diff --git a/.circleci/config.yml b/.circleci/config.yml index b730ae0ff595a..30c39ce4fdc9b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -53,13 +53,15 @@ jobs: - OMP_NUM_THREADS: 2 - MKL_NUM_THREADS: 2 - CONDA_ENV_NAME: testenv - - PYTHON_VERSION: 3 + - PYTHON_VERSION: '3.9' - NUMPY_VERSION: 'latest' - SCIPY_VERSION: 'latest' - MATPLOTLIB_VERSION: 'latest' - CYTHON_VERSION: 'latest' - SCIKIT_IMAGE_VERSION: 'latest' - - SPHINX_VERSION: 'min' + # Bump the sphinx version from time to time. Avoid latest sphinx version + # that tends to break things slightly too often + - SPHINX_VERSION: 4.2.0 - PANDAS_VERSION: 'latest' - SPHINX_GALLERY_VERSION: 'latest' - NUMPYDOC_VERSION: 'latest' @@ -109,34 +111,17 @@ jobs: name: linting command: ./build_tools/circle/linting.sh - pypy3: - docker: - - image: condaforge/miniforge3 - environment: - # Avoid the interactive dialog when installing tzdata - - DEBIAN_FRONTEND: noninteractive - steps: - - restore_cache: - keys: - - pypy3-ccache-{{ .Branch }} - - pypy3-ccache - - run: apt-get -yq update && apt-get -yq install git ssh - - checkout - - run: conda init bash && source ~/.bashrc - - run: ./build_tools/circle/build_test_pypy.sh - - save_cache: - key: pypy3-ccache-{{ .Branch }}-{{ .BuildNum }} - paths: - - ~/.ccache - - ~/.cache/pip - linux-arm64: machine: image: ubuntu-2004:202101-01 resource_class: arm.medium environment: + # Use the latest supported version of python + - PYTHON_VERSION: '3.9' - OMP_NUM_THREADS: 2 - OPENBLAS_NUM_THREADS: 2 + - NUMPY_VERSION: 'latest' + - SCIPY_VERSION: 'latest' - CYTHON_VERSION: 'latest' - JOBLIB_VERSION: 'latest' - THREADPOOLCTL_VERSION: 'latest' @@ -147,18 +132,16 @@ jobs: - checkout - run: ./build_tools/circle/checkout_merge_commit.sh - restore_cache: - key: v1-datasets-{{ .Branch }} + key: linux-arm64-{{ .Branch }} - run: ./build_tools/circle/build_test_arm.sh - save_cache: - key: doc-ccache-{{ .Branch }}-{{ .BuildNum }} + key: linux-arm64-{{ .Branch }} paths: - - ~/.ccache + - ~/.cache/ccache - ~/.cache/pip - - save_cache: - key: v1-datasets-{{ .Branch }} - paths: - ~/scikit_learn_data - + # The source build folder. + - ~/project/build deploy: docker: - image: circleci/python:3.7 @@ -187,24 +170,9 @@ workflows: - doc-min-dependencies: requires: - lint - - pypy3: - filters: - branches: - only: - - 0.20.X - deploy: requires: - doc - pypy: - triggers: - - schedule: - cron: "0 0 * * *" - filters: - branches: - only: - - main - jobs: - - pypy3 linux-arm64: jobs: - linux-arm64 diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index bb8f6abc4924d..b4103c5334c0f 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -16,3 +16,6 @@ # PR 20412: Use experimental_string_processing=true in Black 3ae7c7615343bbd36acece57825d8b0d70fd9da4 + +# PR 20502: Runs Black on examples +70a185ae59b4362633d18b0d0083abb1b6f7370c diff --git a/.gitattributes b/.gitattributes index 163f2a4fe2030..3b69ec8a7c3f5 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1 +1 @@ -/doc/whats_new.rst merge=union +/doc/whats_new/v*.rst merge=union diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 8634e3fdf159a..d852e18a0812e 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -1,6 +1,6 @@ name: Bug Report description: Create a report to help us reproduce and correct the bug -labels: ['Bug: triage'] +labels: ['Bug', 'Needs Triage'] body: - type: markdown @@ -19,7 +19,7 @@ body: attributes: label: Steps/Code to Reproduce description: | - Please add a minimal example that we can reproduce the error by running the code. Be as succinct as possible, do not depend on external data. In short, we are going to copy-paste your code and we expect to get the same result as you. Example: + Please add a minimal code example that can reproduce the error when running it. Be as succinct as possible, **do not depend on external data files**: instead you can generate synthetic data using `numpy.random`, [sklearn.datasets.make_regression](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html), [sklearn.datasets.make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html) or a few lines of Python code. Example: ```python from sklearn.feature_extraction.text import CountVectorizer @@ -37,6 +37,10 @@ body: ``` If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com. + + In short, **we are going to copy-paste your code** to run it and we expect to get the same result as you. + + We acknowledge that crafting a minimal reproducible code example requires some effort on your side but it really helps the maintainers quickly reproduce the problem and analyze its cause without any ambiguity. Ambiguous bug reports tend to be slower to fix because they will require more effort and back and forth discussion between the maintainers and the reporter to pin-point the precise conditions necessary to reproduce the problem. placeholder: | ``` Sample code to reproduce the problem @@ -55,8 +59,21 @@ body: - type: textarea attributes: label: Actual Results - description: > - Please paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full** traceback of the exception. + description: | + Please paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full traceback** of the exception. For instance the code above raises the following exception: + + ```python-traceback + --------------------------------------------------------------------------- + TypeError Traceback (most recent call last) + in + 4 vectorizer = CountVectorizer(input=docs, analyzer='word') + 5 lda_features = vectorizer.fit_transform(docs) + ----> 6 lda_model = LatentDirichletAllocation( + 7 n_topics=10, + 8 learning_method='online', + + TypeError: __init__() got an unexpected keyword argument 'n_topics' + ``` placeholder: > Please paste or specifically describe the actual output or traceback. validations: diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 5e711832f8838..df6843304f443 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,4 +1,4 @@ -blank_issues_enabled: false +blank_issues_enabled: true contact_links: - name: Discussions url: https://github.com/scikit-learn/scikit-learn/discussions/new diff --git a/.github/ISSUE_TEMPLATE/doc_improvement.yml b/.github/ISSUE_TEMPLATE/doc_improvement.yml index 7be0368bb5e4f..48d0c3de89103 100644 --- a/.github/ISSUE_TEMPLATE/doc_improvement.yml +++ b/.github/ISSUE_TEMPLATE/doc_improvement.yml @@ -1,6 +1,6 @@ name: Documentation improvement description: Create a report to help us improve the documentation. Alternatively you can just open a pull request with the suggested change. -labels: [Documentation] +labels: [Documentation, 'Needs Triage'] body: - type: textarea diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml index 75c14293ac2a2..51a2cdd94920d 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.yml +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -1,6 +1,6 @@ name: Feature request description: Suggest a new algorithm, enhancement to an existing algorithm, etc. -labels: ['New Feature'] +labels: ['New Feature', 'Needs Triage'] body: - type: markdown diff --git a/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md similarity index 100% rename from PULL_REQUEST_TEMPLATE.md rename to .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/workflows/check-changelog.yml b/.github/workflows/check-changelog.yml index 30642fab0cd33..eb1f0e2a1fa6b 100644 --- a/.github/workflows/check-changelog.yml +++ b/.github/workflows/check-changelog.yml @@ -8,6 +8,7 @@ on: jobs: check: + name: A reviewer will let you know if it is required or can be bypassed runs-on: ubuntu-latest if: ${{ contains(github.event.pull_request.labels.*.name, 'No Changelog Needed') == 0 }} steps: @@ -18,7 +19,7 @@ jobs: - uses: actions/checkout@v2 with: fetch-depth: '0' - - name: Check the changelog + - name: Check the changelog entry run: | set -xe changed_files=$(git diff --name-only origin/main) diff --git a/.github/workflows/label-blank-issue.yml b/.github/workflows/label-blank-issue.yml new file mode 100644 index 0000000000000..fce4fe6f9c74e --- /dev/null +++ b/.github/workflows/label-blank-issue.yml @@ -0,0 +1,14 @@ +name: Labels Blank issues + +on: + issues: + types: [opened] + +jobs: + label-blank-issues: + runs-on: ubuntu-latest + steps: + - uses: andymckay/labeler@1.0.4 + with: + add-labels: "Needs Triage" + ignore-if-labeled: true diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index a280c29c31683..7570b19a56af6 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -47,36 +47,145 @@ jobs: # Ensure that a wheel builder finishes even if another fails fail-fast: false matrix: - os: [windows-latest, ubuntu-latest, macos-latest] - python: [37, 38, 39] - bitness: [32, 64] - manylinux_image: [manylinux1, manylinux2010] include: - # Run 32 and 64 bit version in parallel for Linux and Windows + # Window 64 bit - os: windows-latest + python: 37 bitness: 64 platform_id: win_amd64 - os: windows-latest + python: 38 + bitness: 64 + platform_id: win_amd64 + - os: windows-latest + python: 39 + bitness: 64 + platform_id: win_amd64 + - os: windows-latest + python: 310 + bitness: 64 + platform_id: win_amd64 + + # Window 32 bit + - os: windows-latest + python: 37 bitness: 32 platform_id: win32 + - os: windows-latest + python: 38 + bitness: 32 + platform_id: win32 + - os: windows-latest + python: 39 + bitness: 32 + platform_id: win32 + + # Linux 64 bit manylinux2014 + - os: ubuntu-latest + python: 37 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 38 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 39 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 + + # Linux 64 bit manylinux2010 + - os: ubuntu-latest + python: 37 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2010 + - os: ubuntu-latest + python: 38 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2010 + - os: ubuntu-latest + python: 39 + bitness: 64 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2010 + + # NumPy on Python 3.10 only supports 64bit and is only available with manylinux2014 - os: ubuntu-latest + python: 310 bitness: 64 platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 + + # Linux 32 bit manylinux2014 + - os: ubuntu-latest + python: 37 + bitness: 32 + platform_id: manylinux_i686 + manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 38 + bitness: 32 + platform_id: manylinux_i686 + manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 39 + bitness: 32 + platform_id: manylinux_i686 + manylinux_image: manylinux2014 + + # Linux 32 bit manylinux2010 + - os: ubuntu-latest + python: 37 + bitness: 32 + platform_id: manylinux_i686 + manylinux_image: manylinux2010 + - os: ubuntu-latest + python: 38 + bitness: 32 + platform_id: manylinux_i686 + manylinux_image: manylinux2010 - os: ubuntu-latest + python: 39 bitness: 32 platform_id: manylinux_i686 + manylinux_image: manylinux2010 + + # MacOS x86_64 - os: macos-latest bitness: 64 + python: 37 platform_id: macosx_x86_64 - exclude: - os: macos-latest - bitness: 32 - # Remove manylinux1 from the windows and osx build matrix since - # manylinux_image is not used for these platforms - - os: windows-latest - manylinux_image: manylinux1 + bitness: 64 + python: 38 + platform_id: macosx_x86_64 + - os: macos-latest + bitness: 64 + python: 39 + platform_id: macosx_x86_64 + - os: macos-latest + bitness: 64 + python: 310 + platform_id: macosx_x86_64 + + # MacOS arm64 + - os: macos-latest + bitness: 64 + python: 38 + platform_id: macosx_arm64 - os: macos-latest - manylinux_image: manylinux1 + bitness: 64 + python: 39 + platform_id: macosx_arm64 + - os: macos-latest + bitness: 64 + python: 310 + platform_id: macosx_arm64 steps: - name: Checkout scikit-learn @@ -93,10 +202,11 @@ jobs: OPENBLAS_NUM_THREADS=2 SKLEARN_SKIP_NETWORK_TESTS=1 SKLEARN_BUILD_PARALLEL=3 - MACOSX_DEPLOYMENT_TARGET=10.13 CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }} + CIBW_ARCHS: all CIBW_MANYLINUX_X86_64_IMAGE: ${{ matrix.manylinux_image }} CIBW_MANYLINUX_I686_IMAGE: ${{ matrix.manylinux_image }} + CIBW_TEST_SKIP: "*-macosx_arm64" CIBW_REPAIR_WHEEL_COMMAND_WINDOWS: bash build_tools/github/repair_windows_wheels.sh {wheel} {dest_dir} ${{ matrix.bitness }} CIBW_BEFORE_TEST_WINDOWS: bash build_tools/github/build_minimal_windows_image.sh ${{ matrix.python }} ${{ matrix.bitness }} CIBW_TEST_REQUIRES: pytest pandas threadpoolctl @@ -124,6 +234,8 @@ jobs: - name: Setup Python uses: actions/setup-python@v2 + with: + python-version: '3.9' # update once build dependencies are available - name: Build source distribution run: bash build_tools/github/build_source.sh diff --git a/.travis.yml b/.travis.yml index 328940a165fee..fb01bbb7a4f14 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,6 +2,9 @@ # new container-based Travis workers language: python dist: xenial +# Only used to install cibuildwheel, CIBW_BUILD determines the python version being +# built in the docker image itself. Also: travis does not have 3.10 yet. +python: 3.9 cache: apt: true @@ -18,15 +21,16 @@ env: - OPENBLAS_NUM_THREADS=2 - SKLEARN_BUILD_PARALLEL=3 - SKLEARN_SKIP_NETWORK_TESTS=1 + - PYTHONUNBUFFERED=1 # Custom environment variables for the ARM wheel builder - CIBW_BUILD_VERBOSITY=1 - - CIBW_TEST_REQUIRES="pytest pytest-xdist threadpoolctl" - CIBW_TEST_COMMAND="bash {project}/build_tools/travis/test_wheels.sh" - - CIBW_ENVIRONMENT="CPU_COUNT=2 + - CIBW_ENVIRONMENT="CPU_COUNT=4 OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2 - SKLEARN_BUILD_PARALLEL=3 - SKLEARN_SKIP_NETWORK_TESTS=1" + SKLEARN_BUILD_PARALLEL=10 + SKLEARN_SKIP_NETWORK_TESTS=1 + PYTHONUNBUFFERED=1" jobs: include: @@ -36,29 +40,45 @@ jobs: # a scheduled task to build the weekly dev build on the main branch. The # weekly frequency is meant to avoid depleting the Travis CI credits too # fast. - - python: 3.7 - os: linux - arch: arm64 + - os: linux + arch: arm64-graviton2 + dist: focal + virt: vm + group: edge if: type = cron or commit_message =~ /\[cd build\]/ env: - - BUILD_WHEEL=true - CIBW_BUILD=cp37-manylinux_aarch64 + - BUILD_WHEEL=true - - python: 3.8 - os: linux - arch: arm64 + - os: linux + arch: arm64-graviton2 + dist: focal + virt: vm + group: edge if: type = cron or commit_message =~ /\[cd build\]/ env: - - BUILD_WHEEL=true - CIBW_BUILD=cp38-manylinux_aarch64 + - BUILD_WHEEL=true - - python: 3.9 - os: linux - arch: arm64 + - os: linux + arch: arm64-graviton2 + dist: focal + virt: vm + group: edge if: type = cron or commit_message =~ /\[cd build\]/ env: - - BUILD_WHEEL=true - CIBW_BUILD=cp39-manylinux_aarch64 + - BUILD_WHEEL=true + + - os: linux + arch: arm64-graviton2 + dist: focal + virt: vm + group: edge + if: type = cron or commit_message =~ /\[cd build\]/ + env: + - CIBW_BUILD=cp310-manylinux_aarch64 + - BUILD_WHEEL=true install: source build_tools/travis/install.sh || travis_terminate 1 script: source build_tools/travis/script.sh || travis_terminate 1 diff --git a/MANIFEST.in b/MANIFEST.in index 5a4b582bd9886..10f2f5c1c679c 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -23,6 +23,7 @@ recursive-exclude benchmarks * recursive-exclude .binder * recursive-exclude .circleci * exclude .codecov.yml +exclude .git-blame-ignore-revs exclude .mailmap exclude .pre-commit-config.yaml exclude azure-pipelines.yml diff --git a/README.rst b/README.rst index da817e3714639..05ae7f9c8d78c 100644 --- a/README.rst +++ b/README.rst @@ -1,27 +1,27 @@ .. -*- mode: rst -*- -|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_ +|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_ |Benchmark|_ .. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=main .. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=main +.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token +.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn + .. |Travis| image:: https://api.travis-ci.com/scikit-learn/scikit-learn.svg?branch=main -.. _Travis: https://travis-ci.com/scikit-learn/scikit-learn +.. _Travis: https://app.travis-ci.com/github/scikit-learn/scikit-learn .. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9 .. _Codecov: https://codecov.io/gh/scikit-learn/scikit-learn -.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token -.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn - .. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule .. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule .. |PythonVersion| image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue .. _PythonVersion: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue -.. |PyPi| image:: https://badge.fury.io/py/scikit-learn.svg -.. _PyPi: https://badge.fury.io/py/scikit-learn +.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn +.. _PyPi: https://pypi.org/project/scikit-learn .. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg .. _Black: https://github.com/psf/black @@ -29,19 +29,21 @@ .. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg .. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn +.. |Benchmark| image:: https://img.shields.io/badge/Benchmarked%20by-asv-blue +.. _`Benchmark`: https://scikit-learn.org/scikit-learn-benchmarks/ .. |PythonMinVersion| replace:: 3.7 .. |NumPyMinVersion| replace:: 1.14.6 .. |SciPyMinVersion| replace:: 1.1.0 .. |JoblibMinVersion| replace:: 0.11 .. |ThreadpoolctlMinVersion| replace:: 2.0.0 -.. |MatplotlibMinVersion| replace:: 2.2.2 +.. |MatplotlibMinVersion| replace:: 2.2.3 .. |Scikit-ImageMinVersion| replace:: 0.14.5 .. |PandasMinVersion| replace:: 0.25.0 .. |SeabornMinVersion| replace:: 0.9.0 .. |PytestMinVersion| replace:: 5.0.1 -.. image:: doc/logos/scikit-learn-logo.png +.. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png :target: https://scikit-learn.org/ **scikit-learn** is a Python module for machine learning built on top of @@ -183,9 +185,14 @@ Communication - Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn - Gitter: https://gitter.im/scikit-learn/scikit-learn - Twitter: https://twitter.com/scikit_learn +- Twitter (commits): https://twitter.com/sklearn_commits - Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn - Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions - Website: https://scikit-learn.org +- LinkedIn: https://www.linkedin.com/company/scikit-learn +- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists +- Facebook: https://www.facebook.com/scikitlearnofficial/ +- Instagram: https://www.instagram.com/scikitlearnofficial/ Citation ~~~~~~~~ diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000000000..251dbb054df52 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,17 @@ +# Security Policy + +## Supported Versions + +| Version | Supported | +| --------- | ------------------ | +| 1.0.1 | :white_check_mark: | +| < 1.0.1 | :x: | + +## Reporting a Vulnerability + +Please report security vulnerabilities by email to `security@scikit-learn.org`. +This email is an alias to a subset of the scikit-learn maintainers' team. + +If the security vulnerability is accepted, a patch will be crafted privately +in order to prepare a dedicated bugfix release as timely as possible (depending +on the complexity of the fix). diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 2c19db7c9aaae..b2c1b2c4aba03 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -48,7 +48,7 @@ jobs: pip install pytest flake8 mypy==0.782 black==21.6b0 displayName: Install linters - bash: | - black --check . + black --check --diff . displayName: Run black - bash: | ./build_tools/circle/linting.sh @@ -81,6 +81,7 @@ jobs: # Tests that require large downloads over the networks are skipped in CI. # Here we make sure, that they are still run on a regular basis. SKLEARN_SKIP_NETWORK_TESTS: '0' + CREATE_ISSUE_ON_TRACKER: 'true' # Check compilation with intel C++ compiler (ICC) - template: build_tools/azure/posix.yml @@ -97,13 +98,36 @@ jobs: ) ) matrix: - pylatest_conda_mkl: + pylatest_conda_forge_mkl: DISTRIB: 'conda' + CONDA_CHANNEL: 'conda-forge' PYTHON_VERSION: '*' BLAS: 'mkl' COVERAGE: 'false' BUILD_WITH_ICC: 'true' +- template: build_tools/azure/posix-docker.yml + parameters: + name: Linux_Nightly_PyPy + vmImage: ubuntu-20.04 + dependsOn: [linting, git_commit] + condition: | + and( + succeeded(), + not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')), + or( + eq(variables['Build.Reason'], 'Schedule'), + contains(dependencies['git_commit']['outputs']['commit.message'], '[pypy]') + ) + ) + matrix: + pypy3: + DISTRIB: 'conda-mamba-pypy3' + DOCKER_CONTAINER: 'condaforge/mambaforge-pypy3:4.10.3-5' + PILLOW_VERSION: 'none' + PANDAS_VERSION: 'none' + CREATE_ISSUE_ON_TRACKER: 'true' + # Will run all the time regardless of linting outcome. - template: build_tools/azure/posix.yml parameters: @@ -116,11 +140,13 @@ jobs: not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')) ) matrix: - pylatest_conda_mkl: + pylatest_conda_forge_mkl: DISTRIB: 'conda' + CONDA_CHANNEL: 'conda-forge' PYTHON_VERSION: '*' BLAS: 'mkl' COVERAGE: 'true' + SHOW_SHORT_SUMMARY: 'true' # Check compilation with Ubuntu bionic 18.04 LTS and scipy from conda-forge - template: build_tools/azure/posix.yml @@ -135,8 +161,9 @@ jobs: ne(variables['Build.Reason'], 'Schedule') ) matrix: - py37_conda: + py37_conda_forge_openblas_ubuntu_1804: DISTRIB: 'conda' + CONDA_CHANNEL: 'conda-forge' PYTHON_VERSION: '3.7' BLAS: 'openblas' COVERAGE: 'false' @@ -164,14 +191,16 @@ jobs: THREADPOOLCTL_VERSION: 'min' COVERAGE: 'false' # Linux + Python 3.7 build with OpenBLAS and without SITE_JOBLIB - py37_conda_openblas: + py37_conda_defaults_openblas: DISTRIB: 'conda' + CONDA_CHANNEL: 'defaults' # Anaconda main channel PYTHON_VERSION: '3.7' BLAS: 'openblas' NUMPY_VERSION: 'min' SCIPY_VERSION: 'min' MATPLOTLIB_VERSION: 'min' - THREADPOOLCTL_VERSION: '2.0.0' + THREADPOOLCTL_VERSION: '2.2.0' + SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES: '1' # Linux environment to test the latest available dependencies and MKL. # It runs tests requiring lightgbm, pandas and PyAMG. pylatest_pip_openblas_pandas: @@ -182,9 +211,9 @@ jobs: TEST_DOCSTRINGS: 'true' CHECK_WARNINGS: 'true' -- template: build_tools/azure/posix-32.yml +- template: build_tools/azure/posix-docker.yml parameters: - name: Linux32 + name: Linux_Docker vmImage: ubuntu-20.04 dependsOn: [linting, git_commit] condition: | @@ -196,16 +225,17 @@ jobs: matrix: debian_atlas_32bit: DISTRIB: 'debian-32' + DOCKER_CONTAINER: 'i386/debian:10.9' JOBLIB_VERSION: 'min' # disable pytest xdist due to unknown bug with 32-bit container PYTEST_XDIST_VERSION: 'none' PYTEST_VERSION: 'min' - THREADPOOLCTL_VERSION: 'min' + THREADPOOLCTL_VERSION: '2.2.0' - template: build_tools/azure/posix.yml parameters: name: macOS - vmImage: macOS-10.14 + vmImage: macOS-10.15 dependsOn: [linting, git_commit] condition: | and( @@ -218,16 +248,18 @@ jobs: DISTRIB: 'conda' BLAS: 'mkl' CONDA_CHANNEL: 'conda-forge' + CPU_COUNT: '3' pylatest_conda_mkl_no_openmp: DISTRIB: 'conda' BLAS: 'mkl' SKLEARN_TEST_NO_OPENMP: 'true' SKLEARN_SKIP_OPENMP_TEST: 'true' + CPU_COUNT: '3' - template: build_tools/azure/windows.yml parameters: name: Windows - vmImage: vs2017-win2016 + vmImage: windows-latest dependsOn: [linting, git_commit] condition: | and( @@ -236,13 +268,17 @@ jobs: ne(variables['Build.Reason'], 'Schedule') ) matrix: - py37_conda_mkl: + py37_conda_forge_mkl: + DISTRIB: 'conda' + CONDA_CHANNEL: 'conda-forge' PYTHON_VERSION: '3.7' CHECK_WARNINGS: 'true' PYTHON_ARCH: '64' PYTEST_VERSION: '*' COVERAGE: 'true' - DISTRIB: 'conda' + # Temporary fix for setuptools to use disutils from standard lib + # https://github.com/numpy/numpy/issues/17216 + SETUPTOOLS_USE_DISTUTILS: 'stdlib' py37_pip_openblas_32bit: PYTHON_VERSION: '3.7' PYTHON_ARCH: '32' diff --git a/benchmarks/bench_hist_gradient_boosting.py b/benchmarks/bench_hist_gradient_boosting.py index 7f7dec004b809..58477e8894fd1 100644 --- a/benchmarks/bench_hist_gradient_boosting.py +++ b/benchmarks/bench_hist_gradient_boosting.py @@ -140,7 +140,9 @@ def one_run(n_samples): lightgbm_score_duration = None if args.lightgbm: print("Fitting a LightGBM model...") - lightgbm_est = get_equivalent_estimator(est, lib="lightgbm") + lightgbm_est = get_equivalent_estimator( + est, lib="lightgbm", n_classes=args.n_classes + ) tic = time() lightgbm_est.fit(X_train, y_train, sample_weight=sample_weight_train) diff --git a/benchmarks/bench_hist_gradient_boosting_threading.py b/benchmarks/bench_hist_gradient_boosting_threading.py index 264c9f0dbd704..a43debf043dee 100644 --- a/benchmarks/bench_hist_gradient_boosting_threading.py +++ b/benchmarks/bench_hist_gradient_boosting_threading.py @@ -135,7 +135,9 @@ def get_estimator_and_data(): for libname in ["lightgbm", "xgboost", "catboost"]: if getattr(args, libname): print(libname) - est = get_equivalent_estimator(sklearn_est, lib=libname) + est = get_equivalent_estimator( + sklearn_est, lib=libname, n_classes=args.n_classes + ) pprint(est.get_params()) @@ -169,7 +171,9 @@ def one_run(n_threads, n_samples): lightgbm_score_duration = None if args.lightgbm: print("Fitting a LightGBM model...") - lightgbm_est = get_equivalent_estimator(est, lib="lightgbm") + lightgbm_est = get_equivalent_estimator( + est, lib="lightgbm", n_classes=args.n_classes + ) lightgbm_est.set_params(num_threads=n_threads) tic = time() diff --git a/benchmarks/bench_mnist.py b/benchmarks/bench_mnist.py index e0844e70475e4..84111d0a37969 100644 --- a/benchmarks/bench_mnist.py +++ b/benchmarks/bench_mnist.py @@ -6,7 +6,7 @@ Benchmark on the MNIST dataset. The dataset comprises 70,000 samples and 784 features. Here, we consider the task of predicting 10 classes - digits from 0 to 9 from their raw images. By contrast to the -covertype dataset, the feature space is homogenous. +covertype dataset, the feature space is homogeneous. Example of output : [..] diff --git a/benchmarks/bench_random_projections.py b/benchmarks/bench_random_projections.py index 7c03b3de365ca..6da894c0ead1e 100644 --- a/benchmarks/bench_random_projections.py +++ b/benchmarks/bench_random_projections.py @@ -43,10 +43,10 @@ def compute_time(t_start, delta): return delta.seconds + delta.microseconds / mu_second -def bench_scikit_transformer(X, transfomer): +def bench_scikit_transformer(X, transformer): gc.collect() - clf = clone(transfomer) + clf = clone(transformer) # start time t_start = datetime.now() @@ -195,7 +195,7 @@ def print_row(clf_type, time_fit, time_transform): ########################################################################### n_nonzeros = int(opts.ratio_nonzeros * opts.n_features) - print("Dataset statics") + print("Dataset statistics") print("===========================") print("n_samples \t= %s" % opts.n_samples) print("n_features \t= %s" % opts.n_features) diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index b69b01fb5a8dd..5bd845147b0f3 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -5,9 +5,20 @@ set -x UNAMESTR=`uname` +if [[ "$DISTRIB" == "conda-mamba-pypy3" ]]; then + # condaforge/mambaforge-pypy3 needs compilers + apt-get -yq update + apt-get -yq install build-essential +fi + make_conda() { TO_INSTALL="$@" - conda create -n $VIRTUALENV --yes $TO_INSTALL + if [[ "$DISTRIB" == *"mamba"* ]]; then + mamba create -n $VIRTUALENV --yes $TO_INSTALL + else + conda config --show + conda create -n $VIRTUALENV --yes $TO_INSTALL + fi source activate $VIRTUALENV } @@ -25,15 +36,21 @@ setup_ccache() { # imports get_dep source build_tools/shared.sh -if [[ "$DISTRIB" == "conda" ]]; then +if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then if [[ "$CONDA_CHANNEL" != "" ]]; then - TO_INSTALL="-c $CONDA_CHANNEL" + TO_INSTALL="--override-channels -c $CONDA_CHANNEL" else TO_INSTALL="" fi - TO_INSTALL="$TO_INSTALL python=$PYTHON_VERSION ccache pip blas[build=$BLAS]" + if [[ "$DISTRIB" == *"pypy"* ]]; then + TO_INSTALL="$TO_INSTALL pypy" + else + TO_INSTALL="$TO_INSTALL python=$PYTHON_VERSION" + fi + + TO_INSTALL="$TO_INSTALL ccache pip blas[build=$BLAS]" TO_INSTALL="$TO_INSTALL $(get_dep numpy $NUMPY_VERSION)" TO_INSTALL="$TO_INSTALL $(get_dep scipy $SCIPY_VERSION)" @@ -97,9 +114,8 @@ elif [[ "$DISTRIB" == "conda-pip-latest" ]]; then python -m pip install --only-binary :all: scikit-image || true python -m pip install pandas matplotlib pyamg - # do not install dependencies for lightgbm since it requires scikit-learn - # and install a version less than 3.0.0 until the issue #18316 is solved. - python -m pip install "lightgbm<3.0.0" --no-deps + # do not install dependencies for lightgbm since it requires scikit-learn. + python -m pip install "lightgbm>=3.0.0" --no-deps elif [[ "$DISTRIB" == "conda-pip-scipy-dev" ]]; then # FIXME: temporary fix to link against system libraries on linux export LDFLAGS="$LDFLAGS -Wl,--sysroot=/" @@ -113,7 +129,7 @@ elif [[ "$DISTRIB" == "conda-pip-scipy-dev" ]]; then echo "Installing joblib master" pip install https://github.com/joblib/joblib/archive/master.zip echo "Installing pillow master" - pip install https://github.com/python-pillow/Pillow/archive/master.zip + pip install https://github.com/python-pillow/Pillow/archive/main.zip fi python -m pip install $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \ diff --git a/build_tools/azure/posix-32.yml b/build_tools/azure/posix-docker.yml similarity index 91% rename from build_tools/azure/posix-32.yml rename to build_tools/azure/posix-docker.yml index 039236a70fbe5..3487b4742e2d1 100644 --- a/build_tools/azure/posix-32.yml +++ b/build_tools/azure/posix-docker.yml @@ -16,6 +16,7 @@ jobs: JUNITXML: 'test-data.xml' OMP_NUM_THREADS: '2' OPENBLAS_NUM_THREADS: '2' + CPU_COUNT: '2' SKLEARN_SKIP_NETWORK_TESTS: '1' NUMPY_VERSION: 'latest' SCIPY_VERSION: 'latest' @@ -30,12 +31,17 @@ jobs: THREADPOOLCTL_VERSION: 'latest' COVERAGE: 'false' TEST_DOCSTRINGS: 'false' + BLAS: 'openblas' + # Set in azure-pipelines.yml + DISTRIB: '' + DOCKER_CONTAINER: '' + SHOW_SHORT_SUMMARY: 'false' strategy: matrix: ${{ insert }}: ${{ parameters.matrix }} steps: - # Container is detached and sleeping, allowing steps to run commmands + # Container is detached and sleeping, allowing steps to run commands # in the container. The TEST_DIR is mapped allowing the host to access # the JUNITXML file - script: > @@ -45,7 +51,7 @@ jobs: -w /io --detach --name skcontainer - -e DISTRIB=debian-32 + -e DISTRIB=$DISTRIB -e TEST_DIR=/temp_dir -e JUNITXML=$JUNITXML -e VIRTUALENV=testvenv @@ -63,7 +69,9 @@ jobs: -e OMP_NUM_THREADS=$OMP_NUM_THREADS -e OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS -e SKLEARN_SKIP_NETWORK_TESTS=$SKLEARN_SKIP_NETWORK_TESTS - i386/debian:10.9 + -e BLAS=$BLAS + -e CPU_COUNT=$CPU_COUNT + $DOCKER_CONTAINER sleep 1000000 displayName: 'Start container' - script: > diff --git a/build_tools/azure/posix.yml b/build_tools/azure/posix.yml index 79101c8eef227..23e8e40d3bb7c 100644 --- a/build_tools/azure/posix.yml +++ b/build_tools/azure/posix.yml @@ -9,6 +9,7 @@ jobs: - job: ${{ parameters.name }} dependsOn: ${{ parameters.dependsOn }} condition: ${{ parameters.condition }} + timeoutInMinutes: 120 pool: vmImage: ${{ parameters.vmImage }} variables: @@ -17,6 +18,7 @@ jobs: JUNITXML: 'test-data.xml' OMP_NUM_THREADS: '2' OPENBLAS_NUM_THREADS: '2' + CPU_COUNT: '2' SKLEARN_SKIP_NETWORK_TESTS: '1' CCACHE_DIR: $(Pipeline.Workspace)/ccache CCACHE_COMPRESS: '1' @@ -33,6 +35,8 @@ jobs: THREADPOOLCTL_VERSION: 'latest' COVERAGE: 'true' TEST_DOCSTRINGS: 'false' + CREATE_ISSUE_ON_TRACKER: 'false' + SHOW_SHORT_SUMMARY: 'false' strategy: matrix: ${{ insert }}: ${{ parameters.matrix }} @@ -49,6 +53,7 @@ jobs: key: '"$(Agent.JobName)"' path: $(CCACHE_DIR) displayName: ccache + continueOnError: true - script: | build_tools/azure/install.sh displayName: 'Install' @@ -58,10 +63,6 @@ jobs: - script: | build_tools/azure/test_docs.sh displayName: 'Test Docs' - - script: | - build_tools/azure/test_docstring.sh - displayName: "Numpydoc validation" - condition: eq(variables['TEST_DOCSTRINGS'], 'true') - script: | build_tools/azure/test_pytest_soft_dependency.sh displayName: 'Test Soft Dependency' @@ -72,6 +73,30 @@ jobs: testRunTitle: ${{ format('{0}-$(Agent.JobName)', parameters.name) }} displayName: 'Publish Test Results' condition: succeededOrFailed() + - task: UsePythonVersion@0 + inputs: + versionSpec: '3.9' + displayName: Place Python into path to update issue tracker + condition: and(succeededOrFailed(), eq(variables['CREATE_ISSUE_ON_TRACKER'], 'true'), + eq(variables['Build.Reason'], 'Schedule')) + - bash: | + set -ex + if [[ $(BOT_GITHUB_TOKEN) == "" ]]; then + echo "GitHub Token is not set. Issue tracker will not be updated." + exit + fi + + LINK_TO_RUN="https://dev.azure.com/$BUILD_REPOSITORY_NAME/_build/results?buildId=$BUILD_BUILDID&view=logs&j=$SYSTEM_JOBID" + CI_NAME="$SYSTEM_JOBIDENTIFIER" + ISSUE_REPO="$BUILD_REPOSITORY_NAME" + + pip install defusedxml PyGithub + python maint_tools/create_issue_from_juint.py $(BOT_GITHUB_TOKEN) $CI_NAME $ISSUE_REPO $LINK_TO_RUN $JUNIT_FILE + displayName: 'Update issue tracker' + env: + JUNIT_FILE: $(TEST_DIR)/$(JUNITXML) + condition: and(succeededOrFailed(), eq(variables['CREATE_ISSUE_ON_TRACKER'], 'true'), + eq(variables['Build.Reason'], 'Schedule')) - script: | build_tools/azure/upload_codecov.sh condition: and(succeeded(), eq(variables['COVERAGE'], 'true')) diff --git a/build_tools/azure/test_docstring.sh b/build_tools/azure/test_docstring.sh deleted file mode 100755 index 7b41e13273383..0000000000000 --- a/build_tools/azure/test_docstring.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -set -e - -if [[ "$DISTRIB" =~ ^conda.* ]]; then - source activate $VIRTUALENV -elif [[ "$DISTRIB" == "ubuntu" ]]; then - source $VIRTUALENV/bin/activate -fi - -if [[ "$BUILD_WITH_ICC" == "true" ]]; then - source /opt/intel/oneapi/setvars.sh -fi - -pytest maint_tools/test_docstrings.py diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh index 6e05d7d858e52..23c643bb39d64 100755 --- a/build_tools/azure/test_script.sh +++ b/build_tools/azure/test_script.sh @@ -12,18 +12,20 @@ if [[ "$BUILD_WITH_ICC" == "true" ]]; then source /opt/intel/oneapi/setvars.sh fi -python --version -python -c "import numpy; print('numpy %s' % numpy.__version__)" -python -c "import scipy; print('scipy %s' % scipy.__version__)" -python -c "\ -try: - import pandas - print('pandas %s' % pandas.__version__) -except ImportError: - print('pandas not installed') -" -python -c "import multiprocessing as mp; print('%d CPUs' % mp.cpu_count())" -pip list +mkdir -p $TEST_DIR +cp setup.cfg $TEST_DIR +cd $TEST_DIR + +python -c "import sklearn; sklearn.show_versions()" +python -m threadpoolctl -i sklearn + +if ! command -v conda &> /dev/null +then + pip list +else + # conda list provides more info than pip list (when available) + conda list +fi TEST_CMD="python -m pytest --showlocals --durations=20 --junitxml=$JUNITXML" @@ -34,22 +36,25 @@ if [[ "$COVERAGE" == "true" ]]; then # report that otherwise hides the test failures and forces long scrolls in # the CI logs. export COVERAGE_PROCESS_START="$BUILD_SOURCESDIRECTORY/.coveragerc" - TEST_CMD="$TEST_CMD --cov-config=$COVERAGE_PROCESS_START --cov sklearn --cov-report=" + TEST_CMD="$TEST_CMD --cov-config='$COVERAGE_PROCESS_START' --cov sklearn --cov-report=" fi if [[ -n "$CHECK_WARNINGS" ]]; then # numpy's 1.19.0's tostring() deprecation is ignored until scipy and joblib removes its usage TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Wignore:tostring:DeprecationWarning" + + # Python 3.10 deprecates disutils and is imported by numpy interally during import time + TEST_CMD="$TEST_CMD -Wignore:The\ distutils:DeprecationWarning" fi if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then - TEST_CMD="$TEST_CMD -n2" + TEST_CMD="$TEST_CMD -n$CPU_COUNT" fi -mkdir -p $TEST_DIR -cp setup.cfg $TEST_DIR -cd $TEST_DIR +if [[ "$SHOW_SHORT_SUMMARY" == "true" ]]; then + TEST_CMD="$TEST_CMD -ra" +fi set -x -$TEST_CMD --pyargs sklearn +eval "$TEST_CMD --pyargs sklearn" set +x diff --git a/build_tools/azure/windows.yml b/build_tools/azure/windows.yml index f835fd02cf58e..795ac391343df 100644 --- a/build_tools/azure/windows.yml +++ b/build_tools/azure/windows.yml @@ -20,6 +20,8 @@ jobs: PYTEST_XDIST: 'true' PYTEST_XDIST_VERSION: 'latest' TEST_DIR: '$(Agent.WorkFolder)/tmp_folder' + SHOW_SHORT_SUMMARY: 'false' + CPU_COUNT: '2' strategy: matrix: ${{ insert }}: ${{ parameters.matrix }} diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh index 8facdc59a6c56..aa932a620d8e1 100755 --- a/build_tools/circle/build_doc.sh +++ b/build_tools/circle/build_doc.sh @@ -145,8 +145,8 @@ fi MINICONDA_PATH=$HOME/miniconda # Install dependencies with miniconda -wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \ - -O miniconda.sh +wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh \ + -O miniconda.sh chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH export PATH="/usr/lib/ccache:$MINICONDA_PATH/bin:$PATH" @@ -165,7 +165,7 @@ fi source build_tools/shared.sh # packaging won't be needed once setuptools starts shipping packaging>=17.0 -conda create -n $CONDA_ENV_NAME --yes --quiet \ +mamba create -n $CONDA_ENV_NAME --yes --quiet \ python="${PYTHON_VERSION:-*}" \ "$(get_dep numpy $NUMPY_VERSION)" \ "$(get_dep scipy $SCIPY_VERSION)" \ @@ -173,9 +173,15 @@ conda create -n $CONDA_ENV_NAME --yes --quiet \ "$(get_dep matplotlib $MATPLOTLIB_VERSION)" \ "$(get_dep sphinx $SPHINX_VERSION)" \ "$(get_dep pandas $PANDAS_VERSION)" \ - joblib memory_profiler packaging seaborn pillow pytest coverage + joblib memory_profiler packaging seaborn pillow pytest coverage \ + compilers source activate testenv +# Pin PyWavelet to 1.1.1 that is the latest version that support our minumum +# NumPy version required. If PyWavelets 1.2+ is installed, it would require +# NumPy 1.17+ that trigger a bug with Pandas 0.25: +# https://github.com/numpy/numpy/issues/18355#issuecomment-774610226 +pip install PyWavelets==1.1.1 pip install "$(get_dep scikit-image $SCIKIT_IMAGE_VERSION)" pip install "$(get_dep sphinx-gallery $SPHINX_GALLERY_VERSION)" pip install "$(get_dep numpydoc $NUMPYDOC_VERSION)" diff --git a/build_tools/circle/build_test_arm.sh b/build_tools/circle/build_test_arm.sh index 3d555f66227c4..67beaae5dba31 100755 --- a/build_tools/circle/build_test_arm.sh +++ b/build_tools/circle/build_test_arm.sh @@ -4,6 +4,8 @@ set -e set -x UNAMESTR=`uname` +N_CORES=`nproc --all` + setup_ccache() { echo "Setting up ccache" @@ -12,8 +14,10 @@ setup_ccache() { for name in gcc g++ cc c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++; do ln -s $(which ccache) "/tmp/ccache/${name}" done - export PATH="/tmp/ccache/:${PATH}" - ccache -M 256M + export PATH="/tmp/ccache:${PATH}" + # Unset ccache limits + ccache -F 0 + ccache -M 0 } # imports get_dep @@ -21,40 +25,64 @@ source build_tools/shared.sh sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test sudo apt-get update -sudo apt-get install python3-virtualenv ccache -python3 -m virtualenv --system-site-packages --python=python3 testenv -source testenv/bin/activate -pip install --upgrade pip + +# Setup conda environment +MINICONDA_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh" + +# Install Mambaforge +wget $MINICONDA_URL -O mambaforge.sh +MINICONDA_PATH=$HOME/miniconda +chmod +x mambaforge.sh && ./mambaforge.sh -b -p $MINICONDA_PATH +export PATH=$MINICONDA_PATH/bin:$PATH +mamba init --all --verbose +mamba update --yes conda + +# Create environment and install dependencies +mamba create -n testenv --yes $(get_dep python $PYTHON_VERSION) +source activate testenv + +# Use the latest by default +mamba install --verbose -y ccache \ + pip \ + $(get_dep numpy $NUMPY_VERSION) \ + $(get_dep scipy $SCIPY_VERSION) \ + $(get_dep cython $CYTHON_VERSION) \ + $(get_dep joblib $JOBLIB_VERSION) \ + $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \ + $(get_dep pytest $PYTEST_VERSION) \ + $(get_dep pytest-xdist $PYTEST_XDIST_VERSION) setup_ccache -python -m pip install $(get_dep cython $CYTHON_VERSION) \ - $(get_dep joblib $JOBLIB_VERSION) -python -m pip install $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \ - $(get_dep pytest $PYTEST_VERSION) \ - $(get_dep pytest-xdist $PYTEST_XDIST_VERSION) if [[ "$COVERAGE" == "true" ]]; then - python -m pip install codecov pytest-cov -fi - -if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then - python -m pip install pytest-xdist + mamba install --verbose -y codecov pytest-cov fi if [[ "$TEST_DOCSTRINGS" == "true" ]]; then # numpydoc requires sphinx - python -m pip install sphinx - python -m pip install numpydoc + mamba install --verbose -y sphinx + mamba install --verbose -y numpydoc fi python --version -# Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI -# workers with 2 cores when building the compiled extensions of scikit-learn. -export SKLEARN_BUILD_PARALLEL=3 +# Set parallelism to $N_CORES + 1 to overlap IO bound tasks with CPU bound tasks on CI +# workers with $N_CORES cores when building the compiled extensions of scikit-learn. +export SKLEARN_BUILD_PARALLEL=$(($N_CORES + 1)) + +# Disable the build isolation and build in the tree so that the same folder can be +# cached between CI runs. +# TODO: remove the '--use-feature' flag when made obsolete in pip 21.3. +pip install --verbose --no-build-isolation --use-feature=in-tree-build . + +# Report cache usage +ccache -s --verbose + +mamba list -python -m pip list -pip install --verbose --editable . -ccache -s +# Changing directory not to have module resolution use scikit-learn source +# directory but to the installed package. +cd /tmp python -c "import sklearn; sklearn.show_versions()" python -m threadpoolctl --import sklearn -python -m pytest sklearn +# Test using as many workers as available cores +pytest --pyargs -n $N_CORES sklearn diff --git a/build_tools/circle/list_versions.py b/build_tools/circle/list_versions.py index 1f7b39cdca32e..68e198f8bdb38 100755 --- a/build_tools/circle/list_versions.py +++ b/build_tools/circle/list_versions.py @@ -34,7 +34,7 @@ def human_readable_data_quantity(quantity, multiple=1024): def get_file_extension(version): if "dev" in version: - # The 'dev' branch should be explictly handled + # The 'dev' branch should be explicitly handled return "zip" current_version = LooseVersion(version) diff --git a/build_tools/codespell_ignore_words.txt b/build_tools/codespell_ignore_words.txt new file mode 100644 index 0000000000000..fbe501d04f29f --- /dev/null +++ b/build_tools/codespell_ignore_words.txt @@ -0,0 +1,45 @@ +aggresive +aline +ba +basf +boun +bre +cach +complies +coo +copys +deine +didi +feld +fo +fpr +fro +fwe +gool +hart +hist +ines +inout +ist +jaques +linke +lod +mape +mor +nd +nmae +ocur +pullrequest +ro +soler +suh +suprised +te +technic +teh +thi +usal +vie +wan +winn +yau diff --git a/build_tools/generate_authors_table.py b/build_tools/generate_authors_table.py index a16403e3b4a86..fae664fa8b652 100644 --- a/build_tools/generate_authors_table.py +++ b/build_tools/generate_authors_table.py @@ -43,7 +43,14 @@ def get_contributors(): # get core devs and triage team core_devs = [] triage_team = [] - for team_id, lst in zip((11523, 3593183), (core_devs, triage_team)): + comm_team = [] + core_devs_id = 11523 + triage_team_id = 3593183 + comm_team_id = 5368696 + for team_id, lst in zip( + (core_devs_id, triage_team_id, comm_team_id), + (core_devs, triage_team, comm_team), + ): for page in [1, 2]: # 30 per page reply = get(f"https://api.github.com/teams/{team_id}/members?page={page}") lst.extend(reply.json()) @@ -59,6 +66,7 @@ def get_contributors(): # keep only the logins core_devs = set(c["login"] for c in core_devs) triage_team = set(c["login"] for c in triage_team) + comm_team = set(c["login"] for c in comm_team) members = set(c["login"] for c in members) # add missing contributors with GitHub accounts @@ -75,13 +83,15 @@ def get_contributors(): core_devs = [get_profile(login) for login in core_devs] emeritus = [get_profile(login) for login in emeritus] triage_team = [get_profile(login) for login in triage_team] + comm_team = [get_profile(login) for login in comm_team] # sort by last name core_devs = sorted(core_devs, key=key) emeritus = sorted(emeritus, key=key) triage_team = sorted(triage_team, key=key) + comm_team = sorted(comm_team, key=key) - return core_devs, emeritus, triage_team + return core_devs, emeritus, triage_team, comm_team def get_profile(login): @@ -145,7 +155,7 @@ def generate_list(contributors): if __name__ == "__main__": - core_devs, emeritus, triage_team = get_contributors() + core_devs, emeritus, triage_team, comm_team = get_contributors() with open(REPO_FOLDER / "doc" / "authors.rst", "w+") as rst_file: rst_file.write(generate_table(core_devs)) @@ -155,3 +165,6 @@ def generate_list(contributors): with open(REPO_FOLDER / "doc" / "triage_team.rst", "w+") as rst_file: rst_file.write(generate_table(triage_team)) + + with open(REPO_FOLDER / "doc" / "communication_team.rst", "w+") as rst_file: + rst_file.write(generate_table(comm_team)) diff --git a/build_tools/github/build_minimal_windows_image.sh b/build_tools/github/build_minimal_windows_image.sh old mode 100644 new mode 100755 index 67a6180952591..18905a5f1f21e --- a/build_tools/github/build_minimal_windows_image.sh +++ b/build_tools/github/build_minimal_windows_image.sh @@ -6,8 +6,8 @@ set -x PYTHON_VERSION=$1 BITNESS=$2 -if [[ "$PYTHON_VERSION" == "36" || "$BITNESS" == "32" ]]; then - # Python 3.6 and 32-bit architectures are not supported +if [[ "$BITNESS" == "32" ]]; then + # 32-bit architectures are not supported # by the official Docker images: Tests will just be run # on the host (instead of the minimal Docker container). exit 0 diff --git a/build_tools/github/build_source.sh b/build_tools/github/build_source.sh old mode 100644 new mode 100755 diff --git a/build_tools/github/build_wheels.sh b/build_tools/github/build_wheels.sh old mode 100644 new mode 100755 index c484513b648e3..0a8f93e3d2c0b --- a/build_tools/github/build_wheels.sh +++ b/build_tools/github/build_wheels.sh @@ -8,11 +8,21 @@ if [[ "$RUNNER_OS" == "macOS" ]]; then # Make sure to use a libomp version binary compatible with the oldest # supported version of the macos SDK as libomp will be vendored into the # scikit-learn wheels for macos. The list of binaries are in - # https://packages.macports.org/libomp/. Currently, the oldest - # supported macos version is: High Sierra / 10.13. When upgrading this, be - # sure to update the MACOSX_DEPLOYMENT_TARGET environment variable in - # wheels.yml accordingly. Note that Darwin_17 == High Sierra / 10.13. - wget https://packages.macports.org/libomp/libomp-12.0.0_0+universal.darwin_17.i386-x86_64.tbz2 -O libomp.tbz2 + # https://packages.macports.org/libomp/. + if [[ "$CIBW_BUILD" == *-macosx_arm64 ]]; then + # arm64 builds must cross compile because CI is on x64 + export PYTHON_CROSSENV=1 + # SciPy requires 12.0 on arm to prevent kernel panics + # https://github.com/scipy/scipy/issues/14688 + # We use the same deployment target to match SciPy. + export MACOSX_DEPLOYMENT_TARGET=12.0 + wget https://packages.macports.org/libomp/libomp-11.0.1_0.darwin_20.arm64.tbz2 -O libomp.tbz2 + else + # Currently, the oldest supported macos version is: High Sierra / 10.13. + # Note that Darwin_17 == High Sierra / 10.13. + export MACOSX_DEPLOYMENT_TARGET=10.13 + wget https://packages.macports.org/libomp/libomp-11.0.1_0+universal.darwin_17.i386-x86_64.tbz2 -O libomp.tbz2 + fi sudo tar -C / -xvjf libomp.tbz2 opt export CC=/usr/bin/clang diff --git a/build_tools/github/check_build_trigger.sh b/build_tools/github/check_build_trigger.sh old mode 100644 new mode 100755 index b80fe4674864d..8309b33eded2f --- a/build_tools/github/check_build_trigger.sh +++ b/build_tools/github/check_build_trigger.sh @@ -5,8 +5,9 @@ set -x COMMIT_MSG=$(git log --no-merges -1 --oneline) -# The commit marker "[cd build]" will trigger the build when required +# The commit marker "[cd build]" or "[cd build gh]" will trigger the build when required if [[ "$GITHUB_EVENT_NAME" == schedule || - "$COMMIT_MSG" =~ \[cd\ build\] ]]; then + "$COMMIT_MSG" =~ \[cd\ build\] || + "$COMMIT_MSG" =~ \[cd\ build\ gh\] ]]; then echo "::set-output name=build::true" fi diff --git a/build_tools/github/check_wheels.py b/build_tools/github/check_wheels.py index 4abd0c123df7a..ef9bd77254fb5 100644 --- a/build_tools/github/check_wheels.py +++ b/build_tools/github/check_wheels.py @@ -8,14 +8,8 @@ with gh_wheel_path.open("r") as f: wheel_config = yaml.safe_load(f) -build_matrix = wheel_config["jobs"]["build_wheels"]["strategy"]["matrix"] -n_python_versions = len(build_matrix["python"]) - -# For each python version we have: 7 wheels -# 1 osx wheel (x86_64) -# 4 linux wheel (i686 + x86_64) * (manylinux1 + manylinux2010) -# 2 windows wheel (win32 + wind_amd64) -n_wheels = 7 * n_python_versions +build_matrix = wheel_config["jobs"]["build_wheels"]["strategy"]["matrix"]["include"] +n_wheels = len(build_matrix) # plus one more for the sdist n_wheels += 1 diff --git a/build_tools/github/repair_windows_wheels.sh b/build_tools/github/repair_windows_wheels.sh old mode 100644 new mode 100755 diff --git a/build_tools/github/test_source.sh b/build_tools/github/test_source.sh old mode 100644 new mode 100755 diff --git a/build_tools/github/test_wheels.sh b/build_tools/github/test_wheels.sh old mode 100644 new mode 100755 diff --git a/build_tools/github/test_windows_wheels.sh b/build_tools/github/test_windows_wheels.sh old mode 100644 new mode 100755 index ff3f823be754b..cf33252d551ba --- a/build_tools/github/test_windows_wheels.sh +++ b/build_tools/github/test_windows_wheels.sh @@ -6,8 +6,8 @@ set -x PYTHON_VERSION=$1 BITNESS=$2 -if [[ "$PYTHON_VERSION" == "36" || "$BITNESS" == "32" ]]; then - # For Python 3.6 and 32-bit architecture use the regular +if [[ "$BITNESS" == "32" ]]; then + # 32-bit architectures use the regular # test command (outside of the minimal Docker container) cp $CONFTEST_PATH $CONFTEST_NAME pytest --pyargs sklearn diff --git a/build_tools/github/upload_anaconda.sh b/build_tools/github/upload_anaconda.sh old mode 100644 new mode 100755 diff --git a/build_tools/shared.sh b/build_tools/shared.sh index 4aa260675b9be..59592acbbb93b 100644 --- a/build_tools/shared.sh +++ b/build_tools/shared.sh @@ -5,7 +5,7 @@ get_dep() { # do not install with none echo elif [[ "${version%%[^0-9.]*}" ]]; then - # version number is explicity passed + # version number is explicitly passed echo "$package==$version" elif [[ "$version" == "latest" ]]; then # use latest diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh old mode 100644 new mode 100755 index 1e8e2963711ef..178260c8dabcb --- a/build_tools/travis/install.sh +++ b/build_tools/travis/install.sh @@ -4,10 +4,8 @@ # defined in the ".travis.yml" file. In particular, it is # important that we call to the right installation script. -set -e - if [[ $BUILD_WHEEL == true ]]; then - source build_tools/travis/install_wheels.sh + source build_tools/travis/install_wheels.sh || travis_terminate 1 else - source build_tools/travis/install_main.sh + source build_tools/travis/install_main.sh || travis_terminate 1 fi diff --git a/build_tools/travis/install_wheels.sh b/build_tools/travis/install_wheels.sh old mode 100644 new mode 100755 index 4bb52f51f27f7..0f6cdf256e71b --- a/build_tools/travis/install_wheels.sh +++ b/build_tools/travis/install_wheels.sh @@ -1,6 +1,4 @@ #!/bin/bash -set -e - -python -m pip install cibuildwheel -python -m cibuildwheel --output-dir wheelhouse +python -m pip install cibuildwheel || travis_terminate $? +python -m cibuildwheel --output-dir wheelhouse || travis_terminate $? diff --git a/build_tools/travis/script.sh b/build_tools/travis/script.sh old mode 100644 new mode 100755 index 2b7aecb295d82..6e8b7e3deaee1 --- a/build_tools/travis/script.sh +++ b/build_tools/travis/script.sh @@ -5,8 +5,6 @@ # continuous deployment jobs, we have to execute the scripts for # testing the continuous integration jobs. -set -e - if [[ $BUILD_WHEEL != true ]]; then # This trick will make Travis terminate the continuation of the pipeline bash build_tools/travis/test_script.sh || travis_terminate 1 diff --git a/build_tools/travis/test_wheels.sh b/build_tools/travis/test_wheels.sh old mode 100644 new mode 100755 index be2328e3d44d6..aa3d0d8c0ef1b --- a/build_tools/travis/test_wheels.sh +++ b/build_tools/travis/test_wheels.sh @@ -1,9 +1,9 @@ #!/bin/bash -set -e +pip install --upgrade pip || travis_terminate $? +pip install pytest pytest-xdist || travis_terminate $? -# Faster run of the source code tests -pytest -n $CPU_COUNT --pyargs sklearn +python -m pytest -n $CPU_COUNT --pyargs sklearn || travis_terminate $? # Test that there are no links to system libraries -python -m threadpoolctl -i sklearn +python -m threadpoolctl -i sklearn || travis_terminate $? diff --git a/doc/README.md b/doc/README.md index 18d4bde4f5862..8cace706efd35 100644 --- a/doc/README.md +++ b/doc/README.md @@ -1,6 +1,6 @@ # Documentation for scikit-learn -This directory contains the full manual and web site as displayed at +This directory contains the full manual and website as displayed at http://scikit-learn.org. See http://scikit-learn.org/dev/developers/contributing.html#documentation for detailed information about the documentation. diff --git a/doc/about.rst b/doc/about.rst index cc0ecbb2288e0..2fb3f1b48192b 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -18,6 +18,7 @@ been leading the development. Governance ---------- + The decision making process and governance structure of scikit-learn is laid out in the :ref:`governance document `. @@ -47,6 +48,15 @@ maintenance: .. include:: triage_team.rst +Communication Team +------------------ + +The following people help with :ref:`communication around scikit-learn +`. + +.. include:: communication_team.rst + + Emeritus Core Developers ------------------------ @@ -85,8 +95,8 @@ citations to the following paper: If you want to cite scikit-learn for its API or design, you may also want to consider the following paper: - `API design for machine learning software: experiences from the scikit-learn - project `_, Buitinck *et al.*, 2013. + :arxiv:`API design for machine learning software: experiences from the scikit-learn + project <1309.0238>`, Buitinck *et al.*, 2013. Bibtex entry:: @@ -217,7 +227,7 @@ July 2017.
-.. image:: themes/scikit-learn/static/img/sydney-primary.jpeg +.. image:: images/sydney-primary.jpeg :width: 100pt :align: center :target: https://sydney.edu.au/ @@ -320,7 +330,7 @@ Past Sponsors
-.. image:: themes/scikit-learn/static/img/columbia.png +.. image:: images/columbia.png :width: 50pt :align: center :target: https://www.columbia.edu/ @@ -481,7 +491,7 @@ part-time.
-.. image:: themes/scikit-learn/static/img/telecom.png +.. image:: images/telecom.png :width: 50pt :align: center :target: https://www.telecom-paristech.fr/ @@ -508,7 +518,7 @@ funded a scikit-learn coding sprint in 2015.
-.. image:: themes/scikit-learn/static/img/digicosme.png +.. image:: images/digicosme.png :width: 100pt :align: center :target: https://digicosme.lri.fr @@ -533,7 +543,7 @@ Hug to work full-time on scikit-learn in 2020.
-.. image:: themes/scikit-learn/static/img/czi_logo.svg +.. image:: images/czi_logo.svg :width: 100pt :align: center :target: https://chanzuckerberg.com diff --git a/doc/authors.rst b/doc/authors.rst index 861f2c0a884e4..9a2b1f89d5d06 100644 --- a/doc/authors.rst +++ b/doc/authors.rst @@ -6,87 +6,91 @@ img.avatar {border-radius: 10px;}
-
+

