| Name | rex-xai JSON |
| Version |
0.4.1
JSON |
| download |
| home_page | None |
| Summary | causal Responsibility-based eXplanations of black-box-classifiers |
| upload_time | 2025-07-22 08:31:23 |
| maintainer | None |
| docs_url | None |
| author | David Kelly |
| requires_python | >=3.10 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ReX: Causal *R*esponsibility *EX*planations for image classifiers
<!--- BADGES: START --->
[](https://rex-xai.readthedocs.io/en/latest/)
[](https://github.com/ReX-XAI/ReX/actions/workflows/test-package-and-comment.yml)
[](https://github.com/ReX-XAI/ReX.jl/blob/main/LICENSE)
<!--- BADGES: END --->
---
ReX is a causal explainability tool for image classifiers. It also works on tabular and 3D data.
Given an input image and a classifier, ReX calculates a causal responsibility map across the data and identifies a minimal, sufficient, explanation.
  
ReX is black-box, that is, agnostic to the internal structure of the classifier.
ReX finds single explanations, non-contiguous explanations (for partially obscured images), multiple independent explanations, contrastive explanations and lots of other things!
It has a host of options and parameters, allowing you to fine tune it to your data.
For background information and detailed usage instructions, see our [documentation](https://rex-xai.readthedocs.io/en/latest/).
<!--inclusion-marker-start-do-not-remove-->
## Installation
ReX can be installed using `pip`.
We recommend creating a virtual environment to install ReX.
ReX has been tested using versions of Python >= 3.10.
The following instructions assume `conda`:
```bash
conda create -n rex python=3.13
conda activate rex
pip install rex_xai
```
This should install an executable `rex` in your path.
To check that ReX is installed correctly, run:
```bash
ReX --help
```
To build from source, clone the repository and run:
```bash
git clone git@github.com:ReX-XAI/ReX.git
cd ReX
conda create -n rex python=3.13
conda activate rex
pip install .
```
> **Note:**
>
> By default, `onnxruntime` will be installed.
> If you wish to use a GPU, you should uninstall `onnxruntime` and install `onnxruntime-gpu` instead.
> You can alternatively clone the project and edit the `pyproject.toml` to read "onnxruntime-gpu >= 1.17.0" rather than "onnxruntime >= 1.17.0".
If you want to use ReX with 3D data, you will need to install some optional extra dependencies:
```bash
pip install 'rex_xai[3D]'
```
<!--inclusion-marker-end-do-not-remove-->
## Feedback
Bug reports, questions, and suggestions for enhancements are welcome - please [check the GitHub Issues](https://github.com/ReX-XAI/ReX/issues) to see if there is already a relevant issue, or [open a new one](https://github.com/ReX-XAI/ReX/issues/new)!
## How to Contribute
Your contributions are highly valued and welcomed. To get started, please review the guidelines outlined in the [CONTRIBUTING.md](/CONTRIBUTING.md) file. We look forward to your participation!
Raw data
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"description": "# ReX: Causal *R*esponsibility *EX*planations for image classifiers\n\n![ReX logo with dinosaur](https://raw.githubusercontent.com/ReX-XAI/ReX/main/assets/rex_logo.png \"ReX Logo with dinosaur\")\n\n<!--- BADGES: START --->\n\n[](https://rex-xai.readthedocs.io/en/latest/)\n[](https://github.com/ReX-XAI/ReX/actions/workflows/test-package-and-comment.yml)\n[](https://github.com/ReX-XAI/ReX.jl/blob/main/LICENSE)\n\n<!--- BADGES: END --->\n\n---\n\nReX is a causal explainability tool for image classifiers. It also works on tabular and 3D data.\n\nGiven an input image and a classifier, ReX calculates a causal responsibility map across the data and identifies a minimal, sufficient, explanation.\n\n![ladybird](https://raw.githubusercontent.com/ReX-XAI/ReX/main/tests/test_data/ladybird.jpg \"Original Image\") ![responsibility map](https://raw.githubusercontent.com/ReX-XAI/ReX/main/assets/ladybird_rm.png \"Responsibility Map\") ![minimal explanation](https://raw.githubusercontent.com/ReX-XAI/ReX/main/assets/ladybird_301.png \"Explanation\")\n\nReX is black-box, that is, agnostic to the internal structure of the classifier.\nReX finds single explanations, non-contiguous explanations (for partially obscured images), multiple independent explanations, contrastive explanations and lots of other things!\nIt has a host of options and parameters, allowing you to fine tune it to your data.\n\nFor background information and detailed usage instructions, see our [documentation](https://rex-xai.readthedocs.io/en/latest/).\n\n<!--inclusion-marker-start-do-not-remove-->\n\n## Installation\n\nReX can be installed using `pip`.\nWe recommend creating a virtual environment to install ReX.\nReX has been tested using versions of Python >= 3.10.\nThe following instructions assume `conda`:\n\n```bash\nconda create -n rex python=3.13\nconda activate rex\npip install rex_xai\n```\n\nThis should install an executable `rex` in your path.\nTo check that ReX is installed correctly, run:\n\n```bash\nReX --help\n```\n\nTo build from source, clone the repository and run:\n\n```bash\ngit clone git@github.com:ReX-XAI/ReX.git\ncd ReX\nconda create -n rex python=3.13\nconda activate rex\npip install .\n```\n\n> **Note:**\n>\n> By default, `onnxruntime` will be installed.\n> If you wish to use a GPU, you should uninstall `onnxruntime` and install `onnxruntime-gpu` instead.\n> You can alternatively clone the project and edit the `pyproject.toml` to read \"onnxruntime-gpu >= 1.17.0\" rather than \"onnxruntime >= 1.17.0\".\n\nIf you want to use ReX with 3D data, you will need to install some optional extra dependencies:\n\n```bash\npip install 'rex_xai[3D]'\n```\n\n<!--inclusion-marker-end-do-not-remove-->\n\n## Feedback\n\nBug reports, questions, and suggestions for enhancements are welcome - please [check the GitHub Issues](https://github.com/ReX-XAI/ReX/issues) to see if there is already a relevant issue, or [open a new one](https://github.com/ReX-XAI/ReX/issues/new)!\n\n## How to Contribute\n\nYour contributions are highly valued and welcomed. To get started, please review the guidelines outlined in the [CONTRIBUTING.md](/CONTRIBUTING.md) file. We look forward to your participation!\n\n",
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