Computing Auc And Roc Curve From Multi-class Data In Scikit-learn (sklearn)?

I am trying to use the scikit-learn module to compute AUC and plot ROC curves for the output of three different classifiers to compare their performance. I am very new to this top

Solution 1:

You need to use label_binarize function and then you can plot a multi-class ROC.

Example using Iris data:

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_curve, auc
from sklearn.multiclassimport OneVsRestClassifier
from itertools import cycle
plt.style.use('ggplot')

iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)

classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=0))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

fpr = dict()
tpr = dict()
roc_auc = dict()
for i inrange(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
colors = cycle(['blue', 'red', 'green'])
for i, color inzip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=1.5,
             label='ROC curve of class {0} (area = {1:0.2f})'''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=1.5)
plt.xlim([-0.05, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic for multi-class data')
plt.legend(loc="lower right")
plt.show()