def METHOD_NAME(self):
    X = pd.DataFrame(np.random.uniform(low=0.0, high=100, size=(10, 10)))
    y = pd.Series(np.random.uniform(low=0.0, high=100, size=(10,)))
    selector = PearsonSelector(threshold_between_features=0.3,
                               threshold_with_target=0.3,
                               flag_highly_correlated_features=True,
                               n_features_to_select=3)
    selector.evaluate(X=X, y=y, savepath=os.getcwd())
    Xselect = selector.transform(X=X)
    self.assertEqual(Xselect.shape, (10, 3))
    self.assertTrue(os.path.exists('PearsonSelector_fullcorrelationmatrix.csv'))
    self.assertTrue(os.path.exists('PearsonSelector_highlycorrelatedfeatures.csv'))
    self.assertTrue(os.path.exists('PearsonSelector_highlycorrelatedfeaturesflagged.csv'))
    self.assertTrue(os.path.exists('PearsonSelector_highlycorrelatedwithtarget.csv'))
    os.remove('PearsonSelector_fullcorrelationmatrix.csv')
    os.remove('PearsonSelector_highlycorrelatedfeatures.csv')
    os.remove('PearsonSelector_highlycorrelatedfeaturesflagged.csv')
    os.remove('PearsonSelector_highlycorrelatedwithtarget.csv')
    os.remove('selected_features.txt')
    return