SymphonyFairVis
A component that can compare subgroups of data across different metrics, originally introduced to address machine learning fairness problems.
The component is based on the ClassificationSpec, but requires as additional arguments:
prediction_columnlabel_column
Installation
pip install symphony_fairvis
Usage
To learn how to use Symphony, see the documentation.
Development
To learn about how to build Symphony from source and how to contribute to the framework, please look at CONTRIBUTING.md and the development documentation.
1 maintainer
Metadata
1 maintainer
Release files for symphony-fairvis 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| symphony_fairvis-1.0.2.tar.gz | 6.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| symphony_fairvis-1.0.2-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 16.9 MB
Release files / symphony_fairvis-1.0.2.tar.gz
| Download URL | symphony_fairvis-1.0.2.tar.gz |
|---|---|
| Size | 6.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.2 CPython/3.11.4
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Release files / symphony_fairvis-1.0.2-py2.py3-none-any.whl
| Download URL | symphony_fairvis-1.0.2-py2.py3-none-any.whl |
|---|---|
| Size | 10.9 MB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.4
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