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https://github.com/xai-demonstrator/visualime
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
https://github.com/xai-demonstrator/visualime
computer-vision explainable-ai interpretable-machine-learning lime xai
Last synced: about 2 months ago
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Implementation of LIME focused on producing user-centric local explanations for image classifiers.
- Host: GitHub
- URL: https://github.com/xai-demonstrator/visualime
- Owner: XAI-Demonstrator
- License: apache-2.0
- Created: 2022-03-30T17:31:43.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-17T14:21:26.000Z (about 2 months ago)
- Last Synced: 2024-11-17T15:27:05.170Z (about 2 months ago)
- Topics: computer-vision, explainable-ai, interpretable-machine-learning, lime, xai
- Language: Python
- Homepage: https://visualime.readthedocs.io/
- Size: 134 KB
- Stars: 6
- Watchers: 3
- Forks: 1
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# VisuaLIME
[](https://github.com/XAI-Demonstrator/visualime/actions/workflows/unit-test.yml)
[](https://coveralls.io/github/XAI-Demonstrator/visualime?branch=main)
[](https://visualime.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/Apache-2.0)
[](https://pypi.org/project/visualime/)
[](https://pypi.org/project/visualime/)
_VisuaLIME_ is an implementation of _LIME_ (Local Interpretable Model-Agnostic Explanations) [1]
focused on producing visual local explanations for image classifiers.
In contrast to the [reference implementation](https://github.com/marcotcr/lime), _VisuaLIME_
exclusively supports image classification and gives its users full control over the
properties of the generated explanations.
It was written to produce stable, reliable, and expressive explanations at scale.
_VisuaLIME_ was created as part of the
[XAI Demonstrator project](https://github.com/XAI-Demonstrator/xai-demonstrator).
**A full documentation is available on [visualime.readthedocs.io](https://visualime.readthedocs.io/).**
## Getting Started
💡 _If you're new to_ LIME, _you might want to check out the_
[Grokking LIME](https://github.com/ionicsolutions/grokking-lime)
_talk/tutorial for a general introduction prior to diving into_ VisuaLIME.
To install _VisuaLIME_, run:
```shell
pip install visualime
```
_VisuaLIME_ provides two functions that package its building blocks into a reference explanation
pipeline:
```python
import numpy as np
from visualime.explain import explain_classification, render_explanation
image = ... # a numpy array of shape (width, height, 3) representing an RGB image
def predict_fn(images: np.ndarray) -> np.ndarray:
# a function that takes a numpy array of shape (num_of_samples, width, height, 3)
# representing num_of_samples RGB images and returns a numpy array of
# shape (num_of_samples, num_of_classes) where each entry corresponds to the
# classifiers output for the respective image
predictions = ...
return predictions
segment_mask, segment_weights = explain_classification(image, predict_fn)
explanation = render_explanation(
image,
segment_mask,
segment_weights,
positive="green",
negative="red",
coverage=0.2,
)
```
For a full example, see
[the example notebook on GitHub](https://github.com/xai-demonstrator/visualime/blob/main/example.ipynb).
## Roadmap
- Verify that the algorithm matches the original LIME and document differences
- Add performance benchmarks and optimize implementation of the algorithm
- Include utilities to assess and tune explanations for stability and faithfulness
- Create a user guide that walks through a best practice example of implementing
a fully configurable LIME explainer
## References
[1] Ribeiro et al.: _"Why Should I Trust You?": Explaining the Predictions of Any Classifier_
([arXiv:1602.04938](https://arxiv.org/abs/1602.04938), 2016)