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ogrisel
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Updated examples and tests that use scipy's lena
scipy.misc.lena will be removed in scipy version 0.17. This changes the example to use scipy.misc.face instead.
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‎circle.yml

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dependencies:
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cache_directories:
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- "~/scikit_learn_data"
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- "~/scikit-learn.github.io"
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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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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 at %s" % 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)

‎doc/modules/clustering.rst

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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|
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|face_kmeans| |face_discretize|
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===================================== =====================================
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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.
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:ref:`raccoon face <example_cluster_plot_face_ward_segmentation.py>` example.
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.. topic:: Examples:
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* :ref:`example_cluster_plot_lena_ward_segmentation.py`: Ward clustering
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to split the image of lena in regions.
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* :ref:`example_cluster_plot_face_ward_segmentation.py`: Ward clustering
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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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.. figure:: ../auto_examples/decomposition/images/plot_image_denoising_001.png

‎doc/modules/feature_extraction.rst

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to perform `out-of-core`_ scaling. This means that we can learn from data that
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does not fit into the computer's main memory.
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.. _out-of-core: http://en.wikipedia.org/wiki/Out-of-core_algorithm
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.. _out-of-core: http://en.wikipedia.org/wiki/Out-of-core_algorithm
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A strategy to implement out-of-core scaling is to stream data to the estimator
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A strategy to implement out-of-core scaling is to stream data to the estimator
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in mini-batches. Each mini-batch is vectorized using :class:`HashingVectorizer`
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so as to guarantee that the input space of the estimator has always the same
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dimensionality. The amount of memory used at any time is thus bounded by the
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size of a mini-batch. Although there is no limit to the amount of data that can
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be ingested using such an approach, from a practical point of view the learning
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time is often limited by the CPU time one wants to spend on the task.
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For a full-fledged example of out-of-core scaling in a text classification
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For a full-fledged example of out-of-core scaling in a text classification
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task see :ref:`example_applications_plot_out_of_core_classification.py`.
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(:ref:`hierarchical_clustering`) can cluster together only neighboring pixels
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of an image, thus forming contiguous patches:
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.. figure:: ../auto_examples/cluster/images/plot_lena_ward_segmentation_001.png
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:target: ../auto_examples/cluster/plot_lena_ward_segmentation.html
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.. figure:: ../auto_examples/cluster/images/plot_face_ward_segmentation_001.png
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:target: ../auto_examples/cluster/plot_face_ward_segmentation.html
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:align: center
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.. note:: **Examples**
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* :ref:`example_cluster_plot_lena_ward_segmentation.py`
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* :ref:`example_cluster_plot_face_ward_segmentation.py`
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* :ref:`example_cluster_plot_segmentation_toy.py`
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* :ref:`example_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
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‎doc/themes/scikit-learn/layout.html

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<img src="_images/plot_gmm_pdf.png"></a>
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</div>
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<div class="item">
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<a href="{{ pathto('auto_examples/cluster/plot_lena_ward_segmentation') }}">
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<img src="_images/plot_lena_ward_segmentation.png"></a>
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<a href="{{ pathto('auto_examples/cluster/plot_face_ward_segmentation') }}">
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<img src="_images/plot_face_ward_segmentation.png"></a>
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</div>
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</div>
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<!-- Carousel nav -->

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