62 lines
1.6 KiB
Python
62 lines
1.6 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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.. note::
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See also :ref:`example_plot_roc_crossval.py`
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"""
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print(__doc__)
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import numpy as np
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import pylab as pl
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from sklearn import svm, datasets
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from sklearn.utils import shuffle
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from sklearn.metrics import roc_curve, auc
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random_state = np.random.RandomState(0)
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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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# Make it a binary classification problem by removing the third class
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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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# Add noisy features to make the problem harder
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X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
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# shuffle and split training and test sets
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X, y = shuffle(X, y, random_state=random_state)
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half = int(n_samples / 2)
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X_train, X_test = X[:half], X[half:]
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y_train, y_test = y[:half], y[half:]
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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_train, y_train).predict_proba(X_test)
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# Compute ROC curve and area the curve
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fpr, tpr, thresholds = roc_curve(y_test, 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.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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