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""" test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propagation.LabelPropagation, {'kernel': 'knn', 'n_neighbors': 2}),
(label_propagation.LabelSpreading, {'kernel': 'rbf'}),
(label_propagation.LabelSpreading, {'kernel': 'knn', 'n_neighbors': 2})
]
def test_fit_transduction():
samples = [[1., 0.], [0., 2.], [1., 3.]]
labels = [0, 1, -1]
for estimator, parameters in ESTIMATORS:
clf = estimator(**parameters).fit(samples, labels)
nose.tools.assert_equal(clf.transduction_[2], 1)
def test_distribution():
samples = [[1., 0.], [0., 1.], [1., 1.]]
labels = [0, 1, -1]
for estimator, parameters in ESTIMATORS:
clf = estimator(**parameters).fit(samples, labels)
if parameters['kernel'] == 'knn':
assert_array_almost_equal(clf.predict_proba([[1., 0.0]]),
np.array([[1., 0.]]), 2)
else:
assert_array_almost_equal(np.asarray(clf.label_distributions_[2]),
np.array([.5, .5]), 2)
def test_predict():
samples = [[1., 0.], [0., 2.], [1., 3.]]
labels = [0, 1, -1]
for estimator, parameters in ESTIMATORS:
clf = estimator(**parameters).fit(samples, labels)
assert_array_equal(clf.predict([[0.5, 2.5]]), np.array([1]))
def test_predict_proba():
samples = [[1., 0.], [0., 1.], [1., 2.5]]
labels = [0, 1, -1]
for estimator, parameters in ESTIMATORS:
clf = estimator(**parameters).fit(samples, labels)
assert_array_almost_equal(clf.predict_proba([[1., 1.]]),
np.array([[0.5, 0.5]]))
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