def testKNeighborsGraphExecution(self):
rs = np.random.RandomState(0)
raw_X = rs.rand(10, 5)
raw_Y = rs.rand(8, 5)
X = mt.tensor(raw_X, chunk_size=7)
Y = mt.tensor(raw_Y, chunk_size=(5, 3))
neigh = NearestNeighbors(n_neighbors=3)
neigh.fit(X)
sklearn_neigh = SkNearestNeighbors(n_neighbors=3)
sklearn_neigh.fit(raw_X)
for mode in ['connectivity', 'distance']:
graph = neigh.kneighbors_graph(Y, mode=mode)
result = graph.fetch()
self.assertIsInstance(result, SparseNDArray)
self.assertGreater(len(get_tiled(graph).chunks), 1)
expected = sklearn_neigh.kneighbors_graph(raw_Y, mode=mode)
np.testing.assert_array_equal(result.toarray(),
expected.toarray())
graph2 = neigh.kneighbors_graph(mode=mode)
result2 = graph2.fetch()
self.assertIsInstance(result2, SparseNDArray)
self.assertGreater(len(get_tiled(graph2).chunks), 1)
expected2 = sklearn_neigh.kneighbors_graph(mode=mode)
np.testing.assert_array_equal(result2.toarray(),
expected2.toarray())
X = [[0], [3], [1]]
neigh = NearestNeighbors(n_neighbors=2)
sklearn_neigh = SkNearestNeighbors(n_neighbors=2)
neigh.fit(X)
sklearn_neigh.fit(X)
A = neigh.kneighbors_graph(X).fetch()
expected_A = sklearn_neigh.kneighbors_graph(X)
np.testing.assert_array_equal(A.toarray(), expected_A.toarray())
# test wrong mode
with self.assertRaises(ValueError):
_ = neigh.kneighbors_graph(mode='unknown')
def topk_rbf(X, Y=None, n_neighbors=10, gamma=1e-5):
nn = NearestNeighbors(n_neighbors=10, metric='euclidean', n_jobs=-1)
nn.fit(X)
W = -1 * mt.power(nn.kneighbors_graph(Y, mode='distance'), 2) * gamma
W = mt.exp(W)
assert W.issparse()
return W.T