Super class for gradient-based interpretation methods. This class inherits from InterpretingMethod. It summarizes all implemented gradient-based methods and provides a private function to calculate the gradients w.r.t. to the input for given data. Implemented are:
'Vanilla Gradients' and 'Gradient x Input' (Gradient)
'SmoothGrad' and 'SmoothGrad x Input' (SmoothGrad)
innsight::InterpretingMethod -> GradientBased
times_inputMultiplies the gradients with the input features. This method is called 'Gradient x Input'.
new()Create a new instance of this class.
GradientBased$new( converter, data, channels_first = TRUE, output_idx = NULL, ignore_last_act = TRUE, times_input = TRUE, dtype = "float" )
converterThe converter of class Converter with the stored and torch-converted model.
dataThe given data in an array-like format to be interpreted with the selected gradient-based method.
channels_firstThe format of the given date, i.e. channels on
last dimension (FALSE) or after the batch dimension (TRUE). If the
data has no channels, use the default value TRUE.
output_idxThis vector determines for which outputs the method
will be applied. By default (NULL), all outputs (but limited to the
first 10) are considered.
ignore_last_actSet this boolean value to include the last
activation, or not (default: TRUE). In some cases, the last activation
leads to a saturation problem.
times_inputMultiplies the gradients with the input features. This method is called 'Gradient x Input'.
dtypeThe data type for the calculations. Use
either 'float' for torch::torch_float or 'double' for
torch::torch_double.
plot()This method visualizes the result of the selected method in a
ggplot2::ggplot. You can use the argument data_idx to select
the data points in the given data for the plot. In addition, the
individual output nodes for the plot can be selected with the argument
output_idx. The different results for the selected data points and
outputs are visualized using the method ggplot2::facet_grid.
You can also use the as_plotly argument to generate an interactive
plot based on the plot function plotly::plot_ly.
GradientBased$plot( data_idx = 1, output_idx = NULL, aggr_channels = "sum", as_plotly = FALSE )
data_idxAn integer vector containing the numbers of the data
points whose result is to be plotted, e.g. c(1,3) for the first
and third data point in the given data. Default: c(1).
output_idxAn integer vector containing the numbers of the
output indices whose result is to be plotted, e.g. c(1,4) for the
first and fourth model output. But this vector must be included in the
vector output_idx from the initialization, otherwise, no results were
calculated for this output node and can not be plotted. By default
(NULL), the smallest index of all calculated output nodes is used.
aggr_channelsPass one of 'norm', 'sum', 'mean' or a
custom function to aggregate the channels, e.g. the maximum
(base::max) or minimum (base::min) over the channels or only
individual channels with function(x) x[1]. By default ('sum'),
the sum of all channels is used.
Note: This argument is used only for 2D and 3D inputs.
as_plotlyThis boolean value (default: FALSE) can be used to
create an interactive plot based on the library plotly. This function
takes use of plotly::ggplotly, hence make sure that the suggested
package plotly is installed in your R session.
Advanced: You can first
output the results as a ggplot (as_plotly = FALSE) and then make
custom changes to the plot, e.g. other theme or other fill color. Then
you can manually call the function ggplotly to get an interactive
plotly plot.
Returns either a ggplot2::ggplot (as_plotly = FALSE) or a
plotly::plot_ly (as_plotly = TRUE) with the plotted results.
boxplot()This function visualizes the results of this method in a boxplot, where
the type of visualization depends on the input dimension of the data.
By default a ggplot2::ggplot is returned, but with the argument
as_plotly an interactive plotly::plot_ly plot can be created,
which however requires a successful installation of the package
plotly.
GradientBased$boxplot( output_idx = NULL, data_idx = "all", ref_data_idx = NULL, aggr_channels = "norm", preprocess_FUN = abs, as_plotly = FALSE, individual_data_idx = NULL, individual_max = 20 )
output_idxAn integer vector containing the numbers of the
output indices whose result is to be plotted, e.g. c(1,4) for the
first and fourth model output. But this vector must be included in the
vector output_idx from the initialization, otherwise, no results were
calculated for this output node and can not be plotted. By default
(NULL), the smallest index of all calculated output nodes is used.
data_idxBy default ("all"), all available data is used to
calculate the boxplot information. However, this parameter can be used
to select a subset of them by passing the indices. E.g. with
data_idx = c(1:10, 25, 26) only the first 10 data points and
the 25th and 26th are used to calculate the boxplots.
ref_data_idxThis integer number determines the index for the
reference data point. In addition to the boxplots, it is displayed in
red color and is used to compare an individual result with the summary
statistics provided by the boxplot. With the default value (NULL)
no individual data point is plotted. This index can be chosen with
respect to all available data, even if only a subset is selected with
argument data_idx.
Note: Because of the complexity of 3D inputs, this argument is used
only for 1D and 2D inputs and disregarded for 3D inputs.
aggr_channelsPass one of 'norm', 'sum', 'mean' or a
custom function to aggregate the channels, e.g. the maximum
(base::max) or minimum (base::min) over the channels or only
individual channels with function(x) x[1]. By default ('norm'),
the Euclidean norm of all channels is used.
Note: This argument is used only for 2D and 3D inputs.
preprocess_FUNThis function is applied to the method's result
before calculating the boxplots. Since positive and negative values
often cancel each other out, the absolute value (abs) is used by
default. But you can also use the raw data (identity) to see the
results' orientation, the squared data (function(x) x^2) to weight
the outliers higher or any other function.
as_plotlyThis boolean value (default: FALSE) can be used to
create an interactive plot based on the library plotly instead of
ggplot2. Make sure that the suggested package plotly is installed
in your R session.
individual_data_idxOnly relevant for a plotly plot with input
dimension 1 or 2! This integer vector of data indices determines
the available data points in a dropdown menu, which are drawn in
individually analogous to ref_data_idx only for more data points.
With the default value NULL the first individual_max data points
are used.
Note: If ref_data_idx is specified, this data point will be
added to those from individual_data_idx in the dropdown menu.
individual_maxOnly relevant for a plotly plot with input
dimension 1 or 2! This integer determines the maximum number of
individual data points in the dropdown menu without counting
ref_data_idx. This means that if individual_data_idx has more
than individual_max indices, only the first individual_max will
be used. A too high number can significantly increase the runtime.
Returns either a ggplot2::ggplot (as_plotly = FALSE) or a
plotly::plot_ly (as_plotly = TRUE) with the boxplots.
clone()The objects of this class are cloneable with this method.
GradientBased$clone(deep = FALSE)
deepWhether to make a deep clone.