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Commit 589541e5 authored by Nikita Titov's avatar Nikita Titov Committed by Qiwei Ye
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[docs] documentation build test (#958) 

* added doc test

* fixed 404

* prevented maito: ref creation

* added rst structure test

* fixed unknown directives

* added code for the future check for html syntax

* replaced problem link
parent 078a41dd
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.travis.yml
View file @ 589541e5
...@@ -19,6 +19,7 @@ env: ...@@ -19,6 +19,7 @@ env:
- TASK=regular - TASK=regular
- TASK=mpi PYTHON_VERSION=2.7 - TASK=mpi PYTHON_VERSION=2.7
- TASK=pylint - TASK=pylint
- TASK=check-docs
- TASK=if-else - TASK=if-else
- TASK=sdist PYTHON_VERSION=3.4 - TASK=sdist PYTHON_VERSION=3.4
- TASK=bdist PYTHON_VERSION=3.5 - TASK=bdist PYTHON_VERSION=3.5
...@@ -35,6 +36,8 @@ matrix: ...@@ -35,6 +36,8 @@ matrix:
env: TASK=if-else env: TASK=if-else
- os: osx - os: osx
env: TASK=pylint env: TASK=pylint
- os: osx
env: TASK=check-docs
before_install: before_install:
- test -n $CC && unset CC - test -n $CC && unset CC
......
.travis/setup.sh
View file @ 589541e5
...@@ -4,7 +4,7 @@ if [[ $TRAVIS_OS_NAME == "osx" ]]; then ...@@ -4,7 +4,7 @@ if [[ $TRAVIS_OS_NAME == "osx" ]]; then
brew install openmpi # use openmpi to install gcc brew install openmpi # use openmpi to install gcc
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda${PYTHON_VERSION:0:1}-latest-MacOSX-x86_64.sh wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda${PYTHON_VERSION:0:1}-latest-MacOSX-x86_64.sh
else else
if [[ ${TASK} != "pylint" ]]; then if [[ ${TASK} != "pylint" ]] && [[ ${TASK} != "check-docs" ]]; then
sudo add-apt-repository ppa:george-edison55/cmake-3.x -y sudo add-apt-repository ppa:george-edison55/cmake-3.x -y
sudo apt-get update -q sudo apt-get update -q
sudo apt-get install -y cmake sudo apt-get install -y cmake
......
.travis/test.sh
View file @ 589541e5
...@@ -25,6 +25,18 @@ esac ...@@ -25,6 +25,18 @@ esac
cd $TRAVIS_BUILD_DIR cd $TRAVIS_BUILD_DIR
if [[ ${TASK} == "check-docs" ]]; then
cd docs
sudo apt-get install linkchecker
pip install rstcheck # html5validator
pip install -r requirements.txt
rstcheck --ignore-directives=autoclass,autofunction `find . -type f -name "*.rst"` || exit -1
make html || exit -1
# html5validator --root ./_build/html/ || exit -1 For future (Sphinx 1.6) usage
linkchecker --config=.linkcheckerrc ./_build/html/*.html || exit -1
exit 0
fi
if [[ ${TASK} == "pylint" ]]; then if [[ ${TASK} == "pylint" ]]; then
pip install pep8 pip install pep8
pep8 --ignore=E501 --exclude=./compute,./docs . || exit -1 pep8 --ignore=E501 --exclude=./compute,./docs . || exit -1
......
docs/.linkcheckerrc 0 → 100644
View file @ 589541e5
[checking]
recursionlevel=1
anchors=1
sslverify=0
[filtering]
ignorewarnings=http-robots-denied,https-certificate-error
[output]
# Set to 0 if you want see only warnings and errors
verbose=1
[AnchorCheck]
docs/GPU-Performance.rst
View file @ 589541e5
...@@ -131,33 +131,33 @@ The following table lists the accuracy on test set that CPU and GPU learner can ...@@ -131,33 +131,33 @@ The following table lists the accuracy on test set that CPU and GPU learner can
GPU with the same number of bins can achieve a similar level of accuracy as on the CPU, despite using single precision arithmetic. GPU with the same number of bins can achieve a similar level of accuracy as on the CPU, despite using single precision arithmetic.
For most datasets, using 63 bins is sufficient. For most datasets, using 63 bins is sufficient.
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| | CPU 255 bins | CPU 63 bins | CPU 15 bins | GPU 255 bins | GPU 63 bins | GPU 15 bins | | | CPU 255 bins | CPU 63 bins | CPU 15 bins | GPU 255 bins | GPU 63 bins | GPU 15 bins |
+=====================+================+===============+===============+================+===============+===============+ +===========================+================+===============+===============+================+===============+===============+
| Higgs AUC | 0.845612 | 0.845239 | 0.841066 | 0.845612 | 0.845209 | 0.840748 | | Higgs AUC | 0.845612 | 0.845239 | 0.841066 | 0.845612 | 0.845209 | 0.840748 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Epsilon AUC | 0.950243 | 0.949952 | 0.948365 | 0.950057 | 0.949876 | 0.948365 | | Epsilon AUC | 0.950243 | 0.949952 | 0.948365 | 0.950057 | 0.949876 | 0.948365 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG@1 | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 | | Yahoo-LTR NDCG\ :sub:`1` | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG@3 | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 | | Yahoo-LTR NDCG\ :sub:`3` | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG@5 | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 | | Yahoo-LTR NDCG\ :sub:`5` | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG@10 | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 | | Yahoo-LTR NDCG\ :sub:`10` | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Expo AUC | 0.776217 | 0.771566 | 0.743329 | 0.776285 | 0.77098 | 0.744078 | | Expo AUC | 0.776217 | 0.771566 | 0.743329 | 0.776285 | 0.77098 | 0.744078 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG@1 | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 | | MS-LTR NDCG\ :sub:`1` | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG@3 | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 | | MS-LTR NDCG\ :sub:`3` | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG@5 | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 | | MS-LTR NDCG\ :sub:`5` | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG@10 | 0.527835 | 0.527304 | 0.524603 | 0.528009 | 0.527059 | 0.524722 | | MS-LTR NDCG\ :sub:`10` | 0.527835 | 0.527304 | 0.524603 | 0.528009 | 0.527059 | 0.524722 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Bosch AUC | 0.718115 | 0.721791 | 0.716677 | 0.717184 | 0.724761 | 0.717005 | | Bosch AUC | 0.718115 | 0.721791 | 0.716677 | 0.717184 | 0.724761 | 0.717005 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
We record the wall clock time after 500 iterations, as shown in the figure below: We record the wall clock time after 500 iterations, as shown in the figure below:
......
docs/Parameters.md
View file @ 589541e5
...@@ -38,7 +38,7 @@ The parameter format is `key1=value1 key2=value2 ... ` . And parameters can be s ...@@ -38,7 +38,7 @@ The parameter format is `key1=value1 key2=value2 ... ` . And parameters can be s
* `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520) * `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520)
* `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression") * `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression")
* `binary`, binary classification application * `binary`, binary classification application
* `lambdarank`, [lambdarank](https://pdfs.semanticscholar.org/fc9a/e09f9ced555558fdf1e997c0a5411fb51f15.pdf) application * `lambdarank`, [lambdarank](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf) application
* The label should be `int` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect). * The label should be `int` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect).
* `label_gain` can be used to set the gain(weight) of `int` label. * `label_gain` can be used to set the gain(weight) of `int` label.
* `multiclass`, multi-class classification application, should set `num_class` as well * `multiclass`, multi-class classification application, should set `num_class` as well
...@@ -267,7 +267,7 @@ The parameter format is `key1=value1 key2=value2 ... ` . And parameters can be s ...@@ -267,7 +267,7 @@ The parameter format is `key1=value1 key2=value2 ... ` . And parameters can be s
* `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520) * `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520)
* `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression") * `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression")
* `ndcg`, [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG) * `ndcg`, [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG)
* `map`, [MAP](https://www.kaggle.com/wiki/MeanAveragePrecision) * `map`, [MAP](https://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision)
* `auc`, [AUC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve) * `auc`, [AUC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve)
* `binary_logloss`, [log loss](https://www.kaggle.com/wiki/LogLoss) * `binary_logloss`, [log loss](https://www.kaggle.com/wiki/LogLoss)
* `binary_error`. For one sample `0` for correct classification, `1` for error classification. * `binary_error`. For one sample `0` for correct classification, `1` for error classification.
......
docs/Quick-Start.md
View file @ 589541e5
...@@ -54,7 +54,7 @@ Some important parameters: ...@@ -54,7 +54,7 @@ Some important parameters:
* `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520) * `fair`, [Fair loss](https://www.kaggle.com/c/allstate-claims-severity/discussion/24520)
* `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression") * `poisson`, [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression "Poisson regression")
* `binary`, binary classification application * `binary`, binary classification application
* `lambdarank`, [lambdarank](https://pdfs.semanticscholar.org/fc9a/e09f9ced555558fdf1e997c0a5411fb51f15.pdf) application * `lambdarank`, [lambdarank](https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf) application
* The label should be `int` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect). * The label should be `int` type in lambdarank tasks, and larger number represent the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect).
* `label_gain` can be used to set the gain(weight) of `int` label. * `label_gain` can be used to set the gain(weight) of `int` label.
* `multiclass`, multi-class classification application, should set `num_class` as well * `multiclass`, multi-class classification application, should set `num_class` as well
......
docs/index.rst
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...@@ -28,5 +28,4 @@ Indices and tables ...@@ -28,5 +28,4 @@ Indices and tables
================== ==================
* :ref:`genindex` * :ref:`genindex`
* :ref:`modindex`
* :ref:`search` * :ref:`search`
docs/requirements.txt
View file @ 589541e5
sphinx sphinx<=1.5.6
sphinxcontrib-napoleon sphinxcontrib-napoleon
sphinx_rtd_theme sphinx_rtd_theme
recommonmark recommonmark
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