid]
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)
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instances[0].get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c2": np.array([1.0, 0.0]),
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"c3": np.array([0.5, 0.5]),
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"c4": np.array([1.0]),
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}[cid]
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)
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# Second mock instance
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instances[1].slacks = {
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"c1": 0.00,
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"c3": 0.30,
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"c4": 0.00,
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"c5": 0.00,
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}
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instances[1].get_constraint_category = Mock(
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side_effect=lambda cid: {
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"c1": None,
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"c3": "type-a",
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"c4": "type-b",
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"c5": "type-b",
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}[cid]
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)
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instances[1].get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c3": np.array([0.3, 0.4]),
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"c4": np.array([0.7]),
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"c5": np.array([0.8]),
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}[cid]
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)
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expected_x = {
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"type-a": np.array(
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[
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[1.0, 0.0],
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[0.5, 0.5],
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[0.3, 0.4],
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]
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),
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"type-b": np.array(
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[
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[1.0],
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[0.7],
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[0.8],
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]
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),
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}
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expected_y = {
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"type-a": np.array([[0], [0], [1]]),
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"type-b": np.array([[1], [0], [0]]),
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}
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# Should build X and Y matrices correctly
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actual_x = component.x(instances)
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actual_y = component.y(instances)
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for category in ["type-a", "type-b"]:
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np.testing.assert_array_equal(actual_x[category], expected_x[category])
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np.testing.assert_array_equal(actual_y[category], expected_y[category])
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# Should pass along X and Y matrices to classifiers
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component.fit(instances)
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for category in ["type-a", "type-b"]:
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actual_x = component.classifiers[category].fit.call_args[0][0]
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actual_y = component.classifiers[category].fit.call_args[0][1]
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np.testing.assert_array_equal(actual_x, expected_x[category])
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np.testing.assert_array_equal(actual_y, expected_y[category])
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assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
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ev = component.evaluate(instances[1])
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assert ev["True positive"] == 1
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assert ev["True negative"] == 1
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assert ev["False positive"] == 1
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assert ev["False negative"] == 0
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