Help for package NeuralSens

Version:

1.1.3

Title:

Sensitivity Analysis of Neural Networks

Description:

Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point <doi:10.18637/jss.v102.i07>.

Author:

José Portela González [aut], Antonio Muñoz San Roque [aut], Jaime Pizarroso Gonzalo [aut, ctb, cre]

Maintainer:

Jaime Pizarroso Gonzalo <jpizarroso@comillas.edu>

Imports:

ggplot2, gridExtra, NeuralNetTools, reshape2, caret, fastDummies, stringr, Hmisc, ggforce, scales, ggnewscale, magrittr, ggrepel, ggbreak, dplyr

Suggests:

h2o, RSNNS, nnet, neuralnet, plotly, e1071

RoxygenNote:

7.3.1

NeedsCompilation:

URL:

https://github.com/JaiPizGon/NeuralSens

BugReports:

https://github.com/JaiPizGon/NeuralSens/issues

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Encoding:

UTF-8

LazyData:

true

Packaged:

2024-05-11 19:26:10 UTC; jpizarroso

Repository:

CRAN

Date/Publication:

2024-05-11 19:43:03 UTC

NeuralSens: Sensitivity Analysis of Neural Networks

Description

Visualization and analysis tools to aid in the interpretation of neural network models.

Author(s)

Maintainer: Jaime Pizarroso Gonzalo jpizarroso@comillas.edu [contributor]

Authors:

José Portela González Jose.Portela@iit.comillas.edu
Antonio Muñoz San Roque antonio.munoz@iit.comillas.edu

Activation function of neuron

Description

Evaluate activation function of a neuron

Usage

ActFunc(type = "sigmoid", ...)

Arguments

type

character name of the activation function

...

extra arguments needed to calculate the functions

Value

numeric output of the neuron

References

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

Examples

# Return the sigmoid activation function of a neuron
ActivationFunction <- ActFunc("sigmoid")
# Return the tanh activation function of a neuron
ActivationFunction <- ActFunc("tanh")
# Return the activation function of several layers of neurons
actfuncs <- c("linear","sigmoid","linear")
ActivationFunctions <- sapply(actfuncs, ActFunc)

Sensitivity alpha-curve associated to MLP function

Description

Obtain sensitivity alpha-curves associated to MLP function obtained from the sensitivities returned by SensAnalysisMLP.

Usage

AlphaSensAnalysis(
  sens,
  tol = NULL,
  max_alpha = 15,
  curve_equal_origin = FALSE,
  inp_var = NULL,
  line_width = 1,
  title = "Alpha curve of Lp norm values",
  alpha_bar = 1,
  kind = "line"
)

Arguments

sens

sensitivity object returned by SensAnalysisMLP

tol

difference between M_alpha and maximum sensitivity of the sensitivity of each input variable

max_alpha

maximum alpha value to analyze

curve_equal_origin

make all the curves begin at (1,0)

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.

line_width

int width of the line in the plot.

title

char title of the alpha-curves plot

alpha_bar

int alpha value to show as column plot.

kind

char select the type of plot: "line" or "bar"

Value

alpha-curves of the MLP function

Examples


mod <- RSNNS::mlp(simdata[, c("X1", "X2", "X3")], simdata[, "Y"],
                 maxit = 1000, size = 15, linOut = TRUE)

sens <- SensAnalysisMLP(mod, trData = simdata,
                        output_name = "Y", plot = FALSE)

AlphaSensAnalysis(sens)

Sensitivity alpha-curve associated to MLP function of an input variable

Description

Obtain sensitivity alpha-curve associated to MLP function obtained from the sensitivities returned by SensAnalysisMLP of an input variable.

Usage

AlphaSensCurve(sens, tol = NULL, max_alpha = 100)

Arguments

sens

raw sensitivities of the MLP output with respect to input variable.

tol

difference between M_alpha and maximum sensitivity of the sensitivity of each input variable

max_alpha

maximum alpha value to analyze

Value

alpha-curve of the MLP function

Examples


mod <- RSNNS::mlp(simdata[, c("X1", "X2", "X3")], simdata[, "Y"],
                 maxit = 1000, size = 15, linOut = TRUE)

sens <- SensAnalysisMLP(mod, trData = simdata,
                        output_name = "Y", plot = FALSE)

AlphaSensCurve(sens$raw_sens[[1]][,1])

Change significance of boot SensMLP Class

Description

For a SensMLP Class object, change the significance level of the statistical tests

Usage

ChangeBootAlpha(x, boot.alpha)

Arguments

x

SensMLP object created by SensAnalysisMLP

boot.alpha

float significance level

Value

SensMLP object with changed significance level. All boot related metrics are changed

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


## TRAIN nnet NNET --------------------------------------------------------

set.seed(150)
nnetmod <- caret::train(DEM ~ .,
                 data = fdata.Reg.tr,
                 method = "nnet",
                 tuneGrid = expand.grid(size = c(1), decay = c(0.01)),
                 trControl = caret::trainControl(method="none"),
                 preProcess = c('center', 'scale'),
                 linout = FALSE,
                 trace = FALSE,
                 maxit = 300)
# Try SensAnalysisMLP
sens <- NeuralSens::SensAnalysisMLP(nnetmod, trData = fdata.Reg.tr,
                                    plot = FALSE, boot.R=2, output_name='DEM')
NeuralSens::ChangeBootAlpha(sens, boot.alpha=0.1)

Sensitivity analysis plot over time of the data

Description

Plot of sensitivity of the neural network output respect to the inputs over the time variable from the data provided

Usage

CombineSens(object, comb_type = "mean")

Arguments

object

SensMLP object generated by SensAnalysisMLP with several outputs (classification MLP)

comb_type

Function to combine the matrixes of the raw_sens component of object. It can be "mean", "median" or "sqmean". It can also be a function to combine the rows of the matrixes

Value

SensMLP object with the sensitivities combined

Examples


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

#' ## TRAIN nnet NNET --------------------------------------------------------
# Create a formula to train NNET
form <- paste(names(fdata)[1:ncol(fdata)-1], collapse = " + ")
form <- formula(paste(names(fdata)[5], form, sep = " ~ "))

set.seed(150)
mod <- nnet::nnet(form,
                  data = fdata,
                  linear.output = TRUE,
                  size = hidden_neurons,
                  decay = decay,
                  maxit = iters)
# mod should be a neural network classification model
sens <- SensAnalysisMLP(mod, trData = fdata, output_name = 'Species')
combinesens <- CombineSens(sens, "sqmean")

Plot sensitivities of a neural network model

Description

Function to plot the sensitivities created by SensAnalysisMLP.

