# Module `miplearn.classifiers.tests.test_cv`

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 numpy.linalg import norm
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import SVC

    from miplearn.classifiers.cv import CrossValidatedClassifier

    E = 0.1

    def test_cv():
        # Training set: label is true if point is inside a 2D circle
        x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
        x_train = StandardScaler().fit_transform(x_train)
        n_samples = x_train.shape[0]

        y_train = np.array(
            [
                1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
                for x1 in range(-10, 11)
                for x2 in range(-10, 11)
            ]
        )

        # Support vector machines with linear kernels do not perform well on this
        # data set, so predictor should return the given constant.
        clf = CrossValidatedClassifier(
            classifier=SVC(probability=True, random_state=42),
            threshold=0.90,
            constant=0.0,
            cv=30,
        )
        clf.fit(x_train, y_train)
        assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E

        # Support vector machines with quadratic kernels perform almost perfectly
        # on this data set, so predictor should return their prediction.
        clf = CrossValidatedClassifier(
            classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
            threshold=0.90,
            cv=30,
        )
        clf.fit(x_train, y_train)
        print(y_train - clf.predict(x_train))
        assert norm(y_train - clf.predict(x_train)) < E

## Functions

`def test_cv()`
  Expand source code

      def test_cv():
          # Training set: label is true if point is inside a 2D circle
          x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
          x_train = StandardScaler().fit_transform(x_train)
          n_samples = x_train.shape[0]

          y_train = np.array(
              [
                  1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
                  for x1 in range(-10, 11)
                  for x2 in range(-10, 11)
              ]
          )

          # Support vector machines with linear kernels do not perform well on this
          # data set, so predictor should return the given constant.
          clf = CrossValidatedClassifier(
              classifier=SVC(probability=True, random_state=42),
              threshold=0.90,
              constant=0.0,
              cv=30,
          )
          clf.fit(x_train, y_train)
          assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E

          # Support vector machines with quadratic kernels perform almost perfectly
          # on this data set, so predictor should return their prediction.
          clf = CrossValidatedClassifier(
              classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
              threshold=0.90,
              cv=30,
          )
          clf.fit(x_train, y_train)
          print(y_train - clf.predict(x_train))
          assert norm(y_train - clf.predict(x_train)) < E