mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 09:28:51 -06:00
DropRedundant: Collect data from multiple runs
This commit is contained in:
@@ -1,285 +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.
|
||||
|
||||
import numpy as np
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn import LearningSolver, Instance, InternalSolver
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.relaxation import DropRedundantInequalitiesStep
|
||||
|
||||
|
||||
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)
|
||||
|
||||
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(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
|
||||
component.after_solve(solver, instance, None, {}, {})
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert hasattr(instance, "slacks")
|
||||
assert instance.slacks == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
|
||||
|
||||
def test_drop_redundant_with_check_dropped():
|
||||
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
|
||||
|
||||
|
||||
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].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].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
|
||||
Reference in New Issue
Block a user