Usage

ComputeHessMeasures(sens)

Arguments

sens

SensAnalysisMLP object created by SensAnalysisMLP.

Value

SensAnalysisMLP object with the sensitivities calculated

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)

Plot sensitivities of a neural network model

Description

Function to plot the sensitivities created by SensAnalysisMLP.

Usage

ComputeSensMeasures(sens)

Arguments

sens

SensAnalysisMLP object created by SensAnalysisMLP.

Value

SensAnalysisMLP object with the sensitivities calculated

References

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

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)

Data frame with 4 variables

Description

Training dataset with values of temperature and working day to predict electrical demand

Format

A data frame with 1980 rows and 4 variables:

DATE: date of the measure
DEM: electrical demand
WD: Working Day: index which express how much work is made that day
TEMP: weather temperature

Author(s)

Jose Portela Gonzalez

References

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

Data frame with 3 variables

Description

Validation dataset with values of temperature and working day to predict electrical demand

Format

A data frame with 7 rows and 3 variables:

DATE: date of the measure
WD: Working Day: index which express how much work is made that day
TEMP: weather temperature

Author(s)

Jose Portela Gonzalez

References

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

Second derivative of activation function of neuron

Description

Evaluate second derivative of activation function of a neuron

Usage

Der2ActFunc(type = "sigmoid", ...)

Arguments

type

character name of the activation function

...

extra arguments needed to calculate the functions

Value

numeric output of the neuron

Examples

# Return derivative of the sigmoid activation function of a neuron
ActivationFunction <- Der2ActFunc("sigmoid")
# Return derivative of the tanh activation function of a neuron
ActivationFunction <- Der2ActFunc("tanh")
# Return derivative of the activation function of several layers of neurons
actfuncs <- c("linear","sigmoid","linear")
ActivationFunctions <- sapply(actfuncs, Der2ActFunc)

Third derivative of activation function of neuron

Description

Evaluate third derivative of activation function of a neuron

Usage

Der3ActFunc(type = "sigmoid", ...)

Arguments

type

character name of the activation function

...

extra arguments needed to calculate the functions

Value

numeric output of the neuron

Examples

# Return derivative of the sigmoid activation function of a neuron
ActivationFunction <- Der3ActFunc("sigmoid")
# Return derivative of the tanh activation function of a neuron
ActivationFunction <- Der3ActFunc("tanh")
# Return derivative of the activation function of several layers of neurons
actfuncs <- c("linear","sigmoid","linear")
ActivationFunctions <- sapply(actfuncs, Der3ActFunc)

Derivative of activation function of neuron

Description

Evaluate derivative of activation function of a neuron

Usage

DerActFunc(type = "sigmoid", ...)

Arguments

type

character name of the activation function

...

extra arguments needed to calculate the functions

Value

numeric output of the neuron

References

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

Examples

# Return derivative of the sigmoid activation function of a neuron
ActivationFunction <- DerActFunc("sigmoid")
# Return derivative of the tanh activation function of a neuron
ActivationFunction <- DerActFunc("tanh")
# Return derivative of the activation function of several layers of neurons
actfuncs <- c("linear","sigmoid","linear")
ActivationFunctions <- sapply(actfuncs, DerActFunc)

Second derivatives 3D scatter or surface plot against input values

Description

3D Plot of second derivatives of the neural network output respect to the inputs. This function use plotly instead of ggplot2 to achieve better visualization

Usage

HessDotPlot(
  object,
  fdata = NULL,
  input_vars = "all",
  input_vars2 = "all",
  output_vars = "all",
  surface = FALSE,
  grid = FALSE,
  color = NULL,
  ...
)

Arguments

object

fitted neural network model or array containing the raw second derivatives from the function HessianMLP

fdata

data.frame containing the data to evaluate the second derivatives of the model.

input_vars

character vector with the variables to create the scatter plot in x-axis. If "all", then scatter plots are created for all the input variables in fdata.

input_vars2

character vector with the variables to create the scatter plot in y-axis. If "all", then scatter plots are created for all the input variables in fdata.

output_vars

character vector with the variables to create the scatter plot. If "all", then scatter plots are created for all the output variables in fdata.

surface

logical if TRUE, a 3D surface is created instead of 3D scatter plot (only for combinations of different inputs)

grid

logical. If TRUE, plots created are show together using arrangeGrob. It does not work on Windows platforms due to bugs in plotly library.

color

character specifying the name of a numeric variable of fdata to color the 3D scatter plot.

...

further arguments that should be passed to HessianMLP function

Value

list of 3D geom_point plots for the inputs variables representing the sensitivity of each output respect to the inputs

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 HessDotPlot
NeuralSens::HessDotPlot(nnetmod, fdata = nntrData, surface = TRUE, color = "WD")

Feature sensitivity plot

Description

Show the distribution of the sensitivities of the output in geom_sina() plot which color depends on the input values

Usage

HessFeaturePlot(object, fdata = NULL, ...)

