Make scipy.sparse.csgraph imports explicit (#10145)

lesteve · amueller · commit f6163e73f82a · 2017-11-15T14:10:58.000-08:00
This can cause problems in scipy 1.0
diff --git a/sklearn/manifold/spectral_embedding_.py b/sklearn/manifold/spectral_embedding_.py
@@ -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
@@ -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
diff --git a/sklearn/manifold/tests/test_spectral_embedding.py b/sklearn/manifold/tests/test_spectral_embedding.py
@@ -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
@@ -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
diff --git a/sklearn/semi_supervised/label_propagation.py b/sklearn/semi_supervised/label_propagation.py
@@ -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
@@ -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)
diff --git a/sklearn/utils/graph.py b/sklearn/utils/graph.py
@@ -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
@@ -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)
