Module miplearn.components.tests.test_lazy_dynamic

#  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.

from unittest.mock import Mock

import numpy as np
from numpy.linalg import norm

from miplearn.classifiers import Classifier
from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.learning import LearningSolver
from miplearn.tests import get_test_pyomo_instances

E = 0.1


def test_lazy_fit():
    instances, models = get_test_pyomo_instances()
    instances[0].found_violated_lazy_constraints = ["a", "b"]
    instances[1].found_violated_lazy_constraints = ["b", "c"]
    classifier = Mock(spec=Classifier)
    component = DynamicLazyConstraintsComponent(classifier=classifier)

    component.fit(instances)

    # Should create one classifier for each violation
    assert "a" in component.classifiers
    assert "b" in component.classifiers
    assert "c" in component.classifiers

    # Should provide correct x_train to each classifier
    expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
    expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
    expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
    actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
    actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
    actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
    assert norm(expected_x_train_a - actual_x_train_a) < E
    assert norm(expected_x_train_b - actual_x_train_b) < E
    assert norm(expected_x_train_c - actual_x_train_c) < E

    # Should provide correct y_train to each classifier
    expected_y_train_a = np.array([1.0, 0.0])
    expected_y_train_b = np.array([1.0, 1.0])
    expected_y_train_c = np.array([0.0, 1.0])
    actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
    actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
    actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
    assert norm(expected_y_train_a - actual_y_train_a) < E
    assert norm(expected_y_train_b - actual_y_train_b) < E
    assert norm(expected_y_train_c - actual_y_train_c) < E


def test_lazy_before():
    instances, models = get_test_pyomo_instances()
    instances[0].build_lazy_constraint = Mock(return_value="c1")
    solver = LearningSolver()
    solver.internal_solver = Mock(spec=InternalSolver)
    component = DynamicLazyConstraintsComponent(threshold=0.10)
    component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
    component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
    component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])

    component.before_solve(solver, instances[0], models[0])

    # Should ask classifier likelihood of each constraint being violated
    expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
    expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
    actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
    actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
    assert norm(expected_x_test_a - actual_x_test_a) < E
    assert norm(expected_x_test_b - actual_x_test_b) < E

    # Should ask instance to generate cut for constraints whose likelihood
    # of being violated exceeds the threshold
    instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")

    # Should ask internal solver to add generated constraint
    solver.internal_solver.add_constraint.assert_called_once_with("c1")


def test_lazy_evaluate():
    instances, models = get_test_pyomo_instances()
    component = DynamicLazyConstraintsComponent()
    component.classifiers = {
        "a": Mock(spec=Classifier),
        "b": Mock(spec=Classifier),
        "c": Mock(spec=Classifier),
    }
    component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
    component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
    component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])

    instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
    instances[1].found_violated_lazy_constraints = ["b", "d"]
    assert component.evaluate(instances) == {
        0: {
            "Accuracy": 0.75,
            "F1 score": 0.8,
            "Precision": 1.0,
            "Recall": 2 / 3.0,
            "Predicted positive": 2,
            "Predicted negative": 2,
            "Condition positive": 3,
            "Condition negative": 1,
            "False negative": 1,
            "False positive": 0,
            "True negative": 1,
            "True positive": 2,
            "Predicted positive (%)": 50.0,
            "Predicted negative (%)": 50.0,
            "Condition positive (%)": 75.0,
            "Condition negative (%)": 25.0,
            "False negative (%)": 25.0,
            "False positive (%)": 0,
            "True negative (%)": 25.0,
            "True positive (%)": 50.0,
        },
        1: {
            "Accuracy": 0.5,
            "F1 score": 0.5,
            "Precision": 0.5,
            "Recall": 0.5,
            "Predicted positive": 2,
            "Predicted negative": 2,
            "Condition positive": 2,
            "Condition negative": 2,
            "False negative": 1,
            "False positive": 1,
            "True negative": 1,
            "True positive": 1,
            "Predicted positive (%)": 50.0,
            "Predicted negative (%)": 50.0,
            "Condition positive (%)": 50.0,
            "Condition negative (%)": 50.0,
            "False negative (%)": 25.0,
            "False positive (%)": 25.0,
            "True negative (%)": 25.0,
            "True positive (%)": 25.0,
        },
    }