jobs:

- template: build_tools/azure/posix.yml

parameters:

name: Linux

vmImage: ubuntu-16.04

matrix:

# Linux environment to test that scikit-learn can be built against

# versions of numpy, scipy with ATLAS that comes with Ubuntu Xenial 16.04

# i.e. numpy 1.11 and scipy 0.17

py35_np_atlas:

DISTRIB: 'ubuntu'

PYTHON_VERSION: '3.5'

SKLEARN_SITE_JOBLIB: '1'

JOBLIB_VERSION: '0.11'

# Linux + Python 3.5 build with OpenBLAS and without SITE_JOBLIB

py35_conda_openblas:

DISTRIB: 'conda'

PYTHON_VERSION: '3.5'

INSTALL_MKL: 'false'

NUMPY_VERSION: '1.11.0'

SCIPY_VERSION: '0.17.0'

CYTHON_VERSION: '*'

PILLOW_VERSION: '4.0.0'

COVERAGE: "true"

# Linux environment to test the latest available dependencies and MKL.

# It runs tests requiring pandas and PyAMG.

# It also runs with the site joblib instead of the vendored copy of joblib.

pylatest_conda:

DISTRIB: 'conda'

PYTHON_VERSION: '*'

INSTALL_MKL: 'true'

NUMPY_VERSION: '*'

SCIPY_VERSION: '*'

PANDAS_VERSION: '*'

CYTHON_VERSION: '*'

PYAMG_VERSION: '*'

PILLOW_VERSION: '*'

JOBLIB_VERSION: '*'

COVERAGE: 'true'

CHECK_PYTEST_SOFT_DEPENDENCY: 'true'

TEST_DOCSTRINGS: 'true'

SKLEARN_SITE_JOBLIB: '1'

CHECK_WARNINGS: 'true'

- template: build_tools/azure/posix.yml

parameters:

name: macOS

vmImage: xcode9-macos10.13

matrix:

pylatest_conda:

DISTRIB: 'conda'

PYTHON_VERSION: '*'

INSTALL_MKL: 'true'

NUMPY_VERSION: '*'

SCIPY_VERSION: '*'

CYTHON_VERSION: '*'

PILLOW_VERSION: '*'

JOBLIB_VERSION: '*'

COVERAGE: 'true'

- template: build_tools/azure/windows.yml

parameters:

name: Windows

vmImage: vs2017-win2016

matrix:

py37:

CONDA_PY: '3.7'

CHECK_WARNINGS: 'true'

py35:

CONDA_PY: '3.5'

set CONDA_INSTALL=cmd /C conda install -q -y

set PIP_INSTALL=pip install -q

@echo on

call deactivate

conda remove --all -q -y -n %VIRTUALENV%

conda create -n %VIRTUALENV% -q -y python=%CONDA_PY% numpy scipy cython pytest wheel pillow

call activate %VIRTUALENV%

python -m pip install -U pip

python --version

pip --version

python setup.py bdist_wheel bdist_wininst -b doc\logos\scikit-learn-logo.bmp

pip install --pre --no-index --find-links dist\ scikit-learn

if %errorlevel% neq 0 exit /b %errorlevel%

set -e

UNAMESTR=`uname`

if [[ "$UNAMESTR" == 'Darwin' ]]; then

# install OpenMP not present by default on osx

brew install libomp

# enable OpenMP support for Apple-clang

export CC=/usr/bin/clang

export CXX=/usr/bin/clang++

export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"

export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"

export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include"

export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp"

export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib

# avoid error due to multiple OpenMP libraries loaded simultaneously

export KMP_DUPLICATE_LIB_OK=TRUE

make_conda() {

TO_INSTALL="$@"

conda create -n $VIRTUALENV --yes $TO_INSTALL

source activate $VIRTUALENV

}

if [[ "$DISTRIB" == "conda" ]]; then

TO_INSTALL="python=$PYTHON_VERSION pip pytest pytest-cov \

if [[ "$INSTALL_MKL" == "true" ]]; then

TO_INSTALL="$TO_INSTALL mkl"

else

TO_INSTALL="$TO_INSTALL nomkl"

fi

if [[ -n "$PANDAS_VERSION" ]]; then

TO_INSTALL="$TO_INSTALL pandas=$PANDAS_VERSION"

fi

if [[ -n "$PYAMG_VERSION" ]]; then

TO_INSTALL="$TO_INSTALL pyamg=$PYAMG_VERSION"

fi

if [[ -n "$PILLOW_VERSION" ]]; then

TO_INSTALL="$TO_INSTALL pillow=$PILLOW_VERSION"

fi

if [[ -n "$JOBLIB_VERSION" ]]; then

TO_INSTALL="$TO_INSTALL joblib=$JOBLIB_VERSION"

fi

make_conda $TO_INSTALL

elif [[ "$DISTRIB" == "ubuntu" ]]; then

sudo apt-get install python3-scipy libatlas3-base libatlas-base-dev libatlas-dev python3-virtualenv

python3 -m virtualenv --system-site-packages --python=python3 $VIRTUALENV

source $VIRTUALENV/bin/activate

python -m pip install pytest pytest-cov cython joblib==$JOBLIB_VERSION

if [[ "$COVERAGE" == "true" ]]; then

python -m pip install coverage codecov

if [[ "$TEST_DOCSTRINGS" == "true" ]]; then

python -m pip install sphinx numpydoc # numpydoc requires sphinx

python --version

python -c "import numpy; print('numpy %s' % numpy.__version__)"

python -c "import scipy; print('scipy %s' % scipy.__version__)"

python -c "\

pip list

python -m pip install -e .

