MAINT make it possible to use wheelhouse-uploader

wheelhouse-uploader makes it possible to leverage our CI infrastructure
(travis and appveyor) to automate the generation of binary packages
for OSX and windows (both .exe and .whl packages).

The cloud containers that are used to store the build artifacts of the
CI workers are configured in the setup.cfg file.

More details at: https://github.com/ogrisel/wheelhouse-uploader

setup.cfg

@@ -21,6 +21,15 @@ doctest-extension = rst
 doctest-fixtures = _fixture
 #doctest-options = +ELLIPSIS,+NORMALIZE_WHITESPACE
 
+[wheelhouse_uploader]
+artifact_indexes=
+    # OSX wheels built by travis (only for specific tags):
+    # https://github.com/MacPython/scikit-learn-wheels
+    http://wheels.scipy.org
+    # Windows wheels buit by:
+    # https://ci.appveyor.com/project/sklearn-ci/scikit-learn/
+    http://windows-wheels.scikit-learn.org/
+
 # Uncomment the following under windows to build using:
 # http://sourceforge.net/projects/mingw/
 

setup.py

@@ -38,24 +38,20 @@
 # We can actually import a restricted version of sklearn that
 # does not need the compiled code
 import sklearn
-
 VERSION = sklearn.__version__
 
-###############################################################################
+
 # Optional setuptools features
 # We need to import setuptools early, if we want setuptools features,
 # as it monkey-patches the 'setup' function
-
 # For some commands, use setuptools
 SETUPTOOLS_COMMANDS = set([
     'develop', 'release', 'bdist_egg', 'bdist_rpm',
     'bdist_wininst', 'install_egg_info', 'build_sphinx',
     'egg_info', 'easy_install', 'upload', 'bdist_wheel',
     '--single-version-externally-managed',
 ])
-
-
-if len(SETUPTOOLS_COMMANDS.intersection(sys.argv)) > 0:
+if SETUPTOOLS_COMMANDS.intersection(sys.argv):
     import setuptools
     extra_setuptools_args = dict(
         zip_safe=False,  # the package can run out of an .egg file
@@ -64,8 +60,8 @@
 else:
     extra_setuptools_args = dict()
 
-###############################################################################
 
+# Custom clean command to remove build artifacts
 
 class CleanCommand(Clean):
     description = "Remove build artifacts from the source tree"
@@ -84,8 +80,22 @@ def run(self):
                 if dirname == '__pycache__':
                     shutil.rmtree(os.path.join(dirpath, dirname))
 
+cmdclass = {'clean': CleanCommand}
+
+
+# Optional wheelhouse-uploader features
+# To automate release of binary packages for scikit-learn we need a tool
+# to download the packages generated by travis and appveyor workers (with
+# version number matching the current release) and upload them all at once
+# to PyPI at release time.
+# The URL of the artifact repositories are configured in the setup.cfg file.
+
+WHEELHOUSE_UPLOADER_COMMANDS = set(['fetch_artifacts', 'upload_all'])
+if WHEELHOUSE_UPLOADER_COMMANDS.intersection(sys.argv):
+    import wheelhouse_uploader.cmd
+    cmdclass.update(vars(wheelhouse_uploader.cmd))
+
 
-###############################################################################
 def configuration(parent_package='', top_path=None):
     if os.path.exists('MANIFEST'):
         os.remove('MANIFEST')
@@ -133,7 +143,7 @@ def setup_package():
                                  'Programming Language :: Python :: 3.3',
                                  'Programming Language :: Python :: 3.4',
                                  ],
-                    cmdclass={'clean': CleanCommand},
+                    cmdclass=cmdclass,
                     **extra_setuptools_args)
 
     if (len(sys.argv) >= 2
@@ -143,7 +153,7 @@ def setup_package():
         # For these actions, NumPy is not required.
         #
         # They are required to succeed without Numpy for example when
-        # pip is used to install Scikit when Numpy is not yet present in
+        # pip is used to install Scikit-learn when Numpy is not yet present in
         # the system.
         try:
             from setuptools import setup