This release is a pre-release and may not be stable for production use.
Installation
We are on PyPI now. For stable version, please install using pip:
pip install xgboost
Since this package contains C++ source code, pip needs a C++ compiler from the system to compile the source code on-the-fly. Please follow the following instruction for each supported platform.
Note for Mac OS X users: please install gcc from brew by brew tap homebrew/versions; brew install gcc --without-multilib firstly.
Note for Linux users: please install gcc by sudo apt-get install build-essential firstly or using the corresponding package manager of the system.
Note for windows users: this pip installation may not work on some windows environment, and it may cause unexpected errors. pip installation on windows is currently disabled for further invesigation, please install from github.
For up-to-date version, please install from github.
To make the python module, type ./build.sh in the root directory of project
Make sure you have setuptools
Install with cd python-package; python setup.py install from this directory.
For windows users, please use the Visual Studio project file under windows folder. See also the installation tutorial from Kaggle Otto Forum.
Add MinGW to the system PATH in Windows if you are using the latest version of xgboost which requires compilation:
`python import os os.environ['PATH'] = os.environ['PATH'] + ';C:\\Program Files\\mingw-w64\\x86_64-5.3.0-posix-seh-rt_v4-rev0\\mingw64\\bin' `
Examples
Refer also to the walk through example in demo folder
See also the example scripts for Kaggle Higgs Challenge, including speedtest script on this dataset.
Note
If you want to build xgboost on Mac OS X with multiprocessing support where clang in XCode by default doesn’t support, please install gcc 4.9 or higher using homebrew brew tap homebrew/versions; brew install gcc --without-multilib
If you want to run XGBoost process in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by make no_omp=1. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the sklearn_parallel.py demo.
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