Nothing
R/pguTransformator.R
In pguIMP: 'pguIMP'
#' @title pgu.transformator
#'
#' @description
#' Transforms the data of pguIMP.
#'
#' @details
#' Performs a data transformation in order to achieve a normally distributed version of the dataframe.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dplyr mutate_all near select select_if summarise_all
#' @importFrom magrittr %>%
#' @importFrom MASS boxcox
#' @importFrom purrr discard
#' @importFrom R6 R6Class
#' @importFrom rcompanion transformTukey
#' @importFrom tibble is_tibble tibble
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.transformator <- R6::R6Class("pgu.transformator",
####################
# instance variables
####################
private = list(
.trafoAlphabet = "character",
.trafoParameter = "tbl_df"
),
##################
# accessor methods
##################
active = list(
#' @field trafoAlphabet
#' Returns the instance variable trafoAlphabte.
trafoAlphabet = function(){
return(private$.trafoAlphabet)
},
#' @field trafoParameter
#' Returns the instance variable trafoParameter.
trafoParameter = function(){
return(private$.trafoParameter)
}
),
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.transformator` object.
#' @param data_df
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.transformator` object.
#' (pguIMP::pgu.transformator)
initialize = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
}
# private$.trafoAlphabet <-c("none", "log2", "logNorm", "log10", "arcsine", "tukeyLOP", "boxCox")
private$.trafoAlphabet <-c("none", "log2", "logNorm", "log10", "tukeyLOP", "boxCox")
self$resetTrafoParameter(data_df)
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.transformator` is removed from heap.
finalize = function(){
print("Instance of pgu.transformator removed from heap")
},
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.transformator` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.transformator\n")
cat(rString)
cat("trafoAlphabet:\n")
print(private$.trafoAlphabet)
cat("\ntrafoParameter:\n")
print(private$.trafoParameter)
cat("\n\n")
invisible(self)
}, #function
#' @description
#' Resets instance variable `trafoParameter`
#' @param data
#' Dataframe to be analyzed.
#' (tibble::tibble)
resetTrafoParameter = function(data = "tbl_df"){
features <- data %>%
dplyr::select_if(is.numeric) %>%
colnames()
trafoType <- c(rep(self$trafoAlphabet[1], length(features))) %>%
factor(level = self$trafoAlphabet)
mirrorLogic <- c(rep(FALSE, length(features )))
addConst <- c(rep(0.0, length(features )))
lambda <- c(rep(1.0, length(features )))
private$.trafoParameter <- tibble::tibble(features = as.character(features),
trafoType = as.factor(trafoType),
addConst = as.numeric(addConst),
mirrorLogic = as.logical(mirrorLogic),
lambda_as = as.numeric(lambda),
lambda_lop = as.numeric(lambda),
lambda_bc = as.numeric(lambda))
}, #function
############################
# trafoParameter accessors #
############################
#' @description
#' Returns entry of `trafoType`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (character)
trafoType = function(feature = "character"){
t <- "none"
idx <- self$featureIdx(feature)
if(!is.na(idx)){
t <- self$trafoParameter[[idx, "trafoType"]]
}#if
return(t)
}, #function
#' @description
#' Sets entry of `trafoType`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param type
#' Trafo type parameter. Valid choices are:
#' "none", "exponential", "log2", "logNorm", "log10", "arcsine", "tukeyLOP", "boxCox".
#' (character)
setTrafoType = function(feature = "character", type = "character"){
idx <- self$featureIdx(feature)
t <- factor(type, levels = self$trafoAlphabet)
if(is.na(t)){
rString <- sprintf("\nWarning in pgu.transformator$setTrafoType: type %s is not known\n",
type)
cat(rString)
}#if
else if (!is.na(idx)){
private$.trafoParameter[idx, "trafoType"] <- t
}#else if
}, #function
#' @description
#' Returns entry of `addConst`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
addConstant = function(feature = "character"){
c <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
c <- self$trafoParameter[[idx, "addConst"]]
}#if
return(c)
}, #function
#' @description
#' Returns entry of `mirrorLogic`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (logical)
mirrorLogic = function(feature = "character"){
l <- FALSE
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[[idx, "mirrorLogic"]]
}#if
return(l)
}, #unction
#' @description
#' Sets entry of `mirrorLogic`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param logic
#' Specifies whether the data should be mirrored at the coordinate origin.
