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- 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.multiclass import OneVsRestClassifier
- 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 in range(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 in zip(range(n_classes), colors):
- plt.plot(fpr[i], tpr[i], color=color, lw=lw,
- label='ROC curve of class {0} (area = {1:0.2f})'
- ''.format(i, roc_auc[i]))
- plt.plot([0, 1], [0, 1], 'k--', lw=lw)
- 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()
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