MAINT update links and instructions to download and upload from PyPI

CONTRIBUTING.md

@@ -224,7 +224,7 @@ be placed in ``_build/html/`` and are viewable in a web browser. See the
 
 For building the documentation, you will need
 [sphinx](http://sphinx.pocoo.org/),
-[matplotlib](http://matplotlib.sourceforge.net/), and
+[matplotlib](http://matplotlib.org/), and
 [pillow](http://pillow.readthedocs.io/en/latest/).
 
 When you are writing documentation, it is important to keep a good

doc/developers/maintainer.rst

@@ -37,16 +37,21 @@ Making a release
 
        $ python setup.py sdist register upload
 
-   - Upload manually the tarball on SourceForge:
-     https://sourceforge.net/projects/scikit-learn/files/
 
 5. Push the documentation to the website (see README in doc folder)
 
 
-6. Build binaries for windows and push them to PyPI::
+6. Build binaries using dedicated CI servers by updating the git submodule
+   reference to the new scikit-learn tag of the release at:
 
-    $ python setup.py bdist_wininst upload
+   https://github.com/MacPython/scikit-learn-wheels
+
+   Once the CI has completed successfully, collect the generated binary wheel
+   packages and upload them to PyPI by running the following commands in the
+   scikit-learn source folder (checked out at the release tag)::
+
+       $ pip install -U wheelhouse_uploader
+       $ python setup.py sdist fetch_artifacts upload_all
 
-   And upload them also to sourceforge
 
 7. FOR FINAL RELEASE: Update the release date in What's New

doc/related_projects.rst

@@ -218,7 +218,7 @@ Other packages useful for data analysis and machine learning.
 - `theano <http://deeplearning.net/software/theano/>`_ A CPU/GPU array
   processing framework geared towards deep learning research.
 
-- `statsmodels <http://statsmodels.sourceforge.net/>`_ Estimating and analysing
+- `statsmodels <http://www.statsmodels.org>`_ Estimating and analysing
   statistical models. More focused on statistical tests and less on prediction
   than scikit-learn.
 

setup.py

@@ -32,6 +32,7 @@
 MAINTAINER = 'Andreas Mueller'
 MAINTAINER_EMAIL = 'amueller@ais.uni-bonn.de'
 URL = 'http://scikit-learn.org'
+DOWNLOAD_URL = 'https://pypi.org/project/scikit-learn/#files'
 LICENSE = 'new BSD'
 
 # We can actually import a restricted version of sklearn that
@@ -183,6 +184,7 @@ def setup_package():
                     description=DESCRIPTION,
                     license=LICENSE,
                     url=URL,
+                    download_url=DOWNLOAD_URL,
                     version=VERSION,
                     long_description=LONG_DESCRIPTION,
                     classifiers=['Intended Audience :: Science/Research',