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Merge branch 'master' into mldata
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‎.circleci/config.yml

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path: ~/log.txt
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destination: log.txt
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deploy:
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docker:
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- image: circleci/python:3.6.1
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- deploy:
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requires:
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- python3
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- python2

‎.travis.yml

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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1" CYTHON_VERSION="0.25.2"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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if: type != cron
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# This environment tests the newest supported Anaconda release (5.0.0)
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# It also runs tests requiring Pandas and PyAMG
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# This environment tests the newest supported Anaconda release.
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# It runs tests requiring pandas and PyAMG.
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# It also runs with the site joblib instead of the vendored copy of joblib.
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- env: DISTRIB="conda" PYTHON_VERSION="3.6.2" INSTALL_MKL="true"
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NUMPY_VERSION="1.14.2" SCIPY_VERSION="1.0.0" PANDAS_VERSION="0.20.3"
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CYTHON_VERSION="0.26.1" PYAMG_VERSION="3.3.2" PILLOW_VERSION="4.3.0"
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COVERAGE=true
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JOBLIB_VERSION="0.12" COVERAGE=true
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CHECK_PYTEST_SOFT_DEPENDENCY="true" TEST_DOCSTRINGS="true"
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SKLEARN_SITE_JOBLIB=1
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if: type != cron
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# flake8 linting on diff wrt common ancestor with upstream/master
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- env: RUN_FLAKE8="true" SKIP_TESTS="true"

‎AUTHORS.rst

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@@ -47,6 +47,7 @@ The following people have been core contributors to scikit-learn's development a
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* `Kyle Kastner <http://kastnerkyle.github.io>`_
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* `Manoj Kumar <https://manojbits.wordpress.com>`_
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* Robert Layton
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* `Guillaume Lemaitre <https://github.com/glemaitre>`_
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* `Wei Li <http://kuantkid.github.io/>`_
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* Paolo Losi
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* `Gilles Louppe <http://glouppe.github.io/>`_
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* `Alexandre Passos <http://atpassos.posterous.com>`_
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* `Fabian Pedregosa <http://fa.bianp.net/blog/>`_
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* `Peter Prettenhofer <https://sites.google.com/site/peterprettenhofer/>`_
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* `Hanmin Qin <https://github.com/qinhanmin2014>`_
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* Bertrand Thirion
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* `Joris Van den Bossche <https://github.com/jorisvandenbossche>`_
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* `Jake VanderPlas <http://staff.washington.edu/jakevdp/>`_
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* Nelle Varoquaux
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* `Gael Varoquaux <http://gael-varoquaux.info/>`_
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* Ron Weiss
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* `Roman Yurchak <https://github.com/rth>`_
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Please do not email the authors directly to ask for assistance or report issues.
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Instead, please see `What's the best way to ask questions about scikit-learn

‎README.rst

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- NumPy (>= 1.8.2)
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- SciPy (>= 0.13.3)
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**Scikit-learn 0.20 is the last version to support Python2.7.**
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Scikit-learn 0.21 and later will require Python 3.5 or newer.
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For running the examples Matplotlib >= 1.3.1 is required. A few examples
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require scikit-image >= 0.9.3 and a few examples require pandas >= 0.13.1.
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‎appveyor.yml

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SKLEARN_SKIP_NETWORK_TESTS: 1
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matrix:
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- PYTHON: "C:\\Python27"
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PYTHON_VERSION: "2.7.8"
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PYTHON_ARCH: "32"
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- PYTHON: "C:\\Python27-x64"
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PYTHON_VERSION: "2.7.8"
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- PYTHON: "C:\\Python37-x64"
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PYTHON_VERSION: "3.7.0"
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PYTHON_ARCH: "64"
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- PYTHON: "C:\\Python36"
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PYTHON_VERSION: "3.6.1"
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- PYTHON: "C:\\Python27"
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PYTHON_VERSION: "2.7.8"
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PYTHON_ARCH: "32"
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- PYTHON: "C:\\Python36-x64"
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PYTHON_VERSION: "3.6.1"
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PYTHON_ARCH: "64"
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# Because we only have a single worker, we don't want to waste precious
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# appveyor CI time and make other PRs wait for repeated failures in a failing
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# directly to master instead of just PR builds.
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# credits: JuliaLang developers.
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- ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod `
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https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | `
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https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=500).builds | `
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Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
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throw "There are newer queued builds for this pull request, failing early." }
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‎benchmarks/bench_covertype.py

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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import zero_one_loss
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.utils import check_array
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# Memoize the data extraction and memory map the resulting