Arguments

object

fitted neural network model or array containing the raw sensitivities from the function SensAnalysisMLP

fdata

data.frame containing the data to evaluate the sensitivity of the model. Not needed if the raw sensitivities has been passed as object

...

further arguments that should be passed to SensAnalysisMLP function

Value

list of Feature sensitivity plot as described in https://www.r-bloggers.com/2019/03/a-gentle-introduction-to-shap-values-in-r/

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
hess <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
NeuralSens::HessFeaturePlot(hess)

Constructor of the HessMLP Class

Description

Create an object of HessMLP class

Usage

HessMLP(
  sens = list(),
  raw_sens = list(),
  mlp_struct = numeric(),
  trData = data.frame(),
  coefnames = character(),
  output_name = character()
)

Arguments

sens

list of sensitivity measures, one list per output neuron

raw_sens

list of sensitivities, one array per output neuron

mlp_struct

numeric vector describing the structur of the MLP model

trData

data.frame with the data used to calculate the sensitivities

coefnames

character vector with the name of the predictor(s)

output_name

character vector with the name of the output(s)

Value

HessMLP object

Convert a HessMLP to a SensMLP object

Description

Auxiliary function to turn a HessMLP object to a SensMLP object in order to use the plot-related functions associated with SensMLP

Usage

HessToSensMLP(x)

Arguments

x

HessMLP object

Value

SensMLP object

Sensitivity of MLP models

Description

Function for evaluating the sensitivities of the inputs variables in a mlp model

Usage

HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## Default S3 method:
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc = NULL,
  deractfunc = NULL,
  der2actfunc = NULL,
  preProc = NULL,
  terms = NULL,
  output_name = NULL,
  ...
)

## S3 method for class 'train'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'H2OMultinomialModel'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'H2ORegressionModel'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'list'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc,
  ...
)

## S3 method for class 'mlp'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nn'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nnet'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nnetar'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'numeric'
HessianMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc = NULL,
  preProc = NULL,
  terms = NULL,
  ...
)

Arguments

MLP.fit

fitted neural network model

.returnSens

DEPRECATED

plot

logical whether or not to plot the analysis. By default is TRUE.

.rawSens

DEPRECATED

sens_origin_layer

numeric specifies the layer of neurons with respect to which the derivative must be calculated. The input layer is specified by 1 (default).

sens_end_layer

numeric specifies the layer of neurons of which the derivative is calculated. It may also be 'last' to specify the output layer (default).

sens_origin_input

logical specifies if the derivative must be calculated with respect to the inputs (TRUE) or output (FALSE) of the sens_origin_layer layer of the model. By default is TRUE.

sens_end_input

logical specifies if the derivative calculated is of the output (FALSE) or from the input (TRUE) of the sens_end_layer layer of the model. By default is FALSE.

...

additional arguments passed to or from other methods

trData

data.frame containing the data to evaluate the sensitivity of the model

actfunc

character vector indicating the activation function of each neurons layer.

deractfunc

character vector indicating the derivative of the activation function of each neurons layer.

der2actfunc

character vector indicating the second derivative of the activation function of each neurons layer.

preProc

preProcess structure applied to the training data. See also preProcess

terms

function applied to the training data to create factors. See also train

output_name

character name of the output variable in order to avoid changing the name of the output variable in trData to '.outcome'

Details

In case of using an input of class factor and a package which need to enter the input data as matrix, the dummies must be created before training the neural network.

After that, the training data must be given to the function using the trData argument.

Value

SensMLP object with the sensitivity metrics and sensitivities of the MLP model passed to the function.

Plots

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

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR
## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 100
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 HessianMLP
NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)

# Try HessianMLP to calculate sensitivities with respect to output of hidden neurones
NeuralSens::HessianMLP(nnetmod, trData = nntrData,
                             sens_origin_layer = 2,
                             sens_end_layer = "last",
                             sens_origin_input = FALSE,
                             sens_end_input = FALSE)
## Train caret NNET ------------------------------------------------------------
# Create trainControl
ctrl_tune <- caret::trainControl(method = "boot",
                                 savePredictions = FALSE,
                                 summaryFunction = caret::defaultSummary)
set.seed(150) #For replication
caretmod <- caret::train(form = DEM~.,
                              data = fdata.Reg.tr,
                              method = "nnet",
                              linout = TRUE,
                              tuneGrid = data.frame(size = 3,
                                                    decay = decay),
                              maxit = iters,
                              preProcess = c("center","scale"),
                              trControl = ctrl_tune,
                              metric = "RMSE")

# Try HessianMLP
NeuralSens::HessianMLP(caretmod)

## Train h2o NNET --------------------------------------------------------------
# Create a cluster with 4 available cores
h2o::h2o.init(ip = "localhost",
              nthreads = 4)

# Reset the cluster
h2o::h2o.removeAll()
fdata_h2o <- h2o::as.h2o(x = fdata.Reg.tr, destination_frame = "fdata_h2o")

set.seed(150)
h2omod <-h2o:: h2o.deeplearning(x = names(fdata.Reg.tr)[2:ncol(fdata.Reg.tr)],
                                     y = names(fdata.Reg.tr)[1],
                                     distribution = "AUTO",
                                     training_frame = fdata_h2o,
                                     standardize = TRUE,
                                     activation = "Tanh",
                                     hidden = c(hidden_neurons),
                                     stopping_rounds = 0,
                                     epochs = iters,
                                     seed = 150,
                                     model_id = "nnet_h2o",
                                     adaptive_rate = FALSE,
                                     rate_decay = decay,
                                     export_weights_and_biases = TRUE)

# Try HessianMLP
NeuralSens::HessianMLP(h2omod)

# Turn off the cluster
h2o::h2o.shutdown(prompt = FALSE)
rm(fdata_h2o)

## Train RSNNS NNET ------------------------------------------------------------
# Normalize data using RSNNS algorithms
trData <- as.data.frame(RSNNS::normalizeData(fdata.Reg.tr))
names(trData) <- names(fdata.Reg.tr)
set.seed(150)
RSNNSmod <-RSNNS::mlp(x = trData[,2:ncol(trData)],
                           y = trData[,1],
                           size = hidden_neurons,
                           linOut = TRUE,
                           learnFuncParams=c(decay),
                           maxit=iters)

# Try HessianMLP
NeuralSens::HessianMLP(RSNNSmod, trData = trData, output_name = "DEM")

## USE DEFAULT METHOD ----------------------------------------------------------
NeuralSens::HessianMLP(caretmod$finalModel$wts,
                            trData = fdata.Reg.tr,
                            mlpstr = caretmod$finalModel$n,
                            coefnames = caretmod$coefnames,
                            actfun = c("linear","sigmoid","linear"),
                            output_name = "DEM")

