utils.py 文件源码

def plot_ROC(actual, predictions):
    # plot the FPR vs TPR and AUC for a two class problem (0,1)
    import matplotlib.pyplot as plt
    from sklearn.metrics import roc_curve, auc

    false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, predictions)
    roc_auc = auc(false_positive_rate, true_positive_rate)

    plt.title('Receiver Operating Characteristic')
    plt.plot(false_positive_rate, true_positive_rate, 'b',
    label='AUC = %0.2f'% roc_auc)
    plt.legend(loc='lower right')
    plt.plot([0,1],[0,1],'r--')
    plt.xlim([-0.1,1.2])
    plt.ylim([-0.1,1.2])
    plt.ylabel('True Positive Rate')
    plt.xlabel('False Positive Rate')
    plt.show()