xgboost

History

Maurus Cuelenaere 6bd1869026 Add prediction of feature contributions (#2003 )

* Add prediction of feature contributions

This implements the idea described at http://blog.datadive.net/interpreting-random-forests/
which tries to give insight in how a prediction is composed of its feature contributions
and a bias.

* Support multi-class models

* Calculate learning_rate per-tree instead of using the one from the first tree

* Do not rely on node.base_weight * learning_rate having the same value as the node mean value (aka leaf value, if it were a leaf); instead calculate them (lazily) on-the-fly

* Add simple test for contributions feature

* Check against param.num_nodes instead of checking for non-zero length

* Loop over all roots instead of only the first

2017-05-14 00:58:10 -05:00

xgboost

Add prediction of feature contributions (#2003 )

2017-05-14 00:58:10 -05:00

.gitignore

python package refactor into python-package

2015-07-30 22:04:45 -07:00

.pylintrc

[PYTHON] Refactor trainnig API to use callback

2016-05-19 21:31:23 -07:00

build_trouble_shooting.md

Use bst_float consistently throughout (#1824 )

2016-11-30 10:02:10 -08:00

MANIFEST.in

PyPI (pip installation) setup for 0.6 code (#1445 )

2016-08-10 07:45:56 -05:00

prep_pip.sh

PyPI (pip installation) setup for 0.6 code (#1445 )

2016-08-10 07:45:56 -05:00

README.rst

Use bst_float consistently throughout (#1824 )

2016-11-30 10:02:10 -08:00

setup_pip.py

Inform setuptools that this is a binary package (#1996 )

2017-03-07 09:26:04 -06:00

setup.cfg

fix pushd problem of pip building, convert README to rst for PyPI

2015-11-11 23:03:07 -08:00

setup.py

Make lib path relatrive to fix setup error #1932 (#1947 )

2017-01-09 10:40:24 -08:00

README.rst

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

|PyPI version|

Installation
------------

We are on `PyPI <https://pypi.python.org/pypi/xgboost>`__ 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
   investigation, 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 <https://pypi.python.org/pypi/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 <../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.

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 <http://brew.sh/>`__
   ``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/guide-python/sklearn_parallel.py>`__
   demo.

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