#' (logical)
setMirrorLogic = function(feature = "character", logic = "logical"){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
private$.trafoParameter[idx, "mirrorLogic"] <- logic
}#if
}, #function
#' @description
#' Returns entry of `lambda_lop`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the Tukey-LOP
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaLOP = function(feature = "character"){
lambda <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
lambda <- self$trafoParameter[[idx, "lambda_lop"]]
}#if
return(lambda)
}, #function
#' @description
#' Sets entry of `lambda_lop`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param lambda
#' Sets the feature specific exponential value.
#' (numeric)
setLambdaLOP = function(feature = "character", lambda = "numeric"){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
private$.trafoParameter[idx, "lambda_lop"] <- lambda
}#if
}, #function
#' @description
#' Returns entry of `lambda_bc`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the Box-Cox
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaBC = function(feature = "character"){
l <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[idx, "lambda_bc"]
}#if
return(l)
}, #function
#' @description
#' Returns entry of `lambda_as`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the arcsine
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaAS = function(feature = "character"){
l <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[idx, "lambda_as"]
}#if
return(l)
}, #function
####################
# helper functions #
####################
#' @description
#' Returns the index of a pgu.normDist object wihtin the instance variable `trafoParameter`.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Index of attribute entry in dataframe
#' (numeric)
featureIdx = function(feature = "character"){
idx <- match(feature, self$trafoParameter[["features"]])
if(is.na(idx)){
rString <- sprintf("\nWarning in pgu.transformator: feature %s is not known\n",
feature)
cat(rString)
}#if
return(idx)
}, #function
#' @description
#' Calculates and returns the addConst.
#' A constant that prevents the occurrence of negative values as well as zero,
#' if added to an attribute.
#' @param value
#' The smallest of the attribute's values.
#' (numeric)
#' @return
#' The addConst for the attribute
#' (numeric)
addConstGenerator = function(value = "numeric"){
c <- 0.0
# if (value <= 0.0){
# c <- (-1.0 * value) + .Machine$double.xmin
if (value < 0.0){
c <- (-1.0 * value) + 1
}#if
# else if(dplyr::near(0.0, value)){
else if((value>=0.0) & (value<1.0)){
c <- value + 1
}#else if
return(c)
}, #function
####################
# mirror functions #
####################
#' @description
#' Mirrors the assigned values at the coordinate origin.
#' @param value
#' Value or vector of values.
#' (numeric)
#' @return
#' Value or vector of values.
#' (numeric)
mirrorNumeric = function(value = "numeric"){
return(-1.0 * value)
}, #function
#' @description
#' Calls the class' mirrorNumeric function on all numeric attributes of a data frame.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame
#' (tibble::tibble)
mirrorData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if((!is.na(idx)) & (as.logical(self$trafoParameter[idx,"mirrorLogic"]))){
data[feature] <- self$mirrorNumeric(data[[feature]])
}#if
}#for
return(data)
}, #function
#########################
# translation functions #
#########################
#' @description
#' Calculates the addConst value for each attribute of the assigned data frame,
#' by calling the class' addConstGenerator function.
#' The results are stored in addConst attribute of the trafoParameter instance variable.
#' @param data
#' A data frame.
#' (tibble:tibble)
calculateAddConst = function(data = "tbl_df"){
private$.trafoParameter["addConst"] <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
dplyr::summarise_all(min, na.rm=TRUE) %>%
dplyr::mutate_all(self$addConstGenerator) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Translates the assigned values by a constant.
#' @param value
#' A numeric or a vector of numerics to be translated.
#' (numeric)
#' @param const
#' A constant value.
#' (numeric)
#' @return
#' A numeric or a vector of numerics.
#' (numeric)
translateNumeric = function(value = "numeric", const = "numeric"){
return(value + const)
}, #function
#' @description
#' Translates each attribute of the assigned data frame,
#' by calling the class' translateNumeric function.
#' The respective addConst values of the individual attributes of the data frame
#' serve as const variables.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame.
#' (tibble:tibble)
translateData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$translateNumeric(data[[feature]],
as.numeric(self$trafoParameter[idx, "addConst"]))
}#if
}#for
return(data)
}, #function
#' @description
#' Back-translates the assigned values by a constant.
#' @param value
#' A numeric or a vector of numerics to be back-translated.
#' (numeric)
#' @param const
#' A constant value.
#' (numeric)
#' @return
#' A numeric or a vector of numerics.
#' (numeric)
backTranslateNumeric = function(value = "numeric", const = "numeric"){
return(value - const)
}, #function
#' @description
#' Back-translates each attribute of the assigned data frame,
#' by calling the class' backTranslateNumeric function.
