example/roc_demo.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from itertools import cycle
+
+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
+from scipy import interp
+from sklearn.metrics import roc_auc_score
+
+# Import some data to play with
+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]
+
+# Add noisy features to make the problem harder
+random_state = np.random.RandomState(0)
+n_samples, n_features = X.shape
+X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
+
+# shuffle and split training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
+                                                    random_state=0)
+
+# Learn to predict each class against the other
+classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
+                                 random_state=random_state))
+y_score = classifier.fit(X_train, y_train).decision_function(X_test)
+
+# 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_test[:, i], y_score[:, i])
+    roc_auc[i] = auc(fpr[i], tpr[i])
+
+# Compute micro-average ROC curve and ROC area
+fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
+roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
+
+plt.figure()
+lw = 2
+plt.plot(fpr[2], tpr[2], color='darkorange',
+         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
+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('Receiver operating characteristic example')
+plt.legend(loc="lower right")
+plt.show()