sklearnExample2.py

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
X, y = datasets.make_classification(n_samples=100,n_classes=3,n_features=5, n_informative=3, n_redundant=0,random_state=42)
# 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)
classifier = OneVsRestClassifier(svm.SVC(kernel='linear',
probability=True, ))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
fpr, tpr, _ = roc_curve(y_test[:,0], y_score[:,0])
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr, tpr, label='ROC AUC %0.2f' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="best")
plt.show()