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

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

# Factorize the output
fdata.Reg.cl$DEM <- factor(round(fdata.Reg.cl$DEM, digits = 1))

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

## Train caret NNET ------------------------------------------------------------
# Create trainControl
ctrl_tune <- caret::trainControl(method = "boot",
                                 savePredictions = FALSE,
                                 summaryFunction = caret::defaultSummary)
set.seed(150) #For replication
caretmod <- caret::train(form = DEM~.,
                                data = fdata.Reg.cl,
                                method = "nnet",
                                linout = FALSE,
                                tuneGrid = data.frame(size = hidden_neurons,
                                                      decay = decay),
                                maxit = iters,
                                preProcess = c("center","scale"),
                                trControl = ctrl_tune,
                                metric = "Accuracy")

# Try HessianMLP
NeuralSens::HessianMLP(caretmod)

## Train h2o NNET --------------------------------------------------------------
# Create local cluster with 4 available cores
h2o::h2o.init(ip = "localhost",
              nthreads = 4)

# Reset the cluster
h2o::h2o.removeAll()
fdata_h2o <- h2o::as.h2o(x = fdata.Reg.cl, destination_frame = "fdata_h2o")

set.seed(150)
h2omod <- h2o::h2o.deeplearning(x = names(fdata.Reg.cl)[2:ncol(fdata.Reg.cl)],
                                       y = names(fdata.Reg.cl)[1],
                                       distribution = "AUTO",
                                       training_frame = fdata_h2o,
                                       standardize = TRUE,
                                       activation = "Tanh",
                                       hidden = c(hidden_neurons),
                                       stopping_rounds = 0,
                                       epochs = iters,
                                       seed = 150,
                                       model_id = "nnet_h2o",
                                       adaptive_rate = FALSE,
                                       rate_decay = decay,
                                       export_weights_and_biases = TRUE)

# Try HessianMLP
NeuralSens::HessianMLP(h2omod)

# Apaga el cluster
h2o::h2o.shutdown(prompt = FALSE)
rm(fdata_h2o)

## 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 HessianMLP
NeuralSens::HessianMLP(nnetmod, trData = nntrData)

Neural network structure sensitivity plot

Description

Plot a neural interpretation diagram colored by sensitivities of the model

Usage

PlotSensMLP(
  MLP.fit,
  metric = "mean",
  sens_neg_col = "red",
  sens_pos_col = "blue",
  ...
)

Arguments

MLP.fit

fitted neural network model

metric

metric to plot in the NID. It can be "mean" (default), "median or "sqmean". It can be any metric to combine the raw sensitivities

sens_neg_col

character string indicating color of negative sensitivity measure, default 'red'. The same is passed to argument neg_col of plotnet

sens_pos_col

character string indicating color of positive sensitivity measure, default 'blue'. The same is passed to argument pos_col of plotnet

...

additional arguments passed to plotnet and/or SensAnalysisMLP

Value

A graphics object

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR
## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 100
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
NeuralSens::PlotSensMLP(nnetmod, trData = nntrData)

Sensitivity of MLP models

Description

Function for evaluating the sensitivities of the inputs variables in a mlp model

Usage

SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## Default S3 method:
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc = NULL,
  deractfunc = NULL,
  preProc = NULL,
  terms = NULL,
  output_name = NULL,
  ...
)

## S3 method for class 'train'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  boot.R = NULL,
  boot.seed = 1,
  boot.alpha = 0.05,
  ...
)

## S3 method for class 'H2OMultinomialModel'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'H2ORegressionModel'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'list'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc,
  ...
)

## S3 method for class 'mlp'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nn'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nnet'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  preProc = NULL,
  terms = NULL,
  ...
)

## S3 method for class 'nnetar'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  ...
)

## S3 method for class 'numeric'
SensAnalysisMLP(
  MLP.fit,
  .returnSens = TRUE,
  plot = TRUE,
  .rawSens = FALSE,
  sens_origin_layer = 1,
  sens_end_layer = "last",
  sens_origin_input = TRUE,
  sens_end_input = FALSE,
  trData,
  actfunc = NULL,
  preProc = NULL,
  terms = NULL,
  ...
)

Arguments

MLP.fit

fitted neural network model

.returnSens

DEPRECATED

plot

logical whether or not to plot the analysis. By default is TRUE.

.rawSens

DEPRECATED

sens_origin_layer

numeric specifies the layer of neurons with respect to which the derivative must be calculated. The input layer is specified by 1 (default).

sens_end_layer

numeric specifies the layer of neurons of which the derivative is calculated. It may also be 'last' to specify the output layer (default).

sens_origin_input

logical specifies if the derivative must be calculated with respect to the inputs (TRUE) or output (FALSE) of the sens_origin_layer layer of the model. By default is TRUE.

sens_end_input

logical specifies if the derivative calculated is of the output (FALSE) or from the input (TRUE) of the sens_end_layer layer of the model. By default is FALSE.

...

additional arguments passed to or from other methods

trData

data.frame containing the data to evaluate the sensitivity of the model

actfunc

character vector indicating the activation function of each neurons layer.

deractfunc

character vector indicating the derivative of the activation function of each neurons layer.

preProc

preProcess structure applied to the training data. See also preProcess

terms

function applied to the training data to create factors. See also train

output_name

character name of the output variable in order to avoid changing the name of the output variable in trData to '.outcome'

boot.R

int Number of bootstrap samples to calculate. Used to detect significant inputs and significant non-linearities. Only available for train objects. Defaults to NULL.

boot.seed

int Seed of bootstrap evaluations.

boot.alpha

float Significance level of statistical test.

Details

In case of using an input of class factor and a package which need to enter the input data as matrix, the dummies must be created before training the neural network.

After that, the training data must be given to the function using the trData argument.

Value

SensMLP object with the sensitivity metrics and sensitivities of the MLP model passed to the function.

