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Merge branch 'dfsg' into debian
* dfsg: (29 commits) Release 0.17.1 MAINT remove non-existing cache folder in 0.17.X branch FIX cythonize TSNE MAINT simplify freeing logic for Barnes-Hut SNE memory leak fix Fix memory leak in Barnes-Hut SNE FIX check_build_doc.py false positive detections MAINT more informative output to circle/check_build_doc.py FIX fetch_california_housing FIX in randomized_svd flip sign Updated examples and tests that use scipy's lena DOC whats_new entry for scikit-learn#6258 fix joblib error in LatentDirichletAllocation MAINT fix / speedup travis on 0.17.X MAINT Upgrade pip in appveyor and display version DOC missing changelog entry for scikit-learn#5857 DOC add fix for scikit-learn#6147 to the changelog FIX 6147: ensure that AUC is always a float TST non-regression test for scikit-learn#6147, roc_auc on memmap data Added changelog entry about scikit-learn#6196 Fix reading of bunch pickles ...
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‎.travis.yml

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@@ -9,6 +9,9 @@ addons:
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- libatlas-dev
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- python-scipy
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env:
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global:
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- OMP_NUM_THREADS=4
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- OPENBLAS_NUM_THREADS=4
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matrix:
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# This environment tests that scikit-learn can be built against
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# versions of numpy, scipy with ATLAS that comes with Ubuntu Precise 12.04
@@ -18,7 +21,7 @@ env:
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NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0"
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# This environment tests the newest supported anaconda env
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- DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
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NUMPY_VERSION="1.10.1" SCIPY_VERSION="0.16.0"
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NUMPY_VERSION="1.10.2" SCIPY_VERSION="0.16.1"
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install: source continuous_integration/install.sh
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script: bash continuous_integration/test_script.sh
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after_success:

‎appveyor.yml

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@@ -48,10 +48,12 @@ install:
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# not already installed.
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- "powershell ./continuous_integration/appveyor/install.ps1"
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- "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"
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- "python -m pip install -U pip"
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# Check that we have the expected version and architecture for Python
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- "python --version"
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- "python -c \"import struct; print(struct.calcsize('P') * 8)\""
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- "pip --version"
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# Install the build and runtime dependencies of the project.
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- "%CMD_IN_ENV% pip install --timeout=60 --trusted-host 28daf2247a33ed269873-7b1aad3fab3cc330e1fd9d109892382a.r6.cf2.rackcdn.com -r continuous_integration/appveyor/requirements.txt"

‎circle.yml

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@@ -1,5 +1,17 @@
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dependencies:
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cache_directories:
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- "~/scikit_learn_data"
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- "~/download"
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# Check whether the doc build is required, install build dependencies and
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# run sphinx to build the doc.
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override:
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- ./continuous_integration/circle/build_doc.sh
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test:
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# Grep error on the documentation
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override:
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- cat ~/log.txt && if grep -q "Traceback (most recent call last):" ~/log.txt; then false; else true; fi
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general:
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# Restric the build to the branch master only
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branches:
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only:
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- master
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# Open the doc to the API
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artifacts:
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- "doc/_build/html"
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- "~/log.txt"

‎continuous_integration/circle/build_doc.sh

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set -x
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set -e
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# Introspect the commit to know whether or not we should skip building the
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# documentation: a pull request that does not change any file in doc/ or
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# examples/ folder should be skipped unless the "[doc: build]" is found the
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# commit message.
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BUILD_DOC=`python continuous_integration/circle/check_build_doc.py`
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echo -e $BUILD_DOC
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if [[ $BUILD_DOC == "SKIP:"* ]]; then
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touch ~/log.txt # the "test" segment needs that file
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exit 0
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fi
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# Installing required system packages to support the rendering of match
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# notation in the HTML documentation
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sudo -E apt-get -yq update
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sudo -E apt-get -yq remove texlive-binaries --purge
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sudo -E apt-get -yq --no-install-suggests --no-install-recommends --force-yes \
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install dvipng texlive-latex-base texlive-latex-extra
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# deactivate circleci virtualenv and setup a miniconda env instead
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if [[ `type -t deactivate` ]]; then
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deactivate
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fi
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# Install dependencies with miniconda
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pushd .
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cd
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mkdir -p download
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cd download
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echo "Cached in $HOME/download :"
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ls -l
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if [[ ! -f miniconda.sh ]]
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then
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wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh \
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-O miniconda.sh
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fi
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chmod +x miniconda.sh && ./miniconda.sh -b -p $HOME/miniconda
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cd ..
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export PATH="$HOME/miniconda/bin:$PATH"
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conda update --yes --quiet conda
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popd
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# Configure the conda environment and put it in the path using the
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# provided versions
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conda create -n testenv --yes --quiet python numpy scipy \
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cython nose coverage matplotlib sphinx pillow
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source /home/ubuntu/miniconda/envs/testenv/bin/activate testenv
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# Build and install scikit-learn in dev mode
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python setup.py develop
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# The pipefail is requested to propagate exit code
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set -o pipefail && cd doc && make html 2>&1 | tee ~/log.txt

