Morning! I find myself often requiring the indices and/or values of the top (or bottom) k items in a numpy array. I am aware of solutions involving *partition*/*argpartition *but I find these inelegant (or using *sort *but these are inefficient).
Is this a feature that would benefit the numpy package, or bloat it? I am happy to code it up. Here are some examples: >> import numpy as np >> a = np.array( [ [5,8,1,3,0], [5,6,2,1,3], [1,4,9,1,3], [8,0,4,7,0] ] ) >> # PROPOSED FEATURE: return (ordered) top 4 values in array: >> a.top_k(k=4) array([9, 8, 8, 7]) >> # CURRENT METHOD: return (ordered) top 4 values in array: >> np.sort( np.partition(a.flatten(), -4)[-4:] )[::-1] # faster method array([9, 8, 8, 7]) >> np.sort(a.flatten())[::-1][:4] # slower method array([9, 8, 8, 7]) >> # PROPOSED FEATURE: return INDICES of (ordered) top 4 values in array: >> a.top_k(k=4, return_indices=True) array([12,1,15,18]) >> # CURRENT METHOD: return INDICES of (ordered) top 4 values in array: >> (-a.flatten()).argsort()[:4] array([12,1,15,18]) >> # PROPOSED FEATURE: multidimensional examples: >> a.top_k(k=3, axis=0) array( [8,5,1], [8,6,4], [9,4,2], [7,3,1], [3,3,0] ) >> a.top_k(k=3, axis=1) array( [8,5,3], [6,5,2], [9,4,3], [8,7,4] ) I'd also consider including functionality for bottom k values, and methods for returning indices in the case of tied values (e.g. "first appearance", "random" etc.). Cheers Joe On Tue, 22 Feb 2022 at 15:30, Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote: > Joe, > > Could you show an example that you find inelegant and elaborate on how you > intend to improve it? It's hard to discuss without more specific > information. > > - Joe > > On Tue, Feb 22, 2022, 07:23 Joseph Bolton <joseph.jazz.bol...@gmail.com> > wrote: > >> Morning, >> >> My apologies if this deviates from the vision of numpy: >> >> I find myself often requiring the indices and/or values of the top (or >> bottom) k items in a numpy array. >> >> I am aware of solutions involving partition/argpartition but these are >> inelegant. >> >> I am thinking of 1-dimensional arrays, but this concept extends to an >> arbitrary number of dimensions. >> >> Is this a feature that would benefit the numpy package? I am happy to >> code it up. >> >> Thanks for your time! >> >> Best regards >> Joe >> >> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list -- numpy-discussion@python.org >> To unsubscribe send an email to numpy-discussion-le...@python.org >> https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ >> Member address: jfoxrabinov...@gmail.com >> > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: joseph.jazz.bol...@gmail.com >
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