‎benchmarks/bench_isolation_forest.py

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y_test = y[n_samples_train:]
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print('--- Fitting the IsolationForest estimator...')
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model = IsolationForest(n_jobs=-1, random_state=random_state)
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model = IsolationForest(behaviour='new', n_jobs=-1,
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random_state=random_state)
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tstart = time()
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model.fit(X_train)
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fit_time = time() - tstart

‎benchmarks/bench_mnist.py

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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.kernel_approximation import Nystroem
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from sklearn.kernel_approximation import RBFSampler
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from sklearn.metrics import zero_one_loss

‎benchmarks/bench_plot_nmf.py

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from sklearn.decomposition.nmf import _initialize_nmf
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from sklearn.decomposition.nmf import _beta_divergence
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from sklearn.decomposition.nmf import INTEGER_TYPES, _check_init
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils.extmath import safe_sparse_dot, squared_norm
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from sklearn.utils import check_array

‎benchmarks/bench_rcv1_logreg_convergence.py

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import gc
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import time
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.linear_model import (LogisticRegression, SGDClassifier)
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from sklearn.datasets import fetch_rcv1
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from sklearn.linear_model.sag import get_auto_step_size

‎benchmarks/bench_saga.py

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from sklearn.datasets import fetch_rcv1, load_iris, load_digits, \
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fetch_20newsgroups_vectorized
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from sklearn.externals.joblib import delayed, Parallel, Memory
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from sklearn.utils import delayed, Parallel, Memory
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import log_loss
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from sklearn.model_selection import train_test_split

‎benchmarks/bench_tsne_mnist.py

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import json
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import argparse
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.datasets import fetch_mldata
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from sklearn.manifold import TSNE
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from sklearn.neighbors import NearestNeighbors

‎build_tools/appveyor/requirements.txt

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# Fetch numpy and scipy wheels from the sklearn rackspace wheelhouse.
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# Those wheels were collected from https://www.lfd.uci.edu/~gohlke/pythonlibs/
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# This is a temporary solution. As soon as numpy and scipy provide official
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# wheel for windows we ca delete this --find-links line.
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--find-links http://28daf2247a33ed269873-7b1aad3fab3cc330e1fd9d109892382a.r6.cf2.rackcdn.com/
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# fix the versions of numpy to force the use of numpy and scipy to use the whl
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# of the rackspace folder instead of trying to install from more recent
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# source tarball published on PyPI
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numpy==1.13.0
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scipy==0.19.0
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cython
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numpy
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scipy
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# Pin Cython to avoid bug with 0.28.x on Python 3.7
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cython==0.27.3
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pytest
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wheel
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wheelhouse_uploader

‎build_tools/circle/build_doc.sh

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make_args=html
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fi
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make_args="SPHINXOPTS=-T $make_args" # show full traceback on exception
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# Installing required system packages to support the rendering of math
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# notation in the HTML documentation
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sudo -E apt-get -yq update

‎build_tools/circle/build_test_pypy.sh

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#!/usr/bin/env bash
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set -x
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set -e
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apt-get -yq update
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apt-get -yq install libatlas-dev libatlas-base-dev liblapack-dev gfortran ccache
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pip install virtualenv
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if command -v pypy3; then
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virtualenv -p $(command -v pypy3) pypy-env
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elif command -v pypy; then
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virtualenv -p $(command -v pypy) pypy-env
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fi
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source pypy-env/bin/activate
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python --version
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which python
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pip install --extra-index https://antocuni.github.io/pypy-wheels/ubuntu numpy==1.14.4 Cython pytest
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pip install "scipy>=1.1.0" sphinx numpydoc docutils
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ccache -M 512M
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export CCACHE_COMPRESS=1
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export PATH=/usr/lib/ccache:$PATH
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pip install -e .
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make test

