evaluation.py 文件源码

def plot_allkfolds_ROC(timestamp, cv, fpr_arr, tpr_arr):

    sns.set(style="white", palette="muted", color_codes=True)

    mean_tpr = 0.0
    mean_fpr = 0.0
    all_roc_auc = []
    bins_roc = np.linspace(0, 1, 300)
    with plt.style.context(('seaborn-muted')):
        fig, ax = plt.subplots(figsize=(10, 8))
        for i, (train, test) in enumerate(cv):
            mean_tpr += interp(bins_roc, fpr_arr[i], tpr_arr[i])
            mean_tpr[0] = 0.0
            mean_fpr += interp(bins_roc, fpr_arr[i], tpr_arr[i])
            mean_fpr[0] = 0.0
            roc_auc = metrics.auc(fpr_arr[i], tpr_arr[i])
            all_roc_auc.append(roc_auc)
            ax.plot(fpr_arr[i], tpr_arr[i], lw=1, label='KFold %d (AUC = %0.2f)' % (i, roc_auc))
        ax.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random')

        mean_tpr /= len(cv)
        mean_tpr[-1] = 1.0
        mean_auc = np.mean(all_roc_auc)
        ax.plot(bins_roc, mean_tpr, 'k--',
             label='Mean ROC (AUC = %0.2f)' % mean_auc, lw=2)

        ax.set_xlim([-0.05, 1.05])
        ax.set_ylim([-0.05, 1.05])
        ax.set_xlabel('False Positive Rate')
        ax.set_ylabel('True Positive Rate')
        ax.set_title('Receiver Operating Characteristic')
        ax.legend(loc="lower right")
        plt.savefig('{}_roc.png'.format(timestamp))
    plt.close('all') 
    return mean_auc