Plots

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

References

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

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR
## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 100
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
NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData)

# Try SensAnalysisMLP to calculate sensitivities with respect to output of hidden neurones
NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData,
                             sens_origin_layer = 2,
                             sens_end_layer = "last",
                             sens_origin_input = FALSE,
                             sens_end_input = FALSE)
## Train caret NNET ------------------------------------------------------------
# Create trainControl
ctrl_tune <- caret::trainControl(method = "boot",
                                 savePredictions = FALSE,
                                 summaryFunction = caret::defaultSummary)
set.seed(150) #For replication
caretmod <- caret::train(form = DEM~.,
                              data = fdata.Reg.tr,
                              method = "nnet",
                              linout = TRUE,
                              tuneGrid = data.frame(size = 3,
                                                    decay = decay),
                              maxit = iters,
                              preProcess = c("center","scale"),
                              trControl = ctrl_tune,
                              metric = "RMSE")

# Try SensAnalysisMLP
NeuralSens::SensAnalysisMLP(caretmod)

## Train h2o NNET --------------------------------------------------------------
# Create a cluster with 4 available cores
h2o::h2o.init(ip = "localhost",
              nthreads = 4)

# Reset the cluster
h2o::h2o.removeAll()
fdata_h2o <- h2o::as.h2o(x = fdata.Reg.tr, destination_frame = "fdata_h2o")

set.seed(150)
h2omod <-h2o:: h2o.deeplearning(x = names(fdata.Reg.tr)[2:ncol(fdata.Reg.tr)],
                                     y = names(fdata.Reg.tr)[1],
                                     distribution = "AUTO",
                                     training_frame = fdata_h2o,
                                     standardize = TRUE,
                                     activation = "Tanh",
                                     hidden = c(hidden_neurons),
                                     stopping_rounds = 0,
                                     epochs = iters,
                                     seed = 150,
                                     model_id = "nnet_h2o",
                                     adaptive_rate = FALSE,
                                     rate_decay = decay,
                                     export_weights_and_biases = TRUE)

# Try SensAnalysisMLP
NeuralSens::SensAnalysisMLP(h2omod)

# Turn off the cluster
h2o::h2o.shutdown(prompt = FALSE)
rm(fdata_h2o)

## Train RSNNS NNET ------------------------------------------------------------
# Normalize data using RSNNS algorithms
trData <- as.data.frame(RSNNS::normalizeData(fdata.Reg.tr))
names(trData) <- names(fdata.Reg.tr)
set.seed(150)
RSNNSmod <-RSNNS::mlp(x = trData[,2:ncol(trData)],
                           y = trData[,1],
                           size = hidden_neurons,
                           linOut = TRUE,
                           learnFuncParams=c(decay),
                           maxit=iters)

# Try SensAnalysisMLP
NeuralSens::SensAnalysisMLP(RSNNSmod, trData = trData, output_name = "DEM")

## USE DEFAULT METHOD ----------------------------------------------------------
NeuralSens::SensAnalysisMLP(caretmod$finalModel$wts,
                            trData = fdata.Reg.tr,
                            mlpstr = caretmod$finalModel$n,
                            coefnames = caretmod$coefnames,
                            actfun = c("linear","sigmoid","linear"),
                            output_name = "DEM")

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

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

# Factorize the output
fdata.Reg.cl$DEM <- factor(round(fdata.Reg.cl$DEM, digits = 1))

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

## Train caret NNET ------------------------------------------------------------
# Create trainControl
ctrl_tune <- caret::trainControl(method = "boot",
                                 savePredictions = FALSE,
                                 summaryFunction = caret::defaultSummary)
set.seed(150) #For replication
caretmod <- caret::train(form = DEM~.,
                                data = fdata.Reg.cl,
                                method = "nnet",
                                linout = FALSE,
                                tuneGrid = data.frame(size = hidden_neurons,
                                                      decay = decay),
                                maxit = iters,
                                preProcess = c("center","scale"),
                                trControl = ctrl_tune,
                                metric = "Accuracy")

# Try SensAnalysisMLP
NeuralSens::SensAnalysisMLP(caretmod)

## Train h2o NNET --------------------------------------------------------------
# Create local cluster with 4 available cores
h2o::h2o.init(ip = "localhost",
              nthreads = 4)

# Reset the cluster
h2o::h2o.removeAll()
fdata_h2o <- h2o::as.h2o(x = fdata.Reg.cl, destination_frame = "fdata_h2o")

set.seed(150)
h2omod <- h2o::h2o.deeplearning(x = names(fdata.Reg.cl)[2:ncol(fdata.Reg.cl)],
                                       y = names(fdata.Reg.cl)[1],
                                       distribution = "AUTO",
                                       training_frame = fdata_h2o,
                                       standardize = TRUE,
                                       activation = "Tanh",
                                       hidden = c(hidden_neurons),
                                       stopping_rounds = 0,
                                       epochs = iters,
                                       seed = 150,
                                       model_id = "nnet_h2o",
                                       adaptive_rate = FALSE,
                                       rate_decay = decay,
                                       export_weights_and_biases = TRUE)

# Try SensAnalysisMLP
NeuralSens::SensAnalysisMLP(h2omod)

# Apaga el cluster
h2o::h2o.shutdown(prompt = FALSE)
rm(fdata_h2o)

## 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
NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData)

Sensitivity scatter plot against input values

Description

Plot of sensitivities of the neural network output respect to the inputs

Usage

SensDotPlot(
  object,
  fdata = NULL,
  input_vars = "all",
  output_vars = "all",
  smooth = FALSE,
  nspline = NULL,
  color = NULL,
  grid = FALSE,
  ...
)

Arguments

object

fitted neural network model or array containing the raw sensitivities from the function SensAnalysisMLP

fdata

data.frame containing the data to evaluate the sensitivity of the model.

input_vars

character vector with the variables to create the scatter plot. If "all", then scatter plots are created for all the input variables in fdata.

output_vars

character vector with the variables to create the scatter plot. If "all", then scatter plots are created for all the output variables in fdata.

smooth

logical if TRUE, geom_smooth plots are added to each variable plot

nspline

integer if smooth is TRUE, this determine the degree of the spline used to perform geom_smooth. If nspline is NULL, the square root of the length of the data is used as degrees of the spline.

color

character specifying the name of a numeric variable of fdata to color the scatter plot.

grid

logical. If TRUE, plots created are show together using arrangeGrob

...

further arguments that should be passed to SensAnalysisMLP function

Value

list of geom_point plots for the inputs variables representing the sensitivity of each output respect to the inputs

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 SensDotPlot
NeuralSens::SensDotPlot(nnetmod, fdata = nntrData)

Feature sensitivity plot

Description

Show the distribution of the sensitivities of the output in geom_sina() plot which color depends on the input values

Usage

SensFeaturePlot(object, fdata = NULL, ...)

