I want to make some unit-tests for my app, and I need to compare two arrays. Since array.__eq__
returns a new array (so TestCase.assertEqual
fails), what is the best way to assert for equality?
Currently I'm using
self.assertTrue((arr1 == arr2).all())
but I don't really like it
(np.array([1, 1]) == np.array([1])).all()
will yield True
check out the assert functions in numpy.testing
, e.g.
assert_array_equal
for floating point arrays equality test might fail and assert_almost_equal
is more reliable.
update
A few versions ago numpy obtained assert_allclose
which is now my favorite since it allows us to specify both absolute and relative error and doesn't require decimal rounding as the closeness criterion.
I think (arr1 == arr2).all()
looks pretty nice. But you could use:
numpy.allclose(arr1, arr2)
but it's not quite the same.
An alternative, almost the same as your example is:
numpy.alltrue(arr1 == arr2)
Note that scipy.array is actually a reference numpy.array. That makes it easier to find the documentation.
I find that using self.assertEqual(arr1.tolist(), arr2.tolist())
is the easiest way of comparing arrays with unittest.
I agree it's not the prettiest solution and it's probably not the fastest but it's probably more uniform with the rest of your test cases, you get all the unittest error description and it's really simple to implement.
np.nan
, since np.nan != np.nan
and the self.assertEqual
attempt won't be able to account for that.
Since Python 3.2 you can use assertSequenceEqual(array1.tolist(), array2.tolist())
.
This has the added value of showing you the exact items in which the arrays differ.
float
type. We really need assertSequenceAlmostEqual
self.assertTrue(np.array_equal(x, y, equal_nan=True))
equal_nan = True
if you want to np.nan == np.nan
returns True
or you can use numpy.allclose to compare with torelance.
In my tests I use this:
numpy.testing.assert_array_equal(arr1, arr2)
Use numpy
numpy.array_equal(a, b)
np.linalg.norm(arr1 - arr2) < 1e-6
Success story sharing
unittest
you can useself.assertIsNone(np.testing.assert_array_equal(a, b))
as it returnsNone
if the arrays are equal.