You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
440 lines
12 KiB
440 lines
12 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
from unittest.mock import Mock, call
|
|
|
|
import numpy as np
|
|
|
|
from miplearn.classifiers import Classifier
|
|
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
|
|
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
|
from miplearn.instance import Instance
|
|
from miplearn.solvers.gurobi import GurobiSolver
|
|
from miplearn.solvers.internal import InternalSolver
|
|
from miplearn.solvers.learning import LearningSolver
|
|
from miplearn.features import TrainingSample, Features
|
|
from tests.fixtures.infeasible import get_infeasible_instance
|
|
from tests.fixtures.redundant import get_instance_with_redundancy
|
|
|
|
|
|
def _setup():
|
|
solver = Mock(spec=LearningSolver)
|
|
|
|
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
|
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
|
internal.get_inequality_slacks = Mock(
|
|
side_effect=lambda: {
|
|
"c1": 0.5,
|
|
"c2": 0.0,
|
|
"c3": 0.0,
|
|
"c4": 1.4,
|
|
}
|
|
)
|
|
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
|
internal.is_constraint_satisfied = Mock(return_value=False)
|
|
internal.is_infeasible = Mock(return_value=False)
|
|
|
|
instance = Mock(spec=Instance)
|
|
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]
|
|
)
|
|
instance.get_constraint_category = Mock(
|
|
side_effect=lambda cid: {
|
|
"c1": None,
|
|
"c2": "type-a",
|
|
"c3": "type-a",
|
|
"c4": "type-b",
|
|
}[cid]
|
|
)
|
|
|
|
classifiers = {
|
|
"type-a": Mock(spec=Classifier),
|
|
"type-b": Mock(spec=Classifier),
|
|
}
|
|
classifiers["type-a"].predict_proba = Mock(
|
|
return_value=np.array(
|
|
[
|
|
[0.20, 0.80],
|
|
[0.05, 0.95],
|
|
]
|
|
)
|
|
)
|
|
classifiers["type-b"].predict_proba = Mock(
|
|
return_value=np.array(
|
|
[
|
|
[0.02, 0.98],
|
|
]
|
|
)
|
|
)
|
|
|
|
return solver, internal, instance, classifiers
|
|
|
|
|
|
def test_drop_redundant():
|
|
solver, internal, instance, classifiers = _setup()
|
|
|
|
component = DropRedundantInequalitiesStep()
|
|
component.classifiers = classifiers
|
|
|
|
# LearningSolver calls before_solve
|
|
component.before_solve_mip(
|
|
solver=solver,
|
|
instance=instance,
|
|
model=None,
|
|
stats={},
|
|
features=Features(),
|
|
training_data=TrainingSample(),
|
|
)
|
|
|
|
# Should query list of constraints
|
|
internal.get_constraint_ids.assert_called_once()
|
|
|
|
# Should query category and features for each constraint in the model
|
|
assert instance.get_constraint_category.call_count == 4
|
|
instance.get_constraint_category.assert_has_calls(
|
|
[
|
|
call("c1"),
|
|
call("c2"),
|
|
call("c3"),
|
|
call("c4"),
|
|
]
|
|
)
|
|
|
|
# For constraint with non-null categories, should ask for features
|
|
assert instance.get_constraint_features.call_count == 3
|
|
instance.get_constraint_features.assert_has_calls(
|
|
[
|
|
call("c2"),
|
|
call("c3"),
|
|
call("c4"),
|
|
]
|
|
)
|
|
|
|
# Should ask ML to predict whether constraint should be removed
|
|
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
|
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
|
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
|
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
|
|
|
# Should ask internal solver to remove constraints predicted as redundant
|
|
assert internal.extract_constraint.call_count == 2
|
|
internal.extract_constraint.assert_has_calls(
|
|
[
|
|
call("c3"),
|
|
call("c4"),
|
|
]
|
|
)
|
|
|
|
# LearningSolver calls after_solve
|
|
training_data = TrainingSample()
|
|
component.after_solve_mip(
|
|
solver=solver,
|
|
instance=instance,
|
|
model=None,
|
|
stats={},
|
|
features=Features(),
|
|
training_data=training_data,
|
|
)
|
|
|
|
# Should query slack for all inequalities
|
|
internal.get_inequality_slacks.assert_called_once()
|
|
|
|
# Should store constraint slacks in instance object
|
|
assert training_data.slacks == {
|
|
"c1": 0.5,
|
|
"c2": 0.0,
|
|
"c3": 0.0,
|
|
"c4": 1.4,
|
|
}
|
|
|
|
|
|
def test_drop_redundant_with_check_feasibility():
|
|
solver, internal, instance, classifiers = _setup()
|
|
|
|
component = DropRedundantInequalitiesStep(
|
|
check_feasibility=True,
|
|
