mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 09:28:51 -06:00
Remove experimental LP components
This commit is contained in:
@@ -1,3 +0,0 @@
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# 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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@@ -1,127 +0,0 @@
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# 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.base 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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@@ -1,439 +0,0 @@
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# 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, call
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import numpy as np
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from miplearn.classifiers import Classifier
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from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
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from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
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from miplearn.features import TrainingSample, Features
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from miplearn.instance.base import Instance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.learning import LearningSolver
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from tests.fixtures.infeasible import get_infeasible_instance
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from tests.fixtures.redundant import get_instance_with_redundancy
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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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internal.is_infeasible = 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_mip(
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solver=solver,
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instance=instance,
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model=None,
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stats={},
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features=Features(),
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training_data=TrainingSample(),
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)
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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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training_data = TrainingSample()
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component.after_solve_mip(
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solver=solver,
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instance=instance,
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model=None,
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stats={},
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features=Features(),
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training_data=training_data,
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)
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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 training_data.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_feasibility():
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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_mip(
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solver=solver,
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instance=instance,
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model=None,
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stats={},
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features=Features(),
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training_data=TrainingSample(),
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)
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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].training_data = [
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TrainingSample(
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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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)
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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].training_data = [
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TrainingSample(
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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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)
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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(
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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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]
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),
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"type-b": np.array(
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[
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[False, True],
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[True, False],
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[True, False],
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]
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),
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}
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# Should build X and Y matrices correctly
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actual_x, actual_y = component.x_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) == {
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"type-a": [
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[False, True],
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],
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"type-b": [
|
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[True, False],
|
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[False, True],
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],
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||||
}
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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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||||
|
||||
|
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def test_x_multiple_solves():
|
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instance = Mock(spec=Instance)
|
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instance.training_data = [
|
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TrainingSample(
|
||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
),
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||||
TrainingSample(
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||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
),
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[False, True],
|
||||
[True, False],
|
||||
[True, False],
|
||||
[False, True],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[False, True],
|
||||
[True, False],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x, actual_y = component.x_y([instance])
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
|
||||
def test_usage():
|
||||
for internal_solver in [GurobiSolver]:
|
||||
for instance in [
|
||||
get_instance_with_redundancy(internal_solver),
|
||||
get_infeasible_instance(internal_solver),
|
||||
]:
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
DropRedundantInequalitiesStep(),
|
||||
],
|
||||
)
|
||||
# The following should not crash
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
Reference in New Issue
Block a user