def plot_titanic_scores(y_true, scores_with_line=[], scores_with_points=[],
                        labels_with_line=['Gradient Boosting', 'Random Forest', 'Decision Tree'],
                        labels_with_points=['skope-rules']):
    gradient = np.linspace(0, 1, 10)
    color_list = [ cm.tab10(x) for x in gradient ]
    fig, axes = plt.subplots(1, 2, figsize=(12, 5),
                         sharex=True, sharey=True)
    ax = axes[0]
    n_line = 0
    for i_score, score in enumerate(scores_with_line):
        n_line = n_line + 1
        fpr, tpr, _ = roc_curve(y_true, score)
        ax.plot(fpr, tpr, linestyle='-.', c=color_list[i_score], lw=1, label=labels_with_line[i_score])
    for i_score, score in enumerate(scores_with_points):
        fpr, tpr, _ = roc_curve(y_true, score)
        ax.scatter(fpr[:-1], tpr[:-1], c=color_list[n_line + i_score], s=10, label=labels_with_points[i_score])
    ax.set_title("ROC", fnotallow=20)
    ax.set_xlabel('False Positive Rate', fnotallow=18)
    ax.set_ylabel('True Positive Rate (Recall)', fnotallow=18)
    ax.legend(loc='lower center', fnotallow=8)
    ax = axes[1]
    n_line = 0
    for i_score, score in enumerate(scores_with_line):
        n_line = n_line + 1
precision, recall, _ = precision_recall_curve(y_true, score)
        ax.step(recall, precision, linestyle='-.', c=color_list[i_score], lw=1, where='post', label=labels_with_line[i_score])
    for i_score, score in enumerate(scores_with_points):
precision, recall, _ = precision_recall_curve(y_true, score)
        ax.scatter(recall, precision, c=color_list[n_line + i_score], s=10, label=labels_with_points[i_score])
    ax.set_title("Precision-Recall", fnotallow=20)
    ax.set_xlabel('Recall (True Positive Rate)', fnotallow=18)
    ax.set_ylabel('Precision', fnotallow=18)
    ax.legend(loc='lower center', fnotallow=8)
    plt.show()
plot_titanic_scores(y_test,
                    scores_with_line=[gradient_boost_scoring, random_forest_scoring, decision_tree_scoring],
                    scores_with_points=[skope_rules_scoring]
)