Hi qll,
Sorry for the late reply, lots of things to work on currently.
I'll have a look at the roadmap and the pointers to see what could be done
to enhance the situation.
Cheers,
Matthieu
Le lun. 26 nov. 2018 à 20:09, Roman Yurchak via scikit-learn <
scikit-learn@python.org> a écrit :
> Trie
Tries are interesting, but it appears that while they use less memory
that dicts/maps they are generally slower than dicts for a large number
of elements. See e.g.
https://github.com/pytries/marisa-trie/blob/master/docs/benchmarks.rst.
This is also consistent with the results in the below linke
I think tries might be an interesting datastructure, but it really
depends on where the bottleneck is.
I'm really surprised they are not used more, but maybe that's just
because implementations are missing?
On 11/26/18 8:39 AM, Roman Yurchak via scikit-learn wrote:
Hi Matthieu,
if you are int
Hi Matthieu,
if you are interested in general questions regarding improving
scikit-learn performance, you might be want to have a look at the draft
roadmap
https://github.com/scikit-learn/scikit-learn/wiki/Draft-Roadmap-2018 --
there is a lot topics where suggestions / PRs on improving performa
Hi all,
I've noticed a few questions online (mainly SO) on TfidfVectorizer speed,
and I was wondering about the global effort on speeding up sklearn.
Is there something I can help on this topic (Cython?), as well as a
discussion on this tough subject?
Cheers,
Matthieu
--
Quantitative analyst, P
