Ensemble Integration: a customizable pipeline for generating multi-modal, heterogeneous ensembles
Project description
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for ensemble_integration-0.1.6.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | b44ed2d26ef1d5ed70ec6d48c64e922daa4cfa6c9029fdf9bfbfced25b7f5c75 |
|
MD5 | 328f9c9ebc50ef91180c23ac6274b3a3 |
|
BLAKE2b-256 | 5cc8a23d64c6a79ee15c1ec15da6f7de3981af330a5dc8417e08e9da8cf837bc |
Hashes for ensemble_integration-0.1.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fca67d1df7241d56b07d9b7332f1fcc46c2371fb3ffc6d0e81a0059bdafdd49c |
|
MD5 | 814bb18dfa059a052c079f6142ed4968 |
|
BLAKE2b-256 | 5e85c36ac7f608d09e51372abfefa1836974fe9251d08ffc0928b55dcc077820 |