Arguments

object

fitted neural network model or array containing the raw sensitivities from the function SensAnalysisMLP

fdata

data.frame containing the data to evaluate the sensitivity of the model. Not needed if the raw sensitivities has been passed as object

...

further arguments that should be passed to SensAnalysisMLP function

Value

list of Feature sensitivity plot as described in https://www.r-bloggers.com/2019/03/a-gentle-introduction-to-shap-values-in-r/

References

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

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::SensFeaturePlot(sens)

Constructor of the SensMLP Class

Description

Create an object of SensMLP class

Usage

SensMLP(
  sens = list(),
  raw_sens = list(),
  mlp_struct = numeric(),
  trData = data.frame(),
  coefnames = character(),
  output_name = character(),
  cv = NULL,
  boot = NULL,
  boot.alpha = NULL
)

Arguments

sens

list of sensitivity measures, one data.frame per output neuron

raw_sens

list of sensitivities, one matrix per output neuron

mlp_struct

numeric vector describing the structur of the MLP model

trData

data.frame with the data used to calculate the sensitivities

coefnames

character vector with the name of the predictor(s)

output_name

character vector with the name of the output(s)

cv

list list with critical values of significance for std and mean square.

boot

array bootstrapped sensitivity measures.

boot.alpha

array significance level. Defaults to NULL. Only available for analyzed caret::train models.

Value

SensMLP object

References

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

Plot sensitivities of a neural network model

Description

Function to plot the sensitivities created by HessianMLP.

Usage

SensMatPlot(
  hess,
  sens = NULL,
  output = 1,
  metric = c("mean", "std", "meanSensSQ"),
  senstype = c("matrix", "interactions"),
  ...
)

Arguments

hess

HessMLP object created by HessianMLP.

sens

SensMLP object created by SensAnalysisMLP.

output

numeric or character specifying the output neuron or output name to be plotted. By default is the first output (output = 1).

metric

character specifying the metric to be plotted. It can be "mean", "std" or "meanSensSQ".

senstype

character specifying the type of plot to be plotted. It can be "matrix" or "interactions". If type = "matrix", only the second derivatives are plotted. If type = "interactions" the main diagonal are the first derivatives respect each input variable.

...

further argument passed similar to ggcorrplot arguments.

Details

Most of the code of this function is based on ggcorrplot() function from package ggcorrplot. However, due to the inhability of changing the limits of the color scale, it keeps giving a warning if that function is used and the color scale overwritten.

Value

a list of ggplots, one for each output neuron.

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR
## Parameters of the NNET ------------------------------------------------------
hidden_neurons <- 5
iters <- 100
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 HessianMLP
H <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
NeuralSens::SensMatPlot(H)
S <- NeuralSens::SensAnalysisMLP(nnetmod, trData = nntrData, plot = FALSE)
NeuralSens::SensMatPlot(H, S, senstype = "interactions")

Sensitivity analysis plot over time of the data

Description

Plot of sensitivity of the neural network output respect to the inputs over the time variable from the data provided

Usage

SensTimePlot(
  object,
  fdata = NULL,
  date.var = NULL,
  facet = FALSE,
  smooth = FALSE,
  nspline = NULL,
  ...
)

Arguments

object

fitted neural network model or array containing the raw sensitivities from the function SensAnalysisMLP

fdata

data.frame containing the data to evaluate the sensitivity of the model. Not needed if the raw sensitivities has been passed as object

date.var

Posixct vector with the date of each sample of fdata If NULL, the first variable with Posixct format of fdata is used as dates

facet

logical if TRUE, function facet_grid from ggplot2 is used

smooth

logical if TRUE, geom_smooth plots are added to each variable plot

nspline

integer if smooth is TRUE, this determine the degree of the spline used to perform geom_smooth. If nspline is NULL, the square root of the length of the timeseries is used as degrees of the spline.

...

further arguments that should be passed to SensAnalysisMLP function

Value

list of geom_line plots for the inputs variables representing the sensitivity of each output respect to the inputs over time

References

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

Examples

## Load data -------------------------------------------------------------------
data("DAILY_DEMAND_TR")
fdata <- DAILY_DEMAND_TR
fdata[,3] <- ifelse(as.data.frame(fdata)[,3] %in% c("SUN","SAT"), 0, 1)
## 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 SensTimePlot
NeuralSens::SensTimePlot(nnetmod, fdata = nntrData, date.var = NULL)

Plot sensitivities of a neural network model

Description

Function to plot the sensitivities created by SensAnalysisMLP.

Usage

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

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.

Value

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

References

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

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)

Define function to create a 'diagonal' array or get the diagonal of an array

Description

Define function to create a 'diagonal' array or get the diagonal of an array

Usage

diag3Darray(x = 1, dim = length(x), out = "vector")

Arguments

x

number or vector defining the value of the diagonal of 3D array

dim

integer defining the length of the diagonal. Default is length(x). If length(x) != 1, dim must be equal to length(x).

out

character specifying which type of diagonal to return ("vector" or "matrix"). See Details

Details

The diagonal of a 3D array has been defined as those elements in positions c(int,int,int), i.e., the three digits are the same.

If the diagonal should be returned, out specifies if it should return a "vector" with the elements of position c(int,int,int), or "matrix" with the elements of position c(int,dim,int), i.e., dim = 2 -> elements (1,1,1),(2,1,2),(3,1,3),(1,2,1),(2,2,2),(3,2,3),(3,1,3),(3,2,3),(3,3,3).