Jérémie du Boisberranger

-
+

Joris Van den Bossche

-
+

Loïc Estève

-
+

Thomas J. Fan

-
+

Alexandre Gramfort

-
+

Olivier Grisel

-
+

Yaroslav Halchenko

-
+

Nicolas Hug

-
+

Adrin Jalali

-
+
+

Julien Jerphanion

+
+
+

Guillaume Lemaitre

-
+

Christian Lorentzen

-
+

Jan Hendrik Metzen

-
+

Andreas Mueller

-
+

Vlad Niculae

-
+

Joel Nothman

-
+

Hanmin Qin

-
+

Bertrand Thirion

-
+

Tom Dupré la Tour

-
+

Gael Varoquaux

-
+

Nelle Varoquaux

-
+

Roman Yurchak

-
\ No newline at end of file +
diff --git a/doc/authors_emeritus.rst b/doc/authors_emeritus.rst index bcfd7d7d0514c..a00bb839ce5b6 100644 --- a/doc/authors_emeritus.rst +++ b/doc/authors_emeritus.rst @@ -3,7 +3,6 @@ - Lars Buitinck - David Cournapeau - Noel Dawe -- Shiqiao Du - Vincent Dubourg - Edouard Duchesnay - Alexander Fabisch @@ -28,6 +27,7 @@ - Peter Prettenhofer - (Venkat) Raghav, Rajagopalan - Jacob Schreiber +- Du Shiqiao - Jake Vanderplas - David Warde-Farley -- Ron Weiss \ No newline at end of file +- Ron Weiss diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst index ac5dccb3b5609..308edb4c67c79 100644 --- a/doc/common_pitfalls.rst +++ b/doc/common_pitfalls.rst @@ -560,7 +560,7 @@ bad performance. Similarly, we want a random forest to be robust w.r.t the set of randomly selected features that each tree will be using. For these reasons, it is preferable to evaluate the cross-validation -preformance by letting the estimator use a different RNG on each fold. This +performance by letting the estimator use a different RNG on each fold. This is done by passing a `RandomState` instance (or `None`) to the estimator initialization. diff --git a/doc/communication_team.rst b/doc/communication_team.rst new file mode 100644 index 0000000000000..14855ec447d03 --- /dev/null +++ b/doc/communication_team.rst @@ -0,0 +1,16 @@ +.. raw :: html + + +
+ +
+
+

