10000 The behavior of numpy.apply_along_axis function is not what expected. · Issue #5248 · numpy/numpy · GitHub
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The behavior of numpy.apply_along_axis function is not what expected. #5248

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yorklee70 opened this issue Oct 30, 2014 · 2 comments
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The behavior of numpy.apply_along_axis function is not what expected. #5248

yorklee70 opened this issue Oct 30, 2014 · 2 comments

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@yorklee70
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yorklee70 commented Oct 30, 2014 •
edited by eric-wieser
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When I use python set as elelments of a numpy array, the behavior of numpy.apply_along_axis function is not what expected. For example, I want to get the union set for each column using the following codes:

import numpy
import operator
d = numpy.array([  [ set([1, 11]), set([2, 22]), set([3, 33]) ],
                              [ set([4, 44]), set([5, 55]), set([6, 66]) ]  ])
numpy.apply_along_axis( lambda a:reduce(operator.**or**, a), 0, d)

i.e. I expect to get a result like:

    array([ set(1, 11, 4, 44), set(2, 22, 5, 55), set(3, 33, 6, 66) ]

however, the result is:

    array([ [ set(1, 11, 4, 44), set(2, 22, 5, 55), set(3, 33, 6, 66) ],
               [ set(1, 11, 4, 44), set(2, 22, 5, 55), set(3, 33, 6, 66) ],
               [ set(1, 11, 4, 44), set(2, 22, 5, 55), set(3, 33, 6, 66) ],
               [ set(1, 11, 4, 44), set(2, 22, 5, 55), set(3, 33, 6, 66) ] ])

The expected result was repeated 4 times, which is determined by the first element, actually it equals to the length of the first element.

Can this be viewed as a bug? Anyway it is different with the way of the "apply" function in R language.

York
@eric-wieser
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eric-wieser commented Feb 19, 2017 •
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So, this was kind of fixed by #8441, but also kind of not:

>>> import functools
>>> import operator
>>> import numpy as np
>>> d = np.array([  [ set([1, 11]), set([2, 22]), set([3, 33]) ],  [ set([4, 44]), set([5, 55]), set([6, 66]) ]  ])
>>> np.apply_along_axis( lambda a: functools.reduce(operator.or_, a), 0, d)
array([array({1, 11, 4, 44}, dtype=object),
       array({2, 5, 22, 55}, dtype=object),
       array({33, 66, 3, 6}, dtype=object)], dtype=object)

@eric-wieser
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Closing in favor of #8642

@eric-wieser eric-wieser mentioned this issue Feb 19, 2017
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