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<header>
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<h1 class="title">Module <code>miplearn.components.steps.tests.test_drop_redundant</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># 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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|
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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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|
|
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def test_drop_redundant():
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solver, internal, instance, classifiers = _setup()
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|
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component = DropRedundantInequalitiesStep()
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component.classifiers = classifiers
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|
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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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|
call("c1"),
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call("c2"),
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|
call("c3"),
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|
call("c4"),
|
|
]
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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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|
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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|
|
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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]]))
|
|
|
|
# Should ask internal solver to remove constraints predicted as redundant
|
|
assert internal.extract_constraint.call_count == 2
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|
internal.extract_constraint.assert_has_calls(
|
|
[
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|
call("c3"),
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|
call("c4"),
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|
]
|
|
)
|
|
|
|
# LearningSolver calls after_solve
|
|
training_data = {}
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|
component.after_solve(solver, instance, None, {}, training_data)
|
|
|
|
# Should query slack for all inequalities
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|
internal.get_inequality_slacks.assert_called_once()
|
|
|
|
# Should store constraint slacks in instance object
|
|
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():
|
|
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
|
|
|
|
# LearningSolver call before_solve
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|
component.before_solve(solver, instance, None)
|
|
|
|
# Assert constraints are extracted
|
|
assert internal.extract_constraint.call_count == 2
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|
internal.extract_constraint.assert_has_calls(
|
|
[
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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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|
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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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|
call("<c3>", 1e-3),
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|
call("<c4>", 1e-3),
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|
]
|
|
)
|
|
|
|
# Should add constraints back to LP relaxation
|
|
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
|
|
|
# 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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|
|
|
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|
def test_x_y_fit_predict_evaluate():
|
|
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 = {
|
|
"type-a": Mock(spec=Classifier),
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|
"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(
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|
return_value=np.array(
|
|
[
|
|
[0.50, 0.50],
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|
[0.05, 0.95],
|
|
]
|
|
)
|
|
)
|
|
|
|
# First mock instance
|
|
instances[0].training_data = [
|
|
{
|
|
"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 = [
|
|
{
|
|
"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([[0], [0], [1]]),
|
|
"type-b": np.array([[1], [0], [0]]),
|
|
}
|
|
|
|
# Should build X and Y matrices correctly
|
|
actual_x = component.x(instances)
|
|
actual_y = component.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": [[1]], "type-b": [[0], [1]]}
|
|
|
|
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 = [
|
|
{
|
|
"slacks": {
|
|
"c1": 0.00,
|
|
"c2": 0.05,
|
|
"c3": 0.00,
|
|
"c4": 30.0,
|
|
}
|
|
},
|
|
{
|
|
"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([[1], [0], [0], [1]]),
|
|
"type-b": np.array([[1], [0]]),
|
|
}
|
|
|
|
# Should build X and Y matrices correctly
|
|
component = DropRedundantInequalitiesStep()
|
|
actual_x = component.x([instance])
|
|
actual_y = component.y([instance])
|
|
print(actual_x)
|
|
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])</code></pre>
|
|
</details>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
<h2 class="section-title" id="header-functions">Functions</h2>
|
|
<dl>
|
|
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant"><code class="name flex">
|
|
<span>def <span class="ident">test_drop_redundant</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_drop_redundant():
|
|
solver, internal, instance, classifiers = _setup()
|
|
|
|
component = DropRedundantInequalitiesStep()
|
|
component.classifiers = classifiers
|
|
|
|
# LearningSolver calls before_solve
|
|
component.before_solve(solver, instance, None)
|
|
|
|
# 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 = {}
|
|
component.after_solve(solver, instance, None, {}, 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,
|
|
}</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility"><code class="name flex">
|
|
<span>def <span class="ident">test_drop_redundant_with_check_feasibility</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">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(solver, instance, None)
|
|
|
|
# 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</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves"><code class="name flex">
|
|
<span>def <span class="ident">test_x_multiple_solves</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_x_multiple_solves():
|
|
instance = Mock(spec=Instance)
|
|
instance.training_data = [
|
|
{
|
|
"slacks": {
|
|
"c1": 0.00,
|
|
"c2": 0.05,
|
|
"c3": 0.00,
|
|
"c4": 30.0,
|
|
}
|
|
},
|
|
{
|
|
"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([[1], [0], [0], [1]]),
|
|
"type-b": np.array([[1], [0]]),
|
|
}
|
|
|
|
# Should build X and Y matrices correctly
|
|
component = DropRedundantInequalitiesStep()
|
|
actual_x = component.x([instance])
|
|
actual_y = component.y([instance])
|
|
print(actual_x)
|
|
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])</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate"><code class="name flex">
|
|
<span>def <span class="ident">test_x_y_fit_predict_evaluate</span></span>(<span>)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">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 = [
|
|
{
|
|
"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 = [
|
|
{
|
|
"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([[0], [0], [1]]),
|
|
"type-b": np.array([[1], [0], [0]]),
|
|
}
|
|
|
|
# Should build X and Y matrices correctly
|
|
actual_x = component.x(instances)
|
|
actual_y = component.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": [[1]], "type-b": [[0], [1]]}
|
|
|
|
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</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-functions">Functions</a></h3>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant">test_drop_redundant</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility">test_drop_redundant_with_check_feasibility</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves">test_x_multiple_solves</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate">test_x_y_fit_predict_evaluate</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</nav>
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