Unverified Commit 94fbe5bb authored by Guolin Ke's avatar Guolin Ke Committed by GitHub
Browse files

[docs] updated Microsoft GitHub URL (#2152)

* fix travis badge

* updated GitHub Microsoft URL
parent f46f8b2a
......@@ -5,8 +5,8 @@ Documentation for LightGBM is generated using `Sphinx <http://www.sphinx-doc.org
and `Breathe <https://breathe.readthedocs.io/>`__, which works on top of `Doxygen <http://www.doxygen.nl/index.html>`__ output.
List of parameters and their descriptions in `Parameters.rst <./Parameters.rst>`__
is generated automatically from comments in `config file <https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/config.h>`__
by `this script <https://github.com/Microsoft/LightGBM/blob/master/helpers/parameter_generator.py>`__.
is generated automatically from comments in `config file <https://github.com/microsoft/LightGBM/blob/master/include/LightGBM/config.h>`__
by `this script <https://github.com/microsoft/LightGBM/blob/master/helpers/parameter_generator.py>`__.
After each commit on ``master``, documentation is updated and published to `Read the Docs <https://lightgbm.readthedocs.io/>`__.
......
......@@ -3,7 +3,7 @@ Examples
You can learn how to use LightGBM by these examples.
Comments in configuration files might be outdated. Actual information about parameters always can be found [here](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst).
Comments in configuration files might be outdated. Actual information about parameters always can be found [here](https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst).
Machine Learning Challenge Winning Solutions
============================================
......
......@@ -18,4 +18,4 @@ Here is an example for LightGBM to perform parallel learning for 2 machines.
This parallel learning example is based on socket. LightGBM also supports parallel learning based on mpi.
For more details about the usage of parallel learning, please refer to [this](https://github.com/Microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst).
For more details about the usage of parallel learning, please refer to [this](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst).
......@@ -3,7 +3,7 @@ Python-package Examples
Here is an example for LightGBM to use Python-package.
You should install LightGBM [Python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) first.
You should install LightGBM [Python-package](https://github.com/microsoft/LightGBM/tree/master/python-package) first.
You also need scikit-learn, pandas, matplotlib (only for plot example), and scipy (only for logistic regression example) to run the examples, but they are not required for the package itself. You can install them with pip:
......@@ -19,19 +19,19 @@ python simple_example.py
Examples include:
- [simple_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py)
- [simple_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py)
- Construct Dataset
- Basic train and predict
- Eval during training
- Early stopping
- Save model to file
- [sklearn_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/sklearn_example.py)
- [sklearn_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/sklearn_example.py)
- Create data for learning with sklearn interface
- Basic train and predict with sklearn interface
- Feature importances with sklearn interface
- Self-defined eval metric with sklearn interface
- Find best parameters for the model with sklearn's GridSearchCV
- [advanced_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/advanced_example.py)
- [advanced_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/advanced_example.py)
- Construct Dataset
- Set feature names
- Directly use categorical features without one-hot encoding
......@@ -47,12 +47,12 @@ Examples include:
- Self-defined objective function
- Self-defined eval metric
- Callback function
- [logistic_regression.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/logistic_regression.py)
- [logistic_regression.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/logistic_regression.py)
- Use objective `xentropy` or `binary`
- Use `xentropy` with binary labels or probability labels
- Use `binary` only with binary labels
- Compare speed of `xentropy` versus `binary`
- [plot_example.py](https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/plot_example.py)
- [plot_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/plot_example.py)
- Construct Dataset
- Train and record eval results for further plotting
- Plot metrics recorded during training
......
......@@ -372,7 +372,7 @@ struct Config {
// desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits
// desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values
// desc = **Note**: the forced split logic will be ignored, if the split makes gain worse
// desc = see `this file <https://github.com/Microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
// desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
std::string forcedsplits_filename = "";
// check = >=0.0
......
......@@ -24,7 +24,7 @@ For **macOS** users:
- Starting from version 2.2.1, the library file in distribution wheels is built by the **Apple Clang** (Xcode_8.3.3) compiler. This means that you don't need to install the **gcc** compiler anymore. Instead of that you need to install the **OpenMP** library, which is required for running LightGBM on the system with the **Apple Clang** compiler. You can install the **OpenMP** library by the following command: ``brew install libomp``.