Reshama Shaikh

+
+
+
+

Lauren Burke

+
+
diff --git a/doc/computing/parallelism.rst b/doc/computing/parallelism.rst index 8605650e8eec5..614551d11667b 100644 --- a/doc/computing/parallelism.rst +++ b/doc/computing/parallelism.rst @@ -214,3 +214,9 @@ These environment variables should be set before importing scikit-learn. When this environment variable is set to a non zero value, the tests that need network access are skipped. When this environment variable is not set then network tests are skipped. + +:SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES: + + When this environment variable is set to a non zero value, the `Cython` + derivative, `boundscheck` is set to `True`. This is useful for finding + segfaults. diff --git a/doc/conf.py b/doc/conf.py index 07ef95e2d470f..97741dfd8388a 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -48,6 +48,7 @@ "add_toctree_functions", "sphinx-prompt", "sphinxext.opengraph", + "doi_role", ] # Support for `plot::` directives in sphinx 3.2 requires matplotlib 3.1.0 or newer @@ -240,7 +241,7 @@ "release_highlights" ] = f"auto_examples/release_highlights/{latest_highlights}" -# get version from higlight name assuming highlights have the form +# get version from highlight name assuming highlights have the form # plot_release_highlights_0_22_0 highlight_version = ".".join(latest_highlights.split("_")[-3:-1]) html_context["release_highlights_version"] = highlight_version diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index c1dec51723861..d89067bc58467 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -47,32 +47,52 @@ feature, code or documentation improvement). .. prompt:: bash $ - git clone git://github.com/scikit-learn/scikit-learn.git # add --depth 1 if your connection is slow - cd scikit-learn + git clone git://github.com/scikit-learn/scikit-learn.git # add --depth 1 if your connection is slow + cd scikit-learn If you plan on submitting a pull-request, you should clone from your fork instead. +#. Install a recent version of Python (3.9 is recommended at the time of writing) + for instance using Miniforge3_. Miniforge provides a conda-based distribution + of Python and the most popular scientific libraries. + + If you installed Python with conda, we recommend to create a dedicated + `conda environment`_ with all the build dependencies of scikit-learn + (namely NumPy_, SciPy_, and Cython_): + + .. prompt:: bash $ + + conda create -n sklearn-env -c conda-forge python=3.9 numpy scipy cython + conda activate sklearn-env + +#. **Alternative to conda:** If you run Linux or similar, you can instead use + your system's Python provided it is recent enough (3.7 or higher + at the time of writing). In this case, we recommend to create a dedicated + virtualenv_ and install the scikit-learn build dependencies with pip: + + .. prompt:: bash $ + + python3 -m venv sklearn-env + source sklearn-env/bin/activate + pip install wheel numpy scipy cython + #. Install a compiler with OpenMP_ support for your platform. See instructions for :ref:`compiler_windows`, :ref:`compiler_macos`, :ref:`compiler_linux` and :ref:`compiler_freebsd`. -#. Optional (but recommended): create and activate a dedicated virtualenv_ - or `conda environment`_. - -#. Install Cython_ and build the project with pip in :ref:`editable_mode`: +#. Build the project with pip in :ref:`editable_mode`: .. prompt:: bash $ - pip install cython - pip install --verbose --no-build-isolation --editable . + pip install --verbose --no-build-isolation --editable . #. Check that the installed scikit-learn has a version number ending with `.dev0`: .. prompt:: bash $ - python -c "import sklearn; sklearn.show_versions()" + python -c "import sklearn; sklearn.show_versions()" #. Please refer to the :ref:`developers_guide` and :ref:`pytest_tips` to run the tests on the module of your choice. @@ -100,9 +120,6 @@ runtime: - Joblib (>= |JoblibMinVersion|), - threadpoolctl (>= |ThreadpoolctlMinVersion|). -Those dependencies are **automatically installed by pip** if they were missing -when building scikit-learn from source. - .. note:: For running on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ @@ -188,14 +205,14 @@ to build scikit-learn Cython extensions for each supported platform. Windows ------- -First, install `Build Tools for Visual Studio 2019 -`_. +First, download the `Build Tools for Visual Studio 2019 installer +`_. -.. warning:: +Run the downloaded `vs_buildtools.exe` file, during the installation you will +need to make sure you select "Desktop development with C++", similarly to this +screenshot: - You DO NOT need to install Visual Studio 2019. You only need the "Build - Tools for Visual Studio 2019", under "All downloads" -> "Tools for Visual - Studio 2019". +.. image:: ../images/visual-studio-build-tools-selection.png Secondly, find out if you are running 64-bit or 32-bit Python. The building command depends on the architecture of the Python interpreter. You can check @@ -376,7 +393,7 @@ isolation from the Python packages installed via the system packager. When using an isolated environment, ``pip3`` should be replaced by ``pip`` in the above commands. -When precompiled wheels of the runtime dependencies are not avalaible for your +When precompiled wheels of the runtime dependencies are not available for your architecture (e.g. ARM), you can install the system versions: .. prompt:: bash $ @@ -436,9 +453,12 @@ the base system and these steps will not be necessary. .. _OpenMP: https://en.wikipedia.org/wiki/OpenMP .. _Cython: https://cython.org +.. _NumPy: https://numpy.org +.. _SciPy: https://www.scipy.org .. _Homebrew: https://brew.sh .. _virtualenv: https://docs.python.org/3/tutorial/venv.html .. _conda environment: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html +.. _Miniforge3: https://github.com/conda-forge/miniforge#miniforge3 Alternative compilers ===================== diff --git a/doc/developers/bug_triaging.rst b/doc/developers/bug_triaging.rst index ff96ec9f0faae..1ea461a3511ac 100644 --- a/doc/developers/bug_triaging.rst +++ b/doc/developers/bug_triaging.rst @@ -149,5 +149,7 @@ The following workflow [1]_ is a good way to approach issue triaging: An additional useful step can be to tag the corresponding module e.g. `sklearn.linear_models` when relevant. +#. Remove the "Needs Triage" label from the issue if the label exists. + .. [1] Adapted from the pandas project `maintainers guide `_ diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index e063d7f9846f4..03358342e3509 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -254,33 +254,35 @@ how to set up your git repository: .. prompt:: bash $ - git clone git@github.com:YourLogin/scikit-learn.git # add --depth 1 if your connection is slow - cd scikit-learn + git clone git@github.com:YourLogin/scikit-learn.git # add --depth 1 if your connection is slow + cd scikit-learn -4. Install the development dependencies: - - .. prompt:: bash $ - - pip install cython pytest pytest-cov flake8 mypy black==21.6b0 - -5. Install scikit-learn in editable mode: +3. Follow steps 2-7 in :ref:`install_bleeding_edge` to build scikit-learn in + development mode and return to this document. - .. prompt:: bash $ +4. Install the development dependencies: - pip install --no-build-isolation --editable . + .. prompt:: bash $ - If you receive errors in building scikit-learn, see the - :ref:`install_bleeding_edge` section. + pip install pytest pytest-cov flake8 mypy black==21.6b0 .. _upstream: -6. Add the ``upstream`` remote. This saves a reference to the main +5. Add the ``upstream`` remote. This saves a reference to the main scikit-learn repository, which you can use to keep your repository synchronized with the latest changes: .. prompt:: bash $ - git remote add upstream https://github.com/scikit-learn/scikit-learn.git + git remote add upstream git@github.com:scikit-learn/scikit-learn.git + +6. Check that the `upstream` and `origin` remote aliases are configured correctly + by running `git remote -v` which should display:: + + origin git@github.com:YourLogin/scikit-learn.git (fetch) + origin git@github.com:YourLogin/scikit-learn.git (push) + upstream git@github.com:scikit-learn/scikit-learn.git (fetch) + upstream git@github.com:scikit-learn/scikit-learn.git (push) You should now have a working installation of scikit-learn, and your git repository properly configured. The next steps now describe the process of @@ -536,7 +538,7 @@ profiling and Cython optimizations. For two very well documented and more detailed guides on development workflow, please pay a visit to the `Scipy Development Workflow - `_ - + `_ - and the `Astropy Workflow for Developers `_ sections. @@ -547,7 +549,7 @@ Continuous Integration (CI) * Azure pipelines are used for testing scikit-learn on Linux, Mac and Windows, with different dependencies and settings. * CircleCI is used to build the docs for viewing, for linting with flake8, and - for testing with PyPy and ARM64 / aarch64 on Linux + for testing with ARM64 / aarch64 on Linux Please note that if one of the following markers appear in the latest commit message, the following actions are taken. @@ -557,9 +559,11 @@ message, the following actions are taken. ---------------------- ------------------- [ci skip] CI is skipped completely [cd build] CD is run (wheels and source distribution are built) + [cd build gh] CD is run only for GitHub Actions [lint skip] Azure pipeline skips linting - [scipy-dev] Add a Travis build with our dependencies (numpy, scipy, etc ...) development builds - [icc-build] Add a Travis build with the Intel C compiler (ICC) + [scipy-dev] Build & test with our dependencies (numpy, scipy, etc ...) development builds + [icc-build] Build & test with the Intel C compiler (ICC) + [pypy] Build & test with PyPy [doc skip] Docs are not built [doc quick] Docs built, but excludes example gallery plots [doc build] Docs built including example gallery plots (very long) @@ -1004,7 +1008,7 @@ installed in your current Python environment: asv run --python=same -It's particulary useful when you installed scikit-learn in editable mode to +It's particularly useful when you installed scikit-learn in editable mode to avoid creating a new environment each time you run the benchmarks. By default the results are not saved when using an existing installation. To save the results you must specify a commit hash: diff --git a/doc/developers/maintainer.rst b/doc/developers/maintainer.rst index 8fd439c984660..ffefbb767131a 100644 --- a/doc/developers/maintainer.rst +++ b/doc/developers/maintainer.rst @@ -33,7 +33,7 @@ Before a release - ``maint_tools/sort_whats_new.py`` can put what's new entries into sections. It's not perfect, and requires manual checking of the changes. - If the whats new list is well curated, it may not be necessary. + If the what's new list is well curated, it may not be necessary. - The ``maint_tools/whats_missing.sh`` script may be used to identify pull requests that were merged but likely missing from What's New. @@ -198,7 +198,7 @@ Making a release `Continuous Integration `_. The CD workflow on GitHub Actions is also used to automatically create nightly builds and - publish packages for the developement branch of scikit-learn. See + publish packages for the development branch of scikit-learn. See :ref:`install_nightly_builds`. 4. Once all the CD jobs have completed successfully in the PR, merge it, @@ -211,6 +211,12 @@ Making a release https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Publish+to+Pypi%22 +4.1 You can test the conda-forge builds by submitting a PR to the feedstock + repo: https://github.com/conda-forge/scikit-learn-feedstock. If you want to + publish an RC release on conda-forge, the PR should target the `rc` branch + as opposed to the `master` branch. The two branches need to be kept sync + together otherwise. + 5. If this went fine, you can proceed with tagging. Proceed with caution. Ideally, tags should be created when you're almost certain that the release is ready, since adding a tag to the main repo can trigger certain automated @@ -272,11 +278,11 @@ Making a release git clone --depth 1 --no-checkout git@github.com:scikit-learn/scikit-learn.github.io.git cd scikit-learn.github.io echo stable > .git/info/sparse-checkout - git checkout master + git checkout main rm stable ln -s 0.999 stable sed -i "s/latestStable = '.*/latestStable = '0.999';/" versionwarning.js - git add stable/ versionwarning.js + git add stable versionwarning.js git commit -m "Update stable to point to 0.999" git push origin master @@ -293,14 +299,11 @@ The following GitHub checklist might be helpful in a release PR:: * [ ] merge the PR with `[cd build]` commit message to upload wheels to the staging repo * [ ] upload the wheels and source tarball to https://test.pypi.org * [ ] create tag on the main github repo - * [ ] upload the wheels and source tarball to PyPI - * [ ] https://github.com/scikit-learn/scikit-learn/releases draft * [ ] confirm bot detected at https://github.com/conda-forge/scikit-learn-feedstock and wait for merge + * [ ] upload the wheels and source tarball to PyPI * [ ] https://github.com/scikit-learn/scikit-learn/releases publish - * [ ] fix the binder release version in ``.binder/requirement.txt`` (see - #15847) - * [ ] announce on mailing list and on twitter + * [ ] announce on mailing list and on Twitter, and LinkedIn Merging Pull Requests --------------------- diff --git a/doc/developers/plotting.rst b/doc/developers/plotting.rst index 7a2f6ebf69415..b0e8b3b43ee45 100644 --- a/doc/developers/plotting.rst +++ b/doc/developers/plotting.rst @@ -18,12 +18,14 @@ stored and the plotting is done in a `plot` method. The display object's `__init__` method contains only the data needed to create the visualization. The `plot` method takes in parameters that only have to do with visualization, such as a matplotlib axes. The `plot` method will store the matplotlib artists -as attributes allowing for style adjustments through the display object. A -`plot_*` helper function accepts parameters to do the computation and the -parameters used for plotting. After the helper function creates the display -object with the computed values, it calls the display's plot method. Note that -the `plot` method defines attributes related to matplotlib, such as the line -artist. This allows for customizations after calling the `plot` method. +as attributes allowing for style adjustments through the display object. The +`Display` class should define one or both class methods: `from_estimator` and +`from_predictions`. These methods allows to create the `Display` object from +the estimator and some data or from the true and predicted values. After these +class methods create the display object with the computed values, then call the +display's plot method. Note that the `plot` method defines attributes related +to matplotlib, such as the line artist. This allows for customizations after +calling the `plot` method. For example, the `RocCurveDisplay` defines the following methods and attributes:: @@ -36,20 +38,25 @@ attributes:: self.roc_auc = roc_auc self.estimator_name = estimator_name + @classmethod + def from_estimator(cls, estimator, X, y): + # get the predictions + y_pred = estimator.predict_proba(X)[:, 1] + return cls.from_predictions(y, y_pred, estimator.__class__.__name__) + + @classmethod + def from_predictions(cls, y, y_pred, estimator_name): + # do ROC computation from y and y_pred + fpr, tpr, roc_auc = ... + viz = RocCurveDisplay(fpr, tpr, roc_auc, estimator_name) + return viz.plot() + def plot(self, ax=None, name=None, **kwargs): ... self.line_ = ... self.ax_ = ax self.figure_ = ax.figure_ - def plot_roc_curve(estimator, X, y, pos_label=None, sample_weight=None, - drop_intermediate=True, response_method="auto", - name=None, ax=None, **kwargs): - # do computation - viz = RocCurveDisplay(fpr, tpr, roc_auc, - estimator.__class__.__name__) - return viz.plot(ax=ax, name=name, **kwargs) - Read more in :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` and the :ref:`User Guide `. @@ -57,8 +64,8 @@ Plotting with Multiple Axes --------------------------- Some of the plotting tools like -:func:`~sklearn.inspection.plot_partial_dependence` and -:class:`~sklearn.inspection.PartialDependenceDisplay` support plottong on +:func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` and +:class:`~sklearn.inspection.PartialDependenceDisplay` support plotting on multiple axes. Two different scenarios are supported: 1. If a list of axes is passed in, `plot` will check if the number of axes is @@ -80,8 +87,8 @@ be placed. In this case, we suggest using matplotlib's By default, the `ax` keyword in `plot` is `None`. In this case, the single axes is created and the gridspec api is used to create the regions to plot in. -See for example, :func:`~sklearn.inspection.plot_partial_dependence` which -plots multiple lines and contours using this API. The axes defining the +See for example, :func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator +which plots multiple lines and contours using this API. The axes defining the bounding box is saved in a `bounding_ax_` attribute. The individual axes created are stored in an `axes_` ndarray, corresponding to the axes position on the grid. Positions that are not used are set to `None`. Furthermore, the diff --git a/doc/getting_started.rst b/doc/getting_started.rst index b22febb7f1359..298200f5a2afd 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -9,8 +9,8 @@ etc.). Please refer to our :ref:`installation instructions ``Scikit-learn`` is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for -model fitting, data preprocessing, model selection and evaluation, and many -other utilities. +model fitting, data preprocessing, model selection, model evaluation, +and many other utilities. Fitting and predicting: estimator basics ---------------------------------------- diff --git a/doc/glossary.rst b/doc/glossary.rst index ac04805343bb6..2b4c6af0d1866 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -644,9 +644,8 @@ General Concepts Note that for most distance metrics, we rely on implementations from :mod:`scipy.spatial.distance`, but may reimplement for efficiency in - our context. The :mod:`neighbors` module also duplicates some metric - implementations for integration with efficient binary tree search data - structures. + our context. The :class:`metrics.DistanceMetric` interface is used to implement + distance metrics for integration with efficient neighbors search. pd A shorthand for `Pandas `_ due to the @@ -894,6 +893,7 @@ Class APIs and Estimator Types * :term:`fit` * :term:`transform` * :term:`get_feature_names` + * :term:`get_feature_names_out` meta-estimator meta-estimators @@ -1022,7 +1022,7 @@ such as: Further examples: -* :class:`neighbors.DistanceMetric` +* :class:`metrics.DistanceMetric` * :class:`gaussian_process.kernels.Kernel` * ``tree.Criterion`` @@ -1262,6 +1262,17 @@ Methods to the names of input columns from which output column names can be generated. By default input features are named x0, x1, .... + ``get_feature_names_out`` + Primarily for :term:`feature extractors`, but also used for other + transformers to provide string names for each column in the output of + the estimator's :term:`transform` method. It outputs an array of + strings and may take an array-like of strings as input, corresponding + to the names of input columns from which output column names can + be generated. If `input_features` is not passed in, then the + `feature_names_in_` attribute will be used. If the + `feature_names_in_` attribute is not defined, then the + input names are named `[x0, x1, ..., x(n_features_in_)]`. + ``get_n_splits`` On a :term:`CV splitter` (not an estimator), returns the number of elements one would get if iterating through the return value of diff --git a/doc/governance.rst b/doc/governance.rst index 1d971f16a566e..94afa3c2c1cd5 100644 --- a/doc/governance.rst +++ b/doc/governance.rst @@ -48,8 +48,25 @@ Every new triager will be announced in the mailing list. Triagers are welcome to participate in `monthly core developer meetings `_. +.. _communication_team: + +Communication team +------------------- + +Members of the communication team help with outreach and communication +for scikit-learn. The goal of the team is to develop public awareness of +scikit-learn, of its features and usage, as well as branding. + +For this, they can operate the scikit-learn accounts on various social +networks and produce materials. + +Every new communicator will be announced in the mailing list. +Communicators are welcome to participate in `monthly core developer meetings +`_. + Core developers --------------- + Core developers are community members who have shown that they are dedicated to the continued development of the project through ongoing engagement with the community. They have shown they can be trusted to maintain scikit-learn with diff --git a/doc/themes/scikit-learn/static/img/columbia.png b/doc/images/columbia.png similarity index 100% rename from doc/themes/scikit-learn/static/img/columbia.png rename to doc/images/columbia.png diff --git a/doc/themes/scikit-learn/static/img/czi_logo.svg b/doc/images/czi_logo.svg similarity index 100% rename from doc/themes/scikit-learn/static/img/czi_logo.svg rename to doc/images/czi_logo.svg diff --git a/doc/themes/scikit-learn/static/img/digicosme.png b/doc/images/digicosme.png similarity index 100% rename from doc/themes/scikit-learn/static/img/digicosme.png rename to doc/images/digicosme.png diff --git a/doc/themes/scikit-learn/static/img/sydney-primary.jpeg b/doc/images/sydney-primary.jpeg similarity index 100% rename from doc/themes/scikit-learn/static/img/sydney-primary.jpeg rename to doc/images/sydney-primary.jpeg diff --git a/doc/themes/scikit-learn/static/img/telecom.png b/doc/images/telecom.png similarity index 100% rename from doc/themes/scikit-learn/static/img/telecom.png rename to doc/images/telecom.png diff --git a/doc/images/visual-studio-build-tools-selection.png b/doc/images/visual-studio-build-tools-selection.png new file mode 100644 index 0000000000000..ab4bac71927b0 Binary files /dev/null and b/doc/images/visual-studio-build-tools-selection.png differ diff --git a/doc/install.rst b/doc/install.rst index d0b0f50e78f90..2eaf4f299a027 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -80,9 +80,8 @@ Then run: >pip install -U scikit-learnpip install -U scikit-learnpip3 install -U scikit-learnconda create -n sklearn-envconda create -n sklearn-env -c conda-forge scikit-learnconda activate sklearn-envconda install -c conda-forge scikit-learn In order to check your installation you can use @@ -158,7 +157,7 @@ Installing on Apple Silicon M1 hardware The recently introduced `macos/arm64` platform (sometimes also known as `macos/aarch64`) requires the open source community to upgrade the build -configuation and automation to properly support it. +configuration and automation to properly support it. At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its @@ -204,7 +203,7 @@ It can be installed by typing the following command: Debian/Ubuntu ------------- -The Debian/Ubuntu package is splitted in three different packages called +The Debian/Ubuntu package is split in three different packages called ``python3-sklearn`` (python modules), ``python3-sklearn-lib`` (low-level implementations and bindings), ``python3-sklearn-doc`` (documentation). Only the Python 3 version is available in the Debian Buster (the more recent @@ -247,7 +246,7 @@ command: .. prompt:: bash $ - sudo port install py36-scikit-learn + sudo port install py39-scikit-learn Anaconda and Enthought Deployment Manager for all supported platforms diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst index 3848a189c35d4..b7000bcf7cbb2 100644 --- a/doc/modules/classes.rst +++ b/doc/modules/classes.rst @@ -994,6 +994,7 @@ details. metrics.mean_poisson_deviance metrics.mean_gamma_deviance metrics.mean_tweedie_deviance + metrics.d2_tweedie_score metrics.mean_pinball_loss Multilabel ranking metrics @@ -1057,6 +1058,16 @@ further details. metrics.consensus_score +Distance metrics +---------------- + +.. currentmodule:: sklearn + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + metrics.DistanceMetric Pairwise metrics ---------------- @@ -1316,7 +1327,6 @@ Model validation :template: class.rst neighbors.BallTree - neighbors.DistanceMetric neighbors.KDTree neighbors.KernelDensity neighbors.KNeighborsClassifier diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 0245c48920f11..09637b5d938d1 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -89,14 +89,15 @@ Overview of clustering methods * - :ref:`DBSCAN ` - neighborhood size - Very large ``n_samples``, medium ``n_clusters`` - - Non-flat geometry, uneven cluster sizes, transductive + - Non-flat geometry, uneven cluster sizes, outlier removal, + transductive - Distances between nearest points * - :ref:`OPTICS ` - minimum cluster membership - Very large ``n_samples``, large ``n_clusters`` - Non-flat geometry, uneven cluster sizes, variable cluster density, - transductive + outlier removal, transductive - Distances between points * - :ref:`Gaussian mixtures ` @@ -203,9 +204,9 @@ initializations of the centroids. One method to help address this issue is the k-means++ initialization scheme, which has been implemented in scikit-learn (use the ``init='k-means++'`` parameter). This initializes the centroids to be (generally) distant from each other, leading to probably better results than -random initialization, as shown in the reference. +random initialization, as shown in the reference. -K-means++ can also be called independently to select seeds for other +K-means++ can also be called independently to select seeds for other clustering algorithms, see :func:`sklearn.cluster.kmeans_plusplus` for details and example usage. @@ -544,9 +545,9 @@ graph, and SpectralClustering is initialized with `affinity='precomputed'`:: `_ Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001 - * `"Preconditioned Spectral Clustering for Stochastic + * :arxiv:`"Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge" - `_ + <1309.0238>` David Zhuzhunashvili, Andrew Knyazev .. _hierarchical_clustering: @@ -1383,7 +1384,7 @@ more broadly common names. * `Wikipedia entry for the Adjusted Mutual Information `_ - + .. [VEB2009] Vinh, Epps, and Bailey, (2009). "Information theoretic measures for clusterings comparison". Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09. @@ -1394,13 +1395,13 @@ more broadly common names. Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance". JMLR - + .. [YAT2016] Yang, Algesheimer, and Tessone, (2016). "A comparative analysis of community detection algorithms on artificial networks". Scientific Reports 6: 30750. `doi:10.1038/srep30750 `_. - - + + .. _homogeneity_completeness: @@ -1702,10 +1703,9 @@ cluster analysis. .. topic:: References - * Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the - Interpretation and Validation of Cluster Analysis". Computational - and Applied Mathematics 20: 53–65. - `doi:10.1016/0377-0427(87)90125-7 `_. + * Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the + Interpretation and Validation of Cluster Analysis"<10.1016/0377-0427(87)90125-7>` + . Computational and Applied Mathematics 20: 53–65. Advantages @@ -1738,8 +1738,8 @@ Calinski-Harabasz Index If the ground truth labels are not known, the Calinski-Harabasz index -(:func:`sklearn.metrics.calinski_harabasz_score`) - also known as the Variance -Ratio Criterion - can be used to evaluate the model, where a higher +(:func:`sklearn.metrics.calinski_harabasz_score`) - also known as the Variance +Ratio Criterion - can be used to evaluate the model, where a higher Calinski-Harabasz score relates to a model with better defined clusters. The index is the ratio of the sum of between-clusters dispersion and of @@ -1804,8 +1804,7 @@ number of points in cluster :math:`q`. * Caliński, T., & Harabasz, J. (1974). `"A Dendrite Method for Cluster Analysis" `_. - Communications in Statistics-theory and Methods 3: 1-27. - `doi:10.1080/03610927408827101 `_. + :doi:`Communications in Statistics-theory and Methods 3: 1-27 <10.1080/03610927408827101>`. .. _davies-bouldin_index: @@ -1843,7 +1842,8 @@ Advantages ~~~~~~~~~~ - The computation of Davies-Bouldin is simpler than that of Silhouette scores. -- The index is computed only quantities and features inherent to the dataset. +- The index is solely based on quantities and features inherent to the dataset + as its computation only uses point-wise distances. Drawbacks ~~~~~~~~~ @@ -1879,15 +1879,13 @@ Then the Davies-Bouldin index is defined as: .. topic:: References * Davies, David L.; Bouldin, Donald W. (1979). - "A Cluster Separation Measure" + :doi:`"A Cluster Separation Measure" <10.1109/TPAMI.1979.4766909>` IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1 (2): 224-227. - `doi:10.1109/TPAMI.1979.4766909 `_. * Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001). - "On Clustering Validation Techniques" + :doi:`"On Clustering Validation Techniques" <10.1023/A:1012801612483>` Journal of Intelligent Information Systems, 17(2-3), 107-145. - `doi:10.1023/A:1012801612483 `_. * `Wikipedia entry for Davies-Bouldin index `_. diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index a9195ba9ab022..4c54c496490f0 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -139,6 +139,27 @@ or by name:: >>> pipe['reduce_dim'] PCA() +To enable model inspection, :class:`~sklearn.pipeline.Pipeline` has a +``get_feature_names_out()`` method, just like all transformers. You can use +pipeline slicing to get the feature names going into each step:: + + >>> from sklearn.datasets import load_iris + >>> from sklearn.feature_selection import SelectKBest + >>> iris = load_iris() + >>> pipe = Pipeline(steps=[ + ... ('select', SelectKBest(k=2)), + ... ('clf', LogisticRegression())]) + >>> pipe.fit(iris.data, iris.target) + Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))]) + >>> pipe[:-1].get_feature_names_out() + array(['x2', 'x3'], ...) + +You can also provide custom feature names for the input data using +``get_feature_names_out``:: + + >>> pipe[:-1].get_feature_names_out(iris.feature_names) + array(['petal length (cm)', 'petal width (cm)'], ...) + .. topic:: Examples: * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` @@ -147,6 +168,7 @@ or by name:: * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` + * :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` .. topic:: See Also: @@ -426,21 +448,20 @@ By default, the remaining rating columns are ignored (``remainder='drop'``):: >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.preprocessing import OneHotEncoder >>> column_trans = ColumnTransformer( - ... [('city_category', OneHotEncoder(dtype='int'),['city']), + ... [('categories', OneHotEncoder(dtype='int'), ['city']), ... ('title_bow', CountVectorizer(), 'title')], - ... remainder='drop') + ... remainder='drop', verbose_feature_names_out=False) >>> column_trans.fit(X) - ColumnTransformer(transformers=[('city_category', OneHotEncoder(dtype='int'), + ColumnTransformer(transformers=[('categories', OneHotEncoder(dtype='int'), ['city']), - ('title_bow', CountVectorizer(), 'title')]) + ('title_bow', CountVectorizer(), 'title')], + verbose_feature_names_out=False) - >>> column_trans.get_feature_names() - ['city_category__x0_London', 'city_category__x0_Paris', 'city_category__x0_Sallisaw', - 'title_bow__bow', 'title_bow__feast', 'title_bow__grapes', 'title_bow__his', - 'title_bow__how', 'title_bow__last', 'title_bow__learned', 'title_bow__moveable', - 'title_bow__of', 'title_bow__the', 'title_bow__trick', 'title_bow__watson', - 'title_bow__wrath'] + >>> column_trans.get_feature_names_out() + array(['city_London', 'city_Paris', 'city_Sallisaw', 'bow', 'feast', + 'grapes', 'his', 'how', 'last', 'learned', 'moveable', 'of', 'the', + 'trick', 'watson', 'wrath'], ...) >>> column_trans.transform(X).toarray() array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0], @@ -553,7 +574,7 @@ many estimators. This visualization is activated by setting the >>> from sklearn import set_config >>> set_config(display='diagram') # doctest: +SKIP - >>> # diplays HTML representation in a jupyter context + >>> # displays HTML representation in a jupyter context >>> column_trans # doctest: +SKIP An example of the HTML output can be seen in the diff --git a/doc/modules/cross_decomposition.rst b/doc/modules/cross_decomposition.rst index 981f7d98fbbde..5c9aed46e66ea 100644 --- a/doc/modules/cross_decomposition.rst +++ b/doc/modules/cross_decomposition.rst @@ -64,7 +64,7 @@ Set :math:`X_1` to :math:`X` and :math:`Y_1` to :math:`Y`. Then, for each :math:`C = X_k^T Y_k`. :math:`u_k` and :math:`v_k` are called the *weights*. By definition, :math:`u_k` and :math:`v_k` are - choosen so that they maximize the covariance between the projected + chosen so that they maximize the covariance between the projected :math:`X_k` and the projected target, that is :math:`\text{Cov}(X_k u_k, Y_k v_k)`. - b) Project :math:`X_k` and :math:`Y_k` on the singular vectors to obtain diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst index 36d3d842540c0..6705a3da4a9b0 100644 --- a/doc/modules/cross_validation.rst +++ b/doc/modules/cross_validation.rst @@ -679,7 +679,7 @@ Implementation notes: - With the current implementation full shuffle is not possible in most scenarios. When shuffle=True, the following happens: - 1. All groups a shuffled. + 1. All groups are shuffled. 2. Groups are sorted by standard deviation of classes using stable sort. 3. Sorted groups are iterated over and assigned to folds. @@ -954,7 +954,7 @@ to obtain good results. A high p-value could be due to a lack of dependency between features and labels (there is no difference in feature values between the classes) or because the classifier was not able to use the dependency in the data. In the latter case, using a more appropriate classifier that -is able to utilize the structure in the data, would result in a low +is able to utilize the structure in the data, would result in a lower p-value. Cross-validation provides information about how well a classifier generalizes, @@ -974,7 +974,7 @@ test is therefore only able to show when the model reliably outperforms random guessing. Finally, :func:`~sklearn.model_selection.permutation_test_score` is computed -using brute force and interally fits ``(n_permutations + 1) * n_cv`` models. +using brute force and internally fits ``(n_permutations + 1) * n_cv`` models. It is therefore only tractable with small datasets for which fitting an individual model is very fast. diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst index f90948fdc66b2..eac8f063be258 100644 --- a/doc/modules/decomposition.rst +++ b/doc/modules/decomposition.rst @@ -167,9 +167,9 @@ Note: the implementation of ``inverse_transform`` in :class:`PCA` with .. topic:: References: * Algorithm 4.3 in - `"Finding structure with randomness: Stochastic algorithms for + :arxiv:`"Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions" - `_ + <0909.4061>` Halko, et al., 2009 * `"An implementation of a randomized algorithm for principal component @@ -231,7 +231,7 @@ problem solved is a PCA problem (dictionary learning) with an .. math:: (U^*, V^*) = \underset{U, V}{\operatorname{arg\,min\,}} & \frac{1}{2} ||X-UV||_{\text{Fro}}^2+\alpha||V||_{1,1} \\ - \text{subject to } & ||U_k||_2 = 1 \text{ for all } + \text{subject to } & ||U_k||_2 <= 1 \text{ for all } 0 \leq k < n_{components} :math:`||.||_{\text{Fro}}` stands for the Frobenius norm and :math:`||.