Helix: Python Toolkit for Machine Learning, Feature Importance, and Fuzzy Interpretation
Overview
Helix is an open-source, extensible tool for reproducible Machine Learning Modelling and results interpretation. It was originally designed for QSAR/QSPR modelling in biomaterials discovery, but can be applied to any tabular data classification or regression tasks. Version 1.0.0 contains tools for data visualisation and basic pre-processing, it has a collection of machine learning models and interpretation approaches. The theoretical work underpinning the development of the tool can be found in:
D. Rengasamy, Jimiama M. Mase, Aayush Kumar, Benjamin Rothwell, Mercedes Torres Torres, Morgan R. Alexander, David A. Winkler, Grazziela P. Figueredo, Feature importance in machine learning models: A fuzzy information fusion approach, Neurocomputing, Volume 511,2022, Pages 163-174,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2022.09.053 LINK
D. Rengasamy, B. C. Rothwell; G. P. Figueredo, Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion. Appl. Sci. 2021, 11, 11854. https://doi.org/10.3390/app112411854 Link
To cite the Helix package, please use the following DOI:
Install and run Helix
You will need to install Python 3.11 or 3.12 to use Helix. Make sure you also install pip (The Python package installer). If you don't already have it installed, get Python.
You may need to make sure you have OpenMP installed on your machine before you can install Helix. In the terminal use the following commands for your OS:
On Mac:
brew install libomp
You may need to try brew3 if brew does not work. Make sure you install Homebrew on your Mac to use the brew/brew3 command.
On Linux (Ubuntu)
sudo apt install libomp-dev
On Windows, this doesn't seem to be a problem. You should be able to proceed with installation.
For information on how to install and run Helix, check the instructions.
Usage
Helix will open in your internet browser when you run it. The main screen will appear giving a brief introduction to the app. To the left of the screen you will see a list of pages with the different functionalities of the app. Explanations of how to use the page can be found in the instructions.
Team
- Daniel Lea (Lead Research Software Engineer)
- Eduardo Aguilar (Chemist, Data Scientist, Research Software Engineer)
- Karthikeyan Sivakumar (Data Scientist, Software Engineer)
- Jimiama M Mase (Data Scientist and Engineer)
- Reza Omidvar (Data Scientist, Research Software Engineer)
- James Mitchell-White (Data Scientist, Research Software Engineer)
- Grazziela Figueredo (Associate Professor, Data Scientist, Product Owner, Principal Investigator)
Contact
For bugs, questions, suggestions and collaborations, please contact us
1 maintainer
Metadata
1 maintainer
Release files for helix-ai 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| helix_ai-1.2.0.tar.gz | 824.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| helix_ai-1.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.7 MB
Release files / helix_ai-1.2.0.tar.gz
| Download URL | helix_ai-1.2.0.tar.gz |
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| Size | 824.0 kB |
| Tags | Source |
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No |
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Release files / helix_ai-1.2.0-py3-none-any.whl
| Download URL | helix_ai-1.2.0-py3-none-any.whl |
|---|---|
| Size | 851.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
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