This is a minor release but does contain a range of substantial new
features as well as visual changes, along with some bug fixes. For
users, however, nothing changes that is not set by default as in the
previous version or made aware by warnings. An exception to this are the
graphics that are created using plot(). These now contain a
small box with information about the prediction, the sum of the
relevances and the goal of the method.
By default, the plot() method now creates a small
box within the plot with the prediction for the instance and the
corresponding class. This info box also contains the sum of the
relevances and, if available, the decomposition target of the method.
Displaying the box can be toggled with the show_preds
argument.
The boxplot() function for the interpretation
methods has been renamed plot_global() due to the
inappropriate name (especially for images). The old method
boxplot() can still be used, but throws a warning for image
data and executes the method plot_global()
internally.
IntegratedGradient)ExpectedGradient)DeepSHAP)LIME) and
Shapley values (SHAP). Both can be applied to arbitrary
models (by providing the prediction function pref_fun) and
wrap the suggested packages lime and fastshap.
However, they can only be applied to models with a single input and
output layer.convert(...) for Converter$new(...)run_grad(...) for Gradient$new(...)run_smoothgrad(...) for
SmoothGrad$new(...)run_intgrad(...) for
IntegratedGradient$new(...)run_expgrad(...) for
ExpectedGradient$new(...)run_deeplift(...) for
DeepLift$new(...)run_deepshap(...) for
DeepSHAP$new(...)run_lrp(...) for LRP$new(...)run_cw(...) for
ConnectionWeights$new(...)run_lime(...) for LIME$new(...)run_shap(...) for SHAP$new(...)output_idx, the
new argument output_label for the output label can now also
be specified in order to calculate or visualize only certain output
nodes.if(require("pkgname")) for suggested packages in
examplesThis is a minor release but does contain a range of substantial new
features as well as visual changes, along with some bug fixes. This is
accompanied by internal breaking changes in the R6 classes
Converter and ConvertedModel enabling
non-sequential models with multiple input or output layers. For users,
however, nothing changes that is not set by default as in the previous
version or made aware by warnings.
There are no user-facing changes that are not handled with default values or noted by throwing warnings.
When converting a model to a list, two necessary entries are
added, containing the indices of the layers from the sub-list
layers indicating the input (input_nodes) and
output (output_nodes) layers of the passed model. If one of
these values is not set, a warning is raised and it is assumed that the
model is sequential, i.e., the first layer is the only input layer and
the last layer is the only output layer.
Similarly, for each layer in the sub-list layers the
entries input_layers and output_layers are
added containing the indices of the preceding and following layers for
this layer. If these values are not set, a warning is thrown and it is
assumed that the model is sequential, i.e., the previous entry is the
only preceding and the next entry is the only succeeding layer. The
values 0 and -1 indicate the input and output
layers of the model, respectively.
The functions plot and boxplot for the
interpretability methods no longer return instances of
ggplot2 or plotly, but instances of
the new S4 classes innsight_ggplot2 or
innsight_plotly. However, these objects can be treated like
ordinary objects of ggplot2 or plotly
to some extent and also create the usual visualizations by default (see
this
section in the in-depth explanation for details). Since the results
of models with multiple input and output layers are very complex, the
suggested packages gtable, grid and
gridExtra are needed only in these cases.
Add cli dependency:
Errors, warnings, messages, and progress bars have been revised and unified, and now use the package cli.
Overwrite the default print() function for the R6
classes Converter and InterpretingMethod,
which in particular is inherited by all interpretability
methods.
The Converter class now supports more models and
layers:
Now models created by keras::keras_model are
accepted. In addition, we add support for the following layers of the
keras package: layer_input,
layer_concatenate, layer_add,
layer_activation*, layer_zero_padding_1d,
layer_zero_padding_2d,
layer_batch_normalization,
layer_global_average_pooling_1d,
layer_global_average_pooling_2d,
layer_global_max_pooling_1d and
layer_global_max_pooling_2d
For models created by the torch package, we add
support for nn_batch_norm1d and
nn_batch_norm2d
For models defined as a named list, we add the entries described in the breaking changes and the following layer types (see the in-depth explanation for details):
type = "BatchNorm" for batch normalization layerstype = "GlobalPooling" for all kinds of global pooling
layers, i.e. maximum or average global poolingtype = "Padding" for padding layerstype = "Concatenate" for concatenation layertype = "Add" for an adding layerExtend the arguments output_idx (in all
interpretability methods and the corresponding plot and boxplot
methods), input_dim, input_names,
output_dim, output_names (in
Converter), which now allow lists of these arguments to
define them for multiple input or output layers.
Overwrite the default print() function for the R6
classes Converter and InterpretingMethod,
which in particular is inherited by all interpretability
methods.
Add the S3 function get_result() for instances of
the R6 class InterpretingMethod (i.e., also for all
inherited methods) that forwards to the corresponding class method
$get_result().
In the method LRP it is now possible to set the rule
and the parameter individually for each layer type. In addition, for
batch normalization layers the rule "pass" is added, which
skips this type of layer in the backward pass.
Add the logical argument winner_takes_all to the
methods DeepLift and LRP to treat maximum
pooling layers as an average pooling layer in the backward
pass.
Add the logical argument verbose to all implemented
methods to show or disable the progress bar.
Revise the documentation and use roxygen templates
(@template) for almost all fields and arguments. These are
stored in the folder man-roxygen.
Revise the introduction vignette innsight
(vignette("innsight")).
Add vignette “Example 1: Iris dataset with torch” describing the basic usage of the package with tabular data and only numeric features.
Add vignette “Example 2: Penguin dataset with torch and luz” describing a more advanced usage with tabular data containing numerical and categorical features.
Add article “Example 3: ImageNet with keras” describing the usage of the package with predefined models in keras on the ImageNet dataset.
Add the vignette “In-depth explanation” explaining all methods, arguments and possibilities of the package in great detail. This vignette also includes the depreciated vignette “Custom Model Definition”.
The vignette “Custom Model Definition” is deprecated.
Small speed improvements by using more torch
functions, e.g., torch_clip(x, min = 0) instead of
(x > 0) * x
Some smaller bug fixes
.Rd files using the current CRAN version of roxygen2.NEWS.md file to track changes to the
package.