[docs] move wiki to Read the Docs (#945)

* fixed Python-API references

* moved Features section to ReadTheDocs

* fixed index of ReadTheDocs

* moved Experiments section to ReadTheDocs

* fixed capital letter

* fixed citing

* moved Parallel Learning section to ReadTheDocs

* fixed markdown

* fixed Python-API

* fixed link to Quick-Start

* fixed gpu docker README

* moved Installation Guide from wiki to ReadTheDocs

* removed references to wiki

* fixed capital letters in headings

* hotfixes

* fixed non-Unicode symbols and reference to Python API

* fixed citing references

* fixed links in .md files

* fixed links in .rst files

* store images locally in the repo

* fixed missed word

* fixed indent in Experiments.rst

* fixed 'Duplicate implicit target name' message which is successfully
resolved by adding anchors

* less verbose

* prevented maito: ref creation

* fixed indents

* fixed 404

* fixed 403

* fixed 301

* fixed fake anchors

* fixed file extentions

* fixed Sphinx warnings

* added StrikerRUS profile link to FAQ

* added henry0312 profile link to FAQ

.travis/test.sh

@@ -30,8 +30,10 @@ if [[ ${TASK} == "check-docs" ]]; then
     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
+    rstcheck --report warning --ignore-directives=autoclass,autofunction `find . -type f -name "*.rst"` || exit -1
     make html || exit -1
+    find ./_build/html/ -type f -name '*.html' -exec \
+    sed -i -e 's#\(\.\/[^.]*\.\)\(md\|rst\)#\1html#g' {} \;  # Emulate js function
 #    html5validator --root ./_build/html/ || exit -1  For future (Sphinx 1.6) usage
     linkchecker --config=.linkcheckerrc ./_build/html/*.html || exit -1
     exit 0

CMakeLists.txt

@@ -21,7 +21,7 @@ elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
     message(FATAL_ERROR "Insufficient Clang version")
   endif()
 elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
-    message(FATAL_ERROR "AppleClang wasn't supported. Please see https://github.com/Microsoft/LightGBM/wiki/Installation-Guide#osx")
+    message(FATAL_ERROR "AppleClang wasn't supported. Please see https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#osx")
 endif()
 
 if(APPLE)

R-package/README.md

@@ -24,7 +24,7 @@ For users who wants to install online with GPU or want to choose a specific comp
 
 #### Mac OS X Preparation
 
-gcc with OpenMP support must be installed first. Refer to [wiki](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide#osx) for installing gcc with OpenMP support.
+gcc with OpenMP support must be installed first. Refer to [Installation-Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#osx) for installing gcc with OpenMP support.
 
 ### Install
 
@@ -51,7 +51,7 @@ Note: for the build with Visual Studio/MSBuild in Windows, you should use the Wi
 
 Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). Linux users might require the appropriate user write permissions for packages.
 
-Set `use_gpu` to `TRUE` in `R-package/src/install.libs.R` to enable the build with GPU support. You will need to install Boost and OpenCL first: details for installation can be found in [gpu-support](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide#with-gpu-support).
+Set `use_gpu` to `TRUE` in `R-package/src/install.libs.R` to enable the build with GPU support. You will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version).
 
 You can also install directly from R using the repository with `devtools`:
 
@@ -74,7 +74,7 @@ params <- list(objective="regression", metric="l2")
 model <- lgb.cv(params, dtrain, 10, nfold=5, min_data=1, learning_rate=1, early_stopping_rounds=10)
 ```
 
-Installation with precompiled dll/lib from R using GitHub
+Installation with Precompiled dll/lib from R Using GitHub
 ---------------------------------------------------------
 
 You can install LightGBM R-package from GitHub with devtools thanks to a helper package for LightGBM.
@@ -122,7 +122,9 @@ lgb.dl(commit = "master",
        use_gpu = TRUE)
 ```
 
-For more details about options, please check [Laurae2/lgbdl](https://github.com/Laurae2/lgbdl/) R-package. You may also read [Microsoft/LightGBM#912](https://github.com/Microsoft/LightGBM/issues/912#issuecomment-329496254) for a visual example for LightGBM in Windows with Visual Studio.
+For more details about options, please check [Laurae2/lgbdl](https://github.com/Laurae2/lgbdl/) R-package.
+
+You may also read [Microsoft/LightGBM#912](https://github.com/Microsoft/LightGBM/issues/912#issuecomment-329496254) for a visual example for LightGBM installation in Windows with Visual Studio.
 
 Examples
 --------

README.md

@@ -18,9 +18,9 @@ LightGBM is a gradient boosting framework that uses tree based learning algorith
 - Parallel and GPU learning supported
 - Capable of handling large-scale data
 
-For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
+For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.md).
 
-[Experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
+[Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
 
 News
 ----
@@ -45,7 +45,7 @@ News
 
 12/05/2016 : **Categorical Features as input directly** (without one-hot coding). 
 
