[SciPy-Dev] cKDTree

[Sylvain Corlay]

Would you guys consider in scope for scipy to have implementation of faster
nearest neighbor search methods than KdTree?

Some methods are fairly simple... e.g principal axis tree which use the
principal direction of the dataset to split the dataset into smaller
subsets.  As soon as intrinsic dimensionality is significantly smaller than
the dimension of the space, it is significantly faster.

Besides, only having to compute the (an approximate) principal axis is much
faster than doing an actual PCA.

On Tue, Sep 6, 2016 at 4:14 AM, Jacob Vanderplas <jakevdp at cs.washington.edu>
wrote:

> From my own casual benchmarks, the new scipy cKDTree is much faster than
> any of the scikit-learn options, though it still only supports axis-aligned
> euclidean-like metrics (where sklearn's BallTree supports dozens of
> additional metrics). The cKDTree also has a limited range of query types
> compared to scikit-learn's trees,
>    Jake
>
>  Jake VanderPlas
>  Senior Data Science Fellow
>  Director of Research in Physical Sciences
>  University of Washington eScience Institute
>
> On Mon, Sep 5, 2016 at 12:46 AM, Daπid <davidmenhur at gmail.com> wrote:
>
>> On 4 September 2016 at 23:00, Robert Lucente <rlucente at pipeline.com>
>> wrote:
>> > Please note that I am a newbie and just a lurker.
>> >
>> > I noticed in a recent email that cKDTree was mentioned.
>> >
>> > Q: What is the relationship if any between SciPy an scikit-learn when
>> it comes to cKDTree?
>> >
>> > The reason that I ask are the following 2 links
>> >
>> > https://jakevdp.github.io/blog/2013/04/29/benchmarking-neare
>> st-neighbor-searches-in-python/
>> >
>> > https://github.com/scikit-learn/scikit-learn/issues/3682
>>
>> Note that these benchmarks are from 2013 and 2014. Scipy's KDTree has
>> seen its performance recently improved, twice. Scikit's last update to
>> its KDTree was in 2015. So, we need to run the benchmarks again.
>>
>> /David.
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>
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