https://github.com/gauravpandeylab/eipy

Ensemble Integration: a customizable pipeline for generating multi-modal, heterogeneous ensembles
https://github.com/gauravpandeylab/eipy

classification ensemble interpretation machine-learning multimodal nested-cross-validation predictive-modeling scikit-learn

Last synced: 26 days ago
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Ensemble Integration: a customizable pipeline for generating multi-modal, heterogeneous ensembles

• Host: GitHub
• URL: https://github.com/gauravpandeylab/eipy
• Owner: GauravPandeyLab
• License: gpl-3.0
• Created: 2022-09-01T16:02:22.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-08T01:26:21.000Z (29 days ago)
• Last Synced: 2024-10-10T08:20:57.166Z (26 days ago)
• Topics: classification, ensemble, interpretation, machine-learning, multimodal, nested-cross-validation, predictive-modeling, scikit-learn
• Language: Python
• Homepage: https://eipy.readthedocs.io/en/latest/
• Size: 700 KB
• Stars: 20
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • License: COPYING

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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 `_ 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/ `_.

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.