[scikit-learn] ANN: scikit-learn 0.21 released

[Gael Varoquaux]

Indeed!

Great improvements. And it's a pleasure to see that the releases are more
frequent: a huge value to the community.

Gaël

On Thu, May 16, 2019 at 10:21:09AM +0200, bertrand.thirion wrote:
> Congratulations !
> Bertrand 



> Envoyé depuis mon smartphone Samsung Galaxy.

> -------- Message d'origine --------
> De : Joel Nothman <joel.nothman at gmail.com>
> Date : 16/05/2019 10:03 (GMT+01:00)
> À : Scikit-learn user and developer mailing list <scikit-learn at python.org>
> Objet : [scikit-learn] ANN: scikit-learn 0.21 released

> Thanks to the work of many, many contributors, we have released Scikit-learn
> 0.21. It is available from GitHub, PyPI and Conda-forge, but is not yet
> available on the Anaconda defaults channel.

> * Documentation at https://scikit-learn.org/0.21
> * Release Notes at https://scikit-learn.org/0.21/whats_new
> * Download source or wheels at https://pypi.org/project/scikit-learn/0.21rc2/
> * Install from conda-forge with `conda install -c conda-forge scikit-learn`

> Highlights include:
> * neighbors.NeighborhoodComponentsAnalysis for supervised metric learning,
> which learns a weighted euclidean distance for k-nearest neighbors. https://
> scikit-learn.org/0.21/modules/neighbors.html#nca
> * ensemble.HistGradientBoostingClassifier
> and ensemble.HistGradientBoostingRegressor: experimental implementations of
> efficient binned gradient boosting machines. https://scikit-learn.org/0.21/
> modules/ensemble.html#gradient-tree-boosting
> * impute.IterativeImputer: an experimental API for a non-trivial approach to
> missing value imputation. https://scikit-learn.org/0.21/modules/impute.html#
> multivariate-feature-imputation
> * cluster.OPTICS: a new density-based clustering algorithm. https://
> scikit-learn.org/0.21/modules/clustering.html#optics
> * better printing of estimators as strings, with an option to hide default
> parameters for compactness: https://scikit-learn.org/0.21/auto_examples/
> plot_changed_only_pprint_parameter.html
> * for estimator and library developers: a way to tag your estimator so that it
> can be treated appropriately with check_estimator. https://scikit-learn.org/
> 0.21/developers/contributing.html#estimator-tags

> There are many other enhancements and fixes listed in the release notes (https:
> //scikit-learn.org/0.21/whats_new).

> Please note that Scikit-learn has new dependencies. It requires:
> * joblib >= 0.11, which used to be vendored within Scikit-learn
> * OpenMP, unless the environment variable SKLEARN_NO_OPENMP=1 when the code is
> compiled (and cythonized)
> * Python >= 3.5. Installing Scikit-learn from Python 2 will continue to provide
> version 0.20.

> Thanks again to everyone who contributed and to our sponsors, who helped us to
> develop such a great set of features and fixes since version 0.20 in under 8
> months.

> Happy Learning!

> From the Scikit-learn ]team.

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> scikit-learn at python.org
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-- 
    Gael Varoquaux
    Senior Researcher, INRIA 
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux