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[FIX] Table: Fix ensure_copy for sparse matrices #1456

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Jul 15, 2016
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[FIX] Table: Fix ensure_copy for sparse matrices #1456

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15 changes: 10 additions & 5 deletions Orange/data/table.py
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Original file line number Diff line number Diff line change
Expand Up @@ -912,15 +912,20 @@ def is_copy(self):

def ensure_copy(self):
"""
Ensure that the table owns its data; copy arrays when necessary
Ensure that the table owns its data; copy arrays when necessary.
"""
if self.X.base is not None:
def is_view(x):
# Sparse matrices don't have views like numpy arrays. Since indexing on
# them creates copies in constructor we can skip this check here.
return not sp.issparse(x) and x.base is not None

if is_view(self.X):
self.X = self.X.copy()
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But if they are sparse, no copy is made?

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Currently not, this is left to the constructor. As far as I understand ensure_copy only ensures that the data is not a view and makes a copy if it is. Since sparse matrices do not have views (at least I think so) I propose to skip the check. Currently the copy is made in the constructor anyway and hence we probably do not need to make an other here.

if self._Y.base is not None:
if is_view(self._Y):
self._Y = self._Y.copy()
if self.metas.base is not None:
if is_view(self.metas):
self.metas = self.metas.copy()
if self.W.base is not None:
if is_view(self.W):
self.W = self.W.copy()

def copy(self):
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13 changes: 13 additions & 0 deletions Orange/tests/test_table.py
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Original file line number Diff line number Diff line change
Expand Up @@ -649,6 +649,19 @@ def test_copy(self):
self.assertFalse(np.all(t.Y == copy.Y))
self.assertFalse(np.all(t.metas == copy.metas))

def test_copy_sparse(self):
t = data.Table('iris')
t.X = csr_matrix(t.X)
copy = t.copy()

self.assertEqual((t.X != copy.X).nnz, 0) # sparse matrices match by content
np.testing.assert_equal(t.Y, copy.Y)
np.testing.assert_equal(t.metas, copy.metas)

self.assertNotEqual(id(t.X), id(copy.X))
self.assertNotEqual(id(t._Y), id(copy._Y))
self.assertNotEqual(id(t.metas), id(copy.metas))

def test_concatenate(self):
d1 = data.Domain([data.ContinuousVariable('a1')])
t1 = data.Table.from_numpy(d1, [[1],
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