tutorials

Tutorials

This folder contains DIANNA tutorial notebooks. To install the dependencies for the tutorials, run (in the main dianna folder)

pip install .[notebooks]

🠊 For general demonstration of DIANNA click on the logo or run it in Colab: .

🠊 For tutorials on how to convert an Keras, PyTorch, Scikit-learn or Tensorflow model to ONNX, please see the conversion tutorials.

🠊 For specific XAI methods (explainers):

• Click on the explainer names to watch explanatory videos for the respective method.
• Click on the logos for direct access to a tutorial notebook. Run the tutorials directly in Google Colab by clicking on the Colab buttons.

Datasets and Tasks

Illustrative (Simple)

Data modality	Dataset	Task	Logo
Images	Binary MNIST	Binary digit classification
	Simple Geometric (circles and triangles)	Binary shape classificaiton
	Imagenet	$1000$ classes natural images classificaiton
Text	Stanford sentiment treebank	Positive or negative movie reviews sentiment classification
Timeseries	Coffee dataset	Binary classificaiton of Robusta and Aribica coffee beans
	Weather dataset	Binary classification (warm/cold season) of temperature time-series
Tabular	Penguin dataset	$3$ penguin spicies (Adele, Chinstrap, Gentoo) classificaiton
	Weather dataset	Next day sunshine hours prediction (regression)

Scientific use-cases

Data modality	Dataset	Task	Logo
Images	Simple Scientific (LeafSnap30)	$30$ tree species leaves classification
Text	EU-law statements	Regulatory or non-regulatory classification
Timeseries	Fast Radio Burst (FRB) dataset (not publicly available)	Binary classificaiton of Fast Radio Burst (FRB) timeseries data : noise or a real FRB.
Tabular	Land atmosphere dataset	Prediction of "latent heat flux" (regression). The random forest model is used as an emulator to replace the physical model STEMMUS_SCOPE to predict global maps of latent heat flux.

Models

The ONNX models used in the tutorials are available at dianna/models, or linked from their respective tutorial notebooks.

Summary of all Tutorials

All tutorials can be accessed by clicking on the dataset & task logo in the tables below.

The explainers' output for the models trained on the datasets & tasks which are included in the dashboard are marked with .

Illustrative (Simple)

Modality \ Method	RISE	LIME	KernelSHAP
Images	or		or
	or		or
Text	or	or
Time series	or	or
		or
Tabular	or	or	or
		or	or

To learn more about how we aproach the masking for time-series data, please read our Masking time-series for XAI blog-post.

Scientific use-cases

Modality \ Method	RISE	LIME	KernelSHAP
Images		or
Text		or
Time series	or
Tabular			or

IMPORTANT: Hyperparameters

Settings per explainer

The XAI methods (explainers) are sensitive to the choice of their hyperparameters! In this master Thesis, this sensitivity is researched and useful conclusions are drawn. The default hyperparameters used in DIANNA for each explainer as well as the choices for some tutorials and their data modality (i - images, txt - text, ts - time series and tab - tabular) are given in the tables below. Also the main conclusions (🠊) from the thesis (on images and text) about the hyperparameters effect are listed.

RISE

Hyperparameter	Default value	(i)	(i)	(txt)	(ts)	(ts)
$n_{masks}$	$1000$	default	$5000$	default	$10000$	$5000$
$p_{keep}$	optimized (i, txt), $0.5$ (ts)	$0.1$	$0.1$	default	$0.1$	$0.1$
$n_{features}$	$8$	$6$	default	default	default	$16$

🠊 The most crucial parameter is $p_{keep}$. Lower values of $p_{keep}$ lead to more sentitive explanations (observed for both images and text). Easier classificication tasks usually require a lower $p_keep$ as this will cause more perturbation in the input and therefore a more distinct signal in the model predictions.

🠊 The feature resolution $n_{features}$ exhibited an optimum at a value of $6$. Higher values can offer a finer grained result but require (far) more $n_masks$. This is also dependent on the scale of the phenomena in the input data that we want to take into account in the explanation.

🠊 Larger $n_masks$ will return more consistent results at the cost of computation time. If 2 identical runs yield (very) different results, these will likely contain a lot of (or even mostly) noise and a higher value for $n_masks$ should be used instead.

LIME

Hyperparameter	Default value	(i)	(ts)	(ts)
$n_{samples}$	$5000$	$1000$	$10 000$	$500$	2000
Kernel Width	$25$	default	default	default	default
$n_{features}$	$10$	$30$	default	default	999

🠊 The most crucial parameter is the Kernel width: low values cause high sensitivity, however that observation was dependent on the evaluation metric.

KernelSHAP

Hyperparameter	Default value	(i)	(i)	(tab)
$n_{samples}$	auto/int	$1000$	$2000$	$136588$
$n_{segments}$	$100$	$200$	$200$	default
$sigma$	$0$	default	default	default

🠊 The most crucial parameter is the nubmer of super-pixels $n_{segments}$. Higher values led to higher sensitivity, however that observaiton was dependant on the evaluaiton metric.

🠊 Regularization had only a marginal detrimental effect, the best results were obtained using no regularization (no smoothing, $sigma = 0$) or least squares regression.