#' The respective addConst values of the individual attributes of the data frame
#' serve as const variables.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame.
#' (tibble:tibble)
backTranslateData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$backTranslateNumeric(data[[feature]],
as.numeric(self$trafoParameter[idx, "addConst"]))
}#if
}#for
return(data)
}, #function
#####################
# lambda estimation #
#####################
#' @description
#' Estimates the lambda factor for the given values,
#' that are assigned to a user defined attribute..
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' The specific lambda factor.
#' (numeric)
lambdaEstimator = function(value = "numeric", feature = "character"){
lambda <- 1.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {lambda <- 1.0},
log2 = {lambda <- 1.0},
logNorm = {lambda <- 1-0},
log10 = {lambda <- 1.0},
arcsine = {lambda <- self$normalizeArcSine(value)},
tukeyLOP = {lambda <- self$optimizeTukeyLadderOfPowers(value)},
boxCox = {lambda <- self$optimizeBoxCox(value)},
{rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return(lambda)
}, #function
#' @description
#' Estimates the lambda factor for each attribute of the assigned data frame,
#' by calling the class' lambdaEstimator function.
#' The respective lambda values of the individual attributes of the data frame
#' are stored in the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
estimateLambda_temp = function(data = "tbl_df"){
tempData <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData()
private$.trafoParameter["lambda"] <- lapply(self$trafoParameter[["features"]], function(x){self$lambdaEstimator(value = tempData[[x]], feature = x)}) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Estimates the arcsine transformation lambda factor
#' for each attribute of the assigned data frame.
#' The respective lambda values of the individual attributes of the data frame
#' are stored in the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
estimateLambda = function(data = "tbl_df"){
tempData <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData()
private$.trafoParameter["lambda_as"] <- lapply(self$trafoParameter[["features"]], function(x){self$normalizeArcSine(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
private$.trafoParameter["lambda_lop"] <- lapply(self$trafoParameter[["features"]], function(x){self$optimizeTukeyLadderOfPowers(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
private$.trafoParameter["lambda_bc"] <- lapply(self$trafoParameter[["features"]], function(x){self$optimizeBoxCox(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Estimates the lambda factor for an arcsine transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
normalizeArcSine = function(value = "numeric"){
return(max(value, na.rm=TRUE))
}, #function
#' @description
#' Estimates the lambda factor for a tukeyLOP transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
optimizeTukeyLadderOfPowers = function(value = "numeric"){
lambda <- 1.0
tryCatch({
lambda <-
value %>%
purrr::discard(is.na) %>%
rcompanion::transformTukey(start = -2,
end = 2,
int = 0.01,
plotit = FALSE,
verbose = FALSE,
quiet = TRUE,
statistic = 1,
returnLambda = TRUE)
return(lambda)
},
warning = function(w) {
warningMessage <- sprintf("Warning: Could not optimize Tukey Ladder Of Powers Lambda is set to 1.0:\n%s", w)
warning(warningMessage)
return(lambda)
}, #warning
error = function(e) {
warningMessage <- sprintf("Warning: Could not optimize Tukey Ladder Of Powers Lambda is set to 1.0:\n%s", e)
warning(warningMessage)
return(lambda)
}#error
)#tryCatch
}, #function
#' @description
#' Estimates the lambda factor for a boxcox transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
optimizeBoxCox = function(value = "numeric"){
lambda <- 1.0
tryCatch({
logLikelihoodVector <- MASS::boxcox(value ~ 1,
lambda = seq(-10, 10, 0.1),
plotit = FALSE)
logLikelihood <- tibble::tibble(logLikelihoodVector$x, logLikelihoodVector$y)
colnames(logLikelihood) <- c("lambda", "logLikelihood")
maxLikelihoodIdx <- which(logLikelihood[, "logLikelihood"] == max(logLikelihood[, "logLikelihood"], na.rm = TRUE), arr.ind = TRUE)
lambda <- logLikelihood[[maxLikelihoodIdx[1], "lambda"]]
return(lambda)
},
warning = function(w) {
errorMessage <- sprintf("Warning: Could not optimize Box Cox Lambda is set to 1.0:\n%s", w)
warning(errorMessage)
return(lambda)
}, #warning
error = function(e) {
errorMessage <- sprintf("Warning: Could not optimize Box Cox Lambda is set to 1.0:\n%s", e)
warning(errorMessage)
return(lambda)
}#error
)#tryCatch
}, #function
############################
# transformation functions #
############################
#' @description
#' Transforms the given numeric values,
#' that are assigned to a user defined attribute.
