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Move tests to separate folder
This commit is contained in:
@@ -1,123 +0,0 @@
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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_first=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_first=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_first=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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@@ -1,364 +0,0 @@
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# 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, 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.relaxation import DropRedundantInequalitiesStep
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from miplearn.instance import Instance
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.learning import LearningSolver
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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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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(solver, instance, None)
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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 = {}
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component.after_solve(solver, instance, None, {}, training_data)
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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(solver, instance, None)
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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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{
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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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{
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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([[0], [0], [1]]),
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"type-b": np.array([[1], [0], [0]]),
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}
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# Should build X and Y matrices correctly
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actual_x = component.x(instances)
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actual_y = component.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) == {"type-a": [[1]], "type-b": [[0], [1]]}
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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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def test_x_multiple_solves():
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instance = Mock(spec=Instance)
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instance.training_data = [
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{
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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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"slacks": {
|
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"c1": 0.00,
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"c2": 0.00,
|
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"c3": 1.00,
|
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"c4": 0.0,
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}
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},
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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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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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|
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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],
|
||||
[1.0, 0.0],
|
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[0.5, 0.5],
|
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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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[1.0],
|
||||
]
|
||||
),
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||||
}
|
||||
|
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expected_y = {
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"type-a": np.array([[1], [0], [0], [1]]),
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"type-b": np.array([[1], [0]]),
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}
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# Should build X and Y matrices correctly
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||||
component = DropRedundantInequalitiesStep()
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actual_x = component.x([instance])
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actual_y = component.y([instance])
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||||
print(actual_x)
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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])
|
||||
@@ -1,3 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
@@ -1,57 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.composite import CompositeComponent
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_composite():
|
||||
solver, instance, model = (
|
||||
Mock(spec=LearningSolver),
|
||||
Mock(spec=Instance),
|
||||
Mock(),
|
||||
)
|
||||
|
||||
c1 = Mock(spec=Component)
|
||||
c2 = Mock(spec=Component)
|
||||
cc = CompositeComponent([c1, c2])
|
||||
|
||||
# Should broadcast before_solve
|
||||
cc.before_solve(solver, instance, model)
|
||||
c1.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
c2.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast after_solve
|
||||
cc.after_solve(solver, instance, model, {}, {})
|
||||
c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
|
||||
# Should broadcast fit
|
||||
cc.fit([1, 2, 3])
|
||||
c1.fit.assert_has_calls([call([1, 2, 3])])
|
||||
c2.fit.assert_has_calls([call([1, 2, 3])])
|
||||
|
||||
# Should broadcast lazy_cb
|
||||
cc.lazy_cb(solver, instance, model)
|
||||
c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast iteration_cb
|
||||
cc.iteration_cb(solver, instance, model)
|
||||
