COSMIT pep8, removing unused imports and recommend toarray instead of todense

amueller · amueller · commit 3c46ebaa6e5f · 2012-11-24T20:12:33.000Z
diff --git a/sklearn/feature_extraction/tests/test_feature_hasher.py b/sklearn/feature_extraction/tests/test_feature_hasher.py
@@ -1,11 +1,9 @@
 import numpy as np
-import scipy.sparse as sp
 
 from sklearn.feature_extraction import FeatureHasher
 
 from nose.tools import assert_raises, assert_true
 from numpy.testing import assert_array_equal, assert_equal
-from sklearn.utils.testing import assert_in
 
 
 def test_feature_hasher_dicts():
@@ -68,7 +66,7 @@ def test_hasher_invalid_input():
     assert_raises(ValueError, FeatureHasher, n_features=0)
     assert_raises(TypeError, FeatureHasher, n_features='ham')
 
-    h = FeatureHasher(n_features=np.uint16(2**6))
+    h = FeatureHasher(n_features=np.uint16(2 ** 6))
     assert_raises(ValueError, h.transform, [])
     assert_raises(Exception, h.transform, [[5.5]])
     assert_raises(Exception, h.transform, [[None]])
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
@@ -69,7 +69,7 @@ def array2d(X, dtype=None, order=None, copy=False):
     """Returns at least 2-d array with data from X"""
     if sparse.issparse(X):
         raise TypeError('A sparse matrix was passed, but dense data '
-                        'is required. Use X.todense() to convert to dense.')
+                        'is required. Use X.toarray() to convert to dense.')
     X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
     assert_all_finite(X_2d)
     if X is X_2d and copy:
@@ -78,7 +78,7 @@ def array2d(X, dtype=None, order=None, copy=False):
 
 
 def atleast2d_or_csc(X, dtype=None, order=None, copy=False):
-    """Like numpy.atleast_2d, but converts sparse matrices to CSC format
+    """Like numpy.atleast_2d, but converts sparse matrices to CSC format.
 
     Also, converts np.matrix to np.ndarray.
     """
