xplique 0.2.2

🦊 Xplique (pronounced \ɛks.plik\) is a Python toolkit dedicated to explainability, currently based on Tensorflow. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.
Explore Xplique docs »

Attributions · Concept · Feature Visualization · Metrics

The library is composed of several modules, the Attributions Methods module implements various methods (e.g Saliency, Grad-CAM, Integrated-Gradients...), with explanations, examples and links to official papers. The Feature Visualization module allows to see how neural networks build their understanding of images by finding inputs that maximize neurons, channels, layers or compositions of these elements. The Concepts module allows you to extract human concepts from a model and to test their usefulness with respect to a class. Finally, the Metrics module covers the current metrics used in explainability. Used in conjunction with the Attribution Methods module, it allows you to test the different methods or evaluate the explanations of a model.

📚 Table of contents

• 📚 Table of contents
• 🚀 Quick Start
• 🔥 Tutorials
• 📦 What's Included
• 👍 Contributing
• 👀 See Also
• 🙏 Acknowledgments
• 👨‍🎓 Creator
• 📝 License

🚀 Quick Start

Xplique requires a version of python higher than 3.6 and several libraries including Tensorflow and Numpy. Installation can be done using Pypi:

pip install xplique

Now that Xplique is installed, here are 4 basic examples of what you can do with the available modules.

Attributions Methods

let's start with a simple example, by computing Grad-CAM for several images (or a complete dataset) on a trained model.

from xplique.attributions import GradCAM

# load images, labels and model
# ...

explainer = GradCAM(model)
explanations = explainer.explain(images, labels)
# or just `explainer(images, labels)`

Attributions Metrics

In order to measure if the explanations provided by our method are faithful (it reflects well the functioning of the model) we can use a fidelity metric such as Deletion

from xplique.attributions import GradCAM
from xplique.metrics import Deletion

# load images, labels and model
# ...

explainer = GradCAM(model)
metric = Deletion(model, inputs, labels)

score_grad_cam = metric(explainer)

Concepts Extraction

Concerning the concept-based methods, we can for example extract a concept vector from a layer of a model. In order to do this, we use two datasets, one containing inputs containing the concept: positive_samples, the other containing other entries which do not contain the concept: negative_samples.

from xplique.concepts import Cav

# load a model, samples that contain a concept
# (positive) and samples who don't (negative)
# ...

extractor = Cav(model, 'mixed3')
concept_vector = extractor(positive_samples,
                           negative_samples)

Feature Visualization

Finally, in order to find an image that maximizes a neuron and at the same time a layer, we build two objectives that we combine together. We then call the optimizer which returns our images

from xplique.features_visualizations import Objective
from xplique.features_visualizations import optimize

# load a model...

neuron_obj = Objective.neuron(model, "logits", 200)
channel_obj = Objective.layer(model, "mixed3", 10)

obj = neuron_obj + 2.0 * channel_obj
images = optimize(obj)

🔥 Tutorials

We propose some Hands-on tutorials to get familiar with the library and its api:

• Getting Started
  
• Tabular Data and Regression
  
• Introduction to Metrics
  

You can find a certain number of other practical tutorials just here. This section is actively developed and more contents will be included. We will try to cover all the possible usage of the library, feel free to contact us if you have any suggestions or recommandations towards tutorials you would like to see.

📦 What's Included

All the attributions method presented below handle both Classification and Regression tasks.

Attribution Method	Type of Model	Source	Tabular Data	Images	Time-Series
Deconvolution	TF	Paper	✔	✔	TBD
Grad-CAM	TF	Paper		✔	TBD
Grad-CAM++	TF	Paper		✔	TBD
Gradient Input	TF	Paper	✔	✔	TBD
Guided Backprop	TF	Paper	✔	✔	TBD
Integrated Gradients	TF	Paper	✔	✔	TBD
Kernel SHAP	Callable	Paper	✔	✔	TBD
Lime	Callable	Paper	✔	✔	TBD
Occlusion	Callable	Paper	✔	✔	TBD
Rise	Callable	Paper	TBD	✔	TBD
Saliency	TF	Paper	✔	✔	TBD
SmoothGrad	TF	Paper	✔	✔	TBD
SquareGrad	TF	Paper	✔	✔	TBD
VarGrad	TF	Paper	✔	✔	TBD

Attribution Metrics	Type of Model	Property	Source
MuFidelity	TF	Fidelity	Paper
Deletion	TF	Fidelity	Paper
Insertion	TF	Fidelity	Paper
Average Stability	TF	Stability	Paper
(WIP) MeGe
(WIP) ReCo

Concepts method	Type of Model	Source
Concept Activation Vector (CAV)	TF	Paper
Testing CAV	TF	Paper
(WIP) Robust TCAV

Feature Visualization (Paper)	Type of Model	Details
Neurons	TF	Optimizes for specific neurons
Layer	TF	Optimizes for specific layers
Channel	TF	Optimizes for specific channels
Direction	TF	Optimizes for specific vector
Fourrier Preconditioning	TF	Optimize in Fourier basis (see preconditioning)
Objective combination	TF	Allows to combine objectives

👍 Contributing

Feel free to propose your ideas or come and contribute with us on the Xplique toolbox! We have a specific document where we describe in a simple way how to make your first pull request: just here.

👀 See Also

This library is one approach of many to explain your model. We don't expect it to be the final solution; we create it to explore one point in the space of possibilities.

Other tools to explain your model include:

• Lucid the wonderful library specialized in feature visualization from OpenAI.
• Captum the Pytorch library for Interpretability research
• Tf-explain that implement multiples attribution methods and propose callbacks API for tensorflow.
• Alibi Explain for model inspection and interpretation
• SHAP a very popular library to compute local explanations using the classic Shapley values from game theory and their related extensions

To learn more about Explainable AI in general, see:

• Interpretable Machine Learning an excellent book by Christophe Molnar.
• Interpretability Beyond Feature Attribution by Been Kim.
• A Roadmap for the Rigorous Science of Interpretability by Finale Doshi-Velez.
• DEEL White paper a summary of the DEEL team on the challenges of certifiable AI and the role of explainability for this purpose

🙏 Acknowledgments

This project received funding from the French ”Investing for the Future – PIA3” program within the Artificial and Natural Intelligence Toulouse Institute (ANITI). The authors gratefully acknowledge the support of the DEEL project.

👨‍🎓 Creator

This library was started as a side-project by Thomas FEL who is doing a thesis on explainability.

📝 License

The package is released under MIT license.