xgboost/python-package
lyxthe 53f695acf2 scikit-learn api section documentation correction (#3967)
* update description of early stopping rounds

the description of early stopping round was quite inconsistent in the scikit-learn api section since the fit paragraph tells that when early stopping rounds occurs, the last iteration is returned not the best one, but the predict paragraph tells that when the predict is called without ntree_limit specified, then ntree_limit is equals to best_ntree_limit.

Thus, when reading the fit part, one could think that it is needed to specify what is the best iter when calling the predict, but when reading the predict part, then the best iter is given by default, it is the last iter that you have to specify if needed.

* Update sklearn.py

* Update sklearn.py

fix doc according to the python_lightweight_test error
2018-12-14 00:27:04 -08:00
..

======================
XGBoost Python Package
======================

|PyPI version|

Notes
=====

- Windows users: pip installation may not work on some Windows environments, and it may cause unexpected errors.
  
  Installation from pip on Windows is therefore currently disabled for further investigation; please `install from Github <https://xgboost.readthedocs.io/en/latest/build.html>`_ instead.
- 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/guide-python/sklearn_parallel.py>`__ demo.

Requirements
============

Since this package contains C++ source code, ``pip`` needs a C++ compiler from the system to compile the source code on-the-fly.

macOS
-----

On macOS, ``gcc@5`` is required as later versions remove support for OpenMP. `See here <https://github.com/dmlc/xgboost/issues/1501#issuecomment-292209578>`_ for more info.

Please install ``gcc@5`` from `Homebrew <https://brew.sh/>`_::

    brew install gcc@5

After installing ``gcc@5``, set it as your compiler::

    export CC = gcc-5
    export CXX = g++-5

Linux
-----

Please install ``gcc``::

    sudo apt-get install build-essential      # Ubuntu/Debian
    sudo yum groupinstall 'Development Tools' # CentOS/RHEL

Installation
============

From `PyPI <https://pypi.python.org/pypi/xgboost>`_
---------------------------------------------------

For a stable version, install using ``pip``::

    pip install xgboost

From source
-----------

For an up-to-date version, `install from Github <https://xgboost.readthedocs.io/en/latest/build.html>`_:

-  Run ``./build.sh`` in the root of the repo.
-  Make sure you have `setuptools <https://pypi.python.org/pypi/setuptools>`_ installed: ``pip install setuptools``
-  Install with ``cd python-package; python setup.py install`` from the root of the repo
-  For Windows users, please use the Visual Studio project file under the `Windows folder <../windows/>`_. See also the `installation
   tutorial <https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13043/run-xgboost-from-windows-and-python>`_ 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 <https://github.com/dmlc/xgboost/tree/master/demo/guide-python>`_.
-  See also the `example scripts <https://github.com/dmlc/xgboost/tree/master/demo/kaggle-higgs>`_ for Kaggle
   Higgs Challenge, including `speedtest script <https://github.com/dmlc/xgboost/tree/master/demo/kaggle-higgs/speedtest.py>`_ on this dataset.

.. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg
   :target: http://badge.fury.io/py/xgboost