Data type coverage and bug correction

Release Notes v1.3.1

Data type coverage

The first part of this release concerns Xplique data type coverage extension. It first modifies some methods to extend the coverage.

Non-square images

SobolAttributionMethod and HsicAttributionImage now support non-square images.

Image explanation shape harmonization

For image explanation, depending on the method, the explanation shape could be either $(n, h, w)$, $(n, h, w, 1)$, or $(n, h, w, 3)$. It was decided to harmonize it to $(n, h, w, 1)$.

Reducer for gradient-based methods

For images, most gradient-based provide a value for each channel, however, for consistency, it was decided that for images, explanations will have the shape $(n, h, w, 1)$. Therefore, gradient-based methods need to reduce the channel dimension of their image explanations and the reducer parameter chooses how to do it among {"mean", "min", "max", "sum", None}. In the case None is given, the channel dimension is not reduced. The default value is "mean" for methods except Saliency which is "max" to comply with the paper and GradCAM and GradCAMPP which are not concerned.

Time series

Xplique was initially designed for images but it also supports attribution methods for tabular data and now time series data.

Xplique conciders data with:

• 4 dimensions as images.
• 3 dimensions as time series.
• 2 dimensions as tabular data.

Tutorial

To show how to use Xplique on time series a new tutorial was designed: Attributions: Time Series and Regression.

Plot

The function xplique.plots.plot_timeseries_attributions was modified to match xplique.plots.plot_attributions API. Here is an example from the tutorial on temperature forecasting for the next 24 hours based on weather data from the last 48 hours:

Methods

Rise, Lime, and Kernelshap now support time series natively.

Overview of covered data types and tasks

Attribution Method	Type of Model	Images	Time Series and Tabular Data
Deconvolution	TF	C✔️ OD❌ SS❌	C✔️ R✔️
Grad-CAM	TF	C✔️ OD❌ SS❌	❌
Grad-CAM++	TF	C✔️ OD❌ SS❌	❌
Gradient Input	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
Guided Backprop	TF	C✔️ OD❌ SS❌	C✔️ R✔️
Integrated Gradients	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
Kernel SHAP	TF, PyTorch**, Callable*	C✔️ OD✔️ SS✔️	C✔️ R✔️
Lime	TF, PyTorch**, Callable*	C✔️ OD✔️ SS✔️	C✔️ R✔️
Occlusion	TF, PyTorch**, Callable*	C✔️ OD✔️ SS✔️	C✔️ R✔️
Rise	TF, PyTorch**, Callable*	C✔️ OD✔️ SS✔️	C✔️ R✔️
Saliency	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
SmoothGrad	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
SquareGrad	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
VarGrad	TF, PyTorch**	C✔️ OD✔️ SS✔️	C✔️ R✔️
Sobol Attribution	TF, PyTorch**	C✔️ OD✔️ SS✔️	🔵
Hsic Attribution	TF, PyTorch**	C✔️ OD✔️ SS✔️	🔵
FORGrad enhancement	TF, PyTorch**	C✔️ OD✔️ SS✔️	❌

TF : Tensorflow compatible
C : Classification | R : Regression |
OD : Object Detection | SS : Semantic Segmentation (SS)

* : See the Callable documentation

** : See the Xplique for PyTorch documentation, and the PyTorch models: Getting started notebook.

✔️ : Supported by Xplique | ❌ : Not applicable | 🔵 : Work in Progress

Metrics

Naturally, metrics now support Time series too.

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Bugs correction

The second part of this release is to solve pending issues: #102, #123, #127, #128, #131, and #137.

Memories problem

Indeed, among the reported issues several concerned memory management.

SmoothGrad, VarGrad, and SquareGrad issue #137

SmoothGrad, VarGrad, and SquareGrad now use online statistics to compute explanations, which allows to make batch inferences. Furthermore, their implementation was refactorized with a GradientStatistic abstraction. It does not modify usage.

MuFidelity issue #137

The metric MuFidelity had the same problem as the three previous methods, it was also solved.

HsicAttributionMethod

This method had a different memory problem the batch_size for the model was used correctly, however, when computing the estimator a tensor of size grid_size**2 * nb_design**2 was created. However, for big images and/or small objects in images, the grid_size needs to be increased, furthermore, for the estimator to converge, nb_design should also be increased accordingly. Which creates out-of-memory errors.

Thus an estimator_batch_size (different from the initial batch_size) was introduced to batch over the grid_size**2 dimension. The default value is None, thus conserving the default behavior of the method, but when an out-of-memory occurs, setting an estimator_batch_size smaller than grid_size**2 will reduce the memory cost of the method.

Other issues

Metrics input types issues #102 and #128

Now inputs and targets are sanitized to numpy arrays

Feature visualization latent dtype issue #131

In issue #131, there was a conflict in dtype between the model internal dtype and Xplique dtype. We made sure that the dtype used for the conflicting computation was the model's internal dtype.

Other corrections

Naturally, other problems were reported to us outside of issues or discovered by the team, we also addressed these.

Some refactorization

Lime was refactorized but it does not impact usage.

Small fixes

In HsicAttributionMethod and SobolAttributionMethod there was a difference between the documentation of the perturbation_function and the actual code.

For Craft, there were some remaining prints, but they may be useful, thus Craft's methods with print now take a verbose parameter.