Deal with np.array(sparsearr) densification

docs/generated/sparse.COO.__array__.rst

@@ -0,0 +1,6 @@
+COO\.\_\_array\_\_
+==================
+
+.. currentmodule:: sparse
+
+.. automethod:: COO.__array__

docs/generated/sparse.COO.rst

@@ -68,6 +68,7 @@ COO
       COO.to_scipy_sparse
       COO.tocsc
       COO.tocsr
+      COO.__array__
 
    .. rubric:: :doc:`Other operations <../user_manual/operations/other>`
    .. autosummary::

sparse/core.py

@@ -488,6 +488,31 @@ def nbytes(self):
         """
         return self.data.nbytes + self.coords.nbytes
 
+    def __array__(self, dtype=None):
+        """
+        Helper function that gets called during :code:`np.array(x)` conversion.
+        We deliberately return :code:`NotImplemented` to prevent accidental
+        densification.
+
+        Parameters
+        ----------
+        dtype: type
+            Datatype requested by :code:`np.array(x)`. Has no effect on
+            output.
+
+        Returns
+        -------
+        NotImplemented
+            We do not implement this function, so we return
+            :obj:`NotImplemented`.
+
+        See Also
+        --------
+        numpy.ndarray.__array__ : Numpy equivalent function.
+
+        """
+        return NotImplemented
+
     def __len__(self):
         """
         Get "length" of array, which is by definition the size of the first
@@ -2374,15 +2399,17 @@ def maybe_densify(self, allowed_nnz=1000, allowed_fraction=0.25):
         >>> s.maybe_densify(allowed_nnz=5, allowed_fraction=0.25)
         Traceback (most recent call last):
             ...
-        NotImplementedError: Operation would require converting large sparse array to dense
+        NotImplementedError: Operation would require converting large sparse \
+array to dense. Use .todense() to force densification.
         """
         elements = np.prod(self.shape)
 
         if elements <= allowed_nnz or self.nnz >= elements * allowed_fraction:
             return self.todense()
         else:
             raise NotImplementedError("Operation would require converting "
-                                      "large sparse array to dense")
+                                      "large sparse array to dense. Use "
+                                      ".todense() to force densification.")
 
 
 def tensordot(a, b, axes=2):

sparse/tests/test_core.py

@@ -887,3 +887,16 @@ def test_scalar_shape_construction():
 def test_len():
     s = sparse.random((20, 30, 40))
     assert len(s) == 20
+
+
+def test_numpy_todense():
+    # make sure that .astype(int) is never all zeros
+    s = sparse.random((20, 30, 40)) * 100
+    with pytest.raises(ValueError):
+        assert np.array(s)
+
+    with pytest.raises(ValueError):
+        assert np.array(s, dtype=int)
+
+    with pytest.raises(ValueError):
+        assert np.allclose(s, s.todense())