#' @param value
#' A numeric or a vector of numerics to be tranformed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' A transfromed version of the given numeric or vector of numerics.
#' (numeric)
transformNumeric = function(value = "numeric", feature = "character"){
tf <- numeric()
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {tf <- value},
log2 = {tf <- self$transformLogModulus(value, base=2)},
logNorm = {tf <- self$transformLogModulus(value, base=exp(1))},
log10 = {tf <- self$transformLogModulus(value, base=10)},
arcsine = {tf <- self$transformArcsine(value, lambda=self$trafoParameter[[idx,"lambda_as"]])},
tukeyLOP = {tf <- self$transformTukeyLadderOfPowers(value, lambda=self$trafoParameter[[idx,"lambda_lop"]])},
boxCox = {tf <- self$transformBoxCox(value, lambda=self$trafoParameter[[idx,"lambda_bc"]])},
{private$.trafoParameter[idx, "trafoType"] <- "none"
tf <- value
rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return (tf)
}, #function
#' @description
#' Transforms each attribute of the assigned data frame,
#' by calling the class' tranformNumeric function.
#' The respective lambda values of the individual attributes of the data frame
#' are read from the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
transformData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$transformNumeric(data[[feature]], feature)
}#if
}#for
return(data)
}, #function
#' @description
#' Performes a log transformation for the given values,
#' based on a user defined base value.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param base
#' Logarithmic base.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformLogModulus = function(value = "numeric", base="numeric"){
return(log(value, base=base))
}, #function
#' @description
#' Performes a square root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformSquareRoot = function(value = "numeric"){
return(sqrt(value))
}, #function
#' @description
#' Performes a cube root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformCubeRoot = function(value = "numeric"){
return((value)^(1/3))
}, #function
#' @description
#' Performes an arcsine transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Normalization factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformArcsine = function(value = "numeric", lambda="numeric"){
return(asin(sqrt((value)/lambda)))
}, #function
#' @description
#' Performes an inverse transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformInverse = function(value = "numeric"){
return(1.0/(value))
}, #function
#' @description
#' Performes a tukeyLOP transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformTukeyLadderOfPowers = function(value = "numeric", lambda="numeric"){
if(lambda > 0){
return(value^lambda)
}#if
else if(lambda < 0){
return(-1.0*((value)^lambda))
}#else if
else {
return(self$transformLogModulus(value, base=exp(1)))
}#else
}, #function
#' @description
#' Performes a boxcox transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformBoxCox = function(value = "numeric", lambda="numeric"){
if (lambda == 0){
return(self$transformLogModulus(value, base=exp(1)))
}#if
else{
return(((value)^lambda -1) / lambda)
}#else
}, #function
####################################
# inverse transfromation functions #
####################################
#' @description
#' Inverse transforms the given numeric values,
#' that are assigned to a user defined attribute.
#' @param value
#' A numeric or a vector of numerics to be tranformed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' An inverse transfromed version of the given numeric or vector of numerics.
#' (numeric)
inverseTransformNumeric = function(value = "numeric", feature = "character"){
tf <- numeric()
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {tf <- value},
log2 = {tf <- self$inverseTransformLogModulus(value, base=2)},
logNorm = {tf <- self$inverseTransformLogModulus(value, base=exp(1))},
log10 = {tf <- self$inverseTransformLogModulus(value, base=10)},
squareRoot = {tf <- self$inverseTransformSquareRoot(value)},
cubeRoot = {tf <- self$inverseTransformCubeRoot(value)},
arcsine = {tf <- self$inverseTransformArcsine(value, lambda=self$trafoParameter[[idx,"lambda_as"]])},
inverse = {tf <- self$inverseTransformInverse(value)},
tukeyLOP = {tf <- self$inverseTransformTukeyLadderOfPowers(value, lambda=self$trafoParameter[[idx,"lambda_lop"]])},
boxCox = {tf <- self$inverseTransformBoxCox(value, lambda=self$trafoParameter[[idx,"lambda_bc"]])},
{private$.trafoParameter[idx, "trafoType"] <- "none"
tf <- value
rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return (tf)
}, #function
#' @description
#' Inverse transforms each attribute of the assigned data frame,
#' by calling the class' tranformNumeric function.
