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218 lines
6.7 KiB
218 lines
6.7 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 pytest
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from numpy.testing import assert_array_equal
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from miplearn import GurobiPyomoSolver, LearningSolver, Regressor
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from miplearn.components.objective import ObjectiveValueComponent
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from miplearn.types import TrainingSample, Features
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from tests.fixtures.knapsack import get_knapsack_instance
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import numpy as np
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@pytest.fixture
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def features() -> Features:
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return {
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"Instance": {
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"User features": [1.0, 2.0],
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}
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}
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@pytest.fixture
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def sample() -> TrainingSample:
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return {
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"Lower bound": 1.0,
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"Upper bound": 2.0,
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"LP value": 3.0,
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}
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@pytest.fixture
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def sample_without_lp() -> TrainingSample:
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return {
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"Lower bound": 1.0,
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"Upper bound": 2.0,
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}
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@pytest.fixture
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def sample_without_ub() -> TrainingSample:
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return {
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"Lower bound": 1.0,
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"LP value": 3.0,
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}
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def test_sample_xy(
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features: Features,
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sample: TrainingSample,
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) -> None:
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x_expected = {
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"Lower bound": [[1.0, 2.0, 3.0]],
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"Upper bound": [[1.0, 2.0, 3.0]],
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}
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y_expected = {
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"Lower bound": [[1.0]],
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"Upper bound": [[2.0]],
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}
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xy = ObjectiveValueComponent.sample_xy(features, sample)
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assert xy is not None
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x_actual, y_actual = xy
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assert x_actual == x_expected
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assert y_actual == y_expected
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def test_sample_xy_without_lp(
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features: Features,
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sample_without_lp: TrainingSample,
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) -> None:
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x_expected = {
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"Lower bound": [[1.0, 2.0]],
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"Upper bound": [[1.0, 2.0]],
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}
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y_expected = {
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"Lower bound": [[1.0]],
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"Upper bound": [[2.0]],
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}
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xy = ObjectiveValueComponent.sample_xy(features, sample_without_lp)
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assert xy is not None
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x_actual, y_actual = xy
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assert x_actual == x_expected
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assert y_actual == y_expected
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def test_sample_xy_without_ub(
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features: Features,
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sample_without_ub: TrainingSample,
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) -> None:
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x_expected = {
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"Lower bound": [[1.0, 2.0, 3.0]],
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"Upper bound": [[1.0, 2.0, 3.0]],
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}
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y_expected = {"Lower bound": [[1.0]]}
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xy = ObjectiveValueComponent.sample_xy(features, sample_without_ub)
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assert xy is not None
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x_actual, y_actual = xy
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assert x_actual == x_expected
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assert y_actual == y_expected
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def test_fit_xy() -> None:
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x = {
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"Lower bound": np.array([[0.0, 0.0], [1.0, 2.0]]),
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"Upper bound": np.array([[0.0, 0.0], [1.0, 2.0]]),
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}
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y = {
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"Lower bound": np.array([[100.0]]),
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"Upper bound": np.array([[200.0]]),
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}
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reg = Mock(spec=Regressor)
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reg.clone = Mock(side_effect=lambda: Mock(spec=Regressor))
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comp = ObjectiveValueComponent(regressor=reg)
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assert comp.ub_regressor is None
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assert comp.lb_regressor is None
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comp.fit_xy(x, y)
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assert reg.clone.call_count == 2
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assert comp.ub_regressor is not None
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assert comp.lb_regressor is not None
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assert comp.ub_regressor.fit.call_count == 1
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assert comp.lb_regressor.fit.call_count == 1
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assert_array_equal(comp.ub_regressor.fit.call_args[0][0], x["Upper bound"])
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assert_array_equal(comp.lb_regressor.fit.call_args[0][0], x["Lower bound"])
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assert_array_equal(comp.ub_regressor.fit.call_args[0][1], y["Upper bound"])
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assert_array_equal(comp.lb_regressor.fit.call_args[0][1], y["Lower bound"])
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def test_fit_xy_without_ub() -> None:
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x = {
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"Lower bound": np.array([[0.0, 0.0], [1.0, 2.0]]),
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"Upper bound": np.array([[0.0, 0.0], [1.0, 2.0]]),
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}
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y = {
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"Lower bound": np.array([[100.0]]),
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}
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reg = Mock(spec=Regressor)
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reg.clone = Mock(side_effect=lambda: Mock(spec=Regressor))
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comp = ObjectiveValueComponent(regressor=reg)
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assert comp.ub_regressor is None
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assert comp.lb_regressor is None
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comp.fit_xy(x, y)
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assert reg.clone.call_count == 1
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assert comp.ub_regressor is None
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assert comp.lb_regressor is not None
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assert comp.lb_regressor.fit.call_count == 1
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assert_array_equal(comp.lb_regressor.fit.call_args[0][0], x["Lower bound"])
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assert_array_equal(comp.lb_regressor.fit.call_args[0][1], y["Lower bound"])
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def test_sample_predict(
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features: Features,
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sample: TrainingSample,
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) -> None:
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x, y = ObjectiveValueComponent.sample_xy(features, sample)
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comp = ObjectiveValueComponent()
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comp.lb_regressor = Mock(spec=Regressor)
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comp.ub_regressor = Mock(spec=Regressor)
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comp.lb_regressor.predict = Mock(side_effect=lambda _: np.array([[50.0]]))
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comp.ub_regressor.predict = Mock(side_effect=lambda _: np.array([[60.0]]))
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pred = comp.sample_predict(features, sample)
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assert pred == {
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"Lower bound": 50.0,
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"Upper bound": 60.0,
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}
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assert_array_equal(comp.ub_regressor.predict.call_args[0][0], x["Upper bound"])
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assert_array_equal(comp.lb_regressor.predict.call_args[0][0], x["Lower bound"])
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def test_sample_predict_without_ub(
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features: Features,
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sample_without_ub: TrainingSample,
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) -> None:
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x, y = ObjectiveValueComponent.sample_xy(features, sample_without_ub)
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comp = ObjectiveValueComponent()
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comp.lb_regressor = Mock(spec=Regressor)
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comp.lb_regressor.predict = Mock(side_effect=lambda _: np.array([[50.0]]))
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pred = comp.sample_predict(features, sample_without_ub)
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assert pred == {
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"Lower bound": 50.0,
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}
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assert_array_equal(comp.lb_regressor.predict.call_args[0][0], x["Lower bound"])
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def test_sample_evaluate(features: Features, sample: TrainingSample) -> None:
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comp = ObjectiveValueComponent()
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comp.lb_regressor = Mock(spec=Regressor)
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comp.lb_regressor.predict = lambda _: np.array([[1.05]])
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comp.ub_regressor = Mock(spec=Regressor)
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comp.ub_regressor.predict = lambda _: np.array([[2.50]])
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ev = comp.sample_evaluate(features, sample)
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assert ev == {
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"Lower bound": {
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"Actual value": 1.0,
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"Predicted value": 1.05,
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"Absolute error": 0.05,
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"Relative error": 0.05,
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},
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"Upper bound": {
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"Actual value": 2.0,
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"Predicted value": 2.50,
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"Absolute error": 0.5,
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"Relative error": 0.25,
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},
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}
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def test_usage() -> None:
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solver = LearningSolver(components=[ObjectiveValueComponent()])
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instance = get_knapsack_instance(GurobiPyomoSolver())
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solver.solve(instance)
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solver.fit([instance])
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stats = solver.solve(instance)
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assert stats["Lower bound"] == stats["Objective: Predicted LB"]
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assert stats["Upper bound"] == stats["Objective: Predicted UB"]
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