🌐 Mailman 3 January 2022 - scikit-learn - python.org

[ANN] scikit-learn 0.24.0rc1 is online!
by Olivier Grisel Jan. 19, 2024

Jan. 19, 2024

Please help us test the first release candidate for scikit-learn 0.24.0: pip install scikit-learn==0.24.0rc1 Changelog: https://scikit-learn.org/0.24/whats_new/v0.24.html In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression. Feel free to also retweet the announcement to get more people to test it before the final release (probably in 1 week or 2): https://twitter.com/scikit_learn/status/1334562221498753026 Thanks to anybody who helped make this happen! -- Olivier

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[ANNOUNCEMENT] scikit-learn 1.0 release
by Adrin June 30, 2023

June 30, 2023

Hi everyone, We're happy to announce the 1.0 release which you can install via pip or conda: pip install -U scikit-learn or conda install -c conda-forge scikit-learn You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.0.html New major features include: mandatory keyword arguments in many places, Spline Transformers, Quantile Regressor, Feature Names Support, a more flexible plotting API, Online One-Class SVM, and much more! This version supports Python versions 3.7 to 3.9. A big thanks to all contributors for making this release possible. Regards, Adrin, On the behalf of the scikit-learn maintainer team.

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Metadata Routing Survey
by Adrin Jan. 27, 2022

Jan. 27, 2022

Hi there, We're in the process of hopefully finalizing the user and developer API for metadata routing, and we'd like to hear your thoughts on a few things, especially if you're somebody who develops custom/third party scikit-learn compatible estimators. You can find the survey here <https://docs.google.com/forms/d/e/1FAIpQLSd8G8xwKKJ2gJKmqcyZafi6z3mMQdDDiHf…>. It'll take just a few minutes, and it would really help us finalize the work and have it ready sooner for you to benefit from it. Regards, Adrin

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Using KernelDensity for spatial clustering
by Mahmood Naderan Jan. 25, 2022

Jan. 25, 2022

Hi I would like to group a set of numbers by their spatial distance and since it is a 1D problem, I want to use KernelDensity. Based on the examples I saw in the internet [1], the following code, works: a = array([1,2,3,10,12,11,50,51,52,53]).reshape(-1, 1) kde = KernelDensity(kernel='gaussian').fit(a) s = linspace(0,100) e = kde.score_samples(s.reshape(-1,1)) mi = argrelextrema(e, np.less)[0] With the code, I am able to find the minima of the KDE and then compare the values in `a` with `s[mi]`. For simple and test file, that works. However, I need some more information about the way it works. For example, if I change the linespace boundries, the result changes. What does the linespace do exactly? [1] https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html Regards, Mahmood

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Using KernelDensity for spatial clustering
by Mahmood Jan. 25, 2022

Jan. 25, 2022

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scikit-learn monthly developer meeting: Monday January 31 2022
by Thomas J. Fan Jan. 24, 2022

Jan. 24, 2022

Dear all, The scikit-learn developer monthly meeting will take place on Monday January 31 at 20:00 UTC. - Video call link: https://meet.google.com/ews-uszu-djs - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q - Local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2022&month=… The goal of this meeting is to discuss ongoing development topics for the project. Everybody is welcome. As usual, please follow the code of conduct of the project: https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md Regards, Thomas

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Looking for sklearn.neighbors.kde
by Mahmood Naderan Jan. 24, 2022

Jan. 24, 2022

Hi, I have installed scikit 1.0.2 version via pip as below $ pip3 install --user sklearn Collecting sklearn Downloading sklearn-0.0.tar.gz (1.1 kB) Collecting scikit-learn Downloading scikit_learn-1.0.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB) |████████████████████████████████| 26.7 MB 10.5 MB/s Requirement already satisfied: scipy>=1.1.0 in ./.local/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.6.0) Collecting joblib>=0.11 Using cached joblib-1.1.0-py2.py3-none-any.whl (306 kB) Requirement already satisfied: numpy>=1.14.6 in ./.local/lib/python3.8/site-packages (from scikit-learn->sklearn) (1.20.2) Collecting threadpoolctl>=2.0.0 Using cached threadpoolctl-3.0.0-py3-none-any.whl (14 kB) Building wheels for collected packages: sklearn Building wheel for sklearn (setup.py) ... done Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1316 sha256=8bcf6cce0190ae1dcf2af1315bc71b09a162362ec91e063cdc027b4ec55b5ff1 Stored in directory: /home//.cache/pip/wheels/22/0b/40/fd3f795caaa1fb4c6cb738bc1f56100be1e57da95849bfc897 Successfully built sklearn Installing collected packages: threadpoolctl, joblib, scikit-learn, sklearn Successfully installed joblib-1.1.0 scikit-learn-1.0.2 sklearn-0.0 threadpoolctl-3.0.0 However, I get the following error when I want to use KernelDensity from sklearn.neighbors.kde import KernelDensity ModuleNotFoundError: No module named 'sklearn.neighbors.kde' Any idea how to fix that? Regards, Mahmood

