This class implements the Connection Weights method investigated by Olden et al. (2004) which results in a feature relevance score for each input variable. The basic idea is to multiply up all path weights for each possible connection between an input feature and the output node and then calculate the sum over them. Besides, it is a global interpretation method and independent of the input data. For a neural network with \(3\) hidden layers with weight matrices \(W_1\), \(W_2\) and \(W_3\) this method results in a simple matrix multiplication $$W_1 * W_2 * W_3. $$
J. D. Olden et al. (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178, p. 389–397
converter
The converter of class Converter with the stored and torchconverted model.
channels_first
The data format of the result, i.e. channels on
last dimension (FALSE
) or on the first dimension (TRUE
). If the
data has no channels, use the default value TRUE
.
dtype
The type of the data and parameters (either 'float'
for torch::torch_float or 'double'
for torch::torch_double).
result
The methods result as a torch tensor of size
(dim_in, dim_out) and with data type dtype
.
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.
new()
ConnectionWeights$new(
converter,
output_idx = NULL,
channels_first = TRUE,
dtype = "float"
)
converter
The converter of class Converter with the stored and torchconverted model.
output_idx
This vector determines for which output indices the
method will be applied. By default (NULL
), all outputs (but limited
to the first 10) are considered.
channels_first
The data format of the result, i.e. channels on
last dimension (FALSE
) or on the first dimension (TRUE
). If the
data has no channels, use the default value TRUE
.
dtype
The data type for the calculations. Use
either 'float'
for torch::torch_float or 'double'
for
torch::torch_double.
get_result()
This function returns the result of the Connection Weights method
either as an array ('array'
), a torch tensor ('torch.tensor'
or
'torch_tensor'
) of size (dim_in, dim_out) or as a data.frame
('data.frame'
).
plot()
This method visualizes the result of the ConnectionWeights method in a
ggplot2::ggplot. You can use the argument output_idx
to select
individual output nodes for the plot. The different results for the
selected outputs are visualized using the method ggplot2::facet_grid.
You can also use the as_plotly
argument to generate an interactive
plot based on the plot function plotly::plot_ly.
ConnectionWeights$plot(
output_idx = NULL,
aggr_channels = "sum",
preprocess_FUN = identity,
as_plotly = FALSE
)
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.
preprocess_FUN
This function is applied to the method's result
before generating the plot. By default, the identity function
(identity
) is used.
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 object (as_plotly = TRUE
) with the plotted results.
# Example 1: Torch 
library(torch)
# Create nn_sequential model
model < nn_sequential(
nn_linear(5, 12),
nn_relu(),
nn_linear(12, 1),
nn_sigmoid()
)
# Create Converter with input names
converter < Converter$new(model,
input_dim = c(5),
input_names = list(c("Car", "Cat", "Dog", "Plane", "Horse"))
)
# Apply method Connection Weights
cw < ConnectionWeights$new(converter)
#> Backward pass 'ConnectionWeights':
#>

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# Print the result as a data.frame
cw$get_result("data.frame")
#> feature class value
#> 1 Car Y1 0.04181626
#> 2 Cat Y1 0.21144159
#> 3 Dog Y1 0.14502102
#> 4 Plane Y1 0.02537861
#> 5 Horse Y1 0.05368418
# Plot the result
plot(cw)
# Example 2: Neuralnet 
library(neuralnet)
data(iris)
# Train a Neural Network
nn < neuralnet((Species == "setosa") ~ Petal.Length + Petal.Width,
iris,
linear.output = FALSE,
hidden = c(3, 2), act.fct = "tanh", rep = 1
)
# Convert the trained model
converter < Converter$new(nn)
# Apply the Connection Weights method
cw < ConnectionWeights$new(converter)
#> Backward pass 'ConnectionWeights':
#>

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# Get the result as a torch tensor
cw$get_result(type = "torch.tensor")
#> torch_tensor
#> 54.0289
#> 29.0146
#> [ CPUFloatType{2,1} ]
# Plot the result
plot(cw)
# Example 3: Keras 
library(keras)
if (is_keras_available()) {
# Define a model
model < keras_model_sequential()
model %>%
layer_conv_1d(
input_shape = c(64, 3), kernel_size = 16, filters = 8,
activation = "softplus"
) %>%
layer_conv_1d(kernel_size = 16, filters = 4, activation = "tanh") %>%
layer_conv_1d(kernel_size = 16, filters = 2, activation = "relu") %>%
layer_flatten() %>%
layer_dense(units = 64, activation = "relu") %>%
layer_dense(units = 2, activation = "softmax")
# Convert the model
converter < Converter$new(model)
# Apply the Connection Weights method
cw < ConnectionWeights$new(converter)
# Get the result as data.frame
cw$get_result(type = "data.frame")
# Plot the result for all classes
plot(cw, output_idx = 1:2)
}
#> Loaded Tensorflow version 2.7.0
#> Backward pass 'ConnectionWeights':
#>

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#  Advanced: Plotly 
# If you want to create an interactive plot of your results with custom
# changes, you can take use of the method plotly::ggplotly
library(ggplot2)
library(plotly)
#>
#> Attaching package: ‘plotly’
#> The following object is masked from ‘package:ggplot2’:
#>
#> last_plot
#> The following object is masked from ‘package:stats’:
#>
#> filter
#> The following object is masked from ‘package:graphics’:
#>
#> layout
library(neuralnet)
data(iris)
nn < neuralnet(Species ~ .,
iris,
linear.output = FALSE,
hidden = c(10, 8), act.fct = "tanh", rep = 1, threshold = 0.5
)
# create an converter for this model
converter < Converter$new(nn)
# create new instance of 'LRP'
cw < ConnectionWeights$new(converter)
#> Backward pass 'ConnectionWeights':
#>

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library(plotly)
# Get the ggplot and add your changes
p < plot(cw, output_idx = 1) +
theme_bw() +
scale_fill_gradient2(low = "green", mid = "black", high = "blue")
#> Scale for 'fill' is already present. Adding another scale for 'fill', which
#> will replace the existing scale.
# Now apply the method plotly::ggplotly with argument tooltip = "text"
plotly::ggplotly(p, tooltip = "text")