#' The respective lambda values of the individual attributes of the data frame
#' are read from the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
inverseTransformData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$inverseTransformNumeric(data[[feature]], feature)
}#if
}#for
return(data)
}, #function
#' @description
#' Performes an inverse log transformation for the given values,
#' based on a user defined base value.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param base
#' Logarithmic base.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformLogModulus = function(value = "numeric", base="numeric"){
return (base^value)
}, #function
#' @description
#' Performes an inverse square root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformSquareRoot = function(value = "numeric"){
return(value^2)
}, #function
#' @description
#' Performes an inverse cube root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformCubeRoot = function(value = "numeric"){
return(value^3)
}, #function
#' @description
#' Performes an inverse arcsine transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Normalization factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformArcsine = function(value = "numeric", lambda="numeric"){
return(((sin(value))^2.0)*lambda)
}, #function
#' @description
#' Performes an inverse inverse-transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformInverse = function(value = "numeric"){
return(1.0/value)
}, #function
#' @description
#' Performes an inverse tukeyLOP transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformTukeyLadderOfPowers = function(value = "numeric", lambda="numeric"){
if(lambda > 0){
return(value^(1/lambda))
}#if
else if(lambda == 0){
return(self$inverseTransformLogModulus(value, base=exp(1)))
}#else if
else {
return(((-1.0*value)^(1/lambda)))
}#else
}, #function
#' @description
#' Performes an inverse boxcox transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformBoxCox = function(value = "numeric", lambda="numeric"){
if (lambda == 0){
return(self$inverseTransformLogModulus(value, base=exp(1)))
}#if
else{
return(((value*lambda)+1)^(1/lambda))
}#else
}, #function
####################
# compound methods #
####################
#' @description
#' Estimate all transformation parameters(lambda, addConst,...)
#' for each attribute of a given data frame.
#' The function calls the class' functions calculateAddConst and estimateLambda.
#' The results are stored in the respective attributes of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
fit = function(data = "tbl_df"){
data %>%
self$calculateAddConst()
data %>%
self$estimateLambda()
}, #function
#' @description
#' Mutates the values of each attribute of a given data frame.
#' Here, mutation is defined as the cesecutive sequence of the class'
#' functions mirrorData, tranlsateData and transfromData.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A mutated data frame.
#' (tibble::tibble)
mutateData = function(data = "tbl_df"){
data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData() %>%
self$transformData() %>%
return()
}, #function
#' @description
#' Re-mutates the values of each attribute of a given data frame.
#' Here, re-mutation is defined as the cesecutive sequence of the class'
#' functions inverseTransformData, backTranslateData, mirrorData
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A mutated data frame.
#' (tibble::tibble)
reverseMutateData = function(data = "tbl_df"){
data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$inverseTransformData() %>%
self$backTranslateData() %>%
self$mirrorData() %>%
return()
}#function
)#public
)#class
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#' @title pgu.transformator
#'
#' @description
#' Transforms the data of pguIMP.
#'
#' @details
#' Performs a data transformation in order to achieve a normally distributed version of the dataframe.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dplyr mutate_all near select select_if summarise_all
#' @importFrom magrittr %>%
#' @importFrom MASS boxcox
#' @importFrom purrr discard
#' @importFrom R6 R6Class
#' @importFrom rcompanion transformTukey
#' @importFrom tibble is_tibble tibble
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.transformator <- R6::R6Class("pgu.transformator",
####################
# instance variables
####################
private = list(
.trafoAlphabet = "character",
.trafoParameter = "tbl_df"
),
##################
# accessor methods
##################
active = list(
#' @field trafoAlphabet
#' Returns the instance variable trafoAlphabte.
trafoAlphabet = function(){
return(private$.trafoAlphabet)
},
#' @field trafoParameter
#' Returns the instance variable trafoParameter.
trafoParameter = function(){
return(private$.trafoParameter)
}
),
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.transformator` object.
#' @param data_df
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.transformator` object.
#' (pguIMP::pgu.transformator)
initialize = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
}
# private$.trafoAlphabet <-c("none", "log2", "logNorm", "log10", "arcsine", "tukeyLOP", "boxCox")
private$.trafoAlphabet <-c("none", "log2", "logNorm", "log10", "tukeyLOP", "boxCox")
self$resetTrafoParameter(data_df)
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.transformator` is removed from heap.
finalize = function(){
print("Instance of pgu.transformator removed from heap")
},
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.transformator` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.transformator\n")
cat(rString)
cat("trafoAlphabet:\n")
print(private$.trafoAlphabet)
cat("\ntrafoParameter:\n")
print(private$.trafoParameter)
cat("\n\n")
invisible(self)
}, #function
#' @description
#' Resets instance variable `trafoParameter`
#' @param data
#' Dataframe to be analyzed.