‎continuous_integration/circle/check_build_doc.py

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"""Check whether we or not we should build the documentation
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If the last commit message has a "[doc skip]" marker, do not build
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the doc. On the contrary if a "[doc build]" marker is found, build the doc
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instead of relying on the subsequent rules.
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We always build the documentation for jobs that are not related to a specific
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PR (e.g. a merge to master or a maintenance branch).
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If this is a PR, check that if there are some files in this PR that are under
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the "doc/" or "examples/" folders, otherwise skip.
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If the introspection of the current commit fails for any reason, the default
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behavior is to build the documentation.
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"""
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import sys
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import os
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from subprocess import check_output, CalledProcessError
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def exit(msg="", skip=False):
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print("%s: %s" % ("SKIP" if skip else "BUILD", msg))
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sys.exit(0)
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# Introspect the message for the commit that triggered the build
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commit = os.environ.get('CIRCLE_SHA1')
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if not commit:
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exit("undefined CIRCLE_SHA1 variable")
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try:
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commit_msg = check_output("git log --format=%B -n 1".split() + [commit])
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commit_msg = commit_msg.decode('utf-8')
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except CalledProcessError:
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exit("failed to introspect commit message for %s" % commit)
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if "[doc skip]" in commit_msg:
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exit("[doc skip] marker found", skip=True)
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elif "[doc build]" in commit_msg:
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exit("[doc build] marker found")
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# Check whether this commit is part of a pull request or not
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pr_url = os.environ.get('CI_PULL_REQUEST')
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if not pr_url:
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# The documentation should be always built when executed from one of the
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# main branches
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exit("not a pull request")
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# Introspect the list of files changed by all the commits in this PR.
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# Hardcode the assumption that this is a PR to origin/master of this repo
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# as apparently there is way to reliably get the target of a PR with circle
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# ci
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git_range = "origin/master...%s" % commit
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try:
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check_output("git fetch origin master".split())
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filenames = check_output("git diff --name-only".split() + [git_range])
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except CalledProcessError:
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exit("git introspection failed.")
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filenames = filenames.decode('utf-8').split()
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for filename in filenames:
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if filename.startswith(u'doc/') or filename.startswith(u'examples/'):
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exit("detected doc impacting file modified by PR in range %s: %s"
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% (git_range, filename))
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# This PR does not seem to have any documentation related file changed.
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msg = "no doc impacting files detected:\n" + u"\n".join(filenames)
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exit(msg, skip=True)

‎continuous_integration/install.sh

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# Configure the conda environment and put it in the path using the
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# provided versions
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conda create -n testenv --yes python=$PYTHON_VERSION pip nose \
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numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION
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source activate testenv
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if [[ "$INSTALL_MKL" == "true" ]]; then
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# Make sure that MKL is used
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conda install --yes mkl
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conda create -n testenv --yes python=$PYTHON_VERSION pip nose \
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numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION numpy scipy \
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libgfortran mkl
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else
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# Make sure that MKL is not used
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conda remove --yes --features mkl || echo "MKL not installed"
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conda create -n testenv --yes python=$PYTHON_VERSION pip nose \
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numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION libgfortran
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fi
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source activate testenv
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elif [[ "$DISTRIB" == "ubuntu" ]]; then
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# At the time of writing numpy 1.9.1 is included in the travis