violation_tolerance=1e-3,
|
|
)
|
|
component.classifiers = classifiers
|
|
|
|
# LearningSolver call before_solve
|
|
component.before_solve_mip(
|
|
solver=solver,
|
|
instance=instance,
|
|
model=None,
|
|
stats={},
|
|
features=Features(),
|
|
training_data=TrainingSample(),
|
|
)
|
|
|
|
# Assert constraints are extracted
|
|
assert internal.extract_constraint.call_count == 2
|
|
internal.extract_constraint.assert_has_calls(
|
|
[
|
|
call("c3"),
|
|
call("c4"),
|
|
]
|
|
)
|
|
|
|
# LearningSolver calls iteration_cb (first time)
|
|
should_repeat = component.iteration_cb(solver, instance, None)
|
|
|
|
# Should ask LearningSolver to repeat
|
|
assert should_repeat
|
|
|
|
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
|
internal.is_constraint_satisfied.assert_has_calls(
|
|
[
|
|
call("<c3>", 1e-3),
|
|
call("<c4>", 1e-3),
|
|
]
|
|
)
|
|
|
|
# Should add constraints back to LP relaxation
|
|
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
|
|
|
# LearningSolver calls iteration_cb (second time)
|
|
should_repeat = component.iteration_cb(solver, instance, None)
|
|
assert not should_repeat
|
|
|
|
|
|
def test_x_y_fit_predict_evaluate():
|
|
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
|
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
|
component.classifiers = {
|
|
"type-a": Mock(spec=Classifier),
|
|
"type-b": Mock(spec=Classifier),
|
|
}
|
|
component.classifiers["type-a"].predict_proba = Mock(
|
|
return_value=[
|
|
np.array([0.20, 0.80]),
|
|
]
|
|
)
|
|
component.classifiers["type-b"].predict_proba = Mock(
|
|
return_value=np.array(
|
|
[
|
|
[0.50, 0.50],
|
|
[0.05, 0.95],
|
|
]
|
|
)
|
|
)
|
|
|
|
# First mock instance
|
|
instances[0].training_data = [
|
|
TrainingSample(
|
|
slacks={
|
|
"c1": 0.00,
|
|
"c2": 0.05,
|
|
"c3": 0.00,
|
|
"c4": 30.0,
|
|
}
|
|
)
|
|
]
|
|
instances[0].get_constraint_category = Mock(
|
|
side_effect=lambda cid: {
|
|
"c1": None,
|
|
"c2": "type-a",
|
|
"c3": "type-a",
|
|
"c4": "type-b",
|
|
}[cid]
|
|
)
|
|
instances[0].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]
|
|
)
|
|
|
|
# Second mock instance
|
|
instances[1].training_data = [
|
|
TrainingSample(
|
|
slacks={
|
|
"c1": 0.00,
|
|
"c3": 0.30,
|
|
"c4": 0.00,
|
|
"c5": 0.00,
|
|
}
|
|
)
|
|
]
|
|
instances[1].get_constraint_category = Mock(
|
|
side_effect=lambda cid: {
|
|
"c1": None,
|
|
"c3": "type-a",
|
|
"c4": "type-b",
|
|
"c5": "type-b",
|
|
}[cid]
|
|
)
|
|
instances[1].get_constraint_features = Mock(
|
|
side_effect=lambda cid: {
|
|
"c3": np.array([0.3, 0.4]),
|
|
"c4": np.array([0.7]),
|
|
"c5": np.array([0.8]),
|
|
}[cid]
|
|
)
|
|
|
|
expected_x = {
|
|
"type-a": np.array(
|
|
[
|
|
[1.0, 0.0],
|
|
[0.5, 0.5],
|
|
[0.3, 0.4],
|
|
]
|
|
),
|
|
"type-b": np.array(
|
|
[
|
|
[1.0],
|
|
[0.7],
|
|
[0.8],
|
|
]
|
|
),
|
|
}
|
|
expected_y = {
|
|
"type-a": np.array(
|
|
[
|
|
[True, False],
|
|
[True, False],
|
|
[False, True],
|
|
]
|
|
),
|
|
"type-b": np.array(
|
|
[
|
|
[False, True],
|
|
[True, False],
|
|
[True, False],
|
|
]
|
|
),
|
|
}
|
|
|
|
# Should build X and Y matrices correctly
|
|
actual_x, actual_y = component.x_y(instances)
|
|
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])
|
|
|
|
# Should pass along X and Y matrices to classifiers
|
|
component.fit(instances)
|
|
for category in ["type-a", "type-b"]:
|
|
actual_x = component.classifiers[category].fit.call_args[0][0]
|
|
actual_y = component.classifiers[category].fit.call_args[0][1]
|
|
np.testing.assert_array_equal(actual_x, expected_x[category])
|
|
np.testing.assert_array_equal(actual_y, expected_y[category])
|
|
|
|
assert component.predict(expected_x) == {
|
|
"type-a": [
|
|
[False, True],
|
|
],
|
|
"type-b": [
|
|
[True, False],
|
|
[False, True],
|
|
],
|
|
}
|
|
|
|
ev = component.evaluate(instances[1])
|
|
assert ev["True positive"] == 1
|
|
assert ev["True negative"] == 1
|
|
assert ev["False positive"] == 1
|
|
assert ev["False negative"] == 0
|
|
|
|
|
|
def test_x_multiple_solves():
|
|
instance = Mock(spec=Instance)
|
|
instance.training_data = [
|
|
TrainingSample(
|
|
slacks={
|
|
"c1": 0.00,
|
|
"c2": 0.05,
|
|
"c3": 0.00,
|
|
"c4": 30.0,
|
|
}
|
|
),
|
|
TrainingSample(
|
|
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)
|