mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 09:28:51 -06:00
Reformat source code with Black; add pre-commit hooks and CI checks
This commit is contained in:
@@ -28,9 +28,9 @@ def test_lazy_fit():
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assert "c" in component.classifiers
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# Should provide correct x_train to each classifier
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expected_x_train_a = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
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expected_x_train_b = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
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expected_x_train_c = np.array([[67., 21.75, 1287.92], [70., 23.75, 1199.83]])
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expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
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actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
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actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
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actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
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@@ -56,16 +56,15 @@ def test_lazy_before():
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solver = LearningSolver()
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solver.internal_solver = Mock(spec=InternalSolver)
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component = DynamicLazyConstraintsComponent(threshold=0.10)
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component.classifiers = {"a": Mock(spec=Classifier),
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"b": Mock(spec=Classifier)}
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component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
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component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
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component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
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component.before_solve(solver, instances[0], models[0])
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# Should ask classifier likelihood of each constraint being violated
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expected_x_test_a = np.array([[67., 21.75, 1287.92]])
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expected_x_test_b = np.array([[67., 21.75, 1287.92]])
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expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
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expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
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actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
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actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
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assert norm(expected_x_test_a - actual_x_test_a) < E
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@@ -82,13 +81,15 @@ def test_lazy_before():
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def test_lazy_evaluate():
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instances, models = get_test_pyomo_instances()
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component = DynamicLazyConstraintsComponent()
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component.classifiers = {"a": Mock(spec=Classifier),
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"b": Mock(spec=Classifier),
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"c": Mock(spec=Classifier)}
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component.classifiers = {
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"a": Mock(spec=Classifier),
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"b": Mock(spec=Classifier),
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"c": Mock(spec=Classifier),
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}
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component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
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component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
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component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
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instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
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instances[1].found_violated_lazy_constraints = ["b", "d"]
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assert component.evaluate(instances) == {
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@@ -96,7 +97,7 @@ def test_lazy_evaluate():
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"Accuracy": 0.75,
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"F1 score": 0.8,
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"Precision": 1.0,
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"Recall": 2/3.,
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"Recall": 2 / 3.0,
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"Predicted positive": 2,
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"Predicted negative": 2,
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"Condition positive": 3,
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@@ -135,6 +136,5 @@ def test_lazy_evaluate():
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"False positive (%)": 25.0,
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"True negative (%)": 25.0,
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"True positive (%)": 25.0,
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}
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},
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}
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@@ -4,10 +4,12 @@
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from unittest.mock import Mock, call
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from miplearn import (StaticLazyConstraintsComponent,
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LearningSolver,
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Instance,
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InternalSolver)
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from miplearn import (
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StaticLazyConstraintsComponent,
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LearningSolver,
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Instance,
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InternalSolver,
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)
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from miplearn.classifiers import Classifier
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@@ -23,39 +25,47 @@ def test_usage_with_solver():
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instance = Mock(spec=Instance)
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instance.has_static_lazy_constraints = Mock(return_value=True)
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instance.is_constraint_lazy = Mock(side_effect=lambda cid: {
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"c1": False,
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"c2": True,
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"c3": True,
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"c4": True,
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}[cid])
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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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"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.is_constraint_lazy = Mock(
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side_effect=lambda cid: {
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"c1": False,
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"c2": True,
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"c3": True,
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"c4": True,
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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": [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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"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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component = StaticLazyConstraintsComponent(threshold=0.90,
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use_two_phase_gap=False,
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violation_tolerance=1.0)
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component = StaticLazyConstraintsComponent(
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threshold=0.90, use_two_phase_gap=False, violation_tolerance=1.0
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)
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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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[0.05, 0.95],
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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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[0.02, 0.98],
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])
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]
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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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@@ -67,37 +77,59 @@ def test_usage_with_solver():
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internal.get_constraint_ids.assert_called_once()
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# Should ask if each constraint in the model is lazy
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instance.is_constraint_lazy.assert_has_calls([
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call("c1"), call("c2"), call("c3"), call("c4"),
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])
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instance.is_constraint_lazy.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 the lazy ones, should ask for features
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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 also ask for categories
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assert instance.get_constraint_category.call_count == 3
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instance.get_constraint_category.assert_has_calls([
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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("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 internal solver to remove constraints identified as lazy
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assert internal.extract_constraint.call_count == 3
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internal.extract_constraint.assert_has_calls([
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call("c2"), 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("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 each lazy constraint should be enforced
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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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# For the ones that should be enforced, should ask solver to re-add them
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# to the formulation. The remaining ones should remain in the pool.