- For version smaller than 2.2.1 and not smaller than 2.1.2, **gcc-8** with **OpenMP** support must be installed first. Refer to `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc>`__ for installation of **gcc-8** with **OpenMP** support.
- For version smaller than 2.2.1 and not smaller than 2.1.2, **gcc-8** with **OpenMP** support must be installed first. Refer to `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc>`__ for installation of **gcc-8** with **OpenMP** support.
- For version smaller than 2.1.2, **gcc-7** with **OpenMP** is required.
......@@ -45,7 +45,7 @@ For **Linux** and **macOS** users, installation from sources requires installed
For **macOS** users, you can perform installation either with **Apple Clang** or **gcc**.
- In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang>`__) first and **CMake** version 3.12 or higher is required.
- In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang>`__) first and **CMake** version 3.12 or higher is required.
In some cases **OpenMP** cannot be found which causes installation failures. So, if you encounter errors during the installation process, try to pass paths to **CMake** via ``pip`` options, like
......@@ -53,7 +53,7 @@ For **macOS** users, you can perform installation either with **Apple Clang** or
pip install lightgbm --install-option="--openmp-include-dir=/usr/local/opt/libomp/include/" --install-option="--openmp-library=/usr/local/opt/libomp/lib/libomp.dylib"
- In case you prefer **gcc**, you need to install it (details for installation can be found in `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc>`__) and specify compilers by running ``export CXX=g++-7 CC=gcc-7`` (replace "7" with version of **gcc** installed on your machine) first.
- In case you prefer **gcc**, you need to install it (details for installation can be found in `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc>`__) and specify compilers by running ``export CXX=g++-7 CC=gcc-7`` (replace "7" with version of **gcc** installed on your machine) first.
For **Windows** users, **Visual Studio** (or `VS Build Tools <https://visualstudio.microsoft.com/downloads/>`_) is needed. If you get any errors during installation, you may need to install `CMake`_ (version 3.8 or higher).
......@@ -79,7 +79,7 @@ All remarks from `Build from Sources section <#build-from-sources>`__ are actual
For **Windows** users, compilation with **MinGW-w64** is not supported and `CMake`_ (version 3.8 or higher) is strongly required.
**MPI** libraries are needed: details for installation can be found in `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-mpi-version>`__.
**MPI** libraries are needed: details for installation can be found in `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-mpi-version>`__.
Build GPU Version
~~~~~~~~~~~~~~~~~
......@@ -92,7 +92,7 @@ All remarks from `Build from Sources section <#build-from-sources>`__ are actual
For **Windows** users, `CMake`_ (version 3.8 or higher) is strongly required.
**Boost** and **OpenCL** are needed: details for installation can be found in `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version>`__. Almost always you also need to pass ``OpenCL_INCLUDE_DIR``, ``OpenCL_LIBRARY`` options for **Linux** and ``BOOST_ROOT``, ``BOOST_LIBRARYDIR`` options for **Windows** to **CMake** via ``pip`` options, like
**Boost** and **OpenCL** are needed: details for installation can be found in `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version>`__. Almost always you also need to pass ``OpenCL_INCLUDE_DIR``, ``OpenCL_LIBRARY`` options for **Linux** and ``BOOST_ROOT``, ``BOOST_LIBRARYDIR`` options for **Windows** to **CMake** via ``pip`` options, like
.. code:: sh
......@@ -132,7 +132,7 @@ Build with MinGW-w64 on Windows
`CMake`_ and `MinGW-w64 <https://mingw-w64.org/>`_ should be installed first.
It is recommended to use **Visual Studio** for its better multithreading efficiency in **Windows** for many-core systems (see `FAQ <https://github.com/Microsoft/LightGBM/blob/master/docs/FAQ.rst#lightgbm>`__ Question 4 and Question 8).
It is recommended to use **Visual Studio** for its better multithreading efficiency in **Windows** for many-core systems (see `FAQ <https://github.com/microsoft/LightGBM/blob/master/docs/FAQ.rst#lightgbm>`__ Question 4 and Question 8).
