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rajdeepdrajdeepd
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Merge remote-tracking branch 'upstream/master' into test_mean_shift
2 parents dea8840 + 26f6909 commit aa17ea1

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‎.circleci/config.yml

+10-6
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@@ -3,7 +3,7 @@ version: 2
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jobs:
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doc-min-dependencies:
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docker:
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- image: circleci/python:3.6.1
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- image: circleci/python:3.6
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environment:
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- MINICONDA_PATH: ~/miniconda
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- CONDA_ENV_NAME: testenv
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doc:
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docker:
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- image: circleci/python:3.6.1
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- image: circleci/python:3.6
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environment:
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- MINICONDA_PATH: ~/miniconda
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- CONDA_ENV_NAME: testenv
@@ -61,7 +61,7 @@ jobs:
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lint:
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docker:
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- image: circleci/python:3.6.1
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- image: circleci/python:3.6
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steps:
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- checkout
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- run: ./build_tools/circle/checkout_merge_commit.sh
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deploy:
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docker:
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- image: circleci/python:3.6.1
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- image: circleci/python:3.6
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steps:
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- checkout
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- run: ./build_tools/circle/checkout_merge_commit.sh
@@ -109,9 +109,13 @@ workflows:
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version: 2
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build-doc-and-deploy:
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jobs:
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- doc
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- doc-min-dependencies
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- lint
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- doc:
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requires:
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- lint
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- doc-min-dependencies:
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requires:
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- lint
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- pypy3:
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filters:
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branches:

‎.codecov.yml

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@@ -19,4 +19,6 @@ coverage:
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# https://github.com/codecov/browser-extension
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target: auto
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threshold: 1%
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ignore:
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- "sklearn/externals"

‎.gitignore

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@@ -71,3 +71,8 @@ _configtest.o.d
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# Used by mypy
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.mypy_cache/
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# files generated from a template
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sklearn/utils/seq_dataset.pyx
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sklearn/utils/seq_dataset.pxd
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sklearn/linear_model/sag_fast.pyx

‎.travis.yml

+1-39
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@@ -20,44 +20,6 @@ env:
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matrix:
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include:
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# Latest dependencies on macOS with MKL
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- env: DISTRIB="conda" PYTHON_VERSION="*" INSTALL_MKL="true"
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NUMPY_VERSION="*" SCIPY_VERSION="*" CYTHON_VERSION="*"
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PILLOW_VERSION="*" JOBLIB_VERSION="*" COVERAGE=true
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language: generic
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if: type != cron
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os: osx
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# Linux environment to test that scikit-learn can be built against
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# versions of numpy, scipy with ATLAS that comes with Ubuntu Xenial 16.04
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# i.e. numpy 1.11 and scipy 0.17
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- env: DISTRIB="ubuntu" PYTHON_VERSION="3.5"
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NUMPY_VERSION="1.11.0" SCIPY_VERSION="0.17.0"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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SKLEARN_SITE_JOBLIB=1 JOBLIB_VERSION="0.11"
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if: type != cron
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addons:
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apt:
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packages:
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# these only required by the DISTRIB="ubuntu" builds:
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- python3-scipy
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- libatlas3-base
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- libatlas-base-dev
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- libatlas-dev
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# Linux + Python 3.5 build with OpenBLAS and without SITE_JOBLIB
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- env: DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="false"
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NUMPY_VERSION="1.11.0" SCIPY_VERSION="0.17.0" CYTHON_VERSION="*"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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if: type != cron
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# Linux environment to test the latest available dependencies and MKL.
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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="*" INSTALL_MKL="true"
55-
NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
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CYTHON_VERSION="*" PYAMG_VERSION="*" PILLOW_VERSION="*"
57-
JOBLIB_VERSION="*" COVERAGE=true
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CHECK_PYTEST_SOFT_DEPENDENCY="true" TEST_DOCSTRINGS="true"
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SKLEARN_SITE_JOBLIB=1 CHECK_WARNINGS="true"
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if: type != cron
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# Linux environment to test scikit-learn against numpy and scipy master
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# installed from their CI wheels in a virtualenv with the Python
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# interpreter provided by travis.
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if: type = cron OR commit_message =~ /\[scipy-dev\]/
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install: source build_tools/travis/install.sh
69-
script:
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script:
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- bash build_tools/travis/test_script.sh
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- bash build_tools/travis/test_docs.sh
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- bash build_tools/travis/test_pytest_soft_dependency.sh

‎Makefile

+1-1
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@@ -60,4 +60,4 @@ code-analysis:
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pylint -E -i y sklearn/ -d E1103,E0611,E1101
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flake8-diff:
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./build_tools/travis/flake8_diff.sh
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./build_tools/circle/flake8_diff.sh

‎README.rst

+4-4
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@@ -1,13 +1,13 @@
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.. -*- mode: rst -*-
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3-
|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |Python35|_ |PyPi|_ |DOI|_
3+
|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |Python35|_ |PyPi|_ |DOI|_
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.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=master
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.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=master
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.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master
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.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
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.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/scikit-learn/scikit-learn?branch=master&svg=true
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.. _AppVeyor: https://ci.appveyor.com/project/sklearn-ci/scikit-learn/history
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.. |Codecov| image:: https://codecov.io/github/scikit-learn/scikit-learn/badge.svg?branch=master&service=github
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.. _Codecov: https://codecov.io/github/scikit-learn/scikit-learn?branch=master
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‎appveyor.yml

