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[MRG] Make csgraph imports explicit #10145

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Merged
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Nov 15, 2017
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[MRG] Make csgraph imports explicit #10145

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5 changes: 3 additions & 2 deletions sklearn/manifold/spectral_embedding_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
from scipy.linalg import eigh
from scipy.sparse.linalg import eigsh, lobpcg
from scipy.sparse.csgraph import connected_components
from scipy.sparse.csgraph import laplacian as csgraph_laplacian

from ..base import BaseEstimator
from ..externals import six
Expand Down Expand Up @@ -234,8 +235,8 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
warnings.warn("Graph is not fully connected, spectral embedding"
" may not work as expected.")

laplacian, dd = sparse.csgraph.laplacian(adjacency, normed=norm_laplacian,
return_diag=True)
laplacian, dd = csgraph_laplacian(adjacency, normed=norm_laplacian,
return_diag=True)
if (eigen_solver == 'arpack' or eigen_solver != 'lobpcg' and
(not sparse.isspmatrix(laplacian) or n_nodes < 5 * n_components)):
# lobpcg used with eigen_solver='amg' has bugs for low number of nodes
Expand Down
5 changes: 3 additions & 2 deletions sklearn/manifold/tests/test_spectral_embedding.py
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Expand Up @@ -3,6 +3,7 @@
from numpy.testing import assert_array_equal

from scipy import sparse
from scipy.sparse import csgraph
from scipy.linalg import eigh

from sklearn.manifold.spectral_embedding_ import SpectralEmbedding
Expand Down Expand Up @@ -254,8 +255,8 @@ def test_spectral_embedding_unnormalized():
drop_first=False)

# Verify using manual computation with dense eigh
laplacian, dd = sparse.csgraph.laplacian(sims, normed=False,
return_diag=True)
laplacian, dd = csgraph.laplacian(sims, normed=False,
return_diag=True)
_, diffusion_map = eigh(laplacian)
embedding_2 = diffusion_map.T[:n_components] * dd
embedding_2 = _deterministic_vector_sign_flip(embedding_2).T
Expand Down
3 changes: 2 additions & 1 deletion sklearn/semi_supervised/label_propagation.py
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Expand Up @@ -60,6 +60,7 @@
import warnings
import numpy as np
from scipy import sparse
from scipy.sparse import csgraph

from ..base import BaseEstimator, ClassifierMixin
from ..externals import six
Expand Down Expand Up @@ -514,7 +515,7 @@ def _build_graph(self):
self.nn_fit = None
n_samples = self.X_.shape[0]
affinity_matrix = self._get_kernel(self.X_)
laplacian = sparse.csgraph.laplacian(affinity_matrix, normed=True)
laplacian = csgraph.laplacian(affinity_matrix, normed=True)
laplacian = -laplacian
if sparse.isspmatrix(laplacian):
diag_mask = (laplacian.row == laplacian.col)
Expand Down
5 changes: 3 additions & 2 deletions sklearn/utils/graph.py
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Expand Up @@ -11,6 +11,7 @@
# License: BSD 3 clause

from scipy import sparse
from scipy.sparse import csgraph

from .graph_shortest_path import graph_shortest_path # noqa
from .deprecation import deprecated
Expand Down Expand Up @@ -73,11 +74,11 @@ def single_source_shortest_path_length(graph, source, cutoff=None):
"version 0.19 and will be removed in 0.21. Use "
"scipy.sparse.csgraph.connected_components instead.")
def connected_components(*args, **kwargs):
return sparse.csgraph.connected_components(*args, **kwargs)
return csgraph.connected_components(*args, **kwargs)


@deprecated("sklearn.utils.graph.graph_laplacian was deprecated in version "
"0.19 and will be removed in 0.21. Use "
"scipy.sparse.csgraph.laplacian instead.")
def graph_laplacian(*args, **kwargs):
return sparse.csgraph.laplacian(*args, **kwargs)
return csgraph.laplacian(*args, **kwargs)