c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# If at least one child component returns true, iteration_cb should return True
|
||||
c1.iteration_cb = Mock(return_value=True)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert cc.iteration_cb(solver, instance, model)
|
||||
|
||||
# If all children return False, iteration_cb should return False
|
||||
c1.iteration_cb = Mock(return_value=False)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert not cc.iteration_cb(solver, instance, model)
|
||||
@@ -1,143 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_lazy_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "c"]
|
||||
classifier = Mock(spec=Classifier)
|
||||
component = DynamicLazyConstraintsComponent(classifier=classifier)
|
||||
|
||||
component.fit(instances)
|
||||
|
||||
# Should create one classifier for each violation
|
||||
assert "a" in component.classifiers
|
||||
assert "b" in component.classifiers
|
||||
assert "c" in component.classifiers
|
||||
|
||||
# Should provide correct x_train to each classifier
|
||||
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
|
||||
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
|
||||
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
|
||||
assert norm(expected_x_train_a - actual_x_train_a) < E
|
||||
assert norm(expected_x_train_b - actual_x_train_b) < E
|
||||
assert norm(expected_x_train_c - actual_x_train_c) < E
|
||||
|
||||
# Should provide correct y_train to each classifier
|
||||
expected_y_train_a = np.array([1.0, 0.0])
|
||||
expected_y_train_b = np.array([1.0, 1.0])
|
||||
expected_y_train_c = np.array([0.0, 1.0])
|
||||
actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
|
||||
actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
|
||||
actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
|
||||
assert norm(expected_y_train_a - actual_y_train_a) < E
|
||||
assert norm(expected_y_train_b - actual_y_train_b) < E
|
||||
assert norm(expected_y_train_c - actual_y_train_c) < E
|
||||
|
||||
|
||||
def test_lazy_before():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].build_lazy_constraint = Mock(return_value="c1")
|
||||
solver = LearningSolver()
|
||||
solver.internal_solver = Mock(spec=InternalSolver)
|
||||
component = DynamicLazyConstraintsComponent(threshold=0.10)
|
||||
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
|
||||
|
||||
component.before_solve(solver, instances[0], models[0])
|
||||
|
||||
# Should ask classifier likelihood of each constraint being violated
|
||||
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
|
||||
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
|
||||
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
|
||||
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
|
||||
assert norm(expected_x_test_a - actual_x_test_a) < E
|
||||
assert norm(expected_x_test_b - actual_x_test_b) < E
|
||||
|
||||
# Should ask instance to generate cut for constraints whose likelihood
|
||||
# of being violated exceeds the threshold
|
||||
instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
|
||||
|
||||
# Should ask internal solver to add generated constraint
|
||||
solver.internal_solver.add_constraint.assert_called_once_with("c1")
|
||||
|
||||
|
||||
def test_lazy_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
component = DynamicLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"a": Mock(spec=Classifier),
|
||||
"b": Mock(spec=Classifier),
|
||||
"c": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "d"]
|
||||
assert component.evaluate(instances) == {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"F1 score": 0.8,
|
||||
"Precision": 1.0,
|
||||
"Recall": 2 / 3.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 3,
|
||||
"Condition negative": 1,
|
||||
"False negative": 1,
|
||||
"False positive": 0,
|
||||
"True negative": 1,
|
||||
"True positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 75.0,
|
||||
"Condition negative (%)": 25.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 50.0,
|
||||
},
|
||||
1: {
|
||||
"Accuracy": 0.5,
|
||||
"F1 score": 0.5,
|
||||
"Precision": 0.5,
|
||||
"Recall": 0.5,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 2,
|
||||
"Condition negative": 2,
|
||||
"False negative": 1,
|
||||
"False positive": 1,
|
||||
"True negative": 1,
|
||||
"True positive": 1,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 50.0,
|
||||
"Condition negative (%)": 50.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 25.0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 25.0,
|
||||
},
|
||||
}
|
||||
@@ -1,232 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_static import StaticLazyConstraintsComponent
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_usage_with_solver():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
solver.use_lazy_cb = False
|
||||
solver.gap_tolerance = 1e-4
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.has_static_lazy_constraints = Mock(return_value=True)
|
||||
instance.is_constraint_lazy = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": False,
|
||||
"c2": True,
|
||||
"c3": True,
|
||||
"c4": True,
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
component = StaticLazyConstraintsComponent(
|
||||
threshold=0.90,
|
||||
use_two_phase_gap=False,
|
||||
violation_tolerance=1.0,
|
||||
)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should ask if instance has static lazy constraints
|
||||
instance.has_static_lazy_constraints.assert_called_once()
|
||||
|
||||
# Should ask internal solver for a list of constraints in the model
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should ask if each constraint in the model is lazy
|
||||
instance.is_constraint_lazy.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For the lazy ones, should ask for features
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should also ask for categories
|
||||
assert instance.get_constraint_category.call_count == 3
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask internal solver to remove constraints identified as lazy
|
||||
assert internal.extract_constraint.call_count == 3
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether each lazy constraint should be enforced
