Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Create a list of forks, extensions etc. #724

Open
mnwright opened this issue May 16, 2024 · 4 comments
Open

Create a list of forks, extensions etc. #724

mnwright opened this issue May 16, 2024 · 4 comments

Comments

@mnwright
Copy link
Member

Include things like:

Could also link to packages that heavily rely on ranger, such as:

Anything missing?

@stephematician
Copy link
Contributor

stephematician commented Sep 12, 2024

Well, I'd never want to take away from the mighty ranger! But I did eventually release literanger which:

  1. Is about 15-20% faster in training, and 100% faster in prediction.
  2. In particular, it addresses Interface refactor  #304 .
  3. It also offers efficient and compact serialization of trained models via cereal.

It only supports some of ranger's features. I originally intended to pull it into ranger itself - but I never got that far. I've made sure to include the correct license and attribution as best as I can (let me know if I haven't).

I'm not sure how much future there is literanger, but at least it is also available as a backend for multiple imputation with random forests in MICE (see amices/mice#648).

As always, props to you @mnwright for this package, it was a real inspiration and I learnt a lot in refactoring.

@stephematician
Copy link
Contributor

I suppose along with #304 - it also goes some way towards the 'C++ API' that some other issues have raised (e.g. #644 etc)

@mnwright
Copy link
Member Author

mnwright commented Sep 13, 2024

Nice! Particularly the C++ API is something I have on my list for a long time but never made it and probably won't do it soon.
For the speedup, is there anything specific we might merge back into ranger?

In the long run, I think it would be best to converge back to a single package. I often thought about starting ranger v2 from scratch 😆, maybe that would be a good starting point (but probably won't happen soon).

Btw.: I think for large and high dimensional data, there is no speedup (at least not for training, haven't tried prediction yet).

@stephematician
Copy link
Contributor

stephematician commented Sep 14, 2024

@mnwright - if you're using the CRAN version, then training will be about the same (if not slightly slower) than ranger. I only recently fixed up some performance issues with the help of a profiler. It's hard to say if these could be translated into ranger - some of the speedup seems to be due to better inlining.

I'm also currently in the throes of a resubmission to CRAN as there are some issues with the serialization package (Rcereal), and the maintainer seems awol :(

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants