metrics.py 文件源码

def plot_ROC_by_class(y_true, y_pred, classes, ls='-'):

    print y_true.shape
    print y_pred.shape

    best_thresh = {}
    for class_name, c in classes.items():  # for each class

        # Compute ROC curve
        fpr, tpr, thresholds = roc_curve(y_true[:, c], y_pred[:, c])
        roc_auc = auc(fpr, tpr)

        # Plot ROC curve
        plt.plot(fpr, tpr, label='{}, AUC = {:.3f}'.format(class_name, roc_auc), linestyle=ls)

        # Calculate J statistic
        J = [j_statistic(y_true[:, c], y_pred[:, c], t) for t in thresholds]
        j_best = np.argmax(J)

        # Store best threshold for each class
        best_thresh[class_name] = J[j_best]

    return best_thresh