def visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
              y_test_pred, show_figure=True,
              save_figure=False):  # pragma: no cover
    """Utility function for visualizing the results in examples.
    Internal use only.

    Parameters
    ----------
    clf_name : str
        The name of the detector.

    X_train : numpy array of shape (n_samples, n_features)
        The training samples.

    y_train : list or array of shape (n_samples,)
        The ground truth of training samples.

    X_test : numpy array of shape (n_samples, n_features)
        The test samples.

    y_test : list or array of shape (n_samples,)
        The ground truth of test samples.

    y_train_pred : numpy array of shape (n_samples, n_features)
        The predicted binary labels of the training samples.

    y_test_pred : numpy array of shape (n_samples, n_features)
        The predicted binary labels of the test samples.

    show_figure : bool, optional (default=True)
        If set to True, show the figure.

    save_figure : bool, optional (default=False)
        If set to True, save the figure to the local.

    """

    def _add_sub_plot(X_inliers, X_outliers, sub_plot_title,
                      inlier_color='blue', outlier_color='orange'):
        """Internal method to add subplot of inliers and outliers.

        Parameters
        ----------
        X_inliers : numpy array of shape (n_samples, n_features)
            Outliers.

        X_outliers : numpy array of shape (n_samples, n_features)
            Inliers.

        sub_plot_title : str
            Subplot title.

        inlier_color : str, optional (default='blue')
            The color of inliers.

        outlier_color : str, optional (default='orange')
            The color of outliers.

        """
        plt.axis("equal")
        plt.scatter(X_inliers[:, 0], X_inliers[:, 1], label='inliers',
                    color=inlier_color, s=40)
        plt.scatter(X_outliers[:, 0], X_outliers[:, 1],
                    label='outliers', color=outlier_color, s=50, marker='^')
        plt.title(sub_plot_title, fontsize=15)
        plt.xticks([])
        plt.yticks([])
        plt.legend(loc=3, prop={'size': 10})

    # check input data shapes are consistent
    X_train, y_train, X_test, y_test, y_train_pred, y_test_pred = \
        check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,
                               y_test_pred)

    if X_train.shape[1] != 2:
        raise ValueError("Input data has to be 2-d for visualization. The "
                         "input data has {shape}.".format(shape=X_train.shape))

    X_train_outliers, X_train_inliers = get_outliers_inliers(X_train, y_train)
    X_train_outliers_pred, X_train_inliers_pred = get_outliers_inliers(
        X_train, y_train_pred)

    X_test_outliers, X_test_inliers = get_outliers_inliers(X_test, y_test)
    X_test_outliers_pred, X_test_inliers_pred = get_outliers_inliers(
        X_test, y_test_pred)

    # plot ground truth vs. predicted results
    fig = plt.figure(figsize=(12, 10))
    plt.suptitle("Demo of {clf_name} Detector".format(clf_name=clf_name),
                 fontsize=15)

    fig.add_subplot(221)
    _add_sub_plot(X_train_inliers, X_train_outliers, 'Train Set Ground Truth',
                  inlier_color='blue', outlier_color='orange')

    fig.add_subplot(222)
    _add_sub_plot(X_train_inliers_pred, X_train_outliers_pred,
                  'Train Set Prediction', inlier_color='blue',
                  outlier_color='orange')

    fig.add_subplot(223)
    _add_sub_plot(X_test_inliers, X_test_outliers, 'Test Set Ground Truth',
                  inlier_color='green', outlier_color='red')

    fig.add_subplot(224)
    _add_sub_plot(X_test_inliers_pred, X_test_outliers_pred,
                  'Test Set Prediction', inlier_color='green',
                  outlier_color='red')

    if save_figure:
        plt.savefig('{clf_name}.png'.format(clf_name=clf_name), dpi=300)

    if show_figure:
        plt.show()
Utility function for visualizing the results in examples.
    Internal use only.

    Parameters
    ----------
    clf_name : str
        The name of the detector.

    X_train : numpy array of shape (n_samples, n_features)
        The training samples.

    y_train : list or array of shape (n_samples,)
        The ground truth of training samples.

    X_test : numpy array of shape (n_samples, n_features)
        The test samples.

    y_test : list or array of shape (n_samples,)
        The ground truth of test samples.

    y_train_pred : numpy array of shape (n_samples, n_features)
        The predicted binary labels of the training samples.

    y_test_pred : numpy array of shape (n_samples, n_features)
        The predicted binary labels of the test samples.

    show_figure : bool, optional (default=True)
        If set to True, show the figure.

    save_figure : bool, optional (default=False)
        If set to True, save the figure to the local.

    
pyod/utils/example.py