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129 lines
4.5 KiB
129 lines
4.5 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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from typing import List, cast
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from unittest.mock import Mock
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import numpy as np
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import pytest
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from miplearn.classifiers import Classifier
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from miplearn.classifiers.threshold import MinProbabilityThreshold
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from miplearn.components import classifier_evaluation_dict
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from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
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from miplearn.features.sample import MemorySample
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from miplearn.instance.base import Instance
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from miplearn.solvers.tests import assert_equals
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E = 0.1
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@pytest.fixture
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def training_instances() -> List[Instance]:
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instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
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samples_0 = [
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MemorySample(
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{
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"mip_constr_lazy_enforced": np.array(["c1", "c2"], dtype="S"),
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"static_instance_features": np.array([5.0]),
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},
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),
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MemorySample(
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{
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"mip_constr_lazy_enforced": np.array(["c2", "c3"], dtype="S"),
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"static_instance_features": np.array([5.0]),
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},
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),
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]
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instances[0].get_samples = Mock(return_value=samples_0) # type: ignore
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instances[0].get_constraint_categories = Mock( # type: ignore
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return_value=np.array(["type-a", "type-a", "type-b", "type-b"], dtype="S")
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)
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instances[0].get_constraint_features = Mock( # type: ignore
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return_value=np.array(
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[
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[1.0, 2.0, 3.0],
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[4.0, 5.0, 6.0],
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[1.0, 2.0, 0.0],
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[3.0, 4.0, 0.0],
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]
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)
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)
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instances[0].are_constraints_lazy = Mock( # type: ignore
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return_value=np.zeros(4, dtype=bool)
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)
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samples_1 = [
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MemorySample(
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{
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"mip_constr_lazy_enforced": np.array(["c3", "c4"], dtype="S"),
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"static_instance_features": np.array([8.0]),
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},
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)
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]
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instances[1].get_samples = Mock(return_value=samples_1) # type: ignore
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instances[1].get_constraint_categories = Mock( # type: ignore
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return_value=np.array(["", "type-a", "type-b", "type-b"], dtype="S")
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)
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instances[1].get_constraint_features = Mock( # type: ignore
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return_value=np.array(
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[
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[7.0, 8.0, 9.0],
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[5.0, 6.0, 0.0],
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[7.0, 8.0, 0.0],
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]
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)
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)
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instances[1].are_constraints_lazy = Mock( # type: ignore
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return_value=np.zeros(4, dtype=bool)
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)
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return instances
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def test_sample_xy(training_instances: List[Instance]) -> None:
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comp = DynamicLazyConstraintsComponent()
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comp.pre_fit(
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[
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np.array(["c1", "c3", "c4"], dtype="S"),
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np.array(["c1", "c2", "c4"], dtype="S"),
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]
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)
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x_expected = {
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b"type-a": np.array([[5.0, 1.0, 2.0, 3.0], [5.0, 4.0, 5.0, 6.0]]),
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b"type-b": np.array([[5.0, 1.0, 2.0, 0.0], [5.0, 3.0, 4.0, 0.0]]),
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}
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y_expected = {
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b"type-a": np.array([[False, True], [False, True]]),
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b"type-b": np.array([[True, False], [True, False]]),
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}
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x_actual, y_actual = comp.sample_xy(
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training_instances[0],
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training_instances[0].get_samples()[0],
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)
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assert_equals(x_actual, x_expected)
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assert_equals(y_actual, y_expected)
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def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
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comp = DynamicLazyConstraintsComponent()
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comp.known_cids.extend([b"c1", b"c2", b"c3", b"c4"])
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comp.thresholds[b"type-a"] = MinProbabilityThreshold([0.5, 0.5])
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comp.thresholds[b"type-b"] = MinProbabilityThreshold([0.5, 0.5])
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comp.classifiers[b"type-a"] = Mock(spec=Classifier)
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comp.classifiers[b"type-b"] = Mock(spec=Classifier)
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comp.classifiers[b"type-a"].predict_proba = Mock( # type: ignore
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side_effect=lambda _: np.array([[0.1, 0.9], [0.8, 0.2]])
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)
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comp.classifiers[b"type-b"].predict_proba = Mock( # type: ignore
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side_effect=lambda _: np.array([[0.9, 0.1], [0.1, 0.9]])
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)
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pred = comp.sample_predict(
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training_instances[0],
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training_instances[0].get_samples()[0],
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)
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assert pred == [b"c1", b"c4"]
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ev = comp.sample_evaluate(
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training_instances[0],
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training_instances[0].get_samples()[0],
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)
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assert ev == classifier_evaluation_dict(tp=1, fp=1, tn=1, fn=1)
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