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assert internal.add_constraint.call_count == 2
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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(
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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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internal.add_constraint.reset_mock()
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# LearningSolver calls after_iteration (first time)
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@@ -126,37 +158,45 @@ def test_usage_with_solver():
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def test_fit():
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instance_1 = Mock(spec=Instance)
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instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
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instance_1.get_constraint_category = Mock(side_effect=lambda cid: {
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"c1": "type-a",
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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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"c5": "type-b",
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}[cid])
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instance_1.get_constraint_features = Mock(side_effect=lambda cid: {
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"c1": [1, 1],
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"c2": [1, 2],
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"c3": [1, 3],
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"c4": [1, 4, 0],
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"c5": [1, 5, 0],
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}[cid])
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instance_1.get_constraint_category = Mock(
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side_effect=lambda cid: {
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"c1": "type-a",
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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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"c5": "type-b",
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}[cid]
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)
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instance_1.get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c1": [1, 1],
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"c2": [1, 2],
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"c3": [1, 3],
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"c4": [1, 4, 0],
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"c5": [1, 5, 0],
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}[cid]
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)
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instance_2 = Mock(spec=Instance)
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instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
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instance_2.get_constraint_category = Mock(side_effect=lambda cid: {
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"c1": "type-a",
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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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"c5": "type-b",
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}[cid])
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instance_2.get_constraint_features = Mock(side_effect=lambda cid: {
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"c1": [2, 1],
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"c2": [2, 2],
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"c3": [2, 3],
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"c4": [2, 4, 0],
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"c5": [2, 5, 0],
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}[cid])
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instance_2.get_constraint_category = Mock(
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side_effect=lambda cid: {
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"c1": "type-a",
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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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"c5": "type-b",
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}[cid]
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)
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instance_2.get_constraint_features = Mock(
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side_effect=lambda cid: {
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"c1": [2, 1],
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"c2": [2, 2],
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"c3": [2, 3],
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"c4": [2, 4, 0],
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"c5": [2, 5, 0],
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}[cid]
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)
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instances = [instance_1, instance_2]
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component = StaticLazyConstraintsComponent()
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@@ -171,18 +211,22 @@ def test_fit():
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}
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expected_x = {
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"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
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"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]]
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"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
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}
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expected_y = {
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"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
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"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]]
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"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
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}
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assert component._collect_constraints(instances) == expected_constraints
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assert component.x(instances) == expected_x
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assert component.y(instances) == expected_y
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component.fit(instances)
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component.classifiers["type-a"].fit.assert_called_once_with(expected_x["type-a"],
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expected_y["type-a"])
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component.classifiers["type-b"].fit.assert_called_once_with(expected_x["type-b"],
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expected_y["type-b"])
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component.classifiers["type-a"].fit.assert_called_once_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_once_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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@@ -16,8 +16,10 @@ def test_usage():
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comp.fit(instances)
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assert instances[0].lower_bound == 1183.0
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assert instances[0].upper_bound == 1183.0
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assert np.round(comp.predict(instances), 2).tolist() == [[1183.0, 1183.0],
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[1070.0, 1070.0]]
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assert np.round(comp.predict(instances), 2).tolist() == [
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[1183.0, 1183.0],
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[1070.0, 1070.0],
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]
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def test_obj_evaluate():
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@@ -28,20 +30,20 @@ def test_obj_evaluate():
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comp.fit(instances)
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ev = comp.evaluate(instances)
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assert ev == {
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'Lower bound': {
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'Explained variance': 0.0,
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'Max error': 183.0,
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'Mean absolute error': 126.5,
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'Mean squared error': 19194.5,
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'Median absolute error': 126.5,
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'R2': -5.012843605607331,