‎build_tools/travis/install.sh

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# ~60M is used by .ccache when compiling from scratch at the time of writing
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ccache --max-size 100M --show-stats
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if [[ "$DISTRIB" == "conda" ]]; then
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make_conda() {
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TO_INSTALL="$@"
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# Deactivate the travis-provided virtual environment and setup a
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# conda-based environment instead
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deactivate
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export PATH=$MINICONDA_PATH/bin:$PATH
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conda update --yes conda
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conda create -n testenv --yes $TO_INSTALL
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source activate testenv
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}
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if [[ "$DISTRIB" == "conda" ]]; then
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TO_INSTALL="python=$PYTHON_VERSION pip pytest pytest-cov \
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numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION \
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cython=$CYTHON_VERSION"
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TO_INSTALL="$TO_INSTALL pillow=$PILLOW_VERSION"
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fi
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conda create -n testenv --yes $TO_INSTALL
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source activate testenv
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if [[ -n "$JOBLIB_VERSION" ]]; then
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TO_INSTALL="$TO_INSTALL joblib=$JOBLIB_VERSION"
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fi
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make_conda $TO_INSTALL
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# for python 3.4, conda does not have recent pytest packages
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if [[ "$PYTHON_VERSION" == "3.4" ]]; then
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pip install pytest pytest-cov cython==$CYTHON_VERSION
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elif [[ "$DISTRIB" == "scipy-dev" ]]; then
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# Set up our own virtualenv environment to avoid travis' numpy.
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# This venv points to the python interpreter of the travis build
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# matrix.
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virtualenv --python=python ~/testvenv
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source ~/testvenv/bin/activate
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make_conda python=3.7
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pip install --upgrade pip setuptools
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echo "Installing numpy and scipy master wheels"

‎conftest.py

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# doc/modules/clustering.rst and use sklearn from the local folder rather than
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# the one from site-packages.
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import platform
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from distutils.version import LooseVersion
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import pytest
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from _pytest.doctest import DoctestItem
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def pytest_collection_modifyitems(config, items):
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# FeatureHasher is not compatible with PyPy
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if platform.python_implementation() == 'PyPy':
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skip_marker = pytest.mark.skip(
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reason='FeatureHasher is not compatible with PyPy')
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for item in items:
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if item.name == 'sklearn.feature_extraction.hashing.FeatureHasher':
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item.add_marker(skip_marker)
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# numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
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# run doctests only for numpy >= 1.14.
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skip_doctests = True

‎doc/Makefile

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"results in $(BUILDDIR)/doctest/output.txt."
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download-data:
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python -c "from sklearn.datasets.lfw import check_fetch_lfw; check_fetch_lfw()"
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python -c "from sklearn.datasets.lfw import _check_fetch_lfw; _check_fetch_lfw()"
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# Optimize PNG files. Needs OptiPNG. Change the -P argument to the number of
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# cores you have available, so -P 64 if you have a real computer ;)

‎doc/about.rst

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:align: center
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:target: http://www.sydney.edu.au/
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`The Labex DigiCosme <https://digicosme.lri.fr>`_ funded Nicolas Goix (2015-2016),
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Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias (2018-2019) to work part time
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on scikit-learn during their PhDs. It also funded a scikit-learn coding sprint in 2015.
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.. image:: themes/scikit-learn/static/img/digicosme.png
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:width: 200pt
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:align: center
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:target: https://digicosme.lri.fr
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The following students were sponsored by `Google <https://developers.google.com/open-source/>`_
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to work on scikit-learn through the
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`Google Summer of Code <https://en.wikipedia.org/wiki/Google_Summer_of_Code>`_

‎doc/conftest.py

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import os
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from os.path import exists
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from os.path import join
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import warnings
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import numpy as np
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from sklearn.utils import IS_PYPY
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from sklearn.utils.testing import SkipTest
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from sklearn.utils.testing import check_skip_network
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from sklearn.datasets import get_data_home
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def setup_working_with_text_data():
61+
if IS_PYPY and os.environ.get('CI'< 1012B /span>, None):
62+
raise SkipTest('Skipping too slow test with PyPy on CI')
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check_skip_network()
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cache_path = _pkl_filepath(get_data_home(), CACHE_NAME)
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if not exists(cache_path):
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7580
raise SkipTest("Skipping impute.rst, pandas not installed")
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def setup_unsupervised_learning():
84+
# ignore deprecation warnings from scipy.misc.face
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warnings.filterwarnings('ignore', 'The binary mode of fromstring',
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DeprecationWarning)
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def pytest_runtest_setup(item):
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fname = item.fspath.strpath
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is_index = fname.endswith('datasets/index.rst')
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setup_working_with_text_data()
92103
elif fname.endswith('modules/compose.rst') or is_index:
93104
setup_compose()
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elif IS_PYPY and fname.endswith('modules/feature_extraction.rst'):
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raise SkipTest('FeatureHasher is not compatible with PyPy')
94107
elif fname.endswith('modules/impute.rst'):
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setup_impute()
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elif fname.endswith('statistical_inference/unsupervised_learning.rst'):
110+
setup_unsupervised_learning()
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def pytest_runtest_teardown(item):

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