Disallow auto-densification.

docs/generated/sparse.SparseArray.rst

@@ -19,4 +19,5 @@ SparseArray
       :toctree:
 
       SparseArray.asformat
+      SparseArray.todense
 

docs/generated/sparse.SparseArray.todense.rst

@@ -0,0 +1,6 @@
+SparseArray.todense
+===================
+
+.. currentmodule:: sparse
+
+.. automethod:: SparseArray.todense

sparse/__init__.py

@@ -10,7 +10,7 @@
 
 import os
 
-_AUTO_DENSIFICATION_ENABLED = bool(int(os.environ.get('SPARSE_AUTO_DENSIFY', '1')))
+_AUTO_DENSIFICATION_ENABLED = bool(int(os.environ.get('SPARSE_AUTO_DENSIFY', '0')))
 _AUTO_WARN_ON_TOO_DENSE = bool(int(os.environ.get('SPARSE_WARN_ON_TOO_DENSE', '0')))
 
 del os

sparse/coo/common.py

@@ -612,7 +612,7 @@ def nanmean(x, axis=None, keepdims=False, dtype=None, out=None):
     den = reduce(operator.mul, (x.shape[i] for i in axis), 1)
     den -= nancount
 
-    if np.any(den == 0):
+    if (den == 0).any():
         warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
 
     num = np.sum(x2, axis=axis, dtype=dtype, keepdims=keepdims)

sparse/coo/core.py

@@ -1449,14 +1449,6 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
 
         return result
 
-    def __array__(self, **kwargs):
-        from .. import _AUTO_DENSIFICATION_ENABLED as densify
-        if not densify:
-            raise RuntimeError('Cannot convert a sparse array to dense automatically. '
-                               'To manually densify, use the todense method.')
-
-        return np.asarray(self.todense(), **kwargs)
-
     def linear_loc(self):
         """
         The nonzero coordinates of a flattened version of this array. Note that

sparse/sparse_array.py

@@ -5,6 +5,8 @@
 from functools import reduce
 import operator
 
+import numpy as np
+
 from .utils import _zero_of_dtype
 from .compatibility import int
 
@@ -177,3 +179,38 @@ def asformat(self, format):
         NotImplementedError
             If the format isn't supported.
         """
+
+    @abstractmethod
+    def todense(self):
+        """
+        Convert this :obj:`SparseArray` array to a dense :obj:`numpy.ndarray`. Note that
+        this may take a large amount of memory and time.
+
+        Returns
+        -------
+        numpy.ndarray
+            The converted dense array.
+
+        See Also
+        --------
+        DOK.todense : Equivalent :obj:`DOK` array method.
+        COO.todense : Equivalent :obj:`COO` array method.
+        scipy.sparse.coo_matrix.todense : Equivalent Scipy method.
+
+        Examples
+        --------
+        >>> import sparse
+        >>> x = np.random.randint(100, size=(7, 3))
+        >>> s = sparse.COO.from_numpy(x)
+        >>> x2 = s.todense()
+        >>> np.array_equal(x, x2)
+        True
+        """
+
+    def __array__(self, **kwargs):
+        from . import _AUTO_DENSIFICATION_ENABLED as densify
+        if not densify:
+            raise RuntimeError('Cannot convert a sparse array to dense automatically. '
+                               'To manually densify, use the todense method.')
+
+        return np.asarray(self.todense(), **kwargs)

sparse/tests/test_coo.py

@@ -1645,9 +1645,9 @@ def test_size():
 
 def test_np_array():
     s = sparse.random((20, 30, 40))
-    x = np.array(s)
-    assert isinstance(x, np.ndarray)
-    assert_eq(x, s)
+
+    with pytest.raises(RuntimeError):
+        np.array(s)
 
 
 @pytest.mark.parametrize('shapes', [
@@ -2059,12 +2059,14 @@ def test_failed_densification():
     import os
     from importlib import reload
 
-    os.environ['SPARSE_AUTO_DENSIFY'] = '0'
+    os.environ['SPARSE_AUTO_DENSIFY'] = '1'
     reload(sparse)
 
-    with pytest.raises(RuntimeError):
-        s = sparse.random((3, 4, 5), density=0.5)
-        np.array(s)
+    s = sparse.random((3, 4, 5), density=0.5)
+    x = np.array(s)
+
+    assert isinstance(x, np.ndarray)
+    assert_eq(s, x)
 
     del os.environ['SPARSE_AUTO_DENSIFY']
     reload(sparse)