rename ensure_symmetric -> test_symmetric

sklearn/manifold/mds.py

@@ -11,7 +11,7 @@ import warnings
 
 from ..base import BaseEstimator
 from ..metrics import euclidean_distances
-from ..utils import check_random_state, check_array, ensure_symmetric
+from ..utils import check_random_state, check_array, check_symmetric
 from ..externals.joblib import Parallel
 from ..externals.joblib import delayed
 from ..isotonic import IsotonicRegression
@@ -65,7 +65,7 @@ def _smacof_single(similarities, metric=True, n_components=2, init=None,
         Number of iterations run.
 
     """
-    similarities = ensure_symmetric(similarities, raise_exception=True)
+    similarities = check_symmetric(similarities, raise_exception=True)
 
     n_samples = similarities.shape[0]
     random_state = check_random_state(random_state)

sklearn/manifold/spectral_embedding_.py

@@ -13,7 +13,7 @@ from scipy.sparse.linalg import lobpcg
 
 from ..base import BaseEstimator
 from ..externals import six
-from ..utils import check_random_state, check_array, ensure_symmetric
+from ..utils import check_random_state, check_array, check_symmetric
 from ..utils.graph import graph_laplacian
 from ..utils.sparsetools import connected_components
 from ..utils.arpack import eigsh
@@ -183,7 +183,7 @@ def spectral_embedding(adjacency, n_components=8, eigen_solver=None,
       Andrew V. Knyazev
       http://dx.doi.org/10.1137%2FS1064827500366124
     """
-    adjacency = ensure_symmetric(adjacency)
+    adjacency = check_symmetric(adjacency)
 
     try:
         from pyamg import smoothed_aggregation_solver

sklearn/utils/__init__.py

@@ -12,7 +12,7 @@ from .validation import (as_float_array,
                          assert_all_finite, warn_if_not_float,
                          check_random_state, column_or_1d, check_array,
                          check_consistent_length, check_X_y, indexable,
-                         ensure_symmetric)
+                         check_symmetric)
 from .class_weight import compute_class_weight
 from ..externals.joblib import cpu_count
 

sklearn/utils/validation.py

@@ -406,16 +406,16 @@ def has_fit_parameter(estimator, parameter):
     return parameter in getargspec(estimator.fit)[0]
 
 
-def ensure_symmetric(array, tol=1E-10, raise_warning=True,
-                     raise_exception=False):
+def check_symmetric(array, tol=1E-10, raise_warning=True,
+                    raise_exception=False):
     """
-    Ensure that the array is symmetric two-dimensional array or sparse matrix,
+    Check that the array is symmetric two-dimensional array or sparse matrix,
     returning a symmetrized version and optionally raising a warning or
     exception if the input is not symmetric.
 
     Parameters
     ----------
-    array : object
+    array : nd-array or sparse matrix
         Input object to check / convert
     tol : float
         Absolute tolerance for equivalence of arrays. Default = 1E-10.
@@ -427,7 +427,9 @@ def ensure_symmetric(array, tol=1E-10, raise_warning=True,
     Returns
     -------
     array_sym : object
-        Symmetrized version of the input array
+        Symmetrized version of the input array, i.e. the average of array
+        and array.transpose(). If sparse, then duplicate entries are first
+        summed and zeros are eliminated.
     """
     if (array.ndim != 2) or (array.shape[0] != array.shape[1]):
         raise ValueError("array must be 2-dimensional and symmetric")
@@ -444,6 +446,10 @@ def ensure_symmetric(array, tol=1E-10, raise_warning=True,
         if raise_warning:
             warnings.warn("Array is not symmetric, and will be converted "
                           "to symmetric by average with its transpose.")
-        array = 0.5 * (array + array.T)
+        if sp.issparse(array):
+            conversion = 'to' + array.format
+            array = getattr(0.5 * (array + array.T), conversion)()
+        else:
+            array = 0.5 * (array + array.T)
 
     return array