# Module `miplearn.components.tests.test_primal`

Expand source code

    #  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 miplearn.classifiers import Classifier
    from miplearn.components.primal import PrimalSolutionComponent
    from miplearn.tests import get_test_pyomo_instances

    def test_predict():
        instances, models = get_test_pyomo_instances()
        comp = PrimalSolutionComponent()
        comp.fit(instances)
        solution = comp.predict(instances[0])
        assert "x" in solution
        assert 0 in solution["x"]
        assert 1 in solution["x"]
        assert 2 in solution["x"]
        assert 3 in solution["x"]

    def test_evaluate():
        instances, models = get_test_pyomo_instances()
        clf_zero = Mock(spec=Classifier)
        clf_zero.predict_proba = Mock(
            return_value=np.array(
                [
                    [0.0, 1.0],  # x[0]
                    [0.0, 1.0],  # x[1]
                    [1.0, 0.0],  # x[2]
                    [1.0, 0.0],  # x[3]
                ]
            )
        )
        clf_one = Mock(spec=Classifier)
        clf_one.predict_proba = Mock(
            return_value=np.array(
                [
                    [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].training_data[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,
                }
            },
        }

    def test_primal_parallel_fit():
        instances, models = get_test_pyomo_instances()
        comp = PrimalSolutionComponent()
        comp.fit(instances, n_jobs=2)
        assert len(comp.classifiers) == 2

## Functions

`def test_evaluate()`
  Expand source code

      def test_evaluate():
          instances, models = get_test_pyomo_instances()
          clf_zero = Mock(spec=Classifier)
          clf_zero.predict_proba = Mock(
              return_value=np.array(
                  [
                      [0.0, 1.0],  # x[0]
                      [0.0, 1.0],  # x[1]
                      [1.0, 0.0],  # x[2]
                      [1.0, 0.0],  # x[3]
                  ]
              )
          )
          clf_one = Mock(spec=Classifier)
          clf_one.predict_proba = Mock(
              return_value=np.array(
                  [
                      [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].training_data[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,
                  }
              },
          }

`def test_predict()`
  Expand source code

      def test_predict():
          instances, models = get_test_pyomo_instances()
          comp = PrimalSolutionComponent()
          comp.fit(instances)
          solution = comp.predict(instances[0])
          assert "x" in solution
          assert 0 in solution["x"]
          assert 1 in solution["x"]
          assert 2 in solution["x"]
          assert 3 in solution["x"]

`def test_primal_parallel_fit()`
  Expand source code

      def test_primal_parallel_fit():
          instances, models = get_test_pyomo_instances()
          comp = PrimalSolutionComponent()
          comp.fit(instances, n_jobs=2)
          assert len(comp.classifiers) == 2