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BUG: TestNanFunctions_Median.test_keepdims_out #22835

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#22869
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BUG: TestNanFunctions_Median.test_keepdims_out#22835
#22869
Labels
00 - Bug05 - Testing
Milestone
1.24.1 release
@bnavigator

Description

@bnavigator
bnavigator
opened on Dec 19, 2022

Describe the issue:

The random matrices for TestNanFunctions_Median.test_keepdims_out fail to slice ocassionally while building numpy 1.24 for openSUSE Tumbleweed.

Reproduce the code example:

import numpy as np
np.test(extra_argv=['-k', 'test_keepdims_out'])

Error message:

[ 2386s] _________________ TestNanFunctions_Median.test_keepdims_out[1] _________________
[ 2386s] [gw7] linux -- Python 3.8.16 /usr/bin/python3.8
[ 2386s] 
[ 2386s] self = <numpy.lib.tests.test_nanfunctions.TestNanFunctions_Median object at 0x7f07bc2c7520>
[ 2386s] axis = 1
[ 2386s] 
[ 2386s]     @pytest.mark.parametrize(
[ 2386s]         argnames='axis',
[ 2386s]         argvalues=[
[ 2386s]             None,
[ 2386s]             1,
[ 2386s]             (1, ),
[ 2386s]             (0, 1),
[ 2386s]             (-3, -1),
[ 2386s]         ]
[ 2386s]     )
[ 2386s]     def test_keepdims_out(self, axis):
[ 2386s]         d = np.ones((3, 5, 7, 11))
[ 2386s]         # Randomly set some elements to NaN:
[ 2386s]         w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
[ 2386s]         w = w.astype(np.intp)
[ 2386s]         d[tuple(w)] = np.nan
[ 2386s]         if axis is None:
[ 2386s]             shape_out = (1,) * d.ndim
[ 2386s]         else:
[ 2386s]             axis_norm = normalize_axis_tuple(axis, d.ndim)
[ 2386s]             shape_out = tuple(
[ 2386s]                 1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
[ 2386s]         out = np.empty(shape_out)
[ 2386s] >       result = np.nanmedian(d, axis=axis, keepdims=True, out=out)
[ 2386s] 
[ 2386s] axis       = 1
[ 2386s] axis_norm  = (1,)
[ 2386s] d          = array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ....1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1., nan,  1.]]]])
[ 2386s] out        = array([[[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-...73875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26]]]])
[ 2386s] self       = <numpy.lib.tests.test_nanfunctions.TestNanFunctions_Median object at 0x7f07bc2c7520>
[ 2386s] shape_out  = (3, 1, 7, 11)
[ 2386s] w          = array([[ 0,  1,  1,  1,  2,  1,  0,  0,  1,  1,  1,  1,  2,  2,  2,  0,
[ 2386s]          2,  1,  0,  0,  2,  0,  2,  1,  2,  0...,  9,
[ 2386s]          3,  4,  1, 10,  2, 10,  3,  6,  3,  7,  7, 10,  5,  3,  7,  0,
[ 2386s]          6,  0,  9,  9,  8,  6,  0,  1]])
[ 2386s] 
[ 2386s] ../../../BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/lib/tests/test_nanfunctions.py:834: 
[ 2386s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[ 2386s] <__array_function__ internals>:200: in nanmedian
[ 2386s]     ???
[ 2386s]         args       = (array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ..., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1., nan,  1.]]]]),)
[ 2386s]         dispatcher = <function _nanmedian_dispatcher at 0x7f07c61a59d0>
[ 2386s]         implementation = <function nanmedian at 0x7f07c61a5af0>
[ 2386s]         kwargs     = {'axis': 1, 'keepdims': True, 'out': array([[[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.6367387...3875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26]]]])}
[ 2386s]         public_api = <function nanmedian at 0x7f07c61a5b80>
[ 2386s]         relevant_args = (array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ...3875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26]]]]))
[ 2386s] ../../../BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/lib/nanfunctions.py:1217: in nanmedian
[ 2386s]     return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims,
[ 2386s]         a          = array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ....1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1., nan,  1.]]]])
[ 2386s]         axis       = 1
[ 2386s]         keepdims   = True
[ 2386s]         out        = array([[[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-...73875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26]]]])
[ 2386s]         overwrite_input = False
[ 2386s] ../../../BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/lib/function_base.py:3752: in _ureduce
[ 2386s]     r = func(a, **kwargs)
[ 2386s]         a          = array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ....1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1., nan,  1.]]]])
[ 2386s]         axis       = (1,)
[ 2386s]         func       = <function _nanmedian at 0x7f07c61a58b0>
