Metadata-Version: 2.1
Name: gpboost
Version: 0.7.1
Summary: GPBoost Python Package
Home-page: https://github.com/fabsig/GPBoost
Maintainer: Fabio Sigrist
Maintainer-email: fabiosigrist@gmail.com
License: Apache License, Version 2.0, + see LICENSE file
Description: <img src="https://github.com/fabsig/GPBoost/blob/master/gpboost_sticker.jpg?raw=true"
             alt="GPBoost icon"
             align = "right"
             width="40%" />
        
        # GPBoost Python Package
        
        [![License](https://img.shields.io/badge/Licence-Apache%202.0-green.svg)](https://github.com/fabsig/GPBoost/blob/master/LICENSE)
        [<img src="https://img.shields.io/pypi/pyversions/gpboost.svg?logo=python&logoColor=white">](https://pypi.org/project/gpboost)
        [<img src="https://img.shields.io/pypi/v/gpboost.svg?logo=pypi&logoColor=white">](https://pypi.org/project/gpboost)
        [<img src="https://pepy.tech/badge/gpboost">](https://pepy.tech/project/gpboost)
        
        This is the Python package implementation of the GPBoost library. See https://github.com/fabsig/GPBoost for more information on the modeling background and the software implementation.
        
        ### Table of Contents
        * [Examples](#examples)
        * [Installation](#installation)
          * [Installation from PyPI](#installation-from-pypi-using-precompiled-binaries)
          * [Installation from source](#installation-from-source)
        
        
        ## Examples
        
        * [**Detailed Python examples**](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide):
          * [GPBoost algorithm](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/GPBoost_algorithm.py) for combining tree-boosting with Gaussian process and random effects models
          * [GPBoost algorithm for binary classification and other non-Gaussian data](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/classification_non_Gaussian_data.py) (Poisson regression, etc.)
          * [Parameter tuning](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/parameter_tuning.py) using deterministic or random grid search
          * [Linear Gaussian process and mixed effects model examples](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/linear_Gaussian_process_mixed_effects_models.py)
          * [Generalized linear Gaussian process and mixed effects model examples](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/generalized_linear_Gaussian_process_mixed_effects_models.py)
          * [GPBoost algorithm applied to panel data](https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/panel_data_example.py)
        * **Blog posts** on how to 
           * [Combine tree-boosting with grouped random effects models](https://towardsdatascience.com/tree-boosted-mixed-effects-models-4df610b624cb) 
           * [Combine tree-boosting with Gaussian processes for spatial data](https://towardsdatascience.com/tree-boosting-for-spatial-data-789145d6d97d)
           * [Use GPBoost for generalized linear mixed effects models (GLMMs)](https://towardsdatascience.com/generalized-linear-mixed-effects-models-in-r-and-python-with-gpboost-89297622820c) 
        * [This demo](https://htmlpreview.github.io/?https://github.com/fabsig/GPBoost/blob/master/examples/GPBoost_demo.html) on how GPBoost can be used in R and Python
        
        
        ## Installation
        
        #### Before you install
        
        * [setuptools](https://pypi.org/project/setuptools) is needed. You can install this using ``pip install setuptools -U``
        
        * 32-bit Python is not supported. Please install the 64-bit version. See [build 32-bit version with 32-bit Python section](#build-32-bit-version-with-32-bit-python).
        
        ### Installation from [PyPI](https://pypi.org/project/gpboost) using precompiled binaries
        
        ```sh
        pip install gpboost -U
        ```
        
        * You may need to install [wheel](https://pythonwheels.com) via ``pip install wheel`` first.
        
        #### Requirements
        
        * For **Windows** users, [VC runtime](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads) is needed if **Visual Studio** (2015 or newer) is not installed.
        
        * For **Linux** users, **glibc** >= 2.14 is required. Also, in some rare cases, when you get the ``OSError: libgomp.so.1: cannot open shared object file: No such file or directory`` error when importing GPBoost, you need to install the OpenMP runtime library separately (use your package manager and search for ``lib[g|i]omp`` for doing this).
        
        * For **macOS** users:
        
          * The library file in distribution wheels is built by the **Apple Clang** (Xcode version 12.3 is used starting from GPBoost version 0.3.0) compiler. You need to install the **OpenMP** library. You can install the **OpenMP** library by the following command: ``brew install libomp``.
        
        ### Installation from source
        
        Installation from source can be either done from PyPI or GitHub.
        
        #### Requirements for installation from source
        
        * Installation from source requires that you have installed [**CMake**](https://cmake.org/).
        
        * 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 the [Installation Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang)) first and **CMake** version 3.16 or higher is required. Only Apple Clang version 8.1 or higher is supported.
        
          * In case you prefer **gcc**, you need to install it (details for installation can be found in the [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. 
        
        
        #### Installation from source from PyPI
        
        ```sh
        pip install --no-binary :all: gpboost
        ```
        
        ##### Build with MinGW-w64 on Windows
        
        ```sh
        pip install gpboost --install-option=--mingw
        ```
        
        * [CMake](https://cmake.org/) and [MinGW-w64](https://mingw-w64.org/) should be installed first.
        
        
        ##### Build 32-bit version with 32-bit Python
        
        ```sh
        pip install gpboost --install-option=--bit32
        ```
        
        By default, installation in an environment with 32-bit Python is prohibited. However, you can remove this prohibition on your own risk by passing the ``bit32`` option (not recommended).
        
        
        #### Installation from source from GitHub
        
        ```sh
        git clone --recursive https://github.com/fabsig/GPBoost.git
        cd GPBoost/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
        ```
        
        Note: ``sudo`` (or administrator rights in **Windows**) may be needed to perform the command.
        
        ##### Build with MinGW-w64 on Windows
        
        ```sh
        python setup.py install --mingw
        ```
        * [CMake](https://cmake.org/) and [MinGW-w64](https://mingw-w64.org/) should be installed first.
        
        If you get any errors during installation or due to any other reasons, you may want to build a dynamic library from source by any method you prefer and then just run ``python setup.py install --precompile``.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