||_{1,1}` @@ -273,26 +273,39 @@ Exact Kernel PCA ---------------- :class:`KernelPCA` is an extension of PCA which achieves non-linear -dimensionality reduction through the use of kernels (see :ref:`metrics`). It +dimensionality reduction through the use of kernels (see :ref:`metrics`) [Scholkopf1997]_. It has many applications including denoising, compression and structured prediction (kernel dependency estimation). :class:`KernelPCA` supports both ``transform`` and ``inverse_transform``. -.. figure:: ../auto_examples/decomposition/images/sphx_glr_plot_kernel_pca_001.png +.. figure:: ../auto_examples/decomposition/images/sphx_glr_plot_kernel_pca_002.png :target: ../auto_examples/decomposition/plot_kernel_pca.html :align: center :scale: 75% +.. note:: + :meth:`KernelPCA.inverse_transform` relies on a kernel ridge to learn the + function mapping samples from the PCA basis into the original feature + space [Bakir2004]_. Thus, the reconstruction obtained with + :meth:`KernelPCA.inverse_transform` is an approximation. See the example + linked below for more details. + .. topic:: Examples: * :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py` .. topic:: References: - * Kernel PCA was introduced in "Kernel principal component analysis" - Bernhard Schoelkopf, Alexander J. Smola, and Klaus-Robert Mueller. 1999. - In Advances in kernel methods, MIT Press, Cambridge, MA, USA 327-352. + .. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. + `"Kernel principal component analysis." + `_ + International conference on artificial neural networks. + Springer, Berlin, Heidelberg, 1997. + .. [Bakir2004] Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf. + `"Learning to find pre-images." + `_ + Advances in neural information processing systems 16 (2004): 449-456. .. _kPCA_Solvers: @@ -341,13 +354,13 @@ components is less than 10 (strict) and the number of samples is more than 200 * *randomized* solver: - - Algorithm 4.3 in - `"Finding structure with randomness: Stochastic algorithms for + * Algorithm 4.3 in + :arxiv:`"Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions" - `_ + <0909.4061>` Halko, et al., 2009 - - `"An implementation of a randomized algorithm for principal component + * `"An implementation of a randomized algorithm for principal component analysis" `_ A. Szlam et al. 2014 @@ -513,7 +526,7 @@ dictionary fixed, and then updating the dictionary to best fit the sparse code. .. math:: (U^*, V^*) = \underset{U, V}{\operatorname{arg\,min\,}} & \frac{1}{2} ||X-UV||_{\text{Fro}}^2+\alpha||U||_{1,1} \\ - \text{subject to } & ||V_k||_2 = 1 \text{ for all } + \text{subject to } & ||V_k||_2 <= 1 \text{ for all } 0 \leq k < n_{\mathrm{atoms}} @@ -829,7 +842,7 @@ and the intensity of the regularization with the :attr:`alpha_W` and :attr:`alph (:math:`\alpha_W` and :math:`\alpha_H`) parameters. The priors are scaled by the number of samples (:math:`n\_samples`) for `H` and the number of features (:math:`n\_features`) for `W` to keep their impact balanced with respect to one another and to the data fit -term as independant as possible of the size of the training set. Then the priors terms +term as independent as possible of the size of the training set. Then the priors terms are: .. math:: diff --git a/doc/modules/density.rst b/doc/modules/density.rst index 115d318183577..9e542b803ef68 100644 --- a/doc/modules/density.rst +++ b/doc/modules/density.rst @@ -136,9 +136,9 @@ The form of these kernels is as follows: :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h` The kernel density estimator can be used with any of the valid distance -metrics (see :class:`~sklearn.neighbors.DistanceMetric` for a list of available metrics), though -the results are properly normalized only for the Euclidean metric. One -particularly useful metric is the +metrics (see :class:`~sklearn.metrics.DistanceMetric` for a list of +available metrics), though the results are properly normalized only +for the Euclidean metric. One particularly useful metric is the `Haversine distance `_ which measures the angular distance between points on a sphere. Here is an example of using a kernel density estimate for a visualization diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 91fc892f79d0a..98ad4cc68b75a 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -1210,8 +1210,8 @@ Finally, many parts of the implementation of `_ .. [R2007] G. Ridgeway, "Generalized Boosted Models: A guide to the gbm package", 2007 - .. [XGBoost] Tianqi Chen, Carlos Guestrin, `"XGBoost: A Scalable Tree - Boosting System" `_ + .. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree + Boosting System" <1603.02754>` .. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient BoostingDecision Tree" `_ diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index e074d93fad7b8..a07d722defb9b 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -1,4 +1,4 @@ -.. _feature_extraction: +.. _feature_extraction: ================== Feature extraction @@ -53,8 +53,8 @@ is a traditional numerical feature:: [ 0., 1., 0., 12.], [ 0., 0., 1., 18.]]) - >>> vec.get_feature_names() - ['city=Dubai', 'city=London', 'city=San Francisco', 'temperature'] + >>> vec.get_feature_names_out() + array(['city=Dubai', 'city=London', 'city=San Francisco', 'temperature'], ...) :class:`DictVectorizer` accepts multiple string values for one feature, like, e.g., multiple categories for a movie. @@ -69,10 +69,9 @@ and its year of release. array([[0.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 2.003e+03], [1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 2.011e+03], [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.974e+03]]) - >>> vec.get_feature_names() == ['category=animation', 'category=drama', - ... 'category=family', 'category=thriller', - ... 'year'] - True + >>> vec.get_feature_names_out() + array(['category=animation', 'category=drama', 'category=family', + 'category=thriller', 'year'], ...) >>> vec.transform({'category': ['thriller'], ... 'unseen_feature': '3'}).toarray() array([[0., 0., 0., 1., 0.]]) @@ -111,8 +110,9 @@ suitable for feeding into a classifier (maybe after being piped into a with 6 stored elements in Compressed Sparse ... format> >>> pos_vectorized.toarray() array([[1., 1., 1., 1., 1., 1.]]) - >>> vec.get_feature_names() - ['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat', 'word-2=the'] + >>> vec.get_feature_names_out() + array(['pos+1=PP', 'pos-1=NN', 'pos-2=DT', 'word+1=on', 'word-1=cat', + 'word-2=the'], ...) As you can imagine, if one extracts such a context around each individual word of a corpus of documents the resulting matrix will be very wide @@ -340,10 +340,9 @@ Each term found by the analyzer during the fit is assigned a unique integer index corresponding to a column in the resulting matrix. This interpretation of the columns can be retrieved as follows:: - >>> vectorizer.get_feature_names() == ( - ... ['and', 'document', 'first', 'is', 'one', - ... 'second', 'the', 'third', 'this']) - True + >>> vectorizer.get_feature_names_out() + array(['and', 'document', 'first', 'is', 'one', 'second', 'the', + 'third', 'this'], ...) >>> X.toarray() array([[0, 1, 1, 1, 0, 0, 1, 0, 1], @@ -406,8 +405,8 @@ however, similar words are useful for prediction, such as in classifying writing style or personality. There are several known issues in our provided 'english' stop word list. It -does not aim to be a general, 'one-size-fits-all' solution as some tasks -may require a more custom solution. See [NQY18]_ for more details. +does not aim to be a general, 'one-size-fits-all' solution as some tasks +may require a more custom solution. See [NQY18]_ for more details. Please take care in choosing a stop word list. Popular stop word lists may include words that are highly informative to @@ -742,9 +741,8 @@ decide better:: >>> ngram_vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(2, 2)) >>> counts = ngram_vectorizer.fit_transform(['words', 'wprds']) - >>> ngram_vectorizer.get_feature_names() == ( - ... [' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp']) - True + >>> ngram_vectorizer.get_feature_names_out() + array([' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp'], ...) >>> counts.toarray().astype(int) array([[1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1]]) @@ -758,17 +756,15 @@ span across words:: >>> ngram_vectorizer.fit_transform(['jumpy fox']) <1x4 sparse matrix of type '<... 'numpy.int64'>' with 4 stored elements in Compressed Sparse ... format> - >>> ngram_vectorizer.get_feature_names() == ( - ... [' fox ', ' jump', 'jumpy', 'umpy ']) - True + >>> ngram_vectorizer.get_feature_names_out() + array([' fox ', ' jump', 'jumpy', 'umpy '], ...) >>> ngram_vectorizer = CountVectorizer(analyzer='char', ngram_range=(5, 5)) >>> ngram_vectorizer.fit_transform(['jumpy fox']) <1x5 sparse matrix of type '<... 'numpy.int64'>' with 5 stored elements in Compressed Sparse ... format> - >>> ngram_vectorizer.get_feature_names() == ( - ... ['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox']) - True + >>> ngram_vectorizer.get_feature_names_out() + array(['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox'], ...) The word boundaries-aware variant ``char_wb`` is especially interesting for languages that use white-spaces for word separation as it generates diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index 6aa9cb417aa5d..94fc69305cf6d 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -88,14 +88,14 @@ estimate the noise level of data. An illustration of the log-marginal-likelihood (LML) landscape shows that there exist two local maxima of LML. -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_001.png +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_003.png :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html :align: center The first corresponds to a model with a high noise level and a large length scale, which explains all variations in the data by noise. -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_002.png +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_004.png :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html :align: center @@ -106,7 +106,7 @@ hyperparameters, the gradient-based optimization might also converge to the high-noise solution. It is thus important to repeat the optimization several times for different initializations. -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_003.png +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_005.png :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html :align: center @@ -142,7 +142,7 @@ Moreover, the noise level of the data is learned explicitly by GPR by an additional WhiteKernel component in the kernel and by the regularization parameter alpha of KRR. -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_compare_gpr_krr_001.png +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_compare_gpr_krr_005.png :target: ../auto_examples/gaussian_process/plot_compare_gpr_krr.html :align: center @@ -220,7 +220,7 @@ overall noise level is very small, indicating that the data can be very well explained by the model. The figure shows also that the model makes very confident predictions until around 2015 -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_co2_001.png +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_co2_003.png :target: ../auto_examples/gaussian_process/plot_gpr_co2.html :align: center diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index 331607c86e657..19910d5a16da3 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -538,9 +538,8 @@ since it has reached the last iteration (3) with the highest score: Optimization `_, in proc. of Machine Learning Research, 2016. .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, - `Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization - `_, in Machine Learning Research - 18, 2018. + :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization + <1603.06560>`, in Machine Learning Research 18, 2018. .. _grid_search_tips: diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst index b7de01e308519..b82da8e43582a 100644 --- a/doc/modules/kernel_approximation.rst +++ b/doc/modules/kernel_approximation.rst @@ -246,8 +246,8 @@ or store training examples. .. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection" `_ Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010 - .. [PP2013] `"Fast and scalable polynomial kernels via explicit feature maps" - `_ + .. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps" + <10.1145/2487575.2487591>` Pham, N., & Pagh, R. - 2013 .. [CCF2002] `"Finding frequent items in data streams" `_ diff --git a/doc/modules/lda_qda.rst b/doc/modules/lda_qda.rst index 076c8fbb38498..02b6c88cb7001 100644 --- a/doc/modules/lda_qda.rst +++ b/doc/modules/lda_qda.rst @@ -187,7 +187,7 @@ an estimate for the covariance matrix). Setting this parameter to a value between these two extrema will estimate a shrunk version of the covariance matrix. -The shrinked Ledoit and Wolf estimator of covariance may not always be the +The shrunk Ledoit and Wolf estimator of covariance may not always be the best choice. For example if the distribution of the data is normally distributed, the Oracle Shrinkage Approximating estimator :class:`sklearn.covariance.OAS` @@ -234,7 +234,7 @@ For QDA, the use of the SVD solver relies on the fact that the covariance matrix :math:`\Sigma_k` is, by definition, equal to :math:`\frac{1}{n - 1} X_k^tX_k = \frac{1}{n - 1} V S^2 V^t` where :math:`V` comes from the SVD of the (centered) matrix: :math:`X_k = U S V^t`. It turns out that we can compute the -log-posterior above without having to explictly compute :math:`\Sigma`: +log-posterior above without having to explicitly compute :math:`\Sigma`: computing :math:`S` and :math:`V` via the SVD of :math:`X` is enough. For LDA, two SVDs are computed: the SVD of the centered input matrix :math:`X` and the SVD of the class-wise mean vectors. diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index fcc8ed5437ad4..7243990bb5ffe 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -298,6 +298,7 @@ features, it is often faster than :class:`LassoCV`. .. centered:: |lasso_cv_1| |lasso_cv_2| +.. _lasso_lars_ic: Information-criteria based model selection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -306,22 +307,92 @@ Alternatively, the estimator :class:`LassoLarsIC` proposes to use the Akaike information criterion (AIC) and the Bayes Information criterion (BIC). It is a computationally cheaper alternative to find the optimal value of alpha as the regularization path is computed only once instead of k+1 times -when using k-fold cross-validation. However, such criteria needs a -proper estimation of the degrees of freedom of the solution, are -derived for large samples (asymptotic results) and assume the model -is correct, i.e. that the data are generated by this model. -They also tend to break when the problem is badly conditioned -(more features than samples). - -.. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_001.png - :target: ../auto_examples/linear_model/plot_lasso_model_selection.html +when using k-fold cross-validation. + +Indeed, these criteria are computed on the in-sample training set. In short, +they penalize the over-optimistic scores of the different Lasso models by +their flexibility (cf. to "Mathematical details" section below). + +However, such criteria need a proper estimation of the degrees of freedom of +the solution, are derived for large samples (asymptotic results) and assume the +correct model is candidates under investigation. They also tend to break when +the problem is badly conditioned (e.g. more features than samples). + +.. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_lasso_lars_ic_001.png + :target: ../auto_examples/linear_model/plot_lasso_lars_ic.html :align: center :scale: 50% +.. _aic_bic: + +**Mathematical details** + +The definition of AIC (and thus BIC) might differ in the literature. In this +section, we give more information regarding the criterion computed in +scikit-learn. The AIC criterion is defined as: + +.. math:: + AIC = -2 \log(\hat{L}) + 2 d + +where :math:`\hat{L}` is the maximum likelihood of the model and +:math:`d` is the number of parameters (as well referred to as degrees of +freedom in the previous section). + +The definition of BIC replace the constant :math:`2` by :math:`\log(N)`: + +.. math:: + BIC = -2 \log(\hat{L}) + \log(N) d + +where :math:`N` is the number of samples. + +For a linear Gaussian model, the maximum log-likelihood is defined as: + +.. math:: + \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2} + +where :math:`\sigma^2` is an estimate of the noise variance, +:math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted +targets, and :math:`n` is the number of samples. + +Plugging the maximum log-likelihood in the AIC formula yields: + +.. math:: + AIC = n \log(2 \pi \sigma^2) + \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sigma^2} + 2 d + +The first term of the above expression is sometimes discarded since it is a +constant when :math:`\sigma^2` is provided. In addition, +it is sometimes stated that the AIC is equivalent to the :math:`C_p` statistic +[12]_. In a strict sense, however, it is equivalent only up to some constant +and a multiplicative factor. + +At last, we mentioned above that :math:`\sigma^2` is an estimate of the +noise variance. In :class:`LassoLarsIC` when the parameter `noise_variance` is +not provided (default), the noise variance is estimated via the unbiased +estimator [13]_ defined as: + +.. math:: + \sigma^2 = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n - p} + +where :math:`p` is the number of features and :math:`\hat{y}_i` is the +predicted target using an ordinary least squares regression. Note, that this +formula is valid only when `n_samples > n_features`. .. topic:: Examples: * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py` + * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py` + +.. topic:: References + + .. [12] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani. + "On the degrees of freedom of the lasso." + The Annals of Statistics 35.5 (2007): 2173-2192. + <0712.0881.pdf>` + + .. [13] `Cherkassky, Vladimir, and Yunqian Ma. + "Comparison of model selection for regression." + Neural computation 15.7 (2003): 1691-1714. + `_ Comparison with the regularization parameter of SVM ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -934,7 +1005,9 @@ to warm-starting (see :term:`Glossary `). .. [6] Mark Schmidt, Nicolas Le Roux, and Francis Bach: `Minimizing Finite Sums with the Stochastic Average Gradient. `_ - .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: `SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. `_ + .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: + :arxiv:`SAGA: A Fast Incremental Gradient Method With Support for + Non-Strongly Convex Composite Objectives. <1407.0202>` .. [8] https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm @@ -1492,12 +1565,12 @@ problem. The current implementation is based on `_ Econometrica: journal of the Econometric Society, 33-50. - * Portnoy, S., & Koenker, R. (1997). The Gaussian hare and the Laplacian + * Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. - Statistical Science, 12, 279-300. https://doi.org/10.1214/ss/1030037960 + Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`. - * Koenker, R. (2005). Quantile Regression. - Cambridge University Press. https://doi.org/10.1017/CBO9780511754098 + * Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`. + Cambridge University Press. .. _polynomial_regression: diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index e4df915d3eb33..730b094581276 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -563,8 +563,9 @@ or *informedness*. Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies `_, 2015. - .. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. `ExSTraCS 2.0: description and evaluation of a scalable learning - classifier system `_, Evol. Intel. (2015) 8: 89. + .. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. :doi:`ExSTraCS 2.0: description + and evaluation of a scalable learning classifier + system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89. .. _cohen_kappa: @@ -1588,7 +1589,7 @@ Additionally DET curves can be consulted for threshold analysis and operating point selection. This is particularly helpful if a comparison of error types is required. -One the other hand DET curves do not provide their metric as a single number. +On the other hand DET curves do not provide their metric as a single number. Therefore for either automated evaluation or comparison to other classification tasks metrics like the derived area under ROC curve might be better suited. @@ -2355,6 +2356,34 @@ the difference in errors decreases. Finally, by setting, ``power=2``:: we would get identical errors. The deviance when ``power=2`` is thus only sensitive to relative errors. +.. _d2_tweedie_score: + +D² score, the coefficient of determination +------------------------------------------- + +The :func:`d2_tweedie_score` function computes the percentage of deviance +explained. It is a generalization of R², where the squared error is replaced by +the Tweedie deviance. D², also known as McFadden's likelihood ratio index, is +calculated as + +.. math:: + + D^2(y, \hat{y}) = 1 - \frac{\text{D}(y, \hat{y})}{\text{D}(y, \bar{y})} \,. + +The argument ``power`` defines the Tweedie power as for +:func:`mean_tweedie_deviance`. Note that for `power=0`, +:func:`d2_tweedie_score` equals :func:`r2_score` (for single targets). + +Like R², the best possible score is 1.0 and it can be negative (because the +model can be arbitrarily worse). A constant model that always predicts the +expected value of y, disregarding the input features, would get a D² score +of 0.0. + +A scorer object with a specific choice of ``power`` can be built by:: + + >>> from sklearn.metrics import d2_tweedie_score, make_scorer + >>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5) + .. _pinball_loss: Pinball loss @@ -2387,7 +2416,7 @@ Here is a small example of usage of the :func:`mean_pinball_loss` function:: >>> mean_pinball_loss(y_true, y_true, alpha=0.9) 0.0 -It is possible to build a scorer object with a specific choice of alpha:: +It is possible to build a scorer object with a specific choice of ``alpha``:: >>> from sklearn.metrics import make_scorer >>> mean_pinball_loss_95p = make_scorer(mean_pinball_loss, alpha=0.95) diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst index 043d158825a6f..1cf85b08db66a 100644 --- a/doc/modules/multiclass.rst +++ b/doc/modules/multiclass.rst @@ -102,6 +102,7 @@ can provide additional strategies beyond what is built-in: - :class:`neural_network.MLPClassifier` - :class:`neighbors.RadiusNeighborsClassifier` - :class:`ensemble.RandomForestClassifier` + - :class:`linear_model.RidgeClassifier` - :class:`linear_model.RidgeClassifierCV` diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst index f394f011af11a..2d1209ed8b42e 100644 --- a/doc/modules/neighbors.rst +++ b/doc/modules/neighbors.rst @@ -562,13 +562,20 @@ First, the precomputed graph can be re-used multiple times, for instance while varying a parameter of the estimator. This can be done manually by the user, or using the caching properties of the scikit-learn pipeline: + >>> import tempfile >>> from sklearn.manifold import Isomap >>> from sklearn.neighbors import KNeighborsTransformer >>> from sklearn.pipeline import make_pipeline + >>> from sklearn.datasets import make_regression + >>> cache_path = tempfile.gettempdir() # we use a temporary folder here + >>> X, _ = make_regression(n_samples=50, n_features=25, random_state=0) >>> estimator = make_pipeline( - ... KNeighborsTransformer(n_neighbors=5, mode='distance'), - ... Isomap(neighbors_algorithm='precomputed'), - ... memory='/path/to/cache') + ... KNeighborsTransformer(mode='distance'), + ... Isomap(n_components=3, metric='precomputed'), + ... memory=cache_path) + >>> X_embedded = estimator.fit_transform(X) + >>> X_embedded.shape + (50, 3) Second, precomputing the graph can give finer control on the nearest neighbors estimation, for instance enabling multiprocessing though the parameter diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst index b9f4e17c6a575..b8bcf4c70a1dc 100644 --- a/doc/modules/neural_networks_supervised.rst +++ b/doc/modules/neural_networks_supervised.rst @@ -184,7 +184,7 @@ Algorithms MLP trains using `Stochastic Gradient Descent `_, -`Adam `_, or +:arxiv:`Adam <1412.6980>`, or `L-BFGS `__. Stochastic Gradient Descent (SGD) updates parameters using the gradient of the loss function with respect to a parameter that needs adaptation, i.e. diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst index 496b840e0c6da..cce39e4080071 100644 --- a/doc/modules/outlier_detection.rst +++ b/doc/modules/outlier_detection.rst @@ -332,7 +332,7 @@ chosen 1) greater than the minimum number of objects a cluster has to contain, so that other objects can be local outliers relative to this cluster, and 2) smaller than the maximum number of close by objects that can potentially be local outliers. -In practice, such informations are generally not available, and taking +In practice, such information is generally not available, and taking n_neighbors=20 appears to work well in general. When the proportion of outliers is high (i.e. greater than 10 \%, as in the example below), n_neighbors should be greater (n_neighbors=35 in the example diff --git a/doc/modules/partial_dependence.rst b/doc/modules/partial_dependence.rst index d2ed83c4e3fd8..ba99be02c8e4a 100644 --- a/doc/modules/partial_dependence.rst +++ b/doc/modules/partial_dependence.rst @@ -55,20 +55,20 @@ independent of the house age, whereas for values less than 2 there is a strong dependence on age. The :mod:`sklearn.inspection` module provides a convenience function -:func:`plot_partial_dependence` to create one-way and two-way partial +:func:`~PartialDependenceDisplay.from_estimator` to create one-way and two-way partial dependence plots. In the below example we show how to create a grid of partial dependence plots: two one-way PDPs for the features ``0`` and ``1`` and a two-way PDP between the two features:: >>> from sklearn.datasets import make_hastie_10_2 >>> from sklearn.ensemble import GradientBoostingClassifier - >>> from sklearn.inspection import plot_partial_dependence + >>> from sklearn.inspection import PartialDependenceDisplay >>> X, y = make_hastie_10_2(random_state=0) >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, ... max_depth=1, random_state=0).fit(X, y) >>> features = [0, 1, (0, 1)] - >>> plot_partial_dependence(clf, X, features) + >>> PartialDependenceDisplay.from_estimator(clf, X, features) <...> You can access the newly created figure and Axes objects using ``plt.gcf()`` @@ -82,7 +82,7 @@ the PDPs should be created via the ``target`` argument:: >>> mc_clf = GradientBoostingClassifier(n_estimators=10, ... max_depth=1).fit(iris.data, iris.target) >>> features = [3, 2, (3, 2)] - >>> plot_partial_dependence(mc_clf, X, features, target=0) + >>> PartialDependenceDisplay.from_estimator(mc_clf, X, features, target=0) <...> The same parameter ``target`` is used to specify the target in multi-output @@ -138,20 +138,20 @@ and the house price in the PD line. However, the ICE lines show that there are some exceptions, where the house price remains constant in some ranges of the median income. -The :mod:`sklearn.inspection` module's :func:`plot_partial_dependence` +The :mod:`sklearn.inspection` module's :meth:`PartialDependenceDisplay.from_estimator` convenience function can be used to create ICE plots by setting ``kind='individual'``. In the example below, we show how to create a grid of ICE plots: >>> from sklearn.datasets import make_hastie_10_2 >>> from sklearn.ensemble import GradientBoostingClassifier - >>> from sklearn.inspection import plot_partial_dependence + >>> from sklearn.inspection import PartialDependenceDisplay >>> X, y = make_hastie_10_2(random_state=0) >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, ... max_depth=1, random_state=0).fit(X, y) >>> features = [0, 1] - >>> plot_partial_dependence(clf, X, features, + >>> PartialDependenceDisplay.from_estimator(clf, X, features, ... kind='individual') <...> @@ -160,7 +160,7 @@ feature of interest. Hence, it is recommended to use ICE plots alongside PDPs. They can be plotted together with ``kind='both'``. - >>> plot_partial_dependence(clf, X, features, + >>> PartialDependenceDisplay.from_estimator(clf, X, features, ... kind='both') <...> @@ -262,8 +262,8 @@ estimators that support it, and 'brute' is used for the rest. C. Molnar, `Interpretable Machine Learning `_, Section 5.1, 2019. - A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, `Peeking Inside the + A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, :arxiv:`Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual - Conditional Expectation `_, + Conditional Expectation <1309.6392>`, Journal of Computational and Graphical Statistics, 24(1): 44-65, Springer, 2015. diff --git a/doc/modules/permutation_importance.rst b/doc/modules/permutation_importance.rst index 833c9fb9a696e..477a5cf3fdbbc 100644 --- a/doc/modules/permutation_importance.rst +++ b/doc/modules/permutation_importance.rst @@ -184,5 +184,5 @@ example: .. topic:: References: - .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, - 2001. https://doi.org/10.1023/A:1010933404324 + .. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, + Machine Learning, 45(1), 5-32, 2001. diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst index bd506b0151536..035f2b90203ca 100644 --- a/doc/modules/preprocessing.rst +++ b/doc/modules/preprocessing.rst @@ -977,14 +977,12 @@ Interestingly, a :class:`SplineTransformer` of ``degree=0`` is the same as .. topic:: References: - * Eilers, P., & Marx, B. (1996). Flexible Smoothing with B-splines and - Penalties. Statist. Sci. 11 (1996), no. 2, 89--121. - `doi:10.1214/ss/1038425655 `_ - - * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. A review of - spline function procedures in R. BMC Med Res Methodol 19, 46 (2019). - `doi:10.1186/s12874-019-0666-3 - `_ + * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and + Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121. + + * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of + spline function procedures in R <10.1186/s12874-019-0666-3>`. + BMC Med Res Methodol 19, 46 (2019). .. _function_transformer: diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index 0b618289b84ec..49a1008eae23f 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -123,7 +123,7 @@ Please refer to the :ref:`mathematical section below The first two loss functions are lazy, they only update the model parameters if an example violates the margin constraint, which makes training very efficient and may result in sparser models (i.e. with more zero -coefficents), even when L2 penalty is used. +coefficients), even when L2 penalty is used. Using ``loss="log"`` or ``loss="modified_huber"`` enables the ``predict_proba`` method, which gives a vector of probability estimates @@ -408,7 +408,7 @@ parameters, we minimize the regularized training error given by where :math:`L` is a loss function that measures model (mis)fit and :math:`R` is a regularization term (aka penalty) that penalizes model complexity; :math:`\alpha > 0` is a non-negative hyperparameter that controls -the regularization stength. +the regularization strength. Different choices for :math:`L` entail different classifiers or regressors: diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst index fcf1d3e23976b..085d52b89ea8b 100644 --- a/doc/modules/svm.rst +++ b/doc/modules/svm.rst @@ -623,7 +623,7 @@ misclassified or within the margin boundary. Ideally, the value :math:`y_i (w^T \phi (x_i) + b)` would be :math:`\geq 1` for all samples, which indicates a perfect prediction. But problems are usually not always perfectly separable with a hyperplane, so we allow some samples to be at a distance :math:`\zeta_i` from -their correct margin boundary. The penalty term `C` controls the strengh of +their correct margin boundary. The penalty term `C` controls the strength of this penalty, and as a result, acts as an inverse regularization parameter (see note below). diff --git a/doc/related_projects.rst b/doc/related_projects.rst index e23d70b3963fd..1488195381dcc 100644 --- a/doc/related_projects.rst +++ b/doc/related_projects.rst @@ -123,6 +123,11 @@ enhance the functionality of scikit-learn's estimators. - `sklearn-porter `_ Transpile trained scikit-learn models to C, Java, Javascript and others. +- `m2cgen `_ + A lightweight library which allows to transpile trained machine learning + models including many scikit-learn estimators into a native code of C, Java, + Go, R, PHP, Dart, Haskell, Rust and many other programming languages. + - `treelite `_ Compiles tree-based ensemble models into C code for minimizing prediction latency. diff --git a/doc/roadmap.rst b/doc/roadmap.rst index 2bead90522739..df8811b968d7e 100644 --- a/doc/roadmap.rst +++ b/doc/roadmap.rst @@ -51,7 +51,7 @@ external to the core library. (i.e. rectangular data largely invariant to column and row order; predicting targets with simple structure) * improve the ease for users to develop and publish external components -* improve inter-operability with modern data science tools (e.g. Pandas, Dask) +* improve interoperability with modern data science tools (e.g. Pandas, Dask) and infrastructures (e.g. distributed processing) Many of the more fine-grained goals can be found under the `API tag diff --git a/doc/sphinxext/doi_role.py b/doc/sphinxext/doi_role.py new file mode 100644 index 0000000000000..f851a12ec69ea --- /dev/null +++ b/doc/sphinxext/doi_role.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- +""" + doilinks + ~~~~~~~~ + Extension to add links to DOIs. With this extension you can use e.g. + :doi:`10.1016/S0022-2836(05)80360-2` in your documents. This will + create a link to a DOI resolver + (``https://doi.org/10.1016/S0022-2836(05)80360-2``). + The link caption will be the raw DOI. + You can also give an explicit caption, e.g. + :doi:`Basic local alignment search tool <10.1016/S0022-2836(05)80360-2>`. + + :copyright: Copyright 2015 Jon Lund Steffensen. Based on extlinks by + the Sphinx team. + :license: BSD. +""" + +from docutils import nodes, utils + +from sphinx.util.nodes import split_explicit_title + + +def reference_role(typ, rawtext, text, lineno, inliner, options={}, content=[]): + text = utils.unescape(text) + has_explicit_title, title, part = split_explicit_title(text) + if typ in ["arXiv", "arxiv"]: + full_url = "https://arxiv.org/abs/" + part + if not has_explicit_title: + title = "arXiv:" + part + pnode = nodes.reference(title, title, internal=False, refuri=full_url) + return [pnode], [] + if typ in ["doi", "DOI"]: + full_url = "https://doi.org/" + part + if not has_explicit_title: + title = "DOI:" + part + pnode = nodes.reference(title, title, internal=False, refuri=full_url) + return [pnode], [] + + +def setup_link_role(app): + app.add_role("arxiv", reference_role, override=True) + app.add_role("arXiv", reference_role, override=True) + app.add_role("doi", reference_role, override=True) + app.add_role("DOI", reference_role, override=True) + + +def setup(app): + app.connect("builder-inited", setup_link_role) + return {"version": "0.1", "parallel_read_safe": True} diff --git a/doc/templates/index.html b/doc/templates/index.html index d2d956395f545..2d250cc54bd71 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -155,6 +155,12 @@