[ 2386s]         index_out  = (slice(None, None, None), 0, slice(None, None, None), slice(None, None, None))
[ 2386s]         keepdims   = True
[ 2386s]         kwargs     = {'axis': 1, 'out': array([[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-...3673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26]]]), 'overwrite_input': False}
[ 2386s]         nd         = 4
[ 2386s]         out        = array([[[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-...73875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]           -8.63673875e-26, -8.63673875e-26]]]])
[ 2386s] ../../../BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/lib/nanfunctions.py:1094: in _nanmedian
[ 2386s]     return _nanmedian_small(a, axis, out, overwrite_input)
[ 2386s]         a          = array([[[[ 1., nan,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ..., nan,  1.,  1.],
[ 2386s]          [nan,  1.,  1., ....1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1.,  1.,  1.],
[ 2386s]          [ 1.,  1.,  1., ...,  1., nan,  1.]]]])
[ 2386s]         axis       = 1
[ 2386s]         out        = array([[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26...63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26]]])
[ 2386s]         overwrite_input = False
[ 2386s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[ 2386s] 
[ 2386s] a = masked_array(
[ 2386s]   data=[[[[1.0, --, 1.0, ..., 1.0, 1.0, 1.0],
[ 2386s]           [1.0, 1.0, 1.0, ..., --, 1.0, 1.0],
[ 2386s]           [-...se, False, ..., False, False, False],
[ 2386s]           [False, False, False, ..., False,  True, False]]]],
[ 2386s]   fill_value=1e+20)
[ 2386s] axis = 1
[ 2386s] out = array([[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26...63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26]]])
[ 2386s] overwrite_input = False
[ 2386s] 
[ 2386s]     def _nanmedian_small(a, axis=None, out=None, overwrite_input=False):
[ 2386s]         """
[ 2386s]         sort + indexing median, faster for small medians along multiple
[ 2386s]         dimensions due to the high overhead of apply_along_axis
[ 2386s]     
[ 2386s]         see nanmedian for parameter usage
[ 2386s]         """
[ 2386s]         a = np.ma.masked_array(a, np.isnan(a))
[ 2386s]         m = np.ma.median(a, axis=axis, overwrite_input=overwrite_input)
[ 2386s]         for i in range(np.count_nonzero(m.mask.ravel())):
[ 2386s] >           warnings.warn("All-NaN slice encountered", RuntimeWarning,
[ 2386s]                           stacklevel=4)
[ 2386s] E           RuntimeWarning: All-NaN slice encountered
[ 2386s] 
[ 2386s] a          = masked_array(
[ 2386s]   data=[[[[1.0, --, 1.0, ..., 1.0, 1.0, 1.0],
[ 2386s]           [1.0, 1.0, 1.0, ..., --, 1.0, 1.0],
[ 2386s]           [-...se, False, ..., False, False, False],
[ 2386s]           [False, False, False, ..., False,  True, False]]]],
[ 2386s]   fill_value=1e+20)
[ 2386s] axis       = 1
[ 2386s] i          = 0
[ 2386s] m          = masked_array(
[ 2386s]   data=[[[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[ 2386s]          [1.0, 1.0, 1.0, 1.0, 1.0, 1.0...         [False, False, False, False, False, False, False, False, False,
[ 2386s]           False, False]]],
[ 2386s]   fill_value=1e+20)
[ 2386s] out        = array([[[-8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26...63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26, -8.63673875e-26,
[ 2386s]          -8.63673875e-26, -8.63673875e-26]]])
[ 2386s] overwrite_input = False
[ 2386s] 
[ 2386s] ../../../BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/lib/nanfunctions.py/home/abuild/rpmbuild/BUILDROOT/python-numpy-1.24.0-124.1.x86_64/usr/lib64/python3.8/site-packages/numpy/_pytesttester.py:143: DeprecationWarning: 
[ 2386s] 
[ 2386s]   `numpy.distutils` is deprecated since NumPy 1.23.0, as a result
[ 2386s]   of the deprecation of `distutils` itself. It will be removed for
[ 2386s]   Python >= 3.12. For older Python versions it will remain present.
[ 2386s]   It is recommended to use `setuptools < 60.0` for those Python versions.
[ 2386s]   For more details, see:
[ 2386s]     https://numpy.org/devdocs/reference/distutils_status_migration.html 
[ 2386s] 
[ 2386s] 
[ 2386s]   from numpy.distutils import cpuinfo
[ 2386s] :1111: RuntimeWarning
[ 2386s] =========================== short test summary info ============================
[ 2386s] FAILED lib/tests/test_nanfunctions.py::TestNanFunctions_Median::test_keepdims_out[1]
[ 2386s] = 1 failed, 23068 passed, 2238 skipped, 35 xfailed, 3 xpassed in 436.92s (0:07:16) =

NumPy/Python version information:

1.24.0 3.10.9 (main, Dec 08 2022, 14:49:06) [GCC]

Context for the issue:

This is a flaky issue. Sometimes the test passes, sometimes not. On different parametrizations.

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