#' (tibble::tibble)
resetTrafoParameter = function(data = "tbl_df"){
features <- data %>%
dplyr::select_if(is.numeric) %>%
colnames()
trafoType <- c(rep(self$trafoAlphabet[1], length(features))) %>%
factor(level = self$trafoAlphabet)
mirrorLogic <- c(rep(FALSE, length(features )))
addConst <- c(rep(0.0, length(features )))
lambda <- c(rep(1.0, length(features )))
private$.trafoParameter <- tibble::tibble(features = as.character(features),
trafoType = as.factor(trafoType),
addConst = as.numeric(addConst),
mirrorLogic = as.logical(mirrorLogic),
lambda_as = as.numeric(lambda),
lambda_lop = as.numeric(lambda),
lambda_bc = as.numeric(lambda))
}, #function
############################
# trafoParameter accessors #
############################
#' @description
#' Returns entry of `trafoType`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (character)
trafoType = function(feature = "character"){
t <- "none"
idx <- self$featureIdx(feature)
if(!is.na(idx)){
t <- self$trafoParameter[[idx, "trafoType"]]
}#if
return(t)
}, #function
#' @description
#' Sets entry of `trafoType`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param type
#' Trafo type parameter. Valid choices are:
#' "none", "exponential", "log2", "logNorm", "log10", "arcsine", "tukeyLOP", "boxCox".
#' (character)
setTrafoType = function(feature = "character", type = "character"){
idx <- self$featureIdx(feature)
t <- factor(type, levels = self$trafoAlphabet)
if(is.na(t)){
rString <- sprintf("\nWarning in pgu.transformator$setTrafoType: type %s is not known\n",
type)
cat(rString)
}#if
else if (!is.na(idx)){
private$.trafoParameter[idx, "trafoType"] <- t
}#else if
}, #function
#' @description
#' Returns entry of `addConst`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
addConstant = function(feature = "character"){
c <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
c <- self$trafoParameter[[idx, "addConst"]]
}#if
return(c)
}, #function
#' @description
#' Returns entry of `mirrorLogic`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (logical)
mirrorLogic = function(feature = "character"){
l <- FALSE
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[[idx, "mirrorLogic"]]
}#if
return(l)
}, #unction
#' @description
#' Sets entry of `mirrorLogic`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param logic
#' Specifies whether the data should be mirrored at the coordinate origin.
#' (logical)
setMirrorLogic = function(feature = "character", logic = "logical"){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
private$.trafoParameter[idx, "mirrorLogic"] <- logic
}#if
}, #function
#' @description
#' Returns entry of `lambda_lop`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the Tukey-LOP
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaLOP = function(feature = "character"){
lambda <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
lambda <- self$trafoParameter[[idx, "lambda_lop"]]
}#if
return(lambda)
}, #function
#' @description
#' Sets entry of `lambda_lop`
#' for user defined attribute.
#' @param feature
#' Attribute's name.
#' (character)
#' @param lambda
#' Sets the feature specific exponential value.
#' (numeric)
setLambdaLOP = function(feature = "character", lambda = "numeric"){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
private$.trafoParameter[idx, "lambda_lop"] <- lambda
}#if
}, #function
#' @description
#' Returns entry of `lambda_bc`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the Box-Cox
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaBC = function(feature = "character"){
l <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[idx, "lambda_bc"]
}#if
return(l)
}, #function
#' @description
#' Returns entry of `lambda_as`
#' for user defined attribute.
#' Lambda is a specific optimization parameter
#' that is derived from the arcsine
#' transfromation procedure.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Value of entry.
#' (numeric)
lambdaAS = function(feature = "character"){
l <- 0.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
l <- self$trafoParameter[idx, "lambda_as"]
}#if
return(l)
}, #function
####################
# helper functions #
####################
#' @description
#' Returns the index of a pgu.normDist object wihtin the instance variable `trafoParameter`.
#' @param feature
#' Attribute's name.
#' (character)
#' @return
#' Index of attribute entry in dataframe
#' (numeric)
featureIdx = function(feature = "character"){
idx <- match(feature, self$trafoParameter[["features"]])
if(is.na(idx)){
rString <- sprintf("\nWarning in pgu.transformator: feature %s is not known\n",
feature)
cat(rString)
}#if
return(idx)
}, #function
#' @description
#' Calculates and returns the addConst.
#' A constant that prevents the occurrence of negative values as well as zero,
#' if added to an attribute.
#' @param value
#' The smallest of the attribute's values.