-12/02/2016 : Release [**python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
+12/02/2016 : Release [**Python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
 
 More detailed update logs : [Key Events](https://github.com/Microsoft/LightGBM/blob/master/docs/Key-Events.md).
 
@@ -61,16 +61,16 @@ JPMML: https://github.com/jpmml/jpmml-lightgbm
 Get Started and Documentation
 -----------------------------
 
-Install by following the guide for the [command line program](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide), [Python package](https://github.com/Microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/Microsoft/LightGBM/tree/master/R-package). Then please see the [Quick Start](https://github.com/Microsoft/LightGBM/wiki/Quick-Start) guide.
+Install by following the [guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst) for the command line program, [Python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/Microsoft/LightGBM/tree/master/R-package). Then please see the [Quick Start](https://github.com/Microsoft/LightGBM/blob/master/docs/Quick-Start.md) guide.
 
 Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
 
-Next you will want to read:
+Next you may want to read:
 
 * [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks
-* [**Features**](https://github.com/Microsoft/LightGBM/wiki/Features) and algorithms supported by LightGBM
+* [**Features**](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.md) and algorithms supported by LightGBM
 * [**Parameters**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.md) is an exhaustive list of customization you can make
-* [**Parallel Learning**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.md) can speed up computation
+* [**Parallel Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.md) can speed up computation
 * [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters
 
 Documentation for contributors:
@@ -83,7 +83,7 @@ Support
 
 * Ask a question [on Stack Overflow with the `lightgbm` tag ](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.
 * Discuss on the [LightGBM Gitter](https://gitter.im/Microsoft/LightGBM).
-* Open **bug reports** and **feature requests** (not questions) on [Github issues](https://github.com/Microsoft/LightGBM/issues).
+* Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/Microsoft/LightGBM/issues).
 
 How to Contribute
 -----------------

docker/README.md

@@ -8,14 +8,14 @@ Follow the general installation instructions
 [on the Docker site](https://docs.docker.com/installation/):
 
 * [OSX](https://docs.docker.com/installation/mac/): [docker toolbox](https://www.docker.com/toolbox)
-* [ubuntu](https://docs.docker.com/installation/ubuntulinux/)
+* [Ubuntu](https://docs.docker.com/installation/ubuntulinux/)
 
-## Running the container
+## Running the Container
 
-Build the container, for python users: 
+Build the container, for python users:
 
-    $ docker build -t lightgbm -f dockerfile-python .
+    docker build -t lightgbm -f dockerfile-python .
 
 After build finished, run the container:
 
-    $ docker run --rm -it lightgbm
+    docker run --rm -it lightgbm

docker/gpu/README.md

@@ -1,36 +1,46 @@
-# Dockerfile for LightGBM supporting GPU with Python
-A docker file with lightgbm utilizing nvidia-docker. The file is based on the nvidia/cuda:8.0 image. lightgbm can be utilized in gpu and cpu modes and via python (2.7 & 3.5)
-### Contents
+# Dockerfile for LightGBM GPU Version with Python
+
+A docker file with LightGBM utilizing nvidia-docker. The file is based on the nvidia/cuda:8.0 image. LightGBM can be utilized in GPU and CPU modes and via Python (2.7 & 3.5)
+
+## Contents
+
 - LightGBM (cpu + gpu)
 - Python 2.7 (Conda) + scikit-learn notebooks pandas matplotlib
 - Python 3.5 (Conda) + scikit-learn notebooks pandas matplotlib
 
 Running the container starts a jupyter notebook at localhost:8888
 
 jupyter password: keras
-### Requirements
+
+## Requirements
+
 Requires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine.
-### Quickstart
 
-##### Build Docker Image
+## Quickstart
+
+### Build Docker Image
+
 ```sh
 mkdir lightgbm-docker
 cd lightgbm-docker
 wget https://github.com/Microsoft/LightGBM/blob/master/docker/gpu/dockerfile.gpu
-cd lightgbm-docker
 docker build -f dockerfile.gpu -t lightgbm-gpu .
 ```
-##### Run Image
+
+### Run Image
+
 ```sh
 nvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu
 ```
 
-##### Attach with Command Line Access (if required)
+### Attach with Command Line Access (if required)
+
 ```sh
 docker exec -it lightgbm-gpu bash
 ```
-##### Jupyter Notebook
+
+### Jupyter Notebook
+
 ```sh
 localhost:8888
 ```
-

docs/.linkcheckerrc

@@ -7,7 +7,7 @@ sslverify=0
 ignorewarnings=http-robots-denied,https-certificate-error
 
 [output]
-# Set to 0 if you want see only warnings and errors
-verbose=1
+# Set to 1 if you want see the full output, not only warnings and errors
+verbose=0
 
 [AnchorCheck]

docs/Advanced-Topic.md

@@ -31,4 +31,4 @@
 
 ## Parallel Learning
 
-* Refer to [Parallel Learning Guide](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide).
+* Refer to [Parallel Learning Guide](./Parallel-Learning-Guide.rst).