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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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Showing with 60 additions and 33 deletions
.travis.yml
View file @ 589541e5
......@@ -19,6 +19,7 @@ env:
- TASK=regular
- TASK=mpi PYTHON_VERSION=2.7
- TASK=pylint
- TASK=check-docs
- TASK=if-else
- TASK=sdist PYTHON_VERSION=3.4
- TASK=bdist PYTHON_VERSION=3.5
......@@ -35,6 +36,8 @@ matrix:
env: TASK=if-else
- os: osx
env: TASK=pylint
- os: osx
env: TASK=check-docs
before_install:
- test -n $CC && unset CC
......
.travis/setup.sh
View file @ 589541e5
......@@ -4,7 +4,7 @@ if [[ $TRAVIS_OS_NAME == "osx" ]]; then
brew install openmpi # use openmpi to install gcc
wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda${PYTHON_VERSION:0:1}-latest-MacOSX-x86_64.sh
else
if [[ ${TASK} != "pylint" ]]; then
if [[ ${TASK} != "pylint" ]] && [[ ${TASK} != "check-docs" ]]; then
sudo add-apt-repository ppa:george-edison55/cmake-3.x -y
sudo apt-get update -q
sudo apt-get install -y cmake
......
.travis/test.sh
View file @ 589541e5
......@@ -25,6 +25,18 @@ esac
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
pip install pep8
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
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.
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| | 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 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| 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@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@10 | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG\ :sub:`1` | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG\ :sub:`3` | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| Yahoo-LTR NDCG\ :sub:`5` | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| 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 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG@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@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:`1` | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG\ :sub:`3` | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| MS-LTR NDCG\ :sub:`5` | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 |
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
| 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 |
+---------------------+----------------+---------------+---------------+----------------+---------------+---------------+
+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+
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
* `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")
* `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).
* `label_gain` can be used to set the gain(weight) of `int` label.
* `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
* `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")
* `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)
* `binary_logloss`, [log loss](https://www.kaggle.com/wiki/LogLoss)
* `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:
* `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")
* `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).
* `label_gain` can be used to set the gain(weight) of `int` label.
* `multiclass`, multi-class classification application, should set `num_class` as well
......
docs/index.rst
View file @ 589541e5
......@@ -28,5 +28,4 @@ Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
docs/requirements.txt
View file @ 589541e5
sphinx
sphinx<=1.5.6
sphinxcontrib-napoleon
sphinx_rtd_theme
recommonmark
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