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with(
|
||||
[[1.0, 0.0], [0.5, 0.5]]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
|
||||
|
||||
# For the ones that should be enforced, should ask solver to re-add them
|
||||
# to the formulation. The remaining ones should remain in the pool.
|
||||
assert internal.add_constraint.call_count == 2
|
||||
internal.add_constraint.assert_has_calls(
|
||||
[
|
||||
call("<c3>"),
|
||||
call("<c4>"),
|
||||
]
|
||||
)
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert should_repeat
|
||||
|
||||
# Should ask internal solver to verify if constraints in the pool are
|
||||
# satisfied and add the ones that are not
|
||||
internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
|
||||
internal.is_constraint_satisfied.reset_mock()
|
||||
internal.add_constraint.assert_called_once_with("<c2>")
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
# The lazy constraint pool should be empty by now, so no calls should be made
|
||||
internal.is_constraint_satisfied.assert_not_called()
|
||||
internal.add_constraint.assert_not_called()
|
||||
|
||||
# Should update instance object
|
||||
assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
|
||||
|
||||
|
||||
def test_fit():
|
||||
instance_1 = Mock(spec=Instance)
|
||||
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
|
||||
instance_1.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_1.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [1, 1],
|
||||
"c2": [1, 2],
|
||||
"c3": [1, 3],
|
||||
"c4": [1, 4, 0],
|
||||
"c5": [1, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instance_2 = Mock(spec=Instance)
|
||||
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
|
||||
instance_2.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_2.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [2, 1],
|
||||
"c2": [2, 2],
|
||||
"c3": [2, 3],
|
||||
"c4": [2, 4, 0],
|
||||
"c5": [2, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instances = [instance_1, instance_2]
|
||||
component = StaticLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
|
||||
expected_constraints = {
|
||||
"type-a": ["c1", "c2", "c3"],
|
||||
"type-b": ["c4", "c5"],
|
||||
}
|
||||
expected_x = {
|
||||
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
|
||||
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
|
||||
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
|
||||
}
|
||||
assert component._collect_constraints(instances) == expected_constraints
|
||||
assert component.x(instances) == expected_x
|
||||
assert component.y(instances) == expected_y
|
||||
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_once_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_once_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)
|
||||
@@ -1,50 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Regressor
|
||||
from miplearn.components.objective import ObjectiveValueComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_usage():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(instances)
|
||||
assert instances[0].training_data[0]["Lower bound"] == 1183.0
|
||||
assert instances[0].training_data[0]["Upper bound"] == 1183.0
|
||||
assert np.round(comp.predict(instances), 2).tolist() == [
|
||||
[1183.0, 1183.0],
|
||||
[1070.0, 1070.0],
|
||||
]
|
||||
|
||||
|
||||
def test_obj_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
reg = Mock(spec=Regressor)
|
||||
reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
|
||||
comp = ObjectiveValueComponent(regressor=reg)
|
||||
comp.fit(instances)
|
||||
ev = comp.evaluate(instances)
|
||||
assert ev == {
|
||||
"Lower bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
"Upper bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
}
|
||||
@@ -1,111 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.primal import PrimalSolutionComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances)
|
||||
solution = comp.predict(instances[0])
|
||||
assert "x" in solution
|
||||
assert 0 in solution["x"]
|
||||
assert 1 in solution["x"]
|
||||
assert 2 in solution["x"]
|
||||
assert 3 in solution["x"]
|
||||
|
||||
|
||||
def test_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
clf_zero = Mock(spec=Classifier)
|
||||
clf_zero.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.0, 1.0], # x[0]
|
||||
[0.0, 1.0], # x[1]
|
||||
[1.0, 0.0], # x[2]
|
||||
[1.0, 0.0], # x[3]
|
||||
]
|
||||
)
|
||||
)
|
||||
clf_one = Mock(spec=Classifier)
|
||||
clf_one.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[1.0, 0.0], # x[0] instances[0]
|
||||
[1.0, 0.0], # x[1] instances[0]
|
||||
[0.0, 1.0], # x[2] instances[0]
|
||||
[1.0, 0.0], # x[3] instances[0]
|
||||
]
|
||||
)
|
||||
)
|
||||
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
|
||||
comp.fit(instances[:1])
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
|
||||
ev = comp.evaluate(instances[:1])
|
||||
assert ev == {
|
||||
"Fix one": {
|
||||
0: {
|
||||
"Accuracy": 0.5,
|
||||
"Condition negative": 1,
|
||||
"Condition negative (%)": 25.0,
|
||||
"Condition positive": 3,
|
||||
"Condition positive (%)": 75.0,
|
||||
"F1 score": 0.5,
|
||||
"False negative": 2,
|
||||
"False negative (%)": 50.0,
|
||||
"False positive": 0,
|
||||
"False positive (%)": 0.0,
|
||||
"Precision": 1.0,
|
||||
"Predicted negative": 3,
|
||||
"Predicted negative (%)": 75.0,
|
||||
"Predicted positive": 1,
|
||||
"Predicted positive (%)": 25.0,
|
||||
"Recall": 0.3333333333333333,
|
||||
"True negative": 1,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
"Fix zero": {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"Condition negative": 3,
|
||||
"Condition negative (%)": 75.0,
|
||||
"Condition positive": 1,
|
||||
"Condition positive (%)": 25.0,
|
||||
"F1 score": 0.6666666666666666,
|
||||
"False negative": 0,
|
||||
"False negative (%)": 0.0,
|
||||
"False positive": 1,
|
||||
"False positive (%)": 25.0,
|
||||
"Precision": 0.5,
|
||||
"Predicted negative": 2,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Recall": 1.0,
|
||||
"True negative": 2,
|
||||
"True negative (%)": 50.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def test_primal_parallel_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances, n_jobs=2)
|
||||
assert len(comp.classifiers) == 2
|
||||
Reference in New Issue
Block a user