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"Lower bound": {
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"Explained variance": 0.0,
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"Max error": 183.0,
|
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"Mean absolute error": 126.5,
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"Mean squared error": 19194.5,
|
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"Median absolute error": 126.5,
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"R2": -5.012843605607331,
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},
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"Upper bound": {
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"Explained variance": 0.0,
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"Max error": 183.0,
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"Mean absolute error": 126.5,
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"Mean squared error": 19194.5,
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"Median absolute error": 126.5,
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"R2": -5.012843605607331,
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},
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'Upper bound': {
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'Explained variance': 0.0,
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'Max error': 183.0,
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'Mean absolute error': 126.5,
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'Mean squared error': 19194.5,
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'Median absolute error': 126.5,
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'R2': -5.012843605607331,
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}
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}
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@@ -25,71 +25,82 @@ def test_predict():
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def test_evaluate():
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instances, models = get_test_pyomo_instances()
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clf_zero = Mock(spec=Classifier)
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clf_zero.predict_proba = Mock(return_value=np.array([
|
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[0., 1.], # x[0]
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[0., 1.], # x[1]
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[1., 0.], # x[2]
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[1., 0.], # x[3]
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]))
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clf_zero.predict_proba = Mock(
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return_value=np.array(
|
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[
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[0.0, 1.0], # x[0]
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[0.0, 1.0], # x[1]
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[1.0, 0.0], # x[2]
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[1.0, 0.0], # x[3]
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||||
]
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||||
)
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)
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clf_one = Mock(spec=Classifier)
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||||
clf_one.predict_proba = Mock(return_value=np.array([
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[1., 0.], # x[0] instances[0]
|
||||
[1., 0.], # x[1] instances[0]
|
||||
[0., 1.], # x[2] instances[0]
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[1., 0.], # x[3] instances[0]
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]))
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comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
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threshold=0.50)
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clf_one.predict_proba = Mock(
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||||
return_value=np.array(
|
||||
[
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||||
[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].solution == {"x": {0: 1,
|
||||
1: 0,
|
||||
2: 1,
|
||||
3: 1}}
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[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}}}
|
||||
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():
|
||||
|
||||
@@ -4,10 +4,7 @@
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn import (RelaxationComponent,
|
||||
LearningSolver,
|
||||
Instance,
|
||||
InternalSolver)
|
||||
from miplearn import RelaxationComponent, LearningSolver, Instance, InternalSolver
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
|
||||
@@ -16,41 +13,49 @@ def _setup():
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.get_constraint_slacks = Mock(side_effect=lambda: {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
})
|
||||
internal.get_constraint_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": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid])
|
||||
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": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [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=[
|
||||
classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
])
|
||||
classifiers["type-b"].predict_proba = \
|
||||
Mock(return_value=[
|
||||
]
|
||||
)
|
||||
classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
])
|
||||
]
|
||||
)
|
||||
|
||||
return solver, internal, instance, classifiers
|
||||
|
||||
@@ -72,25 +77,39 @@ def test_usage():
|
||||
|
||||
# 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"),
|
||||
])
|
||||
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"),
|
||||
])
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with([[1.0, 0.0], [0.5, 0.5]])
|
||||
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]])
|
||||
|
||||
# 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"),
|
||||
])
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
component.after_solve(solver, instance, None, None)
|
||||
@@ -111,8 +130,7 @@ def test_usage():
|
||||
def test_usage_with_check_dropped():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = RelaxationComponent(check_dropped=True,
|
||||
violation_tolerance=1e-3)
|
||||
component = RelaxationComponent(check_dropped=True, violation_tolerance=1e-3)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
@@ -120,9 +138,12 @@ def test_usage_with_check_dropped():
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls([
|
||||
call("c3"), call("c4"),
|
||||
])
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
@@ -131,15 +152,15 @@ def test_usage_with_check_dropped():
|
||||
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),
|
||||
])
|
||||
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>")
|
||||
])
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
@@ -148,21 +169,22 @@ def test_usage_with_check_dropped():
|
||||
|
||||
def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = RelaxationComponent(slack_tolerance=0.05,
|
||||
threshold=0.80)
|
||||
component = RelaxationComponent(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=[
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
])
|
||||
component.classifiers["type-b"].predict_proba = \
|
||||
Mock(return_value=[
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
])
|
||||
]
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].slacks = {
|
||||
@@ -171,17 +193,21 @@ def test_x_y_fit_predict_evaluate():
|
||||
"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": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid])
|
||||
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": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].slacks = {
|
||||
@@ -190,26 +216,27 @@ def test_x_y_fit_predict_evaluate():
|
||||
"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": [0.3, 0.4],
|
||||
"c4": [0.7],
|
||||
"c5": [0.8],
|
||||
}[cid])
|
||||
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": [0.3, 0.4],
|
||||
"c4": [0.7],
|
||||
"c5": [0.8],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": [[1.0, 0.0], [0.5, 0.5], [0.3, 0.4]],
|
||||
"type-b": [[1.0], [0.7], [0.8]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0], [0], [1]],
|
||||
"type-b": [[1], [0], [0]]
|
||||
}
|
||||
expected_y = {"type-a": [[0], [0], [1]], "type-b": [[1], [0], [0]]}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
assert component.x(instances) == expected_x
|
||||
@@ -217,13 +244,16 @@ def test_x_y_fit_predict_evaluate():
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_with(expected_x["type-a"], expected_y["type-a"])
|
||||
component.classifiers["type-b"].fit.assert_called_with(expected_x["type-b"], expected_y["type-b"])
|
||||
component.classifiers["type-a"].fit.assert_called_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)
|
||||
|
||||
assert component.predict(expected_x) == {
|
||||
"type-a": [[1]],
|
||||
"type-b": [[0], [1]]
|
||||
}
|
||||
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
|
||||
Reference in New Issue
Block a user