parameters:

name: ''

vmImage: ''

matrix: []

jobs:

- job: ${{ parameters.name }}

pool:

vmImage: ${{ parameters.vmImage }}

variables:

TEST_DIR: '$(Agent.WorkFolder)/tmp_folder'

VIRTUALENV: 'testvenv'

JUNITXML: 'test-data.xml'

OMP_NUM_THREADS: '4'

OPENBLAS_NUM_THREADS: '4'

SKLEARN_SKIP_NETWORK_TESTS: '1'

strategy:

matrix:

${{ insert }}: ${{ parameters.matrix }}

steps:

- bash: echo "##vso[task.prependpath]$CONDA/bin"

displayName: Add conda to PATH

condition: eq(variables['DISTRIB'], 'conda')

- script: |

- script: |

- script: |

- script: |

- task: PublishTestResults@2

inputs:

testResultsFiles: '$(TEST_DIR)/$(JUNITXML)'

testRunTitle: '$(Agent.JobName)'

displayName: 'Publish Test Results'

condition: succeededOrFailed()

- script: |

set -e

if [[ "$DISTRIB" == "conda" ]]; then

source activate $VIRTUALENV

elif [[ "$DISTRIB" == "ubuntu" ]]; then

source $VIRTUALENV/bin/activate

make test-doc

set -e

source activate $VIRTUALENV

conda remove -y py pytest || pip uninstall -y py pytest

if [[ "$COVERAGE" == "true" ]]; then

# Need to append the coverage to the existing .coverage generated by

# running the tests

CMD="coverage run --append"

CMD="python"

pushd $TEST_DIR

$CMD -m sklearn.utils.tests.test_estimator_checks

call activate %VIRTUALENV%

mkdir %TMP_FOLDER%

cd %TMP_FOLDER%

if "%CHECK_WARNINGS%" == "true" (

pytest --junitxml=%JUNITXML% --showlocals --durations=20 -Werror::DeprecationWarning -Werror::FutureWarning --pyargs sklearn

) else (

pytest --junitxml=%JUNITXML% --showlocals --durations=20 --pyargs sklearn

)

set -e

if [[ "$DISTRIB" == "conda" ]]; then

source activate $VIRTUALENV

elif [[ "$DISTRIB" == "ubuntu" ]]; then

source $VIRTUALENV/bin/activate

python --version

python -c "import numpy; print('numpy %s' % numpy.__version__)"

python -c "import scipy; print('scipy %s' % scipy.__version__)"

python -c "\

python -c "import multiprocessing as mp; print('%d CPUs' % mp.cpu_count())"

pip list

TEST_CMD="python -m pytest --showlocals --durations=20 --junitxml=$JUNITXML --pyargs"

if [[ "$COVERAGE" == "true" ]]; then

TEST_CMD="$TEST_CMD --cov sklearn"

if [[ -n "$CHECK_WARNINGS" ]]; then

TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning"

mkdir -p $TEST_DIR

cp setup.cfg $TEST_DIR

cd $TEST_DIR

set -x

$TEST_CMD sklearn

set +x

set -e

export PATH=$HOME/miniconda3/bin:$PATH

source activate $VIRTUALENV

cp $TEST_DIR/.coverage $BUILD_REPOSITORY_LOCALPATH

codecov --root $BUILD_REPOSITORY_LOCALPATH -t $CODECOV_TOKEN || echo "codecov upload failed"

parameters:

name: ''

vmImage: ''

matrix: []

jobs:

- job: ${{ parameters.name }}

pool:

vmImage: ${{ parameters.vmImage }}

variables:

VIRTUALENV: 'testvenv'

JUNITXML: 'test-data.xml'

SKLEARN_SKIP_NETWORK_TESTS: '1'

TMP_FOLDER: '$(Agent.WorkFolder)\tmp_folder'

strategy:

matrix:

${{ insert }}: ${{ parameters.matrix }}

steps:

- powershell: Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"

displayName: Add conda to PATH

- script: |

- script: |

- task: PublishTestResults@2

inputs:

testResultsFiles: '$(TMP_FOLDER)\$(JUNITXML)'

testRunTitle: 'Windows-$(CONDA_PY)'

displayName: 'Publish Test Results'

condition: succeededOrFailed()