Metadata-Version: 2.1
Name: ensemble-integration
Version: 0.0.0
Summary: Ensemble Integration: a customizable pipeline for generating multi-modal, heterogeneous ensembles
License: GNU General Public License version 3
Author: Jamie Bennett
Requires-Python: >=3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: dill (>=0.3.7,<0.4.0)
Requires-Dist: imbalanced-learn (>=0.11)
Requires-Dist: joblib (>=1.3)
Requires-Dist: numpy (>=1.24)
Requires-Dist: pandas (>=1.4)
Requires-Dist: pandoc (>=2.3,<3.0)
Requires-Dist: scikit-learn (>=1.2,<1.3)
Requires-Dist: scipy (>=1.0,<1.12) ; python_version >= "3.8" and python_version < "3.13"
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Requires-Dist: wget (>=3.2,<4.0)
Requires-Dist: xgboost (>=1.7)
Project-URL: Documentation, https://eipy.readthedocs.io/en/latest/
Project-URL: Homepage, https://github.com/GauravPandeyLab/eipy
Description-Content-Type: text/x-rst

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``ensemble-integration``: Integrating multi-modal data for predictive modeling
==============================================================================

``ensemble-integration`` (or ``eipy``) leverages multi-modal data to build classifiers using a late fusion approach. 
In eipy, base predictors are trained on each modality before being ensembled at the late stage. 

This implementation of eipy can utilize `sklearn-like <https://scikit-learn.org/>`_ models only, therefore, for unstructured data,
e.g. images, it is recommended to perform feature selection prior to using eipy. We hope to allow for a wider range of base predictors, 
i.e. deep learning methods, in future releases. A key feature of ``eipy`` is its built-in nested cross-validation approach, allowing for a 
fair comparison of a collection of user-defined ensemble methods.

Documentation including tutorials are available at `https://eipy.readthedocs.io/en/latest/ <https://eipy.readthedocs.io/en/latest/>`_.

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

As usual it is recommended to set up a virtual environment prior to installation. 
You can install ensemble-integration with pip:

``pip install ensemble-integration``

Citation
--------

If you use ``ensemble-integration`` in a scientific publication please cite the following:

Jamie J. R. Bennett, Yan Chak Li and Gaurav Pandey. *An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles*, https://doi.org/10.48550/arXiv.2401.09582.

Yan Chak Li, Linhua Wang, Jeffrey N Law, T M Murali, Gaurav Pandey. *Integrating multimodal data through interpretable heterogeneous ensembles*, Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac065, https://doi.org/10.1093/bioadv/vbac065.


