def test_precision_recall():
"""test Precision-Recall"""
precision, recall, thresholds = precision_recall(y[half:], probas_[:,1])
precision_recall_auc = auc(precision, recall)
assert_array_almost_equal(precision_recall_auc, 0.3197, 3)
X = iris.data
y = iris.target
X, y = X[y!=2], y[y!=2] # Keep also 2 classes (0 and 1)
n_samples, n_features = X.shape
p = range(n_samples) # Shuffle samples
random.seed(0)
random.shuffle(p)
X, y = X[p], y[p]
half = int(n_samples/2)
# Add noisy features
np.random.seed(0)
X = np.c_[X,np.random.randn(n_samples, 200*n_features)]
# Run classifier
classifier = svm.SVC(kernel='linear', probability=True)
probas_ = classifier.fit(X[:half],y[:half]).predict_proba(X[half:])
# Compute Precision-Recall and plot curve
precision, recall, thresholds = precision_recall(y[half:], probas_[:,1])
pl.figure(-1)
pl.clf()
pl.plot(recall, precision, label='Precision-Recall curve')
pl.xlabel('Recall')
pl.ylabel('Precision')
pl.ylim([0.0,1.05])
pl.xlim([0.0,1.0])
pl.title('Precision-Recall example')
pl.legend(loc="lower left")
pl.show()