53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
"""
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=======================================
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Receiver operating characteristic (ROC)
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=======================================
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Example of Receiver operating characteristic (ROC) metric to
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evaluate the quality of the output of a classifier.
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"""
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print __doc__
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import random
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import numpy as np
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import pylab as pl
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from scikits.learn import svm, datasets
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from scikits.learn.metrics import roc_curve, auc
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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X, y = X[y!=2], y[y!=2]
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n_samples, n_features = X.shape
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p = range(n_samples)
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random.seed(0)
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random.shuffle(p)
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X, y = X[p], y[p]
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half = int(n_samples/2)
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# Add noisy features
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X = np.c_[X,np.random.randn(n_samples, 200*n_features)]
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# Run classifier
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classifier = svm.SVC(kernel='linear', probability=True)
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probas_ = classifier.fit(X[:half],y[:half]).predict_proba(X[half:])
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# Compute ROC curve and area the curve
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fpr, tpr, thresholds = roc_curve(y[half:], probas_[:,1])
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roc_auc = auc(fpr, tpr)
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print "Area under the ROC curve : %f" % roc_auc
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# Plot ROC curve
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pl.figure(-1)
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pl.clf()
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pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
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pl.plot([0, 1], [0, 1], 'k--')
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pl.xlim([0.0,1.0])
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pl.ylim([0.0,1.0])
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pl.xlabel('False Positive Rate')
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pl.ylabel('True Positive Rate')
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pl.title('Receiver operating characteristic example')
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pl.legend(loc="lower right")
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pl.show()
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