MLNPCapstone.py 文件源码

def plot_roc(self):

        for learner, clf in self._clf.iteritems():
            # Make the predictions 
            (X_test, y_test) = self._test_data 
            y_pred = clf.predict(X_test)

            # Get (f)alse (p)ositive (r)ate, (t)rue (p)ositive (r)ate
            fpr, tpr, _ = roc_curve(y_test, y_pred)

            # Add this classifier's results to the plot
            plt.plot(fpr, tpr, label='%s (area = %0.2f)'\
                % (learner, auc(fpr, tpr)))

        # Now do the plot
        # NOTE:  plot code stolen from scikit-learn docs (http://bit.ly/236k6M3)
        plt.xlim([-0.05, 1.05])
        plt.ylim([-0.05, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('Receiver Operating Characteristic (ROC)')
        plt.legend(loc="lower right")
        plt.show()