Install from GitHub
'''''''''''''''''''
......@@ -145,7 +145,7 @@ For **Windows** users, if you get any errors during installation and there is th
.. code:: sh
git clone --recursive https://github.com/Microsoft/LightGBM.git
git clone --recursive https://github.com/microsoft/LightGBM.git
cd LightGBM/python-package
# export CXX=g++-7 CC=gcc-7 # macOS users, if you decided to compile with gcc, don't forget to specify compilers (replace "7" with version of gcc installed on your machine)
python setup.py install
......@@ -162,19 +162,19 @@ Run ``python setup.py install --gpu`` to enable GPU support. All remarks from `B
Run ``python setup.py install --hdfs`` to enable HDFS support. All remarks from `Build HDFS Version section <#build-hdfs-version>`__ are actual in this case.
If you get any errors during installation or due to any other reasons, you may want to build dynamic library from sources by any method you prefer (see `Installation Guide <https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst>`__) and then just run ``python setup.py install --precompile``.
If you get any errors during installation or due to any other reasons, you may want to build dynamic library from sources by any method you prefer (see `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst>`__) and then just run ``python setup.py install --precompile``.
Troubleshooting
---------------
In case you are facing any errors during the installation process, you can examine ``$HOME/LightGBM_compilation.log`` file, in which all operations are logged, to get more details about occurred problem. Also, please attach this file to the issue on GitHub to help faster indicate the cause of the error.
Refer to `FAQ <https://github.com/Microsoft/LightGBM/tree/master/docs/FAQ.rst>`_.
Refer to `FAQ <https://github.com/microsoft/LightGBM/tree/master/docs/FAQ.rst>`_.
Examples
--------
Refer to the walk through examples in `Python guide folder <https://github.com/Microsoft/LightGBM/tree/master/examples/python-guide>`_.
Refer to the walk through examples in `Python guide folder <https://github.com/microsoft/LightGBM/tree/master/examples/python-guide>`_.
Development Guide
-----------------
......@@ -194,7 +194,7 @@ E501 (line too long) and W503 (line break occurred before a binary operator) can
Documentation strings (docstrings) are written in the NumPy style.
.. |License| image:: https://img.shields.io/badge/license-MIT-blue.svg
:target: https://github.com/Microsoft/LightGBM/blob/master/LICENSE
:target: https://github.com/microsoft/LightGBM/blob/master/LICENSE
.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/lightgbm.svg
:target: https://pypi.org/project/lightgbm
.. |PyPI Version| image:: https://img.shields.io/pypi/v/lightgbm.svg
......
# coding: utf-8
"""LightGBM, Light Gradient Boosting Machine.
Contributors: https://github.com/Microsoft/LightGBM/graphs/contributors.
Contributors: https://github.com/microsoft/LightGBM/graphs/contributors.
"""
from __future__ import absolute_import
......
......@@ -319,7 +319,7 @@ if __name__ == "__main__":
packages=find_packages(),
include_package_data=True,
license='The MIT License (Microsoft)',
url='https://github.com/Microsoft/LightGBM',
url='https://github.com/microsoft/LightGBM',
classifiers=['Development Status :: 5 - Production/Stable',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
......
......@@ -192,7 +192,7 @@ class TestSklearn(unittest.TestCase):
for name, estimator in ((lgb.sklearn.LGBMClassifier.__name__, lgb.sklearn.LGBMClassifier),
(lgb.sklearn.LGBMRegressor.__name__, lgb.sklearn.LGBMRegressor)):
check_parameters_default_constructible(name, estimator)
# we cannot leave default params (see https://github.com/Microsoft/LightGBM/issues/833)
# we cannot leave default params (see https://github.com/microsoft/LightGBM/issues/833)
estimator = estimator(min_child_samples=1, min_data_in_bin=1)
for check in _yield_all_checks(name, estimator):
check_name = check.func.__name__ if hasattr(check, 'func') else check.__name__
......

Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 14
VisualStudioVersion = 14.0.25420.1
......
<?xml version="1.0" encoding="utf-8"?>
<?xml version="1.0" encoding="utf-8"?>
<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
<ItemGroup Label="Projectconfigurations">
<ProjectConfiguration Include="Debug_mpi|x64">
......@@ -283,4 +283,4 @@
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
<ImportGroup Label="ExtensionTargets">
</ImportGroup>
</Project>
\ No newline at end of file
</Project>
<?xml version="1.0" encoding="utf-8"?>
<?xml version="1.0" encoding="utf-8"?>
<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
<ItemGroup>
<Filter Include="src">
......@@ -303,4 +303,4 @@
<Filter>src\io</Filter>
</ClCompile>
</ItemGroup>
</Project>
\ No newline at end of file
</Project>
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