Value

array with all elements zero except the diagonal, with dimensions c(dim,dim,dim)

Examples

x <- diag3Darray(c(1,4,6), dim = 3)
x
# , , 1
#
# [,1] [,2] [,3]
# [1,]    1    0    0
# [2,]    0    0    0
# [3,]    0    0    0
#
# , , 2
#
# [,1] [,2] [,3]
# [1,]    0    0    0
# [2,]    0    4    0
# [3,]    0    0    0
#
# , , 3
#
# [,1] [,2] [,3]
# [1,]    0    0    0
# [2,]    0    0    0
# [3,]    0    0    6
diag3Darray(x)
# 1, 4, 6

Define function to change the diagonal of array

Description

Define function to change the diagonal of array

Usage

diag3Darray(x) <- value

Arguments

x

3D array whose diagonal must be c hanged

value

vector defining the new values of diagonal.

Details

The diagonal of a 3D array has been defined as those elements in positions c(int,int,int), i.e., the three digits are the same.

Value

array with all elements zero except the diagonal, with dimensions c(dim,dim,dim)

Examples

x <- array(1, dim = c(3,3,3))
diag3Darray(x) <- c(2,2,2)
x
#  , , 1
#
#  [,1] [,2] [,3]
#  [1,]    2    1    1
#  [2,]    1    1    1
#  [3,]    1    1    1
#
#  , , 2
#
#  [,1] [,2] [,3]
#  [1,]    1    1    1
#  [2,]    1    2    1
#  [3,]    1    1    1
#
#  , , 3
#
#  [,1] [,2] [,3]
#  [1,]    1    1    1
#  [2,]    1    1    1
#  [3,]    1    1    2

Define function to create a 'diagonal' array or get the diagonal of an array

Description

Define function to create a 'diagonal' array or get the diagonal of an array

Usage

diag4Darray(x = 1, dim = length(x))

Arguments

x

number or vector defining the value of the diagonal of 4D array

dim

integer defining the length of the diagonal. Default is length(x). If length(x) != 1, dim must be equal to length(x).

Details

The diagonal of a 4D array has been defined as those elements in positions c(int,int,int,int), i.e., the four digits are the same.

Value

array with all elements zero except the diagonal, with dimensions c(dim,dim,dim)

Examples

x <- diag4Darray(c(1,3,6,2), dim = 4)
x
# , , 1, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 2, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 3, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 4, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 1, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 2, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    3    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 3, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 4, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 1, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 2, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 3, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    6    0
# [4,]    0    0    0    0
#
# , , 4, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 1, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 2, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 3, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    0
#
# , , 4, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    0    0    0    0
# [2,]    0    0    0    0
# [3,]    0    0    0    0
# [4,]    0    0    0    2
diag4Darray(x)
# 1, 3, 6, 2

Define function to change the diagonal of array

Description

Define function to change the diagonal of array

Usage

diag4Darray(x) <- value

Arguments

x

3D array whose diagonal must be c hanged

value

vector defining the new values of diagonal.

Details

The diagonal of a 3D array has been defined as those elements in positions c(int,int,int), i.e., the three digits are the same.

Value

array with all elements zero except the diagonal, with dimensions c(dim,dim,dim)

Examples

x <- array(1, dim = c(4,4,4,4))
diag4Darray(x) <- c(2,2,2,2)
x
# , , 1, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    2    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 2, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 3, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 4, 1
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 1, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 2, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    2    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 3, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 4, 2
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 1, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 2, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 3, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    2    1
# [4,]    1    1    1    1
#
# , , 4, 3
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 1, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 2, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 3, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    1
#
# , , 4, 4
#
#      [,1] [,2] [,3] [,4]
# [1,]    1    1    1    1
# [2,]    1    1    1    1
# [3,]    1    1    1    1
# [4,]    1    1    1    2

Find Critical Value

Description

This function finds the smallest x such that the probability of a random variable being less than or equal to x is greater than or equal to 1 - alpha. It uses the uniroot function to find where the empirical cumulative distribution function (ECDF) crosses 1 - alpha.

Usage

find_critical_value(ecdf_func, alpha)

Arguments

ecdf_func

An ECDF function representing the distribution of a random variable.

alpha

A numeric value specifying the significance level.

Value

The smallest x such that P(X <= x) >= 1 - alpha.

Examples

data <- rnorm(100)
ecdf_data <- ecdf(data)
critical_val <- find_critical_value(ecdf_data, 0.05)

Check if object is of class `HessMLP`

Description

Check if object is of class HessMLP

Usage

is.HessMLP(object)

Arguments

object

HessMLP object

Value

TRUE if object is a HessMLP object

Check if object is of class `SensMLP`

Description

Check if object is of class SensMLP

Usage

is.SensMLP(object)

Arguments

object

SensMLP object

Value

TRUE if object is a SensMLP object

References

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

k-StepM Algorithm for Hypothesis Testing

Description

This function implements the k-stepM algorithm for multiple hypothesis testing. It tests each hypothesis using the critical value calculated from the ECDF of the k-max differences, updating the critical value, and iterating until all hypotheses are tested.

Usage

kStepMAlgorithm(original_stats, bootstrap_stats, num_hypotheses, alpha, k)

Arguments

original_stats

A numeric vector of original test statistics for each hypothesis.

bootstrap_stats

A numeric matrix of bootstrap test statistics, with rows representing bootstrap samples and columns representing hypotheses.

num_hypotheses

An integer specifying the total number of hypotheses.

alpha

A numeric value specifying the significance level.

k

An integer specifying the threshold number for controlling the k-familywise error rate.

Value

A list containing two elements: 'signif', a logical vector indicating which hypotheses are rejected, and 'cv', a numeric vector of critical values used for each hypothesis.

References

Romano, Joseph P., Azeem M. Shaikh, and Michael Wolf. "Formalized data snooping based on generalized error rates." Econometric Theory 24.2 (2008): 404-447.

Examples

original_stats <- rnorm(10)
bootstrap_stats <- matrix(rnorm(1000), ncol = 10)
result <- kStepMAlgorithm(original_stats, bootstrap_stats, 10, 0.05, 1)

Plot method for the HessMLP Class

Description

Plot the sensitivities and sensitivity metrics of a HessMLP object.

