Class TestBayesianOptimizer

tests/core_tests.py:1226–1368

class TestBayesianOptimizer(unittest.TestCase):
    def test_set_max(self):
        # case 1: the estimator is not fitted yet
        regressor = mock.MockEstimator()
        learner = modAL.models.learners.BayesianOptimizer(estimator=regressor)
        self.assertEqual(-np.inf, learner.y_max)

        # case 2: the estimator is fitted already
        for n_samples in range(1, 100):
            X = np.random.rand(n_samples, 2)
            y = np.random.rand(n_samples, )
            max_val = np.max(y)

            regressor = mock.MockEstimator()
            learner = modAL.models.learners.BayesianOptimizer(
                estimator=regressor,
                X_training=X, y_training=y
            )
            np.testing.assert_almost_equal(max_val, learner.y_max)

    def test_set_new_max(self):
        for n_reps in range(100):
            # case 1: the learner is not fitted yet
            for n_samples in range(1, 10):
                X = np.random.rand(n_samples, 3)
                y = np.random.rand(n_samples)
                max_idx = np.argmax(y)
                regressor = mock.MockEstimator()
                learner = modAL.models.learners.BayesianOptimizer(
                    estimator=regressor)
                learner._set_max(X, y)
                np.testing.assert_equal(learner.X_max, X[max_idx])
                np.testing.assert_equal(learner.y_max, y[max_idx])

            # case 2: new value is not a maximum
            for n_samples in range(1, 10):
                X = np.random.rand(n_samples, 2)
                y = np.random.rand(n_samples)

                regressor = mock.MockEstimator()
                learner = modAL.models.learners.BayesianOptimizer(
                    estimator=regressor,
                    X_training=X, y_training=y
                )

                X_new = np.random.rand()
                y_new = y - np.random.rand()
                X_old_max = learner.X_max
                y_old_max = learner.y_max
                learner._set_max(X_new, y_new)
                np.testing.assert_equal(X_old_max, learner.X_max)
                np.testing.assert_equal(y_old_max, learner.y_max)

            # case 3: new value is a maximum
            for n_samples in range(1, 10):
                X = np.random.rand(n_samples, 2)
                y = np.random.rand(n_samples)