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@@ -64,6 +64,13 @@ install:
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# Install the generated wheel package to test it
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- "pip install --pre --no-index --find-links dist/ scikit-learn"
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# If there is a newer build queued for the same PR, cancel this one.
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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=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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# Not a .NET project, we build scikit-learn in the install step instead
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build: false
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‎azure-pipelines.yml

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# Adapted from https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml
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jobs:
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- template: build_tools/azure/posix.yml
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parameters:
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name: Linux
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vmImage: ubuntu-16.04
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matrix:
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# Linux environment to test that scikit-learn can be built against
9+
# versions of numpy, scipy with ATLAS that comes with Ubuntu Xenial 16.04
10+
# i.e. numpy 1.11 and scipy 0.17
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py35_np_atlas:
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DISTRIB: 'ubuntu'
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PYTHON_VERSION: '3.5'
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SKLEARN_SITE_JOBLIB: '1'
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JOBLIB_VERSION: '0.11'
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# Linux + Python 3.5 build with OpenBLAS and without SITE_JOBLIB
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py35_conda_openblas:
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DISTRIB: 'conda'
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PYTHON_VERSION: '3.5'
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INSTALL_MKL: 'false'
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NUMPY_VERSION: '1.11.0'
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SCIPY_VERSION: '0.17.0'
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CYTHON_VERSION: '*'
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PILLOW_VERSION: '4.0.0'
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COVERAGE: 'true'
26+
# Linux environment to test the latest available dependencies and MKL.
27+
# It runs tests requiring pandas and PyAMG.
28+
# It also runs with the site joblib instead of the vendored copy of joblib.
29+
pylatest_conda:
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DISTRIB: 'conda'
31+
PYTHON_VERSION: '*'
32+
INSTALL_MKL: 'true'
33+
NUMPY_VERSION: '*'
34+
SCIPY_VERSION: '*'
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PANDAS_VERSION: '*'
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CYTHON_VERSION: '*'
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PYAMG_VERSION: '*'
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PILLOW_VERSION: '*'
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JOBLIB_VERSION: '*'
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COVERAGE: 'true'
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CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
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TEST_DOCSTRINGS: 'true'
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SKLEARN_SITE_JOBLIB: '1'
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CHECK_WARNINGS: 'true'
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- template: build_tools/azure/posix.yml
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parameters:
48+
name: macOS
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vmImage: xcode9-macos10.13
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matrix:
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pylatest_conda:
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DISTRIB: 'conda'
53+
PYTHON_VERSION: '*'
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INSTALL_MKL: 'true'
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NUMPY_VERSION: '*'
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SCIPY_VERSION: '*'
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CYTHON_VERSION: '*'
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PILLOW_VERSION: '*'
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JOBLIB_VERSION: '*'
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COVERAGE: 'true'
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- template: build_tools/azure/windows.yml
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parameters:
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name: Windows
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vmImage: vs2017-win2016
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matrix:
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py37_64:
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PYTHON_VERSION: '3.7'
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CHECK_WARNINGS: 'true'
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PYTHON_ARCH: '64'
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COVERAGE: 'true'
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py35_32:
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PYTHON_VERSION: '3.5'
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PYTHON_ARCH: '32'

‎benchmarks/bench_plot_lasso_path.py

+2-2
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import numpy as np
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from sklearn.linear_model import lars_path
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from sklearn.linear_model import lars_path, lars_path_gram
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from sklearn.linear_model import lasso_path
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from sklearn.datasets.samples_generator import make_regression
1515

@@ -45,7 +45,7 @@ def compute_bench(samples_range, features_range):
4545
tstart = time()
4646
G = np.dot(X.T, X) # precomputed Gram matrix
4747
Xy = np.dot(X.T, y)
48-
lars_path(X, y, Xy=Xy, Gram=G, method='lasso')
48+
lars_path_gram(Xy=Xy, Gram=G, n_samples=y.size, method='lasso')
4949
delta = time() - tstart
5050
print("%0.3fs" % delta)
5151
results['lars_path (with Gram)'].append(delta)

‎benchmarks/bench_plot_omp_lars.py

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import numpy as np
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12-
from sklearn.linear_model import lars_path, orthogonal_mp
12+
from sklearn.linear_model import lars_path, lars_path_gram, orthogonal_mp
1313
from sklearn.datasets.samples_generator import make_sparse_coded_signal
1414

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@@ -58,7 +58,8 @@ def compute_bench(samples_range, features_range):
5858
tstart = time()
5959
G = np.dot(X.T, X) # precomputed Gram matrix
6060
Xy = np.dot(X.T, y)
61-
lars_path(X, y, Xy=Xy, Gram=G, max_iter=n_informative)
61+
lars_path_gram(Xy=Xy, Gram=G, n_samples=y.size,
62+
max_iter=n_informative)
6263
delta = time() - tstart
6364
print("%0.3fs" % delta)
6465
lars_gram[i_f, i_s] = delta

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