‎doc/documentation.rst

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<!-- doc versions -->
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<h2>Other Versions</h2>
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<ul>
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<li><a href="http://scikit-learn.org/stable/documentation.html">scikit-learn 0.18 (development)</a></li>
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<li><a href="http://scikit-learn.org/dev/documentation.html">scikit-learn 0.18 (development)</a></li>
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<li>scikit-learn 0.17 (stable)</li>
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<li><a href="http://scikit-learn.org/0.17/documentation.html">scikit-learn 0.16</a></li>
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<li><a href="http://scikit-learn.org/0.15/documentation.html">scikit-learn 0.15</a></li>

‎doc/index.rst

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<ul>
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<li><em>About us</em> See <a href="about.html#people">authors</a></li>
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<li><em>More Machine Learning</em> Find <a href="related_projects.html">related projects</a></li>
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<li><em>Questions?</em> See <a href="faq/">FAQ</a> and <a href="http://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a></li>
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<li><em>Questions?</em> See <a href="faq.html">FAQ</a> and <a href="http://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a></li>
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<li><em>Mailing list:</em> <a href="https://lists.sourceforge.net/lists/listinfo/scikit-learn-general">scikit-learn-general@lists.sourceforge.net</a></li>
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<li><em>IRC:</em> #scikit-learn @ <a href="http://webchat.freenode.net/">freenode</a></li>
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</ul>

‎doc/modules/clustering.rst

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@@ -464,15 +464,15 @@ function of the gradient of the image.
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* :ref:`example_cluster_plot_segmentation_toy.py`: Segmenting objects
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from a noisy background using spectral clustering.
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* :ref:`example_cluster_plot_lena_segmentation.py`: Spectral clustering
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to split the image of lena in regions.
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* :ref:`example_cluster_plot_face_segmentation.py`: Spectral clustering
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to split the image of the raccoon face in regions.
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.. |lena_kmeans| image:: ../auto_examples/cluster/images/plot_lena_segmentation_001.png
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:target: ../auto_examples/cluster/plot_lena_segmentation.html
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.. |face_kmeans| image:: ../auto_examples/cluster/images/plot_face_segmentation_001.png
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:target: ../auto_examples/cluster/plot_face_segmentation.html
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:scale: 65
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.. |lena_discretize| image:: ../auto_examples/cluster/images/plot_lena_segmentation_002.png
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:target: ../auto_examples/cluster/plot_lena_segmentation.html
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.. |face_discretize| image:: ../auto_examples/cluster/images/plot_face_segmentation_002.png
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:target: ../auto_examples/cluster/plot_face_segmentation.html
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:scale: 65
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Different label assignment strategies
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===================================== =====================================
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``assign_labels="kmeans"`` ``assign_labels="discretize"``
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===================================== =====================================
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|lena_kmeans| |lena_discretize|
493+
|face_kmeans| |face_discretize|
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===================================== =====================================
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496496

@@ -619,12 +619,12 @@ merging to nearest neighbors as in :ref:`this example
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<example_cluster_plot_agglomerative_clustering.py>`, or
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using :func:`sklearn.feature_extraction.image.grid_to_graph` to
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enable only merging of neighboring pixels on an image, as in the
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:ref:`Lena <example_cluster_plot_lena_ward_segmentation.py>` example.
622+
:ref:`raccoon face <example_cluster_plot_face_ward_segmentation.py>` example.
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624624
.. topic:: Examples:
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626-
* :ref:`example_cluster_plot_lena_ward_segmentation.py`: Ward clustering
627-
to split the image of lena in regions.
626+
* :ref:`example_cluster_plot_face_ward_segmentation.py`: Ward clustering
627+
to split the image of a raccoon face in regions.
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* :ref:`example_cluster_plot_ward_structured_vs_unstructured.py`: Example of
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Ward algorithm on a swiss-roll, comparison of structured approaches
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* :ref:`example_cluster_plot_kmeans_silhouette_analysis.py` : In this example
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the silhouette analysis is used to choose an optimal value for n_clusters.
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‎doc/modules/decomposition.rst

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learning objects (see :ref:`SparseCoder`).
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The following image shows how a dictionary learned from 4x4 pixel image patches
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extracted from part of the image of Lena looks like.
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extracted from part of the image of a raccoon face looks like.
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457457
.. figure:: ../auto_examples/decomposition/images/plot_image_denoising_001.png

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