def plot_crossval_auc(roc_curves):
cmap = sns.cubehelix_palette(11)
aucs = []
ax = plt.axes()
for fold in roc_curves.keys():
(f, p) = roc_curves[fold]
aucs.append(area_under_curve(f, p))
label_str = "fold {}, roc auc: {:.2f}".format(fold, aucs[-1])
ax.plot(f, p, label=label_str, color=cmap[fold])
ax.plot([0, 1], [0, 1], label="random, roc auc: 0.5", color="black")
ax.legend(loc="lower right")
plt.xlabel("False positive rate")
plt.ylabel("True positive rate")
plt.title(
"ROC curves across 10 different validation folds(tiny convnet "
"trained on small datasets)")
plt.show()
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