def test_radius_neighbors(self):
"""Test query with radius"""
nn = NearestNeighbors(radius=.1)
nn.fit(self.X)
knn = RadiusNeighborsClassifier(radius=.1)
knn.fit(self.X, self.y)
knnr = RadiusNeighborsRegressor(radius=.1)
knnr.fit(self.X, self.modes_location)
for neigh in [nn, knn, knnr]:
dist, links = neigh.radius_neighbors(self.X[:4])
np.testing.assert_array_equal(links[0], np.array([0, 7]))
np.testing.assert_array_equal(links[1], np.array([1]))
np.testing.assert_array_equal(links[2], np.array([2, 17, 22, 27]))
np.testing.assert_array_equal(links[3], np.array([3, 4, 9]))
dist_kneigh = lp_distance(self.X[0], self.X[7])
np.testing.assert_array_almost_equal(dist[0][1], dist_kneigh)
graph = neigh.radius_neighbors_graph(self.X[:4])
for i in range(30):
self.assertEqual(graph[0, i] == 1.0, i in links[0])
self.assertEqual(graph[0, i] == 0.0, i not in links[0])
def test_multivariate_response_score(self):
neigh = RadiusNeighborsRegressor()
y = make_multimodal_samples(n_samples=5, dim_domain=2, random_state=0)
neigh.fit(self.X[:5], y)
# It is not supported the multivariate score by the moment
with np.testing.assert_raises(ValueError):
neigh.score(self.X[:5], y)
def test_radius_functional_response(self):
knnr = RadiusNeighborsRegressor(metric=lp_distance,
weights='distance',
regressor=l2_mean)
knnr.fit(self.X, self.X)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.data_matrix,
self.X.data_matrix)
def test_predict_regressor(self):
"""Test scalar regression, predics mode location"""
# Dummy test, with weight = distance, only the sample with distance 0
# will be returned, obtaining the exact location
knnr = KNeighborsRegressor(weights='distance')
rnnr = RadiusNeighborsRegressor(weights='distance', radius=.1)
knnr.fit(self.X, self.modes_location)
rnnr.fit(self.X, self.modes_location)
np.testing.assert_array_almost_equal(knnr.predict(self.X),
self.modes_location)
np.testing.assert_array_almost_equal(rnnr.predict(self.X),
self.modes_location)
def test_radius_outlier_functional_response(self):
knnr = RadiusNeighborsRegressor(radius=0.001)
knnr.fit(self.X[3:6], self.X[3:6])
# No value given
with np.testing.assert_raises(ValueError):
knnr.predict(self.X[:10])
# Test response
knnr = RadiusNeighborsRegressor(radius=0.001,
outlier_response=self.X[0])
knnr.fit(self.X[:6], self.X[:6])
res = knnr.predict(self.X[:7])
np.testing.assert_array_almost_equal(self.X[0].data_matrix,
res[6].data_matrix)
def test_score_functional_response_exceptions(self):
neigh = RadiusNeighborsRegressor()
neigh.fit(self.X, self.X)
with np.testing.assert_raises(ValueError):
neigh.score(self.X, self.X, sample_weight=[1, 2, 3])
def test_functional_regressor_exceptions(self):
knnr = RadiusNeighborsRegressor()
with np.testing.assert_raises(ValueError):
knnr.fit(self.X[:3], self.X[:4])