Multiclass Roc curves

multi_class_sklearn_roc_curves.py

from sklearn.metrics import roc_curve, auc
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y[:, i], preds[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
    
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y.ravel(), preds.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

for class_num in range(n_classes):
    plt.figure()
    lw = 2
    plt.plot(fpr[class_num], tpr[class_num], color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[class_num])
    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('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC - {}'.format(le.inverse_transform([class_num])[0]))
    plt.legend(loc="lower right")
    plt.show()