News

  • On-going development: What's new (Changelog) +
  • December 2021. scikit-learn 1.0.2 is available for download (Changelog). +
    • +
  • October 2021. scikit-learn 1.0.1 is available for download (Changelog). +
    • +
  • September 2021. scikit-learn 1.0 is available for download (Changelog). +
  • April 2021. scikit-learn 0.24.2 is available for download (Changelog).
  • January 2021. scikit-learn 0.24.1 is available for download (Changelog). @@ -167,18 +173,12 @@

    News

  • May 2020. scikit-learn 0.23.0 is available for download (Changelog).
    • -
  • Scikit-learn from 0.23 requires Python 3.6 or greater. +
  • Scikit-learn from 0.23 requires Python 3.6 or newer.
  • March 2020. scikit-learn 0.22.2 is available for download (Changelog).
  • January 2020. scikit-learn 0.22.1 is available for download (Changelog).
  • December 2019. scikit-learn 0.22 is available for download (Changelog and Release Highlights).
    • -
  • Scikit-learn from 0.21 requires Python 3.5 or greater. -
    • -
  • July 2019. scikit-learn 0.21.3 (Changelog) and 0.20.4 (Changelog) are available for download. -
    • -
  • May 2019. scikit-learn 0.21.0 to 0.21.2 are available for download (Changelog). -
@@ -189,6 +189,12 @@