Usage

## S3 method for class 'HessMLP'
plot(
  x,
  plotType = c("sensitivities", "time", "features", "matrix", "interactions"),
  ...
)

Arguments

x

HessMLP object created by HessianMLP

plotType

character specifying which type of plot should be created. It can be:

"sensitivities" (default): use HessianMLP function
"time": use SensTimePlot function
"features": use HessFeaturePlot function
"matrix": use SensMatPlot function to show the values of second partial derivatives
"interactions": use SensMatPlot function to show the values of second partial derivatives and the first partial derivatives in the diagonal

...

additional parameters passed to plot function of the NeuralSens package

Value

list of graphic objects created by ggplot

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 HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)

plot(sens)
plot(sens,"time")

Plot method for the SensMLP Class

Description

Plot the sensitivities and sensitivity metrics of a SensMLP object.

Usage

## S3 method for class 'SensMLP'
plot(x, plotType = c("sensitivities", "time", "features"), ...)

Arguments

x

SensMLP object created by SensAnalysisMLP

plotType

character specifying which type of plot should be created. It can be:

"sensitivities" (default): use SensAnalysisMLP function
"time": use SensTimePlot function
"features": use SensFeaturePlot function

...

additional parameters passed to plot function of the NeuralSens package

Value

list of graphic objects created by ggplot

References

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

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)

plot(sens)
plot(sens,"time")
plot(sens,"features")

Print method for the HessMLP Class

Description

Print the sensitivities of a HessMLP object.

Usage

## S3 method for class 'HessMLP'
print(x, n = 5, round_digits = NULL, ...)

Arguments

x

HessMLP object created by HessianMLP

n

integer specifying number of sensitivities to print per each output

round_digits

integer number of decimal places, default NULL

...

additional parameters

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 HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
sens

Print method for the SensMLP Class

Description

Print the sensitivities of a SensMLP object.

Usage

## S3 method for class 'SensMLP'
print(x, n = 5, round_digits = NULL, ...)

Arguments

x

SensMLP object created by SensAnalysisMLP

n

integer specifying number of sensitivities to print per each output

round_digits

integer number of decimal places, default NULL

...

additional parameters

References

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

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)
sens

Print method of the summary HessMLP Class

Description

Print the sensitivity metrics of a HessMLP object. This metrics are the mean sensitivity, the standard deviation of sensitivities and the mean of sensitivities square

Usage

## S3 method for class 'summary.HessMLP'
print(x, round_digits = NULL, ...)

Arguments

x

summary.HessMLP object created by summary method of HessMLP object

round_digits

integer number of decimal places, default NULL

...

additional parameters

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 HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
print(summary(sens))

Print method of the summary SensMLP Class

Description

Print the sensitivity metrics of a SensMLP object. This metrics are the mean sensitivity, the standard deviation of sensitivities and the mean of sensitivities square

Usage

## S3 method for class 'summary.SensMLP'
print(x, round_digits = NULL, boot.alpha = NULL, ...)

Arguments

x

summary.SensMLP object created by summary method of SensMLP object

round_digits

integer number of decimal places, default NULL

boot.alpha

float significance level to show statistical metrics. If NULL, boot.alpha inherits from x is used. Defaults to NULL.

...

additional parameters

References

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

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)
print(summary(sens))

Simulated data to test the package functionalities

Description

data.frame with 2000 rows of 4 columns with 3 input variables X1, X2, X3 and one output variable Y. The data is already scaled, and has been generated using the following code:

set.seed(150)

simdata <- data.frame( "X1" = rnorm(2000, 0, 1), "X2" = rnorm(2000, 0, 1), "X3" = rnorm(2000, 0, 1) )

simdata$Y <- simdata$X1^2 + 0.5*simdata$X2 + 0.1*rnorm(2000, 0, 1)

Format

A data frame with 2000 rows and 4 variables:

X1: Random input 1
X2: Random input 2
X3: Random input 3
Y: Output

Author(s)

Jaime Pizarroso Gonzalo

References

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

Summary Method for the HessMLP Class

Description

Print the sensitivity metrics of a HessMLP object. This metrics are the mean sensitivity, the standard deviation of sensitivities and the mean of sensitivities square

Usage

## S3 method for class 'HessMLP'
summary(object, ...)

Arguments

object

HessMLP object created by HessianMLP

...

additional parameters

Value

summary object of the HessMLP object passed

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 HessianMLP
sens <- NeuralSens::HessianMLP(nnetmod, trData = nntrData, plot = FALSE)
summary(sens)

Summary Method for the SensMLP Class

Description

Print the sensitivity metrics of a SensMLP object. This metrics are the mean sensitivity, the standard deviation of sensitivities and the mean of sensitivities square

Usage

## S3 method for class 'SensMLP'
summary(object, ...)

Arguments

object

SensMLP object created by SensAnalysisMLP

...

additional parameters

Value

summary object of the SensMLP object passed

References

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

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)
summary(sens)

NeuralSens: Sensitivity Analysis of Neural Networks

Description

Author(s)

See Also

Activation function of neuron

Description

Usage

Arguments

Value

References

Examples

Sensitivity alpha-curve associated to MLP function

Description

Usage

Arguments

Value

Examples

Sensitivity alpha-curve associated to MLP function of an input variable

Description

Usage

Arguments

Value

Examples

Change significance of boot SensMLP Class

Description

Usage

Arguments

Value

Examples

Sensitivity analysis plot over time of the data

Description

Usage

Arguments

Value

Examples

Plot sensitivities of a neural network model

Description

Usage

Arguments

Value

Examples

Plot sensitivities of a neural network model

Description

Usage

Arguments

Value

References

Examples

Data frame with 4 variables

Description

Format

Author(s)

References

Data frame with 3 variables

Description

Format

Author(s)

References

Second derivative of activation function of neuron

Description

Usage

Arguments

Value

Examples

Third derivative of activation function of neuron

Description

Usage

Arguments

Value

Examples

Derivative of activation function of neuron

Description

Usage

Arguments

Value

References

Examples

Second derivatives 3D scatter or surface plot against input values

Description

Usage