mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-12 12:28:52 -06:00
Reformat source code with Black; add pre-commit hooks and CI checks
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@@ -4,10 +4,7 @@
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from unittest.mock import Mock, call
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from miplearn import (RelaxationComponent,
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LearningSolver,
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Instance,
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InternalSolver)
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from miplearn import RelaxationComponent, LearningSolver, Instance, InternalSolver
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from miplearn.classifiers import Classifier
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@@ -16,41 +13,49 @@ def _setup():
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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_constraint_slacks = Mock(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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internal.get_constraint_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(side_effect=lambda cid: {
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"c2": [1.0, 0.0],
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"c3": [0.5, 0.5],
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"c4": [1.0],
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}[cid])
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instance.get_constraint_category = Mock(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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instance.get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c2": [1.0, 0.0],
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"c3": [0.5, 0.5],
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"c4": [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 = \
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Mock(return_value=[
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classifiers["type-a"].predict_proba = Mock(
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return_value=[
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[0.20, 0.80],
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[0.05, 0.95],
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])
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classifiers["type-b"].predict_proba = \
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Mock(return_value=[
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]
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)
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classifiers["type-b"].predict_proba = Mock(
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return_value=[
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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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@@ -72,25 +77,39 @@ def test_usage():
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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"), call("c2"), call("c3"), call("c4"),
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])
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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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call("c2"), call("c3"), call("c4"),
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])
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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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component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
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component.classifiers["type-a"].predict_proba.assert_called_once_with(
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[[1.0, 0.0], [0.5, 0.5]]
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)
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component.classifiers["type-b"].predict_proba.assert_called_once_with([[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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call("c3"), call("c4"),
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])
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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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component.after_solve(solver, instance, None, None)
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@@ -111,8 +130,7 @@ def test_usage():
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def test_usage_with_check_dropped():
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solver, internal, instance, classifiers = _setup()
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component = RelaxationComponent(check_dropped=True,
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violation_tolerance=1e-3)
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component = RelaxationComponent(check_dropped=True, violation_tolerance=1e-3)
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component.classifiers = classifiers
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# LearningSolver call before_solve
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@@ -120,9 +138,12 @@ def test_usage_with_check_dropped():
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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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call("c3"), call("c4"),
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])
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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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@@ -131,15 +152,15 @@ def test_usage_with_check_dropped():
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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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])
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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([
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call("<c3>"), call("<c4>")
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])
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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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@@ -148,21 +169,22 @@ def test_usage_with_check_dropped():
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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 = RelaxationComponent(slack_tolerance=0.05,
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threshold=0.80)
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component = RelaxationComponent(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 = \
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Mock(return_value=[
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component.classifiers["type-a"].predict_proba = Mock(
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return_value=[
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[0.20, 0.80],
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])
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component.classifiers["type-b"].predict_proba = \
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Mock(return_value=[
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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=[
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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].slacks = {
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@@ -171,17 +193,21 @@ def test_x_y_fit_predict_evaluate():
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"c3": 0.00,
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"c4": 30.0,
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}
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instances[0].get_constraint_category = Mock(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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instances[0].get_constraint_features = Mock(side_effect=lambda cid: {
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"c2": [1.0, 0.0],
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"c3": [0.5, 0.5],
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"c4": [1.0],
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}[cid])
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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": [1.0, 0.0],
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"c3": [0.5, 0.5],
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"c4": [1.0],
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}[cid]
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)
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# Second mock instance
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instances[1].slacks = {
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@@ -190,26 +216,27 @@ def test_x_y_fit_predict_evaluate():
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"c4": 0.00,
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"c5": 0.00,
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}
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instances[1].get_constraint_category = Mock(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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instances[1].get_constraint_features = Mock(side_effect=lambda cid: {
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"c3": [0.3, 0.4],
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"c4": [0.7],
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"c5": [0.8],
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}[cid])
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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": [0.3, 0.4],
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"c4": [0.7],
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"c5": [0.8],
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}[cid]
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)
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expected_x = {
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"type-a": [[1.0, 0.0], [0.5, 0.5], [0.3, 0.4]],
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"type-b": [[1.0], [0.7], [0.8]],
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}
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expected_y = {
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"type-a": [[0], [0], [1]],
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"type-b": [[1], [0], [0]]
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}
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expected_y = {"type-a": [[0], [0], [1]], "type-b": [[1], [0], [0]]}
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# Should build X and Y matrices correctly
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assert component.x(instances) == expected_x
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@@ -217,13 +244,16 @@ def test_x_y_fit_predict_evaluate():
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# Should pass along X and Y matrices to classifiers
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component.fit(instances)
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component.classifiers["type-a"].fit.assert_called_with(expected_x["type-a"], expected_y["type-a"])
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component.classifiers["type-b"].fit.assert_called_with(expected_x["type-b"], expected_y["type-b"])
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component.classifiers["type-a"].fit.assert_called_with(
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expected_x["type-a"],
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expected_y["type-a"],
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)
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component.classifiers["type-b"].fit.assert_called_with(
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expected_x["type-b"],
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expected_y["type-b"],
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)
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assert component.predict(expected_x) == {
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"type-a": [[1]],
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"type-b": [[0], [1]]
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}
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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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