# Module `miplearn.classifiers.tests.test_evaluator`

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.

    import numpy as np
    from sklearn.neighbors import KNeighborsClassifier

    from miplearn.classifiers.evaluator import ClassifierEvaluator

    def test_evaluator():
        clf_a = KNeighborsClassifier(n_neighbors=1)
        clf_b = KNeighborsClassifier(n_neighbors=2)
        x_train = np.array([[0, 0], [1, 0]])
        y_train = np.array([0, 1])
        clf_a.fit(x_train, y_train)
        clf_b.fit(x_train, y_train)
        ev = ClassifierEvaluator()
        assert ev.evaluate(clf_a, x_train, y_train) == 1.0
        assert ev.evaluate(clf_b, x_train, y_train) == 0.5

## Functions

`def test_evaluator()`
  Expand source code

      def test_evaluator():
          clf_a = KNeighborsClassifier(n_neighbors=1)
          clf_b = KNeighborsClassifier(n_neighbors=2)
          x_train = np.array([[0, 0], [1, 0]])
          y_train = np.array([0, 1])
          clf_a.fit(x_train, y_train)
          clf_b.fit(x_train, y_train)
          ev = ClassifierEvaluator()
          assert ev.evaluate(clf_a, x_train, y_train) == 1.0
          assert ev.evaluate(clf_b, x_train, y_train) == 0.5