kshitij12345 commented on a change in pull request #16198: [fix] Update
`test_update_ops_mutation` tolerance
URL: https://github.com/apache/incubator-mxnet/pull/16198#discussion_r326269261
##########
File path: tests/python/unittest/test_ndarray.py
##########
@@ -1887,14 +1888,16 @@ def check_save_load(save_is_np_shape,
load_is_np_shape, shapes, save_throw_excep
check_save_load(True, True, [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0,
0, 0), (2, 1), (0, 5, 0)], False, False)
-@with_seed()
-def test_update_ops_mutation():
- def assert_mutate(x, y, op):
+def _test_update_ops_mutation_impl():
+ assert_allclose = functools.partial(
+ np.testing.assert_allclose, rtol=1e-10)
Review comment:
@Vikas-kum @sxjscience Thanks for taking a look.
The new tolerance of `assert_allclose` ([numpy
doc](https://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.assert_almost_equal.html)
mentions to use `assert_allclose` over `assert_almost_equal`) is now 1e-10,
which means that it is more stricter now when checking for NDArray not being
changed.
A consequence of this as it is used in `assert_mutated` is, it also allows
smaller updates, i.e. -5.9604645e-08 (failure case update) to be valid change .
Thus now we have stricter check on whether any element of NDArray has
changed or not (with stricter tolerance) while allowing smaller valid updates
(i.e. anything above `rtol` of 1e-10 will be a valid update).
> I think it should be I don't care about 7th(or 8th precision or anything
above) precision, it can go arbitrary lower than what I care about.
(**I don't know much about this but**) With the instability of floating
point number (rounding errors), I guess it is better to have some tolerance
like 1e-10. I don't know if it is good idea to go to Machine Epsilon (lowest
possible) as `rtol` for this.
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