Community

  • Questions? See FAQ and stackoverflow
  • Mailing list: scikit-learn@python.org
  • Gitter: gitter.im/scikit-learn
    • +
  • Twitter: @scikit_learn
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  • Twitter (commits): @sklearn_commits
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  • Linkedin: linkedin/scikit-learn
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  • YouTube: youtube.com/scikit-learn
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  • Facebook: @scikitlearnofficial
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  • Instagram: @scikitlearnofficial
  • Communication on all channels should respect PSF's code of conduct.
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    -
    -{% endblock %} - -{% block relbar1 %}{% endblock %} -{% block relbar2 %}{% endblock %} - - -{%- block footer %} -
    - {%- if pagename != 'index' %} - {%- if show_copyright %} - {%- if hasdoc('copyright') %} - {% trans path=pathto('copyright'), copyright=copyright|e %}© {{ copyright }}.{% endtrans %} - {%- else %} - {% trans copyright=copyright|e %}© {{ copyright }}.{% endtrans %} - {%- endif %} - {%- endif %} - {%- if last_updated %} - {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %} - {%- endif %} - {%- if show_source and has_source and sourcename %} - {{ _('Show this page source') }} - {%- endif %} - {%- endif %} -
    - - {%- if pagename != 'index' %} - {%- if parents %} -
    - {% else %} -
    - {% endif %} - {%- for rellink in rellinks|reverse %} -
    - {{ loop.cycle('Previous', 'Next') }} - -
    - {%- endfor %} - {% endif %} -
    - - {% if theme_google_analytics|tobool %} - - - {% endif %} - -{%- endblock %} diff --git a/doc/themes/scikit-learn/static/ML_MAPS_README.rst b/doc/themes/scikit-learn/static/ML_MAPS_README.rst deleted file mode 100644 index 47fe633767995..0000000000000 --- a/doc/themes/scikit-learn/static/ML_MAPS_README.rst +++ /dev/null @@ -1,93 +0,0 @@ -Machine Learning Cheat Sheet (for scikit-learn) -=============================================== - -This document is intended to explain how to edit -the machine learning cheat sheet, originally created -by Andreas Mueller: - -(https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html) - -The image is made interactive using an imagemap, and uses the jQuery Map Hilight plugin module -by David Lynch (https://davidlynch.org/projects/maphilight/docs/) to highlight -the different items on the image upon mouseover. - -Modifying the map on the docs is currently a little bit tedious, -so I'll try to make it as simple as possible. - -1. Editing the layout of the map and its paths. ------------------------------------------------- - -Use a Graphics editor like Inkscape Vector Graphics Editor -to open the ml_map.svg file, in this folder. From there -you can move objects around, etc. as you need. - -Save when done, and make sure to export a .PNG file -to replace the old-outdated ml_map.png, as that file -is used as a background image. - -2. Accessing the paths of the SVG file and exporting them. ----------------------------------------------------------- - -Use an image manipulation package like GIMP Image Editor to open -the ml_map.svg file, in this folder. With GIMP, make sure -to select 'Import paths'. - -Once the image has been opened, you can see all imported paths on the paths tab. -You can edit these or create new paths. In GIMP, right-clicking one of the -paths and choosing: Path Tool will allow you to see the paths on -the image. The paths will be exported later and will be used to -make the click able regions on our image map. - -3. Export paths as SVG files ----------------------------- - -After you've edited a path or created a new one, right click it on -the paths menu and choose 'Export Path..'. This way we extract just -that path on its own as 'new_area.svg' for example. - -4. Edit the SVG file ---------------------- -Using a script made by David Lynch, we will convert the svg files into -html maps. To do this, open the svg file in question in any text editor. -Make sure that the 'width' and 'height' are not in 'in' or 'px', i.e -"100" is OK, but "100px" or "1.25in" are not. - -Then wrap the tags in and tags. -Then the file is ready for the script. - -5. From SVG to HTML map ------------------------ - -Use the provided svg2imagemap.py script on your edited svg file: - -$ python svg2imagemap.py new_area.svg - -where new_area.svg is our file. - -6. Add the new map to the main html file ------------------------------------------- - -Copy the code from the newly created 'new_area.html' -file. Open the ml_map.html file. - -Add the that you copied -after the last tag in the ml_map.html file. - -Add the link address to 'href' and a tooltip to -'title' within your tag. - -If you wish to add the green and blue hover effect -to the area, add -data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}' - -to your area tag, as done in the other tags above. - -Save the file, and you're done. - ------------------------------------------------------ - -I'll take some time to make some scripts to automate this process -a bit more at some point, as it is not difficult to do, -but tedious. - --Jaques Grobler diff --git a/doc/themes/scikit-learn/static/css/bootstrap-responsive.css b/doc/themes/scikit-learn/static/css/bootstrap-responsive.css deleted file mode 100644 index 09e88ce3fecc2..0000000000000 --- a/doc/themes/scikit-learn/static/css/bootstrap-responsive.css +++ /dev/null @@ -1,1109 +0,0 @@ -/*! - * Bootstrap Responsive v2.3.2 - * - * Copyright 2012 Twitter, Inc - * Licensed under the Apache License v2.0 - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Designed and built with all the love in the world @twitter by @mdo and @fat. - */ - -.clearfix { - *zoom: 1; -} - -.clearfix:before, -.clearfix:after { - display: table; - line-height: 0; - content: ""; -} - -.clearfix:after { - clear: both; -} - -.hide-text { - font: 0/0 a; - color: transparent; - text-shadow: none; - background-color: transparent; - border: 0; -} - -.input-block-level { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} - -@-ms-viewport { - width: device-width; -} - -.hidden { - display: none; - visibility: hidden; -} - -.visible-phone { - display: none !important; -} - -.visible-tablet { - display: none !important; -} - -.hidden-desktop { - display: none !important; -} - -.visible-desktop { - display: inherit !important; -} - -@media (min-width: 768px) and (max-width: 979px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important ; - } - .visible-tablet { - display: inherit !important; - } - .hidden-tablet { - display: none !important; - } -} - -@media (max-width: 767px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important; - } - .visible-phone { - display: inherit !important; - } - .hidden-phone { - display: none !important; - } -} - -.visible-print { - display: none !important; -} - -@media print { - .visible-print { - display: inherit !important; - } - .hidden-print { - display: none !important; - } -} - -@media (min-width: 1200px) { - .row { - margin-left: -30px; - *zoom: 1; - } - .row:before, - .row:after { - display: table; - line-height: 0; - content: ""; - } - .row:after { - clear: both; - } - [class*="span"] { - float: left; - min-height: 1px; - margin-left: 30px; - } - .container, - .navbar-static-top .container, - .navbar-fixed-top .container, - .navbar-fixed-bottom .container { - width: 1170px; - } - .span12 { - width: 1170px; - } - .span11 { - width: 1070px; - } - .span10 { - width: 970px; - } - .span9 { - width: 870px; - } - .span8 { - width: 770px; - } - .span7 { - width: 670px; - } - .span6 { - width: 570px; - } - .span5 { - width: 470px; - } - .span4 { - width: 370px; - } - .span3 { - width: 270px; - } - .span2 { - width: 170px; - } - .span1 { - width: 70px; - } - .offset12 { - margin-left: 1230px; - } - .offset11 { - margin-left: 1130px; - } - .offset10 { - margin-left: 1030px; - } - .offset9 { - margin-left: 930px; - } - .offset8 { - margin-left: 830px; - } - .offset7 { - margin-left: 730px; - } - .offset6 { - margin-left: 630px; - } - .offset5 { - margin-left: 530px; - } - .offset4 { - margin-left: 430px; - } - .offset3 { - margin-left: 330px; - } - .offset2 { - margin-left: 230px; - } - .offset1 { - margin-left: 130px; - } - .row-fluid { - width: 100%; - *zoom: 1; - } - .row-fluid:before, - .row-fluid:after { - display: table; - line-height: 0; - content: ""; - } - .row-fluid:after { - clear: both; - } - .row-fluid [class*="span"] { - display: block; - float: left; - width: 100%; - min-height: 30px; - margin-left: 2.564102564102564%; - *margin-left: 2.5109110747408616%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="span"]:first-child { - margin-left: 0; - } - .row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.564102564102564%; - } - .row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; - } - .row-fluid .span11 { - width: 91.45299145299145%; - *width: 91.39979996362975%; - } - .row-fluid .span10 { - width: 82.90598290598291%; - *width: 82.8527914166212%; - } - .row-fluid .span9 { - width: 74.35897435897436%; - *width: 74.30578286961266%; - } - .row-fluid .span8 { - width: 65.81196581196582%; - *width: 65.75877432260411%; - } - .row-fluid .span7 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{ - width: 414px; - } - .span6 { - width: 352px; - } - .span5 { - width: 290px; - } - .span4 { - width: 228px; - } - .span3 { - width: 166px; - } - .span2 { - width: 104px; - } - .span1 { - width: 42px; - } - .offset12 { - margin-left: 764px; - } - .offset11 { - margin-left: 702px; - } - .offset10 { - margin-left: 640px; - } - .offset9 { - margin-left: 578px; - } - .offset8 { - margin-left: 516px; - } - .offset7 { - margin-left: 454px; - } - .offset6 { - margin-left: 392px; - } - .offset5 { - margin-left: 330px; - } - .offset4 { - margin-left: 268px; - } - .offset3 { - margin-left: 206px; - } - .offset2 { - margin-left: 144px; - } - .offset1 { - margin-left: 82px; - } - .row-fluid { - width: 100%; - *zoom: 1; - } - .row-fluid:before, - .row-fluid:after { - display: table; - line-height: 0; - content: ""; - } - .row-fluid:after { - clear: both; - } - .row-fluid [class*="span"] { - display: block; - float: left; - width: 100%; - min-height: 30px; - margin-left: 2.7624309392265194%; - *margin-left: 2.709239449864817%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="span"]:first-child { - margin-left: 0; - } - .row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.7624309392265194%; - } - .row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; - } - .row-fluid .span11 { - width: 91.43646408839778%; - *width: 91.38327259903608%; - } - .row-fluid .span10 { - width: 82.87292817679558%; - *width: 82.81973668743387%; - } - .row-fluid .span9 { - width: 74.30939226519337%; - *width: 74.25620077583166%; - } - .row-fluid .span8 { - width: 65.74585635359117%; - *width: 65.69266486422946%; - } - .row-fluid .span7 { - width: 57.18232044198895%; - *width: 57.12912895262725%; - } - .row-fluid .span6 { - width: 48.61878453038674%; - *width: 48.56559304102504%; - } - .row-fluid .span5 { - width: 40.05524861878453%; - *width: 40.00205712942283%; - } - .row-fluid .span4 { - width: 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20px; - } - input.span12, - textarea.span12, - .uneditable-input.span12 { - width: 710px; - } - input.span11, - textarea.span11, - .uneditable-input.span11 { - width: 648px; - } - input.span10, - textarea.span10, - .uneditable-input.span10 { - width: 586px; - } - input.span9, - textarea.span9, - .uneditable-input.span9 { - width: 524px; - } - input.span8, - textarea.span8, - .uneditable-input.span8 { - width: 462px; - } - input.span7, - textarea.span7, - .uneditable-input.span7 { - width: 400px; - } - input.span6, - textarea.span6, - .uneditable-input.span6 { - width: 338px; - } - input.span5, - textarea.span5, - .uneditable-input.span5 { - width: 276px; - } - input.span4, - textarea.span4, - .uneditable-input.span4 { - width: 214px; - } - input.span3, - textarea.span3, - .uneditable-input.span3 { - width: 152px; - } - input.span2, - textarea.span2, - .uneditable-input.span2 { - width: 90px; - } - input.span1, - textarea.span1, - .uneditable-input.span1 { - width: 28px; - } -} - 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.dropdown-toggle { - vertical-align: top; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.input-append .active, -.input-prepend .active { - background-color: #a9dba9; - border-color: #46a546; -} -.input-prepend .add-on, -.input-prepend .btn { - margin-right: -1px; -} -.input-prepend .add-on:first-child, -.input-prepend .btn:first-child { - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} -.input-append input, -.input-append select, -.input-append .uneditable-input { - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} -.input-append input + .btn-group .btn:last-child, -.input-append select + .btn-group .btn:last-child, -.input-append .uneditable-input + .btn-group .btn:last-child { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} -.input-append .add-on, -.input-append .btn, -.input-append .btn-group { - margin-left: -1px; -} -.input-append .add-on:last-child, -.input-append .btn:last-child, -.input-append .btn-group:last-child > .dropdown-toggle { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} -.input-prepend.input-append input, -.input-prepend.input-append select, -.input-prepend.input-append .uneditable-input { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.input-prepend.input-append input + .btn-group .btn, -.input-prepend.input-append select + .btn-group .btn, -.input-prepend.input-append .uneditable-input + .btn-group .btn { - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} -.input-prepend.input-append .add-on:first-child, -.input-prepend.input-append .btn:first-child { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} -.input-prepend.input-append .add-on:last-child, -.input-prepend.input-append .btn:last-child { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} -.input-prepend.input-append .btn-group:first-child { - margin-left: 0; -} -input.search-query { - padding-right: 14px; - padding-right: 4px \9; - padding-left: 14px; - padding-left: 4px \9; - /* IE7-8 doesn't have border-radius, so don't indent the padding */ - - margin-bottom: 0; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} -/* Allow for input prepend/append in search forms */ -.form-search .input-append .search-query, -.form-search .input-prepend .search-query { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.form-search .input-append .search-query { - -webkit-border-radius: 14px 0 0 14px; - -moz-border-radius: 14px 0 0 14px; - border-radius: 14px 0 0 14px; -} -.form-search .input-append .btn { - -webkit-border-radius: 0 14px 14px 0; - -moz-border-radius: 0 14px 14px 0; - border-radius: 0 14px 14px 0; -} -.form-search .input-prepend .search-query { - -webkit-border-radius: 0 14px 14px 0; - -moz-border-radius: 0 14px 14px 0; - border-radius: 0 14px 14px 0; -} -.form-search .input-prepend .btn { - -webkit-border-radius: 14px 0 0 14px; - -moz-border-radius: 14px 0 0 14px; - border-radius: 14px 0 0 14px; -} -.form-search input, -.form-inline input, -.form-horizontal input, -.form-search textarea, -.form-inline textarea, -.form-horizontal textarea, -.form-search select, -.form-inline select, -.form-horizontal select, -.form-search .help-inline, -.form-inline .help-inline, -.form-horizontal .help-inline, -.form-search .uneditable-input, -.form-inline .uneditable-input, -.form-horizontal .uneditable-input, -.form-search .input-prepend, -.form-inline .input-prepend, -.form-horizontal .input-prepend, -.form-search .input-append, -.form-inline .input-append, -.form-horizontal .input-append { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; - margin-bottom: 0; - vertical-align: middle; -} -.form-search .hide, -.form-inline .hide, -.form-horizontal .hide { - display: none; -} -.form-search label, -.form-inline label, -.form-search .btn-group, -.form-inline .btn-group { - display: inline-block; -} -.form-search .input-append, -.form-inline .input-append, -.form-search .input-prepend, -.form-inline .input-prepend { - margin-bottom: 0; -} -.form-search .radio, -.form-search .checkbox, -.form-inline .radio, -.form-inline .checkbox { - padding-left: 0; - margin-bottom: 0; - vertical-align: middle; -} -.form-search .radio input[type="radio"], -.form-search .checkbox input[type="checkbox"], -.form-inline .radio input[type="radio"], -.form-inline .checkbox input[type="checkbox"] { - float: left; - margin-right: 3px; - margin-left: 0; -} -.control-group { - margin-bottom: 10px; -} -legend + .control-group { - margin-top: 20px; - -webkit-margin-top-collapse: separate; -} -.form-horizontal .control-group { - margin-bottom: 20px; - *zoom: 1; -} -.form-horizontal .control-group:before, -.form-horizontal .control-group:after { - display: table; - content: ""; - line-height: 0; -} -.form-horizontal .control-group:after { - clear: both; -} -.form-horizontal .control-label { - float: left; - width: 160px; - padding-top: 5px; - text-align: right; -} -.form-horizontal .controls { - *display: inline-block; - *padding-left: 20px; - margin-left: 180px; - *margin-left: 0; -} -.form-horizontal .controls:first-child { - *padding-left: 180px; -} -.form-horizontal .help-block { - margin-bottom: 0; -} -.form-horizontal input + .help-block, -.form-horizontal select + .help-block, -.form-horizontal textarea + .help-block, -.form-horizontal .uneditable-input + .help-block, -.form-horizontal .input-prepend + .help-block, -.form-horizontal .input-append + .help-block { - margin-top: 10px; -} -.form-horizontal .form-actions { - padding-left: 180px; -} -.btn { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; - padding: 4px 12px; - margin-bottom: 0; - font-size: 14px; - line-height: 20px; - text-align: center; - vertical-align: middle; - cursor: pointer; - color: #333333; - text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); - background-color: #f5f5f5; - background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); - background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); - background-image: linear-gradient(to bottom, #ffffff, #e6e6e6); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); - border-color: #e6e6e6 #e6e6e6 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #e6e6e6; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - border: 1px solid #cccccc; - *border: 0; - border-bottom-color: #b3b3b3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - *margin-left: .3em; - -webkit-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); - -moz-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); - box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); -} -.btn:hover, -.btn:focus, -.btn:active, -.btn.active, -.btn.disabled, -.btn[disabled] { - color: #333333; - background-color: #e6e6e6; - *background-color: #d9d9d9; -} -.btn:active, -.btn.active { - background-color: #cccccc \9; -} -.btn:first-child { - *margin-left: 0; -} -.btn:hover, -.btn:focus { - color: #333333; - text-decoration: none; - background-position: 0 -15px; - -webkit-transition: background-position 0.1s linear; - -moz-transition: background-position 0.1s linear; - -o-transition: background-position 0.1s linear; - transition: background-position 0.1s linear; -} -.btn:focus { - outline: thin dotted #333; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -.btn.active, -.btn:active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); - -moz-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); - box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); -} -.btn.disabled, -.btn[disabled] { - cursor: default; - background-image: none; - opacity: 0.65; - filter: alpha(opacity=65); - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} -.btn-large { - padding: 11px 19px; - font-size: 17.5px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} -.btn-large [class^="icon-"], -.btn-large [class*=" icon-"] { - margin-top: 4px; -} -.btn-small { - padding: 2px 10px; - font-size: 11.9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} -.btn-small [class^="icon-"], -.btn-small [class*=" icon-"] { - margin-top: 0; -} -.btn-mini [class^="icon-"], -.btn-mini [class*=" icon-"] { - margin-top: -1px; -} -.btn-mini { - padding: 0 6px; - font-size: 10.5px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} -.btn-block { - display: block; - width: 100%; - padding-left: 0; - padding-right: 0; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} -.btn-block + .btn-block { - margin-top: 5px; -} -input[type="submit"].btn-block, -input[type="reset"].btn-block, -input[type="button"].btn-block { - width: 100%; -} -.btn-primary.active, -.btn-warning.active, -.btn-danger.active, -.btn-success.active, -.btn-info.active, -.btn-inverse.active { - color: rgba(255, 255, 255, 0.75); -} -.btn-primary { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #006dcc; - background-image: -moz-linear-gradient(top, #0088cc, #0044cc); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); - background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); - background-image: -o-linear-gradient(top, #0088cc, #0044cc); - background-image: linear-gradient(to bottom, #0088cc, #0044cc); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0); - border-color: #0044cc #0044cc #002a80; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #0044cc; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-primary:hover, -.btn-primary:focus, -.btn-primary:active, -.btn-primary.active, -.btn-primary.disabled, -.btn-primary[disabled] { - color: #ffffff; - background-color: #0044cc; - *background-color: #003bb3; -} -.btn-primary:active, -.btn-primary.active { - background-color: #003399 \9; -} -.btn-warning { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #faa732; - background-image: -moz-linear-gradient(top, #fbb450, #f89406); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); - background-image: -webkit-linear-gradient(top, #fbb450, #f89406); - background-image: -o-linear-gradient(top, #fbb450, #f89406); - background-image: linear-gradient(to bottom, #fbb450, #f89406); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); - border-color: #f89406 #f89406 #ad6704; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #f89406; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-warning:hover, -.btn-warning:focus, -.btn-warning:active, -.btn-warning.active, -.btn-warning.disabled, -.btn-warning[disabled] { - color: #ffffff; - background-color: #f89406; - *background-color: #df8505; -} -.btn-warning:active, -.btn-warning.active { - background-color: #c67605 \9; -} -.btn-danger { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #da4f49; - background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); - background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); - background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); - background-image: linear-gradient(to bottom, #ee5f5b, #bd362f); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0); - border-color: #bd362f #bd362f #802420; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #bd362f; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-danger:hover, -.btn-danger:focus, -.btn-danger:active, -.btn-danger.active, -.btn-danger.disabled, -.btn-danger[disabled] { - color: #ffffff; - background-color: #bd362f; - *background-color: #a9302a; -} -.btn-danger:active, -.btn-danger.active { - background-color: #942a25 \9; -} -.btn-success { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #5bb75b; - background-image: -moz-linear-gradient(top, #62c462, #51a351); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); - background-image: -webkit-linear-gradient(top, #62c462, #51a351); - background-image: -o-linear-gradient(top, #62c462, #51a351); - background-image: linear-gradient(to bottom, #62c462, #51a351); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0); - border-color: #51a351 #51a351 #387038; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #51a351; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-success:hover, -.btn-success:focus, -.btn-success:active, -.btn-success.active, -.btn-success.disabled, -.btn-success[disabled] { - color: #ffffff; - background-color: #51a351; - *background-color: #499249; -} -.btn-success:active, -.btn-success.active { - background-color: #408140 \9; -} -.btn-info { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #49afcd; - background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); - background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); - background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); - background-image: linear-gradient(to bottom, #5bc0de, #2f96b4); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0); - border-color: #2f96b4 #2f96b4 #1f6377; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #2f96b4; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-info:hover, -.btn-info:focus, -.btn-info:active, -.btn-info.active, -.btn-info.disabled, -.btn-info[disabled] { - color: #ffffff; - background-color: #2f96b4; - *background-color: #2a85a0; -} -.btn-info:active, -.btn-info.active { - background-color: #24748c \9; -} -.btn-inverse { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #363636; - background-image: -moz-linear-gradient(top, #444444, #222222); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222)); - background-image: -webkit-linear-gradient(top, #444444, #222222); - background-image: -o-linear-gradient(top, #444444, #222222); - background-image: linear-gradient(to bottom, #444444, #222222); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0); - border-color: #222222 #222222 #000000; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #222222; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.btn-inverse:hover, -.btn-inverse:focus, -.btn-inverse:active, -.btn-inverse.active, -.btn-inverse.disabled, -.btn-inverse[disabled] { - color: #ffffff; - background-color: #222222; - *background-color: #151515; -} -.btn-inverse:active, -.btn-inverse.active { - background-color: #080808 \9; -} -button.btn, -input[type="submit"].btn { - *padding-top: 3px; - *padding-bottom: 3px; -} -button.btn::-moz-focus-inner, -input[type="submit"].btn::-moz-focus-inner { - padding: 0; - border: 0; -} -button.btn.btn-large, -input[type="submit"].btn.btn-large { - *padding-top: 7px; - *padding-bottom: 7px; -} -button.btn.btn-small, -input[type="submit"].btn.btn-small { - *padding-top: 3px; - *padding-bottom: 3px; -} -button.btn.btn-mini, -input[type="submit"].btn.btn-mini { - *padding-top: 1px; - *padding-bottom: 1px; -} -.btn-link, -.btn-link:active, -.btn-link[disabled] { - background-color: transparent; - background-image: none; - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} -.btn-link { - border-color: transparent; - cursor: pointer; - color: #0088cc; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.btn-link:hover, -.btn-link:focus { - color: #005580; - text-decoration: underline; - background-color: transparent; -} -.btn-link[disabled]:hover, -.btn-link[disabled]:focus { - color: #333333; - text-decoration: none; -} -[class^="icon-"], -[class*=" icon-"] { - display: inline-block; - width: 14px; - height: 14px; - *margin-right: .3em; - line-height: 14px; - vertical-align: text-top; - background-image: url("../img/glyphicons-halflings.png"); - background-position: 14px 14px; - background-repeat: no-repeat; - margin-top: 1px; -} -/* White icons with optional class, or on hover/focus/active states of certain elements */ -.icon-white, -.nav-pills > .active > a > [class^="icon-"], -.nav-pills > .active > a > [class*=" icon-"], -.nav-list > .active > a > [class^="icon-"], -.nav-list > .active > a > [class*=" icon-"], -.navbar-inverse .nav > .active > a > [class^="icon-"], -.navbar-inverse .nav > .active > a > [class*=" icon-"], -.dropdown-menu > li > a:hover > [class^="icon-"], -.dropdown-menu > li > a:focus > [class^="icon-"], -.dropdown-menu > li > a:hover > [class*=" icon-"], -.dropdown-menu > li > a:focus > [class*=" icon-"], -.dropdown-menu > .active > a > [class^="icon-"], -.dropdown-menu > .active > a > [class*=" icon-"], -.dropdown-submenu:hover > a > [class^="icon-"], -.dropdown-submenu:focus > a > [class^="icon-"], -.dropdown-submenu:hover > a > [class*=" icon-"], -.dropdown-submenu:focus > a > [class*=" icon-"] { - background-image: url("../img/glyphicons-halflings-white.png"); -} -.icon-glass { - background-position: 0 0; -} -.icon-music { - background-position: -24px 0; -} -.icon-search { - background-position: -48px 0; -} -.icon-envelope { - background-position: -72px 0; -} -.icon-heart { - background-position: -96px 0; -} -.icon-star { - background-position: -120px 0; -} -.icon-star-empty { - background-position: -144px 0; -} -.icon-user { - background-position: -168px 0; -} -.icon-film { - background-position: -192px 0; -} -.icon-th-large { - background-position: -216px 0; -} -.icon-th { - background-position: -240px 0; -} -.icon-th-list { - background-position: -264px 0; -} -.icon-ok { - background-position: -288px 0; -} -.icon-remove { - background-position: -312px 0; -} -.icon-zoom-in { - background-position: -336px 0; -} -.icon-zoom-out { - background-position: -360px 0; -} -.icon-off { - background-position: -384px 0; -} -.icon-signal { - background-position: -408px 0; -} -.icon-cog { - background-position: -432px 0; -} -.icon-trash { - background-position: -456px 0; -} -.icon-home { - background-position: 0 -24px; -} -.icon-file { - background-position: -24px -24px; -} -.icon-time { - background-position: -48px -24px; -} -.icon-road { - background-position: -72px -24px; -} -.icon-download-alt { - background-position: -96px -24px; -} -.icon-download { - background-position: -120px -24px; -} -.icon-upload { - background-position: -144px -24px; -} -.icon-inbox { - background-position: -168px -24px; -} -.icon-play-circle { - background-position: -192px -24px; -} -.icon-repeat { - background-position: -216px -24px; -} -.icon-refresh { - background-position: -240px -24px; -} -.icon-list-alt { - background-position: -264px -24px; -} -.icon-lock { - background-position: -287px -24px; -} -.icon-flag { - background-position: -312px -24px; -} -.icon-headphones { - background-position: -336px -24px; -} -.icon-volume-off { - background-position: -360px -24px; -} -.icon-volume-down { - background-position: -384px -24px; -} -.icon-volume-up { - background-position: -408px -24px; -} -.icon-qrcode { - background-position: -432px -24px; -} -.icon-barcode { - background-position: -456px -24px; -} -.icon-tag { - background-position: 0 -48px; -} -.icon-tags { - background-position: -25px -48px; -} -.icon-book { - background-position: -48px -48px; -} -.icon-bookmark { - background-position: -72px -48px; -} -.icon-print { - background-position: -96px -48px; -} -.icon-camera { - background-position: -120px -48px; -} -.icon-font { - background-position: -144px -48px; -} -.icon-bold { - background-position: -167px -48px; -} -.icon-italic { - background-position: -192px -48px; -} -.icon-text-height { - background-position: -216px -48px; -} -.icon-text-width { - background-position: -240px -48px; -} -.icon-align-left { - background-position: -264px -48px; -} -.icon-align-center { - background-position: -288px -48px; -} -.icon-align-right { - background-position: -312px -48px; -} -.icon-align-justify { - background-position: -336px -48px; -} -.icon-list { - background-position: -360px -48px; -} -.icon-indent-left { - background-position: -384px -48px; -} -.icon-indent-right { - background-position: -408px -48px; -} -.icon-facetime-video { - background-position: -432px -48px; -} -.icon-picture { - background-position: -456px -48px; -} -.icon-pencil { - background-position: 0 -72px; -} -.icon-map-marker { - background-position: -24px -72px; -} -.icon-adjust { - background-position: -48px -72px; -} -.icon-tint { - background-position: -72px -72px; -} -.icon-edit { - background-position: -96px -72px; -} -.icon-share { - background-position: -120px -72px; -} -.icon-check { - background-position: -144px -72px; -} -.icon-move { - background-position: -168px -72px; -} -.icon-step-backward { - background-position: -192px -72px; -} -.icon-fast-backward { - background-position: -216px -72px; -} -.icon-backward { - background-position: -240px -72px; -} -.icon-play { - background-position: -264px -72px; -} -.icon-pause { - background-position: -288px -72px; -} -.icon-stop { - background-position: -312px -72px; -} -.icon-forward { - background-position: -336px -72px; -} -.icon-fast-forward { - background-position: -360px -72px; -} -.icon-step-forward { - background-position: -384px -72px; -} -.icon-eject { - background-position: -408px -72px; -} -.icon-chevron-left { - background-position: -432px -72px; -} -.icon-chevron-right { - background-position: -456px -72px; -} -.icon-plus-sign { - background-position: 0 -96px; -} -.icon-minus-sign { - background-position: -24px -96px; -} -.icon-remove-sign { - background-position: -48px -96px; -} -.icon-ok-sign { - background-position: -72px -96px; -} -.icon-question-sign { - background-position: -96px -96px; -} -.icon-info-sign { - background-position: -120px -96px; -} -.icon-screenshot { - background-position: -144px -96px; -} -.icon-remove-circle { - background-position: -168px -96px; -} -.icon-ok-circle { - background-position: -192px -96px; -} -.icon-ban-circle { - background-position: -216px -96px; -} -.icon-arrow-left { - background-position: -240px -96px; -} -.icon-arrow-right { - background-position: -264px -96px; -} -.icon-arrow-up { - background-position: -289px -96px; -} -.icon-arrow-down { - background-position: -312px -96px; -} -.icon-share-alt { - background-position: -336px -96px; -} -.icon-resize-full { - background-position: -360px -96px; -} -.icon-resize-small { - background-position: -384px -96px; -} -.icon-plus { - background-position: -408px -96px; -} -.icon-minus { - background-position: -433px -96px; -} -.icon-asterisk { - background-position: -456px -96px; -} -.icon-exclamation-sign { - background-position: 0 -120px; -} -.icon-gift { - background-position: -24px -120px; -} -.icon-leaf { - background-position: -48px -120px; -} -.icon-fire { - background-position: -72px -120px; -} -.icon-eye-open { - background-position: -96px -120px; -} -.icon-eye-close { - background-position: -120px -120px; -} -.icon-warning-sign { - background-position: -144px -120px; -} -.icon-plane { - background-position: -168px -120px; -} -.icon-calendar { - background-position: -192px -120px; -} -.icon-random { - background-position: -216px -120px; - width: 16px; -} -.icon-comment { - background-position: -240px -120px; -} -.icon-magnet { - background-position: -264px -120px; -} -.icon-chevron-up { - background-position: -288px -120px; -} -.icon-chevron-down { - background-position: -313px -119px; -} -.icon-retweet { - background-position: -336px -120px; -} -.icon-shopping-cart { - background-position: -360px -120px; -} -.icon-folder-close { - background-position: -384px -120px; - width: 16px; -} -.icon-folder-open { - background-position: -408px -120px; - width: 16px; -} -.icon-resize-vertical { - background-position: -432px -119px; -} -.icon-resize-horizontal { - background-position: -456px -118px; -} -.icon-hdd { - background-position: 0 -144px; -} -.icon-bullhorn { - background-position: -24px -144px; -} -.icon-bell { - background-position: -48px -144px; -} -.icon-certificate { - background-position: -72px -144px; -} -.icon-thumbs-up { - background-position: -96px -144px; -} -.icon-thumbs-down { - background-position: -120px -144px; -} -.icon-hand-right { - background-position: -144px -144px; -} -.icon-hand-left { - background-position: -168px -144px; -} -.icon-hand-up { - background-position: -192px -144px; -} -.icon-hand-down { - background-position: -216px -144px; -} -.icon-circle-arrow-right { - background-position: -240px -144px; -} -.icon-circle-arrow-left { - background-position: -264px -144px; -} -.icon-circle-arrow-up { - background-position: -288px -144px; -} -.icon-circle-arrow-down { - background-position: -312px -144px; -} -.icon-globe { - background-position: -336px -144px; -} -.icon-wrench { - background-position: -360px -144px; -} -.icon-tasks { - background-position: -384px -144px; -} -.icon-filter { - background-position: -408px -144px; -} -.icon-briefcase { - background-position: -432px -144px; -} -.icon-fullscreen { - background-position: -456px -144px; -} -.btn-group { - position: relative; - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; - font-size: 0; - vertical-align: middle; - white-space: nowrap; - *margin-left: .3em; -} -.btn-group:first-child { - *margin-left: 0; -} -.btn-group + .btn-group { - margin-left: 5px; -} -.btn-toolbar { - font-size: 0; - margin-top: 10px; - margin-bottom: 10px; -} -.btn-toolbar > .btn + .btn, -.btn-toolbar > .btn-group + .btn, -.btn-toolbar > .btn + .btn-group { - margin-left: 5px; -} -.btn-group > .btn { - position: relative; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.btn-group > .btn + .btn { - margin-left: -1px; -} -.btn-group > .btn, -.btn-group > .dropdown-menu, -.btn-group > .popover { - font-size: 14px; -} -.btn-group > .btn-mini { - font-size: 10.5px; -} -.btn-group > .btn-small { - font-size: 11.9px; -} -.btn-group > .btn-large { - font-size: 17.5px; -} -.btn-group > .btn:first-child { - margin-left: 0; - -webkit-border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; - border-top-left-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - border-bottom-left-radius: 4px; -} -.btn-group > .btn:last-child, -.btn-group > .dropdown-toggle { - -webkit-border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -moz-border-radius-bottomright: 4px; - border-bottom-right-radius: 4px; -} -.btn-group > .btn.large:first-child { - margin-left: 0; - -webkit-border-top-left-radius: 6px; - -moz-border-radius-topleft: 6px; - border-top-left-radius: 6px; - -webkit-border-bottom-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - border-bottom-left-radius: 6px; -} -.btn-group > .btn.large:last-child, -.btn-group > .large.dropdown-toggle { - -webkit-border-top-right-radius: 6px; - -moz-border-radius-topright: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - -moz-border-radius-bottomright: 6px; - border-bottom-right-radius: 6px; -} -.btn-group > .btn:hover, -.btn-group > .btn:focus, -.btn-group > .btn:active, -.btn-group > .btn.active { - z-index: 2; -} -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} -.btn-group > .btn + .dropdown-toggle { - padding-left: 8px; - padding-right: 8px; - -webkit-box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); - -moz-box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); - box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05); - *padding-top: 5px; - *padding-bottom: 5px; -} -.btn-group > .btn-mini + .dropdown-toggle { - padding-left: 5px; - padding-right: 5px; - *padding-top: 2px; - *padding-bottom: 2px; -} -.btn-group > .btn-small + .dropdown-toggle { - *padding-top: 5px; - *padding-bottom: 4px; -} -.btn-group > .btn-large + .dropdown-toggle { - padding-left: 12px; - padding-right: 12px; - *padding-top: 7px; - *padding-bottom: 7px; -} -.btn-group.open .dropdown-toggle { - background-image: none; - -webkit-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); - -moz-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); - box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05); -} -.btn-group.open .btn.dropdown-toggle { - background-color: #e6e6e6; -} -.btn-group.open .btn-primary.dropdown-toggle { - background-color: #0044cc; -} -.btn-group.open .btn-warning.dropdown-toggle { - background-color: #f89406; -} -.btn-group.open .btn-danger.dropdown-toggle { - background-color: #bd362f; -} -.btn-group.open .btn-success.dropdown-toggle { - background-color: #51a351; -} -.btn-group.open .btn-info.dropdown-toggle { - background-color: #2f96b4; -} -.btn-group.open .btn-inverse.dropdown-toggle { - background-color: #222222; -} -.btn .caret { - margin-top: 8px; - margin-left: 0; -} -.btn-large .caret { - margin-top: 6px; -} -.btn-large .caret { - border-left-width: 5px; - border-right-width: 5px; - border-top-width: 5px; -} -.btn-mini .caret, -.btn-small .caret { - margin-top: 8px; -} -.dropup .btn-large .caret { - border-bottom-width: 5px; -} -.btn-primary .caret, -.btn-warning .caret, -.btn-danger .caret, -.btn-info .caret, -.btn-success .caret, -.btn-inverse .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} -.btn-group-vertical { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; -} -.btn-group-vertical > .btn { - display: block; - float: none; - max-width: 100%; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.btn-group-vertical > .btn + .btn { - margin-left: 0; - margin-top: -1px; -} -.btn-group-vertical > .btn:first-child { - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} -.btn-group-vertical > .btn:last-child { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} -.btn-group-vertical > .btn-large:first-child { - -webkit-border-radius: 6px 6px 0 0; - -moz-border-radius: 6px 6px 0 0; - border-radius: 6px 6px 0 0; -} -.btn-group-vertical > .btn-large:last-child { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} -.nav { - margin-left: 0; - margin-bottom: 20px; - list-style: none; -} -.nav > li > a { - display: block; -} -.nav > li > a:hover, -.nav > li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} -.nav > li > a > img { - max-width: none; -} -.nav > .pull-right { - float: right; -} -.nav-header { - display: block; - padding: 3px 15px; - font-size: 11px; - font-weight: bold; - line-height: 20px; - color: #999999; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - text-transform: uppercase; -} -.nav li + .nav-header { - margin-top: 9px; -} -.nav-list { - padding-left: 15px; - padding-right: 15px; - margin-bottom: 0; -} -.nav-list > li > a, -.nav-list .nav-header { - margin-left: -15px; - margin-right: -15px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); -} -.nav-list > li > a { - padding: 3px 15px; -} -.nav-list > .active > a, -.nav-list > .active > a:hover, -.nav-list > .active > a:focus { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); - background-color: #0088cc; -} -.nav-list [class^="icon-"], -.nav-list [class*=" icon-"] { - margin-right: 2px; -} -.nav-list .