jeremiedbb on kmeans-wheels
sklearn/linear_modeland followed instructions in Contributors Guide. But after few attempts the
/docsdoes not seem to modify local docs build inside
_build. In the browser, API docs didn't change although I modified the sources. Am I missing something?
re 1. you can't use (let alone allocate) numpy arrays when the GIL is released because these are Python objects. Is there a way for you to allocate the arrays somewhere where the GIL is held, and use memory views when the GIL is released? Memory views are safe to use without the GIL
re 2. is it still considered a Python object if you use a
cdefed class and all the attributes are
cdefed as well?
re 3. what vectors? can't you use a view as a field of the struct?
Hey guys, me again: Regarding me previous message :point_up: October 4, 2019 5:28 PM I've gone through some more attempts that don't quite work.
@NicolasHug The blog post helped a bit with my understanding of memory-views, however I still have a few questions: Can a memory-view be initialized
with nogil? And no, a struct member cannot be a memory view.
I tried to make my own class but then got yelled at because it's not of type
Splitter, so that was a bust.
I augmented the
SplitRecord with 2 cpp vectors, but that caused things to go wonky requiring cpp in files that I'm not willing to touch.
I ended up augmenting
SplitRecord with 2 Cython vectors with hard-coded length, but then can't seem to initialize a memory-view into them inside of the
node_split. I'm pretty much stuck (in my current view of things), because I'm trying to do as little modification as possible, but it seems that in order to accomplish my task I'll have to re-write a big chunk of ensemble methods. I'd have to add an input argument to the
node_split method? That doesn't sound like a good idea.
Any ideas? Much appreciated.
Hi all, I'm trying to help my team reduce creating new code when leveraging existing libraries might get the job done. Does anyone have thoughts on how the following can be accomplished? https://stackoverflow.com/q/58533004/1566074
Basically finding the optimal subgroups for a dataset to then feed into an estimator to reduce noise.
masteris easy (just change the import path), the challenging part is to make work out-of-date version with a newer scikit-learn.
try except ImportErroras you suggested I think
help wanted. I suppose a new one can pick this and conclude the PR also taking into account the comments of the reviewers. What's the best here? Create a new PR that refers to the already existing one?