This is an implementation of the Deep Learning Important FeaTures
(DeepLift) algorithm introduced by Shrikumar et al. (2017). It's a local
method for interpreting a single element \(x\) of the dataset concerning
a reference value \(x'\) and returns the contribution of each input
feature from the difference of the output (\(y=f(x)\)) and reference
output (\(y'=f(x')\)) prediction. The basic idea of this method is to
decompose the differencefromreference prediction with respect to the
input features, i.e.
$$\Delta y = y  y' = \sum_i C(x_i).$$
Compared to Layerwise Relevance Propagation (see LRP), the
DeepLift method is an exact decomposition and not an approximation, so we
get real contributions of the input features to the
differencefromreference prediction. There are two ways to handle
activation functions: RescaleRule ('rescale'
) and
RevealCancelRule ('reveal_cancel'
).
A. Shrikumar et al. (2017) Learning important features through propagating activation differences. ICML 2017, p. 48444866
innsight::InterpretingMethod
> DeepLift
x_ref
The reference input of size (1, dim_in) for the interpretation.
rule_name
Name of the applied rule to calculate the contributions
for the nonlinear part of a neural network layer. Either
"rescale"
or "reveal_cancel"
.
new()
Create a new instance of the DeepLift method.
DeepLift$new(
converter,
data,
channels_first = TRUE,
output_idx = NULL,
ignore_last_act = TRUE,
rule_name = "rescale",
x_ref = NULL,
dtype = "float"
)
converter
An instance of the R6 class Converter
.
data
The data for which the contribution scores are to be calculated. It has to be an array or arraylike format of size (batch_size, dim_in).
channels_first
The 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_idx
This 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_act
Set this boolean value to include the last
activation, or not (default: TRUE
). In some cases, the last activation
leads to a saturation problem.
rule_name
Name of the applied rule to calculate the
contributions. Use one of 'rescale'
and 'reveal_cancel'
.
x_ref
The reference input of size (1, dim_in) for the
interpretation. With the default value NULL
you use an input
of zeros.
dtype
The 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.
data_idx
An 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_idx
An 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_channels
Pass 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_plotly
This 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
.
DeepLift$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_idx
An 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_idx
By 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_idx
This 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_channels
Pass 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_FUN
This 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_plotly
This 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_idx
Only 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_max
Only 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.
# Example 1: Torch 
library(torch)
# Create nn_sequential model and data
model < nn_sequential(
nn_linear(5, 12),
nn_relu(),
nn_linear(12, 2),
nn_softmax(dim = 2)
)
data < torch_randn(25, 5)
ref < torch_randn(1, 5)
# Create Converter
converter < Converter$new(model, input_dim = c(5))
# Apply method DeepLift
deeplift < DeepLift$new(converter, data, x_ref = ref)
#> Backward pass 'DeepLift':
#>

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# Print the result as a torch tensor for first two data points
deeplift$get_result("torch.tensor")[1:2]
#> torch_tensor
#> (1,.,.) =
#> 0.0659 0.0058
#> 0.0335 0.0145
#> 0.0072 0.0262
#> 0.2201 0.2676
#> 0.2283 0.0452
#>
#> (2,.,.) =
#> 0.0551 0.0215
#> 0.0392 0.0136
#> 0.0362 0.0828
#> 0.0059 0.1871
#> 0.0878 0.0303
#> [ CPUFloatType{2,5,2} ]
# Plot the result for both classes
plot(deeplift, output_idx = 1:2)
# Plot the boxplot of all datapoints
boxplot(deeplift, output_idx = 1:2)
#  Example 2: Neuralnet 
library(neuralnet)
data(iris)
# Train a neural network
nn < neuralnet((Species == "setosa") ~ Petal.Length + Petal.Width,
iris,
linear.output = FALSE,
hidden = c(3, 2), act.fct = "tanh", rep = 1
)
# Convert the model
converter < Converter$new(nn)
# Apply DeepLift with rescalerule and a reference input of the feature
# means
x_ref < matrix(colMeans(iris[, c(3, 4)]), nrow = 1)
deeplift_rescale < DeepLift$new(converter, iris[, c(3, 4)], x_ref = x_ref)
#> Backward pass 'DeepLift':
#>

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# Get the result as a dataframe and show first 5 rows
deeplift_rescale$get_result(type = "data.frame")[1:5, ]
#> data feature class value
#> 1 data_1 Petal.Length Species == "setosa" 0.1461408
#> 2 data_2 Petal.Length Species == "setosa" 0.1461408
#> 3 data_3 Petal.Length Species == "setosa" 0.1520031
#> 4 data_4 Petal.Length Species == "setosa" 0.1402526
#> 5 data_5 Petal.Length Species == "setosa" 0.1461408
# Plot the result for the first datapoint in the data
plot(deeplift_rescale, data_idx = 1)
# Plot the result as boxplots
boxplot(deeplift_rescale)
#  Example 3: Keras 
library(keras)
if (is_keras_available()) {
data < array(rnorm(10 * 32 * 32 * 3), dim = c(10, 32, 32, 3))
model < keras_model_sequential()
model %>%
layer_conv_2d(
input_shape = c(32, 32, 3), kernel_size = 8, filters = 8,
activation = "softplus", padding = "valid"
) %>%
layer_conv_2d(
kernel_size = 8, filters = 4, activation = "tanh",
padding = "same"
) %>%
layer_conv_2d(
kernel_size = 4, filters = 2, activation = "relu",
padding = "valid"
) %>%
layer_flatten() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 16, activation = "relu") %>%
layer_dense(units = 2, activation = "softmax")
# Convert the model
converter < Converter$new(model)
# Apply the DeepLift method with revealcancel rule
deeplift_revcancel < DeepLift$new(converter, data,
channels_first = FALSE,
rule_name = "reveal_cancel"
)
# Plot the result for the first image and both classes
plot(deeplift_revcancel, output_idx = 1:2)
# Plot the result as boxplots for first class
boxplot(deeplift_revcancel, output_idx = 1)
# You can also create an interactive plot with plotly.
# This is a suggested package, so make sure that it is installed
library(plotly)
boxplot(deeplift_revcancel, as_plotly = TRUE)
}
#> Backward pass 'DeepLift':
#>

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