#' (numeric)
#' @return
#' The addConst for the attribute
#' (numeric)
addConstGenerator = function(value = "numeric"){
c <- 0.0
# if (value <= 0.0){
# c <- (-1.0 * value) + .Machine$double.xmin
if (value < 0.0){
c <- (-1.0 * value) + 1
}#if
# else if(dplyr::near(0.0, value)){
else if((value>=0.0) & (value<1.0)){
c <- value + 1
}#else if
return(c)
}, #function
####################
# mirror functions #
####################
#' @description
#' Mirrors the assigned values at the coordinate origin.
#' @param value
#' Value or vector of values.
#' (numeric)
#' @return
#' Value or vector of values.
#' (numeric)
mirrorNumeric = function(value = "numeric"){
return(-1.0 * value)
}, #function
#' @description
#' Calls the class' mirrorNumeric function on all numeric attributes of a data frame.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame
#' (tibble::tibble)
mirrorData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if((!is.na(idx)) & (as.logical(self$trafoParameter[idx,"mirrorLogic"]))){
data[feature] <- self$mirrorNumeric(data[[feature]])
}#if
}#for
return(data)
}, #function
#########################
# translation functions #
#########################
#' @description
#' Calculates the addConst value for each attribute of the assigned data frame,
#' by calling the class' addConstGenerator function.
#' The results are stored in addConst attribute of the trafoParameter instance variable.
#' @param data
#' A data frame.
#' (tibble:tibble)
calculateAddConst = function(data = "tbl_df"){
private$.trafoParameter["addConst"] <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
dplyr::summarise_all(min, na.rm=TRUE) %>%
dplyr::mutate_all(self$addConstGenerator) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Translates the assigned values by a constant.
#' @param value
#' A numeric or a vector of numerics to be translated.
#' (numeric)
#' @param const
#' A constant value.
#' (numeric)
#' @return
#' A numeric or a vector of numerics.
#' (numeric)
translateNumeric = function(value = "numeric", const = "numeric"){
return(value + const)
}, #function
#' @description
#' Translates each attribute of the assigned data frame,
#' by calling the class' translateNumeric function.
#' The respective addConst values of the individual attributes of the data frame
#' serve as const variables.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame.
#' (tibble:tibble)
translateData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$translateNumeric(data[[feature]],
as.numeric(self$trafoParameter[idx, "addConst"]))
}#if
}#for
return(data)
}, #function
#' @description
#' Back-translates the assigned values by a constant.
#' @param value
#' A numeric or a vector of numerics to be back-translated.
#' (numeric)
#' @param const
#' A constant value.
#' (numeric)
#' @return
#' A numeric or a vector of numerics.
#' (numeric)
backTranslateNumeric = function(value = "numeric", const = "numeric"){
return(value - const)
}, #function
#' @description
#' Back-translates each attribute of the assigned data frame,
#' by calling the class' backTranslateNumeric function.
#' The respective addConst values of the individual attributes of the data frame
#' serve as const variables.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A data frame.
#' (tibble:tibble)
backTranslateData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$backTranslateNumeric(data[[feature]],
as.numeric(self$trafoParameter[idx, "addConst"]))
}#if
}#for
return(data)
}, #function
#####################
# lambda estimation #
#####################
#' @description
#' Estimates the lambda factor for the given values,
#' that are assigned to a user defined attribute..
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' The specific lambda factor.
#' (numeric)
lambdaEstimator = function(value = "numeric", feature = "character"){
lambda <- 1.0
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {lambda <- 1.0},
log2 = {lambda <- 1.0},
logNorm = {lambda <- 1-0},
log10 = {lambda <- 1.0},
arcsine = {lambda <- self$normalizeArcSine(value)},
tukeyLOP = {lambda <- self$optimizeTukeyLadderOfPowers(value)},
boxCox = {lambda <- self$optimizeBoxCox(value)},
{rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return(lambda)
}, #function
#' @description
#' Estimates the lambda factor for each attribute of the assigned data frame,
#' by calling the class' lambdaEstimator function.
#' The respective lambda values of the individual attributes of the data frame
#' are stored in the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
estimateLambda_temp = function(data = "tbl_df"){
tempData <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData()
private$.trafoParameter["lambda"] <- lapply(self$trafoParameter[["features"]], function(x){self$lambdaEstimator(value = tempData[[x]], feature = x)}) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Estimates the arcsine transformation lambda factor
#' for each attribute of the assigned data frame.
