xplique

Xplique (pronounced \ɛks.plik) is a Python toolkit dedicated to explainability. 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. Originally built for Tensorflow's model it also works for PyTorch models partially.

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Attributions · Concept · Feature Visualization · Metrics . Example-based

[!IMPORTANT]
With the release of Keras 3.X since TensorFlow 2.16, some methods may not function as expected. We are actively working on a fix. In the meantime, we recommend using TensorFlow 2.15 or earlier versions for optimal compatibility.

The library is composed of several modules, the Attributions Methods module implements various methods (e.g Saliency, Grad-CAM, FEM, 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.

[!NOTE]
We are proud to announce the release of the Example-based module! This module is dedicated to methods that explain a model by retrieving relevant examples from a dataset. It includes methods that belong to different families: similar examples, contrastive (counter-factuals and semi-factuals) examples, and prototypes (as concepts based methods have a dedicated sections).

Tutorials

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

• Attribution Methods: Getting started

• Attribution Methods: Sanity checks paper

• Attribution Methods: Tabular data and Regression

• Attribution Methods: Object Detection

• Attribution Methods: Semantic Segmentation

• FORGRad: Gradient strikes back with FORGrad

• Attribution Methods: Metrics

• Attribution Methods: Complexity Metrics

• Attribution Methods: Randomization Metrics

• Attribution Methods: Average Drop/Increase/Gain Fidelity Metrics

• PyTorch models: Getting started

• Concepts Methods: Testing with Concept Activation Vectors

• Concepts Methods: CRAFT: Getting started on Tensorflow

• Concepts Methods: CRAFT: Getting started on Pytorch

• Feature Visualization: Getting started

• Modern Feature Visualization with MaCo: Getting started

• Example-based Methods: Getting started

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 recommendations towards tutorials you would like to see.

Quick Start

Xplique requires a version of python higher than 3.7 and several libraries including Tensorflow and Numpy. Installation can be done using Pypi:

pip install xplique

Now that Xplique is installed, here are 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)`

All attributions methods share a common API described in the attributions API documentation.

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

score_grad_cam = metric(explanations)

All attributions metrics share a common API. You can find out more about it here.

Concepts Extraction

CAV

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)

More information on CAV here and on TCAV here.

CRAFT

Use Craft to investigate a single class.

from xplique.concepts import CraftTf as Craft

# Cut the model in two parts: g and h
# Create a Craft concept extractor from these 2 models
craft = Craft(input_to_latent_model = g,
              latent_to_logit_model = h)

# Use Craft to compute the concepts for a specific class
craft.fit(images_preprocessed, class_id=rabbit_class_id)

# Compute Sobol indices to understand which concept matters
importances = craft.estimate_importance()

# Display those concepts by showing the 10 best crops for each concept
craft.plot_concepts_crops(nb_crops=10)

More information in the CRAFT documentation.

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, obj_names = optimize(obj)

Want to know more ? Check the Feature Viz documentation

PyTorch with Xplique

Even though the library was mainly designed to be a Tensorflow toolbox we have been working on a very practical wrapper to facilitate the integration of your PyTorch models into Xplique's framework!

import torch

from xplique.wrappers import TorchWrapper
from xplique.attributions import Saliency
from xplique.metrics import Deletion

# load images, targets and model
# ...

device = 'cuda' if torch.cuda.is_available() else 'cpu'
wrapped_model = TorchWrapper(torch_model, device)

explainer = Saliency(wrapped_model)
explanations = explainer(inputs, targets)

metric = Deletion(wrapped_model, inputs, targets)
score_saliency = metric(explanations)

Want to know more ? Check the PyTorch documentation

What's Included

There are 4 modules in Xplique, Attribution methods, Attribution metrics, Concepts, and Feature visualization. In particular, the attribution methods module supports a huge diversity of tasks:Classification, Regression, Object Detection, and Semantic Segmentation. For diverse data types: Images, Time Series, and Tabular data. The methods compatible with such task are highlighted in the following table:

Table of attributions available