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193 lines
6.0 KiB
193 lines
6.0 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 Sample, 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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"lazy_enforced": {"c1", "c2"},
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"static_instance_features": [5.0],
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},
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),
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MemorySample(
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{
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"lazy_enforced": {"c2", "c3"},
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"static_instance_features": [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={
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"c1": "type-a",
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"c2": "type-a",
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"c3": "type-b",
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"c4": "type-b",
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}
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)
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instances[0].get_constraint_features = Mock( # type: ignore
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return_value={
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"c1": [1.0, 2.0, 3.0],
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"c2": [4.0, 5.0, 6.0],
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"c3": [1.0, 2.0],
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"c4": [3.0, 4.0],
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}
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)
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samples_1 = [
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MemorySample(
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{
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"lazy_enforced": {"c3", "c4"},
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"static_instance_features": [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={
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"c1": None,
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"c2": "type-a",
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"c3": "type-b",
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"c4": "type-b",
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}
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)
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instances[1].get_constraint_features = Mock( # type: ignore
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return_value={
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"c2": [7.0, 8.0, 9.0],
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"c3": [5.0, 6.0],
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"c4": [7.0, 8.0],
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}
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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([{"c1", "c2", "c3", "c4"}])
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x_expected = {
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"type-a": [[5.0, 1.0, 2.0, 3.0], [5.0, 4.0, 5.0, 6.0]],
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"type-b": [[5.0, 1.0, 2.0], [5.0, 3.0, 4.0]],
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}
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y_expected = {
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"type-a": [[False, True], [False, True]],
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"type-b": [[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_fit(training_instances: List[Instance]) -> None:
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# clf = Mock(spec=Classifier)
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# clf.clone = Mock(side_effect=lambda: Mock(spec=Classifier))
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# comp = DynamicLazyConstraintsComponent(classifier=clf)
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# comp.fit(training_instances)
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# assert clf.clone.call_count == 2
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#
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# assert "type-a" in comp.classifiers
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# clf_a = comp.classifiers["type-a"]
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# assert clf_a.fit.call_count == 1 # type: ignore
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# assert_array_equal(
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# clf_a.fit.call_args[0][0], # type: ignore
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# np.array(
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# [
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# [5.0, 1.0, 2.0, 3.0],
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# [5.0, 4.0, 5.0, 6.0],
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# [5.0, 1.0, 2.0, 3.0],
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# [5.0, 4.0, 5.0, 6.0],
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# [8.0, 7.0, 8.0, 9.0],
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# ]
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# ),
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# )
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# assert_array_equal(
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# clf_a.fit.call_args[0][1], # type: ignore
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# np.array(
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# [
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# [False, True],
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# [False, True],
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# [True, False],
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# [False, True],
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# [True, False],
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# ]
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# ),
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# )
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#
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# assert "type-b" in comp.classifiers
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# clf_b = comp.classifiers["type-b"]
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# assert clf_b.fit.call_count == 1 # type: ignore
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# assert_array_equal(
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# clf_b.fit.call_args[0][0], # type: ignore
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# np.array(
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# [
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# [5.0, 1.0, 2.0],
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# [5.0, 3.0, 4.0],
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# [5.0, 1.0, 2.0],
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# [5.0, 3.0, 4.0],
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# [8.0, 5.0, 6.0],
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# [8.0, 7.0, 8.0],
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# ]
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# ),
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# )
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# assert_array_equal(
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# clf_b.fit.call_args[0][1], # type: ignore
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# np.array(
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# [
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# [True, False],
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# [True, False],
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# [False, True],
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# [True, False],
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# [False, True],
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# [False, True],
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# ]
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# ),
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# )
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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(["c1", "c2", "c3", "c4"])
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comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
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comp.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
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comp.classifiers["type-a"] = Mock(spec=Classifier)
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comp.classifiers["type-b"] = Mock(spec=Classifier)
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comp.classifiers["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["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 == ["c1", "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 == {
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"type-a": classifier_evaluation_dict(tp=1, fp=0, tn=0, fn=1),
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"type-b": classifier_evaluation_dict(tp=0, fp=1, tn=1, fn=0),
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}
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