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Upcoming: 2nd edition of "Machine learning with scikit-learn MOOC"
by Gael Varoquaux Jan. 23, 2022

Jan. 23, 2022

Hi everyone, The team at Inria, with the help of the Inria learning lab, will soon be opening the 2nd edition of the "Machine Learning with scikit-learn" MOOC: https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/ The content of the MOOC is visible here (we are still polishing details, this is not final): https://inria.github.io/scikit-learn-mooc/ As you can see, it touches all the basics of machine learning, introduced with scikit-learn, teaching much more than the API of the library. We have put a lot of effort on being didactic. Anna Kondratenko, one of last year's participant, said of last year's edition: "I did a #ScikitLearnMooc course as part of a #100DaysOfCode challenge and I just loved it. Scikit-learn creators managed to make it practice-focused and entertaining at the same time. Also, it is perfect for beginners since it starts from the basics going to more advanced level." https://twitter.com/anacoding/status/1484949583629369344 This year's edition should be significantly more didactic! One of the values of participating to the MOOC, compared to just the material that we provide on the web, is that it is full of coding exercise, that are meant to teach understanding of machine-learning and coding skills. The MOOC is absolutely free, and all the materials are open (in the spirit of scikit-learn). While many people on this list may already know the contents of this MOOC (though, we have inserted many useful reflections), you might know people who could benefit from this course to learn machine learning. Please help us spread the word. Pythonly yours, Gaël -- Gael Varoquaux Research Director, INRIA http://gael-varoquaux.info http://twitter.com/GaelVaroquaux

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VOTE: SLEP006 - Metadata Routing
by Adrin Jan. 21, 2022

Jan. 21, 2022

Hi there, We're calling a vote <https://github.com/scikit-learn/enhancement_proposals/pull/65> on SLEP006 - Metadata Routing, which is available here <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep006…>, and detailed past discussions can be found under these PRs <https://github.com/scikit-learn/enhancement_proposals/pulls?q=is%3Apr++slep…> and these issues <https://github.com/scikit-learn/enhancement_proposals/issues?q=is%3Aissue+s…> . The current proposed implementation is drafted under this pull request <https://github.com/scikit-learn/scikit-learn/pull/22083> where you can find a rendered version of the user guide here <https://171332-843222-gh.circle-artifacts.com/0/doc/metadata_routing.html> and a rendered version of the developer API (only applicable to third party developers and people who write custom estimators) here <https://171332-843222-gh.circle-artifacts.com/0/doc/auto_examples/plot_meta…>. These are found under `metadata_routing.rst` and `plot_metadata_routing.py` under the aforementioned PR <https://github.com/scikit-learn/scikit-learn/pull/22083> respectively. Note that this PR does NOT touch scorers, splitters, or any of the estimators or meta-estimators in scikit-learn. It implements the machinery in `BaseEstimator` for *consumers* only. The PR is also not targeted to `main`, and instead it's to be merged into the `sample-props` branch on the main repo, with follow-up PRs to complete the implementation before merging into `main`. Please leave your votes here in the PR <https://github.com/scikit-learn/enhancement_proposals/pull/65>, and comment on the PR, here on the mailing list, or open new PRs/issues in the enhancement proposals repo or the main repo for specifics if you think further discussion is required. Note that this vote is not to merge the implementation PR, and is rather to accept the SLEP, and the SLEP does NOT discuss implementation details and the API for us and third party developers; but we're more than happy to discuss the implementation and the API during this voting round. We also plan to send out a survey asking third party developers for their opinion of our proposed developer API, parallel to this call for vote. This vote will close on February 21, 2022, and per our governance model it will pass if 2/3 of the cast vote from core developers are in favor, with no quorum/minimum participation requirements. Regards, Adrin

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What is the ECCN (Export Control Classification Number) number & COO (Country of Origin) of scikit-learn
by Anup Arun Yadav Jan. 10, 2022

Jan. 10, 2022

Hey Team, I've subscribed but don't know from where to post, please send URL also Please let me know what is the ECCN (Export Control Classification Number) number & COO (Country of Origin) of scikit-learn. Thanks & Regards, Anup Yadav. Fullstack developer - II -- PITC =============================== Schlumberger Schlumberger Tech. Centre Pvt. Ltd. 4th Floor - Building No 8. Survey No. 144/145, Samrat Ashok Path Commerzone, Yerawada, Pune Maharashtra, India - 411 006 Schlumberger-Private

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