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Interactive visualizations to assess fairness, explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
This repository contains the affectlog_widgets library, which is used for creating interactive, visual Trustworthy AI tools. This project provides a set of widgets for model assessment, fairness analysis, error detection, and more, built on top of machine learning frameworks.
To contribute to affectlog_widgets, make sure to install the following dependencies.
For running tests with code coverage and mocking, use the following:
pytest==7.0.1
pytest-cov
pytest-mock==3.6.1
For parsing requirements, use:
requirements-parser==0.2.0
Ensure you have the necessary tools for building the package:
wheel
Install the following libraries to enable machine learning and fairness evaluation tools:
fairlearn==0.7.0
ml-wrappers>=0.4.0
sktime
pmdarima
Note: For Windows users running Python 3.7, add the following fix for joblib compatibility:
joblib<1.3.0; python_version <= '3.7' and sys_platform == 'win32'
To test notebooks and perform notebook validation, install:
nbformat
papermill
scrapbook
jupyter
nbval
For generating documentation, install the following:
docutils<=0.19
sphinx==5.0.2
sphinx-gallery==0.10.0
pydata-sphinx-theme==0.7.2
Follow these steps to set up your local development environment:
Clone the repository:
git clone <repository_url>
cd affectlog_widgets
Install Dependencies: You can install all dependencies via pip:
pip install -r requirements.txt
Run Tests: To ensure everything is working, run the tests:
pytest
For local development, ensure that trustworthyai is available in your project. You can use the relative path for local development:
../trustworthyai/.
Add this path when running your local environment so the necessary modules can be accessed.
If you're interested in contributing, please create a feature branch from main, make your changes, and open a pull request. Ensure that all tests pass and documentation is updated where necessary.
This project is licensed under the MIT License. See the LICENSE file for details.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.0.6 | 12 Oct 2024 | 10 |
This describes our collected data; it is not a security assessment.
Shared repository data · GitHub
Observed since 20 Oct, 21:03 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:f0309f2b1ad2d94e335ab061fe78d04834c3f319f6e4e66b0e760425657e57e4