SensitivityPlots function

Plot sensitivities of a neural network model

Function to plot the sensitivities created by SensAnalysisMLP.

SensitivityPlots(
  sens = NULL,
  der = TRUE,
  zoom = TRUE,
  quit.legend = FALSE,
  output = 1,
  plot_type = NULL,
  inp_var = NULL,
  title = "Sensitivity Plots",
  dodge_var = FALSE
)

Arguments

• sens: SensAnalysisMLP object created by SensAnalysisMLP or HessMLP object created by HessianMLP.
• der: logical indicating if density plots should be created. By default is TRUE
• zoom: logical indicating if the distributions should be zoomed when there is any of them which is too tiny to be appreciated in the third plot. facet_zoom function from ggforce package is required.
• quit.legend: logical indicating if legend of the third plot should be removed. By default is FALSE
• output: numeric or character specifying the output neuron or output name to be plotted. By default is the first output (output = 1).
• plot_type: character indicating which of the 3 plots to show. Useful when several variables are analyzed. Acceptable values are 'mean_sd', 'square', 'raw' corresponding to first, second and third plot respectively. If NULL, all plots are shown at the same time. By default is NULL.
• inp_var: character indicating which input variable to show in density plot. Only useful when choosing plot_type='raw' to show the density plot of one input variable. If NULL, all variables are plotted in density plot. By default is NULL.
• title: character title of the sensitivity plots
• dodge_var: bool Flag to indicate that x ticks in meanSensSQ plot must dodge between them. Useful with too long input names.

Returns

List with the following plot for each output:

• Plot 1: colorful plot with the classification of the classes in a 2D map
• Plot 2: b/w plot with probability of the chosen class in a 2D map
• Plot 3: plot with the stats::predictions of the data provided if param der is FALSE

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR

## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 250
decay <- 0.1

################################################################################
#########################  REGRESSION NNET #####################################
################################################################################
## Regression dataframe --------------------------------------------------------
# Scale the data
fdata.Reg.tr <- fdata[,2:ncol(fdata)]
fdata.Reg.tr[,3] <- fdata.Reg.tr[,3]/10
fdata.Reg.tr[,1] <- fdata.Reg.tr[,1]/1000

# Normalize the data for some models
preProc <- caret::preProcess(fdata.Reg.tr, method = c("center","scale"))
nntrData <- predict(preProc, fdata.Reg.tr)

#' ## TRAIN nnet NNET --------------------------------------------------------
# Create a formula to train NNET
form <- paste(names(fdata.Reg.tr)[2:ncol(fdata.Reg.tr)], collapse = " + ")
form <- formula(paste(names(fdata.Reg.tr)[1], form, sep = " ~ "))

set.seed(150)
nnetmod <- nnet::nnet(form,
                           data = nntrData,
                           linear.output = TRUE,
                           size = hidden_neurons,
                           decay = decay,
                           maxit = iters)
# Try SensAnalysisMLP
sens <- NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData, plot = FALSE)
NeuralSens::SensitivityPlots(sens)

References

Pizarroso J, Portela J, Muñoz A (2022). NeuralSens: Sensitivity Analysis of Neural Networks. Journal of Statistical Software, 102(7), 1-36.