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128 lines
4.1 KiB
128 lines
4.1 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 unittest.mock import Mock
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from miplearn.classifiers import Classifier
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from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
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from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
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from miplearn.instance import Instance
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from miplearn.problems.knapsack import GurobiKnapsackInstance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.learning import LearningSolver
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def test_convert_tight_usage():
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instance = GurobiKnapsackInstance(
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weights=[3.0, 5.0, 10.0],
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prices=[1.0, 1.0, 1.0],
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capacity=16.0,
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)
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[
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RelaxIntegralityStep(),
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ConvertTightIneqsIntoEqsStep(),
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],
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)
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# Solve original problem
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stats = solver.solve(instance)
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original_upper_bound = stats["Upper bound"]
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# Should collect training data
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assert instance.training_data[0].slacks["eq_capacity"] == 0.0
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# Fit and resolve
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solver.fit([instance])
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stats = solver.solve(instance)
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# Objective value should be the same
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assert stats["Upper bound"] == original_upper_bound
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0
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class SampleInstance(Instance):
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def to_model(self):
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import gurobipy as grb
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m = grb.Model("model")
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x1 = m.addVar(name="x1")
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x2 = m.addVar(name="x2")
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m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
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m.addConstr(x1 <= 2, name="c1")
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m.addConstr(x2 <= 2, name="c2")
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m.addConstr(x1 + x2 <= 3, name="c2")
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return m
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def test_convert_tight_infeasibility():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 1
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assert stats["ConvertTight: Subopt iterations"] == 0
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def test_convert_tight_suboptimality():
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comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 1
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def test_convert_tight_optimal():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0
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