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} -.nav-tabs, -.nav-pills { - *zoom: 1; -} -.nav-tabs:before, -.nav-pills:before, -.nav-tabs:after, -.nav-pills:after { - display: table; - content: ""; - line-height: 0; -} -.nav-tabs:after, -.nav-pills:after { - clear: both; -} -.nav-tabs > li, -.nav-pills > li { - float: left; -} -.nav-tabs > li > a, -.nav-pills > li > a { - padding-right: 12px; - padding-left: 12px; - margin-right: 2px; - line-height: 14px; -} -.nav-tabs { - border-bottom: 1px solid #ddd; -} -.nav-tabs > li { - margin-bottom: -1px; -} -.nav-tabs > li > a { - padding-top: 8px; - padding-bottom: 8px; - line-height: 20px; - border: 1px solid transparent; - -webkit-border-radius: 4px 4px 0 0; - -moz-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} -.nav-tabs > li > a:hover, -.nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #dddddd; -} -.nav-tabs > .active > a, -.nav-tabs > .active > a:hover, -.nav-tabs > .active > a:focus { - color: #555555; - background-color: #ffffff; - border: 1px solid #ddd; - border-bottom-color: transparent; - cursor: default; -} -.nav-pills > li > a { - padding-top: 8px; - padding-bottom: 8px; - margin-top: 2px; - margin-bottom: 2px; - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; -} -.nav-pills > .active > a, -.nav-pills > .active > a:hover, -.nav-pills > .active > a:focus { - color: #ffffff; - background-color: #0088cc; -} -.nav-stacked > li { - float: none; -} -.nav-stacked > li > a { - margin-right: 0; -} -.nav-tabs.nav-stacked { - border-bottom: 0; -} -.nav-tabs.nav-stacked > li > a { - border: 1px solid #ddd; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} -.nav-tabs.nav-stacked > li:first-child > a { - -webkit-border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; - border-top-right-radius: 4px; - -webkit-border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; - border-top-left-radius: 4px; -} -.nav-tabs.nav-stacked > li:last-child > a { - -webkit-border-bottom-right-radius: 4px; - -moz-border-radius-bottomright: 4px; - border-bottom-right-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - border-bottom-left-radius: 4px; -} -.nav-tabs.nav-stacked > li > a:hover, -.nav-tabs.nav-stacked > li > a:focus { - border-color: #ddd; - z-index: 2; -} -.nav-pills.nav-stacked > li > a { - margin-bottom: 3px; -} -.nav-pills.nav-stacked > li:last-child > a { - margin-bottom: 1px; -} -.nav-tabs .dropdown-menu { - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; -} -.nav-pills .dropdown-menu { - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} -.nav .dropdown-toggle .caret { - border-top-color: #0088cc; - border-bottom-color: #0088cc; - margin-top: 6px; -} -.nav .dropdown-toggle:hover .caret, -.nav .dropdown-toggle:focus .caret { - border-top-color: #005580; - border-bottom-color: #005580; -} -/* move down carets for tabs */ -.nav-tabs .dropdown-toggle .caret { - margin-top: 8px; -} -.nav .active .dropdown-toggle .caret { - border-top-color: #fff; - border-bottom-color: #fff; -} -.nav-tabs .active .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} -.nav > .dropdown.active > a:hover, -.nav > .dropdown.active > a:focus { - cursor: pointer; -} -.nav-tabs .open .dropdown-toggle, -.nav-pills .open .dropdown-toggle, -.nav > li.dropdown.open.active > a:hover, -.nav > li.dropdown.open.active > a:focus { - color: #ffffff; - background-color: #999999; - border-color: #999999; -} -.nav li.dropdown.open .caret, -.nav li.dropdown.open.active .caret, -.nav li.dropdown.open a:hover .caret, -.nav li.dropdown.open a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; - opacity: 1; - filter: alpha(opacity=100); -} -.tabs-stacked .open > a:hover, -.tabs-stacked .open > a:focus { - border-color: #999999; -} -.tabbable { - *zoom: 1; -} -.tabbable:before, -.tabbable:after { - display: table; - content: ""; - line-height: 0; -} -.tabbable:after { - clear: both; -} -.tab-content { - overflow: auto; -} -.tabs-below > .nav-tabs, -.tabs-right > .nav-tabs, -.tabs-left > .nav-tabs { - border-bottom: 0; -} -.tab-content > .tab-pane, -.pill-content > .pill-pane { - display: none; -} -.tab-content > .active, -.pill-content > .active { - display: block; -} -.tabs-below > .nav-tabs { - border-top: 1px solid #ddd; -} -.tabs-below > .nav-tabs > li { - margin-top: -1px; - margin-bottom: 0; -} -.tabs-below > .nav-tabs > li > a { - -webkit-border-radius: 0 0 4px 4px; - -moz-border-radius: 0 0 4px 4px; - border-radius: 0 0 4px 4px; -} -.tabs-below > .nav-tabs > li > a:hover, -.tabs-below > .nav-tabs > li > a:focus { - border-bottom-color: transparent; - border-top-color: #ddd; -} -.tabs-below > .nav-tabs > .active > a, -.tabs-below > .nav-tabs > .active > a:hover, -.tabs-below > .nav-tabs > .active > a:focus { - border-color: transparent #ddd #ddd #ddd; -} -.tabs-left > .nav-tabs > li, -.tabs-right > .nav-tabs > li { - float: none; -} -.tabs-left > .nav-tabs > li > a, -.tabs-right > .nav-tabs > li > a { - min-width: 74px; - margin-right: 0; - margin-bottom: 3px; -} -.tabs-left > .nav-tabs { - float: left; - margin-right: 19px; - border-right: 1px solid #ddd; -} -.tabs-left > .nav-tabs > li > a { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} -.tabs-left > .nav-tabs > li > a:hover, -.tabs-left > .nav-tabs > li > a:focus { - border-color: #eeeeee #dddddd #eeeeee #eeeeee; -} -.tabs-left > .nav-tabs .active > a, -.tabs-left > .nav-tabs .active > a:hover, -.tabs-left > .nav-tabs .active > a:focus { - border-color: #ddd transparent #ddd #ddd; - *border-right-color: #ffffff; -} -.tabs-right > .nav-tabs { - float: right; - margin-left: 19px; - border-left: 1px solid #ddd; -} -.tabs-right > .nav-tabs > li > a { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} -.tabs-right > .nav-tabs > li > a:hover, -.tabs-right > .nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #eeeeee #dddddd; -} -.tabs-right > .nav-tabs .active > a, -.tabs-right > .nav-tabs .active > a:hover, -.tabs-right > .nav-tabs .active > a:focus { - border-color: #ddd #ddd #ddd 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.caret, -.navbar .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} -.navbar .pull-right > li > .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right { - left: auto; - right: 0; -} -.navbar .pull-right > li > .dropdown-menu:before, -.navbar .nav > li > .dropdown-menu.pull-right:before { - left: auto; - right: 12px; -} -.navbar .pull-right > li > .dropdown-menu:after, -.navbar .nav > li > .dropdown-menu.pull-right:after { - left: auto; - right: 13px; -} -.navbar .pull-right > li > .dropdown-menu .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { - left: auto; - right: 100%; - margin-left: 0; - margin-right: -1px; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} -.navbar-inverse .navbar-inner { - background-color: #1b1b1b; - background-image: -moz-linear-gradient(top, #222222, #111111); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); - background-image: -webkit-linear-gradient(top, #222222, #111111); - background-image: -o-linear-gradient(top, #222222, #111111); - background-image: linear-gradient(to bottom, #222222, #111111); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0); - border-color: #252525; -} -.navbar-inverse .brand, -.navbar-inverse .nav > li > a { - color: #999999; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); -} -.navbar-inverse .brand:hover, -.navbar-inverse .nav > li > a:hover, -.navbar-inverse .brand:focus, -.navbar-inverse .nav > li > a:focus { - color: #ffffff; -} -.navbar-inverse .brand { - color: #999999; -} -.navbar-inverse .navbar-text { - color: #999999; -} -.navbar-inverse .nav > li > a:focus, -.navbar-inverse .nav > li > a:hover { - background-color: transparent; - color: #ffffff; -} -.navbar-inverse .nav .active > a, -.navbar-inverse .nav .active > a:hover, -.navbar-inverse .nav .active > a:focus { - color: #ffffff; - background-color: #111111; -} -.navbar-inverse .navbar-link { - color: #999999; -} -.navbar-inverse .navbar-link:hover, -.navbar-inverse .navbar-link:focus { - color: #ffffff; -} -.navbar-inverse .divider-vertical { - border-left-color: #111111; - border-right-color: #222222; -} -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle { - background-color: #111111; - color: #ffffff; -} -.navbar-inverse .nav li.dropdown > a:hover .caret, -.navbar-inverse .nav li.dropdown > a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} -.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #999999; - border-bottom-color: #999999; -} -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} -.navbar-inverse .navbar-search .search-query { - color: #ffffff; - background-color: #515151; - border-color: #111111; - -webkit-box-shadow: inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15); - -moz-box-shadow: inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15); - box-shadow: inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15); - -webkit-transition: none; - -moz-transition: none; - -o-transition: none; - transition: none; -} -.navbar-inverse .navbar-search .search-query:-moz-placeholder { - color: #cccccc; -} -.navbar-inverse .navbar-search .search-query:-ms-input-placeholder { - color: #cccccc; -} -.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder { - color: #cccccc; -} -.navbar-inverse .navbar-search .search-query:focus, -.navbar-inverse .navbar-search .search-query.focused { - padding: 5px 15px; - color: #333333; - text-shadow: 0 1px 0 #ffffff; - background-color: #ffffff; - border: 0; - -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - outline: 0; -} -.navbar-inverse .btn-navbar { - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #0e0e0e; - background-image: -moz-linear-gradient(top, #151515, #040404); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404)); - background-image: -webkit-linear-gradient(top, #151515, #040404); - background-image: -o-linear-gradient(top, #151515, #040404); - background-image: linear-gradient(to bottom, #151515, #040404); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0); - border-color: #040404 #040404 #000000; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - *background-color: #040404; - /* Darken IE7 buttons by default so they stand out more given they won't have borders */ - - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.navbar-inverse .btn-navbar:hover, -.navbar-inverse .btn-navbar:focus, -.navbar-inverse .btn-navbar:active, -.navbar-inverse .btn-navbar.active, -.navbar-inverse .btn-navbar.disabled, -.navbar-inverse .btn-navbar[disabled] { - color: #ffffff; - background-color: #040404; - *background-color: #000000; -} -.navbar-inverse .btn-navbar:active, -.navbar-inverse .btn-navbar.active { - background-color: #000000 \9; -} -.breadcrumb { - padding: 8px 15px; - margin: 0 0 20px; - list-style: none; - background-color: #f5f5f5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.breadcrumb > li { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; - text-shadow: 0 1px 0 #ffffff; -} -.breadcrumb > li > .divider { - padding: 0 5px; - color: #ccc; -} -.breadcrumb > .active { - color: #999999; -} -.pagination { - margin: 20px 0; -} -.pagination ul { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; - margin-left: 0; - margin-bottom: 0; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); -} -.pagination ul > li { - display: inline; -} -.pagination ul > li > a, -.pagination ul > li > span { - float: left; - padding: 4px 12px; - line-height: 20px; - text-decoration: none; - background-color: #ffffff; - border: 1px solid #dddddd; - border-left-width: 0; -} -.pagination ul > li > a:hover, -.pagination ul > li > a:focus, -.pagination ul > .active > a, -.pagination ul > .active > span { - background-color: #f5f5f5; -} -.pagination ul > .active > a, -.pagination ul > .active > span { - color: #999999; - cursor: default; -} -.pagination ul > .disabled > span, -.pagination ul > .disabled > a, -.pagination ul > .disabled > a:hover, -.pagination ul > .disabled > a:focus { - color: #999999; - background-color: transparent; - cursor: default; -} -.pagination ul > li:first-child > a, -.pagination ul > li:first-child > span { - border-left-width: 1px; - -webkit-border-top-left-radius: 4px; - -moz-border-radius-topleft: 4px; - border-top-left-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - border-bottom-left-radius: 4px; -} -.pagination ul > li:last-child > a, -.pagination ul > li:last-child > span { - -webkit-border-top-right-radius: 4px; - -moz-border-radius-topright: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -moz-border-radius-bottomright: 4px; - border-bottom-right-radius: 4px; -} -.pagination-centered { - text-align: center; -} -.pagination-right { - text-align: right; -} -.pagination-large ul > li > a, -.pagination-large ul > li > span { - padding: 11px 19px; - font-size: 17.5px; -} -.pagination-large ul > li:first-child > a, -.pagination-large ul > li:first-child > span { - -webkit-border-top-left-radius: 6px; - -moz-border-radius-topleft: 6px; - border-top-left-radius: 6px; - -webkit-border-bottom-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - border-bottom-left-radius: 6px; -} -.pagination-large ul > li:last-child > a, -.pagination-large ul > li:last-child > span { - -webkit-border-top-right-radius: 6px; - -moz-border-radius-topright: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - -moz-border-radius-bottomright: 6px; - border-bottom-right-radius: 6px; -} -.pagination-mini ul > li:first-child > a, -.pagination-small ul > li:first-child > a, -.pagination-mini ul > li:first-child > span, -.pagination-small ul > li:first-child > span { - -webkit-border-top-left-radius: 3px; - -moz-border-radius-topleft: 3px; - border-top-left-radius: 3px; - -webkit-border-bottom-left-radius: 3px; - -moz-border-radius-bottomleft: 3px; - border-bottom-left-radius: 3px; -} -.pagination-mini ul > li:last-child > a, -.pagination-small ul > li:last-child > a, -.pagination-mini ul > li:last-child > span, -.pagination-small ul > li:last-child > span { - -webkit-border-top-right-radius: 3px; - -moz-border-radius-topright: 3px; - border-top-right-radius: 3px; - -webkit-border-bottom-right-radius: 3px; - -moz-border-radius-bottomright: 3px; - border-bottom-right-radius: 3px; -} -.pagination-small ul > li > a, -.pagination-small ul > li > span { - padding: 2px 10px; - font-size: 11.9px; -} -.pagination-mini ul > li > a, -.pagination-mini ul > li > span { - padding: 0 6px; - font-size: 10.5px; -} -.pager { - margin: 20px 0; - list-style: none; - text-align: center; - *zoom: 1; -} -.pager:before, -.pager:after { - display: table; - content: ""; - line-height: 0; -} -.pager:after { - clear: both; -} -.pager li { - display: inline; -} -.pager li > a, -.pager li > span { - display: inline-block; - padding: 5px 14px; - background-color: #fff; - border: 1px solid #ddd; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} -.pager li > a:hover, -.pager li > a:focus { - text-decoration: none; - background-color: #f5f5f5; -} -.pager .next > a, -.pager .next > span { - float: right; -} -.pager .previous > a, -.pager .previous > span { - float: left; -} -.pager .disabled > a, -.pager .disabled > a:hover, -.pager .disabled > a:focus, -.pager .disabled > span { - color: #999999; - background-color: #fff; - cursor: default; -} -.thumbnails { - margin-left: -20px; - list-style: none; - *zoom: 1; -} -.thumbnails:before, -.thumbnails:after { - display: table; - content: ""; - line-height: 0; -} -.thumbnails:after { - clear: both; -} -.row-fluid .thumbnails { - margin-left: 0; -} -.thumbnails > li { - float: left; - margin-bottom: 20px; - margin-left: 20px; -} -.thumbnail { - display: block; - padding: 4px; - line-height: 20px; - border: 1px solid #ddd; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); - -webkit-transition: all 0.2s ease-in-out; - -moz-transition: all 0.2s ease-in-out; - -o-transition: all 0.2s ease-in-out; - transition: all 0.2s ease-in-out; -} -a.thumbnail:hover, -a.thumbnail:focus { - border-color: #0088cc; - -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); - -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); - box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); -} -.thumbnail > img { - display: block; - max-width: 100%; - margin-left: auto; - margin-right: auto; -} -.thumbnail .caption { - padding: 9px; - color: #555555; -} -.alert { - padding: 8px 35px 8px 14px; - margin-bottom: 20px; - text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); - background-color: #fcf8e3; - border: 1px solid #fbeed5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.alert, -.alert h4 { - color: #c09853; -} -.alert h4 { - margin: 0; -} -.alert .close { - position: relative; - top: -2px; - right: -21px; - line-height: 20px; -} -.alert-success { - background-color: #dff0d8; - border-color: #d6e9c6; - color: #468847; -} -.alert-success h4 { - color: #468847; -} -.alert-danger, -.alert-error { - background-color: #f2dede; - border-color: #eed3d7; - color: #b94a48; -} -.alert-danger h4, -.alert-error h4 { - color: #b94a48; -} -.alert-info { - background-color: #d9edf7; - border-color: #bce8f1; - color: #3a87ad; -} -.alert-info h4 { - color: #3a87ad; -} -.alert-block { - padding-top: 14px; - padding-bottom: 14px; -} -.alert-block > p, -.alert-block > ul { - margin-bottom: 0; -} -.alert-block p + p { - margin-top: 5px; -} -@-webkit-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -@-moz-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -@-ms-keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -@-o-keyframes progress-bar-stripes { - from { - background-position: 0 0; - } - to { - background-position: 40px 0; - } -} -@keyframes progress-bar-stripes { - from { - background-position: 40px 0; - } - to { - background-position: 0 0; - } -} -.progress { - overflow: hidden; - height: 20px; - margin-bottom: 20px; - background-color: #f7f7f7; - background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); - background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); - background-image: linear-gradient(to bottom, #f5f5f5, #f9f9f9); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0); - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); - -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.progress .bar { - width: 0%; - height: 100%; - color: #ffffff; - float: left; - font-size: 12px; - text-align: center; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #0e90d2; - background-image: -moz-linear-gradient(top, #149bdf, #0480be); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); - background-image: -webkit-linear-gradient(top, #149bdf, #0480be); - background-image: -o-linear-gradient(top, #149bdf, #0480be); - background-image: linear-gradient(to bottom, #149bdf, #0480be); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0); - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - -webkit-transition: width 0.6s ease; - -moz-transition: width 0.6s ease; - -o-transition: width 0.6s ease; - transition: width 0.6s ease; -} -.progress .bar + .bar { - -webkit-box-shadow: inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15); - -moz-box-shadow: inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15); - box-shadow: inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15); -} -.progress-striped .bar { - background-color: #149bdf; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - -webkit-background-size: 40px 40px; - -moz-background-size: 40px 40px; - -o-background-size: 40px 40px; - background-size: 40px 40px; -} -.progress.active .bar { - -webkit-animation: progress-bar-stripes 2s linear infinite; - -moz-animation: progress-bar-stripes 2s linear infinite; - -ms-animation: progress-bar-stripes 2s linear infinite; - -o-animation: progress-bar-stripes 2s linear infinite; - animation: progress-bar-stripes 2s linear infinite; -} -.progress-danger .bar, -.progress .bar-danger { - background-color: #dd514c; - background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); - background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); - background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); - background-image: linear-gradient(to bottom, #ee5f5b, #c43c35); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0); -} -.progress-danger.progress-striped .bar, -.progress-striped .bar-danger { - background-color: #ee5f5b; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} -.progress-success .bar, -.progress .bar-success { - background-color: #5eb95e; - background-image: -moz-linear-gradient(top, #62c462, #57a957); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); - background-image: -webkit-linear-gradient(top, #62c462, #57a957); - background-image: -o-linear-gradient(top, #62c462, #57a957); - background-image: linear-gradient(to bottom, #62c462, #57a957); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0); -} -.progress-success.progress-striped .bar, -.progress-striped .bar-success { - background-color: #62c462; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} -.progress-info .bar, -.progress .bar-info { - background-color: #4bb1cf; - background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); - background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); - background-image: -o-linear-gradient(top, #5bc0de, #339bb9); - background-image: linear-gradient(to bottom, #5bc0de, #339bb9); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0); -} -.progress-info.progress-striped .bar, -.progress-striped .bar-info { - background-color: #5bc0de; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} -.progress-warning .bar, -.progress .bar-warning { - background-color: #faa732; - background-image: -moz-linear-gradient(top, #fbb450, #f89406); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); - background-image: -webkit-linear-gradient(top, #fbb450, #f89406); - background-image: -o-linear-gradient(top, #fbb450, #f89406); - background-image: linear-gradient(to bottom, #fbb450, #f89406); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); -} -.progress-warning.progress-striped .bar, -.progress-striped .bar-warning { - background-color: #fbb450; - background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); - background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); - background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); -} -.hero-unit { - padding: 60px; - margin-bottom: 30px; - font-size: 18px; - font-weight: 200; - line-height: 30px; - color: inherit; - background-color: #4eacc5; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} -.hero-unit h1 { - margin-bottom: 0; - font-size: 60px; - line-height: 1; - color: inherit; - letter-spacing: -1px; -} -.hero-unit li { - line-height: 30px; -} -.media, -.media-body { - overflow: hidden; - *overflow: visible; - zoom: 1; -} -.media, -.media .media { - margin-top: 15px; -} -.media:first-child { - margin-top: 0; -} -.media-object { - display: block; -} -.media-heading { - margin: 0 0 5px; -} -.media > .pull-left { - margin-right: 10px; -} -.media > .pull-right { - margin-left: 10px; -} -.media-list { - margin-left: 0; - list-style: none; -} -.tooltip { - position: absolute; - z-index: 1030; - display: block; - visibility: visible; - font-size: 11px; - line-height: 1.4; - opacity: 0; - filter: alpha(opacity=0); -} -.tooltip.in { - opacity: 0.8; - filter: alpha(opacity=80); -} -.tooltip.top { - margin-top: -3px; - padding: 5px 0; -} -.tooltip.right { - margin-left: 3px; - padding: 0 5px; -} -.tooltip.bottom { - margin-top: 3px; - padding: 5px 0; -} -.tooltip.left { - margin-left: -3px; - padding: 0 5px; -} -.tooltip-inner { - max-width: 200px; - padding: 8px; - color: #ffffff; - text-align: center; - text-decoration: none; - background-color: #000000; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.tooltip-arrow { - position: absolute; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.tooltip.top .tooltip-arrow { - bottom: 0; - left: 50%; - margin-left: -5px; - border-width: 5px 5px 0; - border-top-color: #000000; -} -.tooltip.right .tooltip-arrow { - top: 50%; - left: 0; - margin-top: -5px; - border-width: 5px 5px 5px 0; - border-right-color: #000000; -} -.tooltip.left .tooltip-arrow { - top: 50%; - right: 0; - margin-top: -5px; - border-width: 5px 0 5px 5px; - border-left-color: #000000; -} -.tooltip.bottom .tooltip-arrow { - top: 0; - left: 50%; - margin-left: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000000; -} -.popover { - position: absolute; - top: 0; - left: 0; - z-index: 1010; - display: none; - max-width: 276px; - padding: 1px; - text-align: left; - background-color: #ffffff; - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - white-space: normal; -} -.popover.top { - margin-top: -10px; -} -.popover.right { - margin-left: 10px; -} -.popover.bottom { - margin-top: 10px; -} -.popover.left { - margin-left: -10px; -} -.popover-title { - margin: 0; - padding: 8px 14px; - font-size: 14px; - font-weight: normal; - line-height: 18px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - -webkit-border-radius: 5px 5px 0 0; - -moz-border-radius: 5px 5px 0 0; - border-radius: 5px 5px 0 0; -} -.popover-title:empty { - display: none; -} -.popover-content { - padding: 9px 14px; -} -.popover .arrow, -.popover .arrow:after { - position: absolute; - display: block; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover .arrow { - border-width: 11px; -} -.popover .arrow:after { - border-width: 10px; - content: ""; -} -.popover.top .arrow { - left: 50%; - margin-left: -11px; - border-bottom-width: 0; - border-top-color: #999; - border-top-color: rgba(0, 0, 0, 0.25); - bottom: -11px; -} -.popover.top .arrow:after { - bottom: 1px; - margin-left: -10px; - border-bottom-width: 0; - border-top-color: #ffffff; -} -.popover.right .arrow { - top: 50%; - left: -11px; - margin-top: -11px; - border-left-width: 0; - border-right-color: #999; - border-right-color: rgba(0, 0, 0, 0.25); -} -.popover.right .arrow:after { - left: 1px; - bottom: -10px; - border-left-width: 0; - border-right-color: #ffffff; -} -.popover.bottom .arrow { - left: 50%; - margin-left: -11px; - border-top-width: 0; - border-bottom-color: #999; - border-bottom-color: rgba(0, 0, 0, 0.25); - top: -11px; -} -.popover.bottom .arrow:after { - top: 1px; - margin-left: -10px; - border-top-width: 0; - border-bottom-color: #ffffff; -} -.popover.left .arrow { - top: 50%; - right: -11px; - margin-top: -11px; - border-right-width: 0; - border-left-color: #999; - border-left-color: rgba(0, 0, 0, 0.25); -} -.popover.left .arrow:after { - right: 1px; - border-right-width: 0; - border-left-color: #ffffff; - bottom: -10px; -} -.modal-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1040; - background-color: #000000; -} -.modal-backdrop.fade { - opacity: 0; -} -.modal-backdrop, -.modal-backdrop.fade.in { - opacity: 0.8; - filter: alpha(opacity=80); -} -.modal { - position: fixed; - top: 10%; - left: 50%; - z-index: 1050; - width: 560px; - margin-left: -280px; - background-color: #ffffff; - border: 1px solid #999; - border: 1px solid rgba(0, 0, 0, 0.3); - *border: 1px solid #999; - /* IE6-7 */ - - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); - -webkit-background-clip: padding-box; - -moz-background-clip: padding-box; - background-clip: padding-box; - outline: none; -} -.modal.fade { - -webkit-transition: opacity .3s linear, top .3s ease-out; - -moz-transition: opacity .3s linear, top .3s ease-out; - -o-transition: opacity .3s linear, top .3s ease-out; - transition: opacity .3s linear, top .3s ease-out; - top: -25%; -} -.modal.fade.in { - top: 10%; -} -.modal-header { - padding: 9px 15px; - border-bottom: 1px solid #eee; -} -.modal-header .close { - margin-top: 2px; -} -.modal-header h3 { - margin: 0; - line-height: 30px; -} -.modal-body { - position: relative; - overflow-y: auto; - max-height: 400px; - padding: 15px; -} -.modal-form { - margin-bottom: 0; -} -.modal-footer { - padding: 14px 15px 15px; - margin-bottom: 0; - text-align: right; - background-color: #f5f5f5; - border-top: 1px solid #ddd; - -webkit-border-radius: 0 0 6px 6px; - -moz-border-radius: 0 0 6px 6px; - border-radius: 0 0 6px 6px; - -webkit-box-shadow: inset 0 1px 0 #ffffff; - -moz-box-shadow: inset 0 1px 0 #ffffff; - box-shadow: inset 0 1px 0 #ffffff; - *zoom: 1; -} -.modal-footer:before, -.modal-footer:after { - display: table; - content: ""; - line-height: 0; -} -.modal-footer:after { - clear: both; -} -.modal-footer .btn + .btn { - margin-left: 5px; - margin-bottom: 0; -} -.modal-footer .btn-group .btn + .btn { - margin-left: -1px; -} -.modal-footer .btn-block + .btn-block { - margin-left: 0; -} -.dropup, -.dropdown { - position: relative; -} -.dropdown-toggle { - *margin-bottom: -3px; -} -.dropdown-toggle:active, -.open .dropdown-toggle { - outline: 0; -} -.caret { - display: inline-block; - width: 0; - height: 0; - vertical-align: top; - border-top: 4px solid #000000; - border-right: 4px solid transparent; - border-left: 4px solid transparent; - content: ""; -} -.dropdown .caret { - margin-top: 8px; - margin-left: 2px; -} -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - list-style: none; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - *border-right-width: 2px; - *border-bottom-width: 2px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} -.dropdown-menu.pull-right { - right: 0; - left: auto; -} -.dropdown-menu .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: normal; - line-height: 20px; - color: #333333; - white-space: nowrap; -} -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus, -.dropdown-submenu:hover > a, -.dropdown-submenu:focus > a { - text-decoration: none; - color: #ffffff; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #ffffff; - text-decoration: none; - outline: 0; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #999999; -} -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); - cursor: default; -} -.open { - *z-index: 1000; -} -.open > .dropdown-menu { - display: block; -} -.dropdown-backdrop { - position: fixed; - left: 0; - right: 0; - bottom: 0; - top: 0; - z-index: 990; -} -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - border-top: 0; - border-bottom: 4px solid #000000; - content: ""; -} -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 1px; -} -.dropdown-submenu { - position: relative; -} -.dropdown-submenu > .dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - -webkit-border-radius: 0 6px 6px 6px; - -moz-border-radius: 0 6px 6px 6px; - border-radius: 0 6px 6px 6px; -} -.dropdown-submenu:hover > .dropdown-menu { - display: block; -} -.dropup .dropdown-submenu > .dropdown-menu { - top: auto; - bottom: 0; - margin-top: 0; - margin-bottom: -2px; - -webkit-border-radius: 5px 5px 5px 0; - -moz-border-radius: 5px 5px 5px 0; - border-radius: 5px 5px 5px 0; -} -.dropdown-submenu > a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} -.dropdown-submenu:hover > a:after { - border-left-color: #ffffff; -} -.dropdown-submenu.pull-left { - float: none; -} -.dropdown-submenu.pull-left > .dropdown-menu { - left: -100%; - margin-left: 10px; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} -.dropdown .dropdown-menu .nav-header { - padding-left: 20px; - padding-right: 20px; -} -.typeahead { - z-index: 1051; - margin-top: 2px; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.accordion { - margin-bottom: 20px; -} -.accordion-group { - margin-bottom: 2px; - border: 1px solid #e5e5e5; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} -.accordion-heading { - border-bottom: 0; -} -.accordion-heading .accordion-toggle { - display: block; - padding: 8px 15px; -} -.accordion-toggle { - cursor: pointer; -} -.accordion-inner { - padding: 9px 15px; - border-top: 1px solid #e5e5e5; -} -.carousel { - position: relative; - margin-bottom: 20px; - line-height: 1; -} -.carousel-inner { - overflow: hidden; - width: 100%; - position: relative; -} -.carousel-inner > .item { - display: none; - position: relative; - -webkit-transition: 0.6s ease-in-out left; - -moz-transition: 0.6s ease-in-out left; - -o-transition: 0.6s ease-in-out left; - transition: 0.6s ease-in-out left; -} -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - display: block; - line-height: 1; -} -.carousel-inner > .active, -.carousel-inner > .next, -.carousel-inner > .prev { - display: block; -} -.carousel-inner > .active { - left: 0; -} -.carousel-inner > .next, -.carousel-inner > .prev { - position: absolute; - top: 0; - width: 100%; -} -.carousel-inner > .next { - left: 100%; -} -.carousel-inner > .prev { - left: -100%; -} -.carousel-inner > .next.left, -.carousel-inner > .prev.right { - left: 0; -} -.carousel-inner > .active.left { - left: -100%; -} -.carousel-inner > .active.right { - left: 100%; -} -.carousel-control { - position: absolute; - top: 40%; - left: 15px; - width: 40px; - height: 40px; - margin-top: -20px; - font-size: 60px; - font-weight: 100; - line-height: 30px; - color: #ffffff; - text-align: center; - background: #222222; - border: 3px solid #ffffff; - -webkit-border-radius: 23px; - -moz-border-radius: 23px; - border-radius: 23px; - opacity: 0.5; - filter: alpha(opacity=50); -} -.carousel-control.right { - left: auto; - right: 15px; -} -.carousel-control:hover, -.carousel-control:focus { - color: #ffffff; - text-decoration: none; - opacity: 0.9; - filter: alpha(opacity=90); -} -.carousel-indicators { - position: absolute; - top: 15px; - right: 15px; - z-index: 5; - margin: 0; - list-style: none; -} -.carousel-indicators li { - display: block; - float: left; - width: 10px; - height: 10px; - margin-left: 5px; - text-indent: -999px; - background-color: #ccc; - background-color: rgba(255, 255, 255, 0.25); - border-radius: 5px; -} -.carousel-indicators .active { - background-color: #fff; -} -.carousel-caption { - position: absolute; - left: 0; - right: 0; - bottom: 0; - padding: 15px; - background: #333333; - background: rgba(0, 0, 0, 0.75); -} -.carousel-caption h4, -.carousel-caption p { - color: #ffffff; - line-height: 20px; -} -.carousel-caption h4 { - margin: 0 0 5px; -} -.carousel-caption p { - margin-bottom: 0; -} -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} -.well-large { - padding: 24px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} -.well-small { - padding: 9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} -.close { - float: right; - font-size: 20px; - font-weight: bold; - line-height: 20px; - color: #000000; - text-shadow: 0 1px 0 #ffffff; - opacity: 0.2; - filter: alpha(opacity=20); -} -.close:hover, -.close:focus { - color: #000000; - text-decoration: none; - cursor: pointer; - opacity: 0.4; - filter: alpha(opacity=40); -} -button.close { - padding: 0; - cursor: pointer; - background: transparent; - border: 0; - -webkit-appearance: none; -} -.pull-right { - float: right; -} -.pull-left { - float: left; -} -.hide { - display: none; -} -.show { - display: block; -} -.invisible { - visibility: hidden; -} -.affix { - position: fixed; -} -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -moz-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} -.fade.in { - opacity: 1; -} -.collapse { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition: height 0.35s ease; - -moz-transition: height 0.35s ease; - -o-transition: height 0.35s ease; - transition: height 0.35s ease; -} -.collapse.in { - height: auto; -} -@-ms-viewport { - width: device-width; -} -.hidden { - display: none; - visibility: hidden; -} -.visible-phone { - display: none !important; -} -.visible-tablet { - display: none !important; -} -.hidden-desktop { - display: none !important; -} -.visible-desktop { - display: inherit !important; -} -@media (min-width: 768px) and (max-width: 979px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important ; - } - .visible-tablet { - display: inherit !important; - } - .hidden-tablet { - display: none !important; - } -} -@media (max-width: 767px) { - .hidden-desktop { - display: inherit !important; - } - .visible-desktop { - display: none !important; - } - .visible-phone { - display: inherit !important; - } - .hidden-phone { - display: none !important; - } -} -.visible-print { - display: none !important; -} -@media print { - .visible-print { - display: inherit !important; - } - .hidden-print { - display: none !important; - } -} -@media (max-width: 767px) { - body { - padding-left: 20px; - padding-right: 20px; - } - .navbar-fixed-top, - .navbar-fixed-bottom, - .navbar-static-top { - margin-left: -20px; - margin-right: -20px; - } - .container-fluid { - padding: 0; - } - .dl-horizontal dt { - float: none; - clear: none; - width: auto; - text-align: left; - } - .dl-horizontal dd { - margin-left: 0; - } - .container { - width: auto; - } - .row-fluid { - width: 100%; - } - .row, - .thumbnails { - margin-left: 0; - } - .thumbnails > li { - float: none; - margin-left: 0; - } - [class*="span"], - .uneditable-input[class*="span"], - .row-fluid [class*="span"] { - float: none; - display: block; - width: 100%; - margin-left: 0; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .span12, - .row-fluid .span12 { - width: 100%; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .row-fluid [class*="offset"]:first-child { - margin-left: 0; - } - .input-large, - .input-xlarge, - .input-xxlarge, - input[class*="span"], - select[class*="span"], - textarea[class*="span"], - .uneditable-input { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - } - .input-prepend input, - .input-append input, - .input-prepend input[class*="span"], - .input-append input[class*="span"] { - display: inline-block; - width: auto; - } - .controls-row [class*="span"] + [class*="span"] { - margin-left: 0; - } - .modal { - position: fixed; - top: 20px; - left: 20px; - right: 20px; - width: auto; - margin: 0; - } - .modal.fade { - top: -100px; - } - .modal.fade.in { - top: 20px; - } -} -@media (max-width: 480px) { - .nav-collapse { - -webkit-transform: translate3d(0, 0, 0); - } - .page-header h1 small { - display: block; - line-height: 20px; - } - input[type="checkbox"], - input[type="radio"] { - border: 1px solid #ccc; - } - .form-horizontal .control-label { - float: none; - width: auto; - padding-top: 0; - text-align: left; - } - .form-horizontal .controls { - margin-left: 0; - } - .form-horizontal .control-list { - padding-top: 0; - } - .form-horizontal .form-actions { - padding-left: 10px; - padding-right: 10px; - } - .media .pull-left, - .media .pull-right { - float: none; - display: block; - margin-bottom: 10px; - } - .media-object { - margin-right: 0; - margin-left: 0; - } - .modal { - top: 10px; - left: 10px; - right: 10px; - } - .modal-header .close { - padding: 10px; - margin: -10px; - } - .carousel-caption { - position: static; - } -} -@media (min-width: 768px) and (max-width: 979px) { - .row { - margin-left: -20px; - *zoom: 1; - } - .row:before, - .row:after { - display: table; - content: ""; - line-height: 0; - } - .row:after { - clear: both; - } - [class*="span"] { - float: left; - min-height: 1px; - margin-left: 20px; - } - .container, - .navbar-static-top .container, - .navbar-fixed-top .container, - .navbar-fixed-bottom .container { - width: 724px; - } - .span12 { - width: 724px; - } - .span11 { - width: 662px; - } - .span10 { - width: 600px; - } - .span9 { - width: 538px; - } - .span8 { - width: 476px; - } - .span7 { - width: 414px; - } - .span6 { - width: 352px; - } - .span5 { - width: 290px; - } - .span4 { - width: 228px; - } - .span3 { - width: 166px; - } - .span2 { - width: 104px; - } - .span1 { - width: 42px; - } - .offset12 { - margin-left: 764px; - } - .offset11 { - margin-left: 702px; - } - .offset10 { - margin-left: 640px; - } - .offset9 { - margin-left: 578px; - } - .offset8 { - margin-left: 516px; - } - .offset7 { - margin-left: 454px; - } - .offset6 { - margin-left: 392px; - } - .offset5 { - margin-left: 330px; - } - .offset4 { - margin-left: 268px; - } - .offset3 { - margin-left: 206px; - } - .offset2 { - margin-left: 144px; - } - .offset1 { - margin-left: 82px; - } - .row-fluid { - width: 100%; - *zoom: 1; - } - .row-fluid:before, - .row-fluid:after { - display: table; - content: ""; - line-height: 0; - } - .row-fluid:after { - clear: both; - } - .row-fluid [class*="span"] { - display: block; - width: 100%; - min-height: 30px; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - float: left; - margin-left: 2.7624309392265194%; - *margin-left: 2.709239449864817%; - } - .row-fluid [class*="span"]:first-child { - margin-left: 0; - } - .row-fluid .controls-row [class*="span"] + [class*="span"] { - margin-left: 2.7624309392265194%; - } - .row-fluid .span12 { - width: 100%; - *width: 99.94680851063829%; - } - .row-fluid .span11 { - width: 91.43646408839778%; - *width: 91.38327259903608%; - } - .row-fluid .span10 { - width: 82.87292817679558%; - *width: 82.81973668743387%; - } - .row-fluid .span9 { - width: 74.30939226519337%; - *width: 74.25620077583166%; - } - .row-fluid .span8 { - width: 65.74585635359117%; - *width: 65.69266486422946%; - } - .row-fluid .span7 { - width: 57.18232044198895%; - *width: 57.12912895262725%; - } - .row-fluid .span6 { - width: 48.61878453038674%; - *width: 48.56559304102504%; - } - .row-fluid .span5 { - width: 40.05524861878453%; - *width: 40.00205712942283%; - } - .row-fluid .span4 { - width: 31.491712707182323%; - *width: 31.43852121782062%; - } - .row-fluid .span3 { - width: 22.92817679558011%; - *width: 22.87498530621841%; - } - .row-fluid .span2 { - width: 14.3646408839779%; - *width: 14.311449394616199%; - } - .row-fluid .span1 { - width: 5.801104972375691%; - *width: 5.747913483013988%; - } - .row-fluid .offset12 { - margin-left: 105.52486187845304%; - *margin-left: 105.41847889972962%; - } - .row-fluid .offset12:first-child { - margin-left: 102.76243093922652%; - *margin-left: 102.6560479605031%; - } - .row-fluid .offset11 { - margin-left: 96.96132596685082%; - *margin-left: 96.8549429881274%; - } - .row-fluid .offset11:first-child { - margin-left: 94.1988950276243%; - *margin-left: 94.09251204890089%; - } - .row-fluid .offset10 { - margin-left: 88.39779005524862%; - *margin-left: 88.2914070765252%; - } - .row-fluid .offset10:first-child { - margin-left: 85.6353591160221%; - *margin-left: 85.52897613729868%; - } - .row-fluid .offset9 { - margin-left: 79.8342541436464%; - *margin-left: 79.72787116492299%; - } - .row-fluid .offset9:first-child { - 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