Mailman 3 September 2018 - scikit-learn

CircleCI
by Manuel CASTEJÓN LIMAS Sept. 10, 2018

Sept. 10, 2018

Dear all, Contrib projects template hints the authors to use TravisCI, CircleCI and Appveyor. Now that CircleCI has moved to version 2, is there any idea on what to do about it? Will the template be updated? Is it ok if we use only CircleCI? What do you, core devs, suggest about that? Best wishes Manuel

3 2

Optimization algorithms in scikit-learn
by Touqir Sajed Sept. 4, 2018

Sept. 4, 2018

Hi, I have been looking for stochastic optimization algorithms in scikit-learn that are faster than SGD and so far I have come across Adam and momentum. Are there other methods implemented in scikit-learn? Particularly, the variance reduction methods such as SVRG ( https://papers.nips.cc/paper/4937-accelerating-stochastic-gradient-descent-… <https://ml-trckr.com/link/https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4937-accel…>) ? These variance reduction methods are the current state of the art in terms of convergence speed while maintaining runtime complexity of order n -- number of features. If they are not implemented yet, I think it would be really great to implement(I am happy to do so) them since nowadays working on large datasets(where LBGFS may not be practical) is the norm where the improvements are definitely worth it. Cheers, Touqir -- Computing Science Master's student at University of Alberta, Canada, specializing in Machine Learning. Website : https://ca.linkedin.com/in/touqir-sajed-6a95b1126 <https://ml-trckr.com/link/https%3A%2F%2Fca.linkedin.com%2Fin%2Ftouqir-sajed…>

3 3

Multi Armed Bandit Algorithms in Scikit-learn
by Touqir Sajed Sept. 4, 2018

Sept. 4, 2018

Hi, This email is intended to initiate a discussion on whether it is worth adding Multi-Armed Bandit (MAB) algorithms in Scikit-learn. For those of you who have not heard of MAB algorithms, they are the simplest form of decision-making algorithms applicable whenever data with labels are not given beforehand and the objective is to try out different decisions, whenever a sample is seen, and learn which decision is the best in the long run. They are the simplest form of Reinforcement Learning algorithms. While they are not applicable for every decision-making tasks, they naturally fit into a number of problem settings where they are more sample efficient and simpler than the more advanced RL algorithms. For a number of applications : https://www.quora.com/In-what-kind-of-real-life-situations-can-we-use-a-mul…. If <https://ml-trckr.com/link/https%3A%2F%2Fwww.quora.com%2FIn-what-kind-of-rea…> you want to know more about their usage, how they work or their advantages, feel free to let me know! I do feel that MAB algorithms should be a part of Scikit-learn since a lot of the interesting problems that we face regarding learning is about decision making. There are quite a few github repos with MAB implementations but their coverage is extremely limited and I do not know of any dedicated library on MABs. Companies like Yahoo, Microsoft, Google use MABs for Ad recommendation and search engine optimization but their code is not made public. Cheers, Touqir -- Computing Science Master's student at University of Alberta, Canada, specializing in Machine Learning. Website : https://ca.linkedin.com/in/touqir-sajed-6a95b1126 <https://ml-trckr.com/link/https%3A%2F%2Fca.linkedin.com%2Fin%2Ftouqir-sajed…>

2 2

Re: [scikit-learn] Contribute to Scikit-learn
by Shuki Cohen Sept. 3, 2018

Sept. 3, 2018

On Mon, Sep 3, 2018 at 1:21 PM Shuki Cohen <shokyco(a)gmail.com> wrote: > Hi all, > > Me and a friend of mine found lack of feature selection functionalities in > Scikit-learn and we thought to contribute in order to answer this need. > More specifically, we want to add: > 1. Sequential Forward Selection algorithm > <https://en.wikipedia.org/wiki/Feature_selection> > 2. Multivariate Feature Selection > <http://staff.utia.cas.cz/novovic/files/CIARP07_NSHP.pdf> > to the Scikit-learn code base, and this mail is to get your approval that > such a project has good chances to be added to the next version. > > Thanks in advance > Shuki & Yaniv >

4 3

ANN Scikit-learn 0.20rc1 release candidate available
by Andreas Mueller Sept. 1, 2018

Sept. 1, 2018

Hey Folks! I'm happy to announce that the scikit-learn 0.20 release candidate 1 is now available via conda-forge and pip. Please help us by testing this release candidate so we can make sure the final release will go seamlessly! You can install the release candidate from conda-forge using conda install scikit-learn=0.20rc1 -c conda-forge/label/rc -c conda-forge (please take into account that if you're using the default conda channel otherwise, this will pull in some other dependencies from conda-forge). You can install the release candidate via pip using pip install --pre scikit-learn The documentation for 0.20 is available at http://scikit-learn.org/0.20/ and will move to http://scikit-learn.org/ upon final release. You can find the release note with all new features and changes here: http://scikit-learn.org/0.20/whats_new.html#version-0-20 Thank you for your help in testing the RC and thank you to everybody that made the release possible! All the best, Andy

3 2