Plot ROC curve.

roc_auc.py

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc

import seaborn as sns
sns.set('talk', 'whitegrid', 'dark', font_scale=1.5, font='Ricty',
        rc={"lines.linewidth": 2, 'grid.linestyle': '--'})

fpr, tpr, _ = roc_curve([1, 0, 1, 0, 1, 0, 0], [0.9, 0.8, 0.7, 0.7, 0.6, 0.5, 0.4])
roc_auc = auc(fpr, tpr)

lw = 2
plt.figure()
plt.plot(fpr, tpr, color='darkorange',
         lw=lw, label='ROC curve (AUC = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('偽陽性率（False Positive Rate）')
plt.ylabel('真陽性率（True Positive Rate）')
plt.title('ROC曲線（Receiver Operating Characteristic curve）')
plt.legend(loc="lower right")
plt.show()
plt.savefig('roc_auc.png')
plt.close()

Would anyone like to see a seaborn-only alternative?

yes, that would be great!