python怎么输出roc曲线_如何在Python中绘制ROC曲线

I am trying to plot a ROC curve to evaluate the accuracy of a prediction model I developed in Python using logistic regression packages. I have computed the true positive rate as well as the false positive rate; however, I am unable to figure out how to plot these correctly using matplotlib and calculate the AUC value. How could I do that?

解决方案

Here are two ways you may try, assuming your model is an sklearn predictor:

import sklearn.metrics as metrics

# calculate the fpr and tpr for all thresholds of the classification

probs = model.predict_proba(X_test)

preds = probs[:,1]

fpr, tpr, threshold = metrics.roc_curve(y_test, preds)

roc_auc = metrics.auc(fpr, tpr)

# method I: plt

import matplotlib.pyplot as plt

plt.title('Receiver Operating Characteristic')

plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)

plt.legend(loc = 'lower right')

plt.plot([0, 1], [0, 1],'r--')

plt.xlim([0, 1])

plt.ylim([0, 1])

plt.ylabel('True Positive Rate')

plt.xlabel('False Positive Rate')

plt.show()

# method II: ggplot

from ggplot import *

df = pd.DataFrame(dict(fpr = fpr, tpr = tpr))

ggplot(df, aes(x = 'fpr', y = 'tpr')) + geom_line() + geom_abline(linetype = 'dashed')

or try

ggplot(df, aes(x = 'fpr', ymin = 0, ymax = 'tpr')) + geom_line(aes(y = 'tpr')) + geom_area(alpha = 0.2) + ggtitle("ROC Curve w/ AUC = %s" % str(roc_auc))