Source: python-shap
Section: python
Priority: optional
Maintainer: Adam Cecile <acecile@le-vert.net>
Build-Depends: debhelper (>= 9), dh-python, python-all-dev, python-setuptools, python3-all-dev, python3-setuptools,
  python-numpy, python3-numpy,
  python-matplotlib, python3-matplotlib,
  python-scipy, python3-scipy,
  python-sklearn, python3-sklearn,
  python-pandas, python3-pandas,
  python-tqdm, python3-tqdm,
  python-ipython, python3-ipython,
  python-pytest <!nocheck>, python3-pytest <!nocheck>,
  python-lightgbm (>= 2.2.1) <!nocheck>, python3-lightgbm (>= 2.2.1) <!nocheck>,
  python3-sphinx <!nodoc>, python3-sphinx-rtd-theme <!nodoc>,
  lsb-release,
Build-Conflicts: python-sphinx <!nodoc>,
Standards-Version: 3.9.8
Homepage: 
X-Python-Version: >= 2.7
X-Python3-Version: >= 3.4

Package: python-shap
Architecture: any
Depends: ${shlibs:Depends}, ${python:Depends}, ${misc:Depends}
Suggests: python-shap-doc
Description: Unified approach to explain machine learning model (Python 2)
 SHAP (SHapley Additive exPlanations) is a unified approach to explain the
 output of any machine learning model.
 .
 SHAP connects game theory with local explanations, uniting several previous
 methods and representing the only possible consistent and locally accurate
 additive feature attribution method based on expectations.
 .
 This package installs the library for Python 2.

Package: python3-shap
Architecture: any
Depends: ${shlibs:Depends}, ${python3:Depends}, ${misc:Depends}
Suggests: python-shap-doc
Description: Unified approach to explain machine learning model (Python 3)
 SHAP (SHapley Additive exPlanations) is a unified approach to explain the
 output of any machine learning model.
 .
 SHAP connects game theory with local explanations, uniting several previous
 methods and representing the only possible consistent and locally accurate
 additive feature attribution method based on expectations.
 .
 This package installs the library for Python 3.

Package: python-shap-doc
Architecture: all
Section: doc
Depends: ${sphinxdoc:Depends}, ${misc:Depends}
Description: Unified approach to explain machine learning model (common documentation)
 SHAP (SHapley Additive exPlanations) is a unified approach to explain the
 output of any machine learning model.
 .
 SHAP connects game theory with local explanations, uniting several previous
 methods and representing the only possible consistent and locally accurate
 additive feature attribution method based on expectations.
 .
 This is the common documentation package.