#' The respective lambda values of the individual attributes of the data frame
#' are stored in the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
estimateLambda = function(data = "tbl_df"){
tempData <- data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData()
private$.trafoParameter["lambda_as"] <- lapply(self$trafoParameter[["features"]], function(x){self$normalizeArcSine(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
private$.trafoParameter["lambda_lop"] <- lapply(self$trafoParameter[["features"]], function(x){self$optimizeTukeyLadderOfPowers(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
private$.trafoParameter["lambda_bc"] <- lapply(self$trafoParameter[["features"]], function(x){self$optimizeBoxCox(value = tempData[[x]])}) %>%
t() %>%
as.numeric()
}, #function
#' @description
#' Estimates the lambda factor for an arcsine transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
normalizeArcSine = function(value = "numeric"){
return(max(value, na.rm=TRUE))
}, #function
#' @description
#' Estimates the lambda factor for a tukeyLOP transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
optimizeTukeyLadderOfPowers = function(value = "numeric"){
lambda <- 1.0
tryCatch({
lambda <-
value %>%
purrr::discard(is.na) %>%
rcompanion::transformTukey(start = -2,
end = 2,
int = 0.01,
plotit = FALSE,
verbose = FALSE,
quiet = TRUE,
statistic = 1,
returnLambda = TRUE)
return(lambda)
},
warning = function(w) {
warningMessage <- sprintf("Warning: Could not optimize Tukey Ladder Of Powers Lambda is set to 1.0:\n%s", w)
warning(warningMessage)
return(lambda)
}, #warning
error = function(e) {
warningMessage <- sprintf("Warning: Could not optimize Tukey Ladder Of Powers Lambda is set to 1.0:\n%s", e)
warning(warningMessage)
return(lambda)
}#error
)#tryCatch
}, #function
#' @description
#' Estimates the lambda factor for a boxcox transformation for the given values,
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The specific lambda factor.
#' (numeric)
optimizeBoxCox = function(value = "numeric"){
lambda <- 1.0
tryCatch({
logLikelihoodVector <- MASS::boxcox(value ~ 1,
lambda = seq(-10, 10, 0.1),
plotit = FALSE)
logLikelihood <- tibble::tibble(logLikelihoodVector$x, logLikelihoodVector$y)
colnames(logLikelihood) <- c("lambda", "logLikelihood")
maxLikelihoodIdx <- which(logLikelihood[, "logLikelihood"] == max(logLikelihood[, "logLikelihood"], na.rm = TRUE), arr.ind = TRUE)
lambda <- logLikelihood[[maxLikelihoodIdx[1], "lambda"]]
return(lambda)
},
warning = function(w) {
errorMessage <- sprintf("Warning: Could not optimize Box Cox Lambda is set to 1.0:\n%s", w)
warning(errorMessage)
return(lambda)
}, #warning
error = function(e) {
errorMessage <- sprintf("Warning: Could not optimize Box Cox Lambda is set to 1.0:\n%s", e)
warning(errorMessage)
return(lambda)
}#error
)#tryCatch
}, #function
############################
# transformation functions #
############################
#' @description
#' Transforms the given numeric values,
#' that are assigned to a user defined attribute.
#' @param value
#' A numeric or a vector of numerics to be tranformed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' A transfromed version of the given numeric or vector of numerics.
#' (numeric)
transformNumeric = function(value = "numeric", feature = "character"){
tf <- numeric()
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {tf <- value},
log2 = {tf <- self$transformLogModulus(value, base=2)},
logNorm = {tf <- self$transformLogModulus(value, base=exp(1))},
log10 = {tf <- self$transformLogModulus(value, base=10)},
arcsine = {tf <- self$transformArcsine(value, lambda=self$trafoParameter[[idx,"lambda_as"]])},
tukeyLOP = {tf <- self$transformTukeyLadderOfPowers(value, lambda=self$trafoParameter[[idx,"lambda_lop"]])},
boxCox = {tf <- self$transformBoxCox(value, lambda=self$trafoParameter[[idx,"lambda_bc"]])},
{private$.trafoParameter[idx, "trafoType"] <- "none"
tf <- value
rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return (tf)
}, #function
#' @description
#' Transforms each attribute of the assigned data frame,
#' by calling the class' tranformNumeric function.
#' The respective lambda values of the individual attributes of the data frame
#' are read from the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
transformData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$transformNumeric(data[[feature]], feature)
}#if
}#for
return(data)
}, #function
#' @description
#' Performes a log transformation for the given values,
#' based on a user defined base value.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param base
#' Logarithmic base.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformLogModulus = function(value = "numeric", base="numeric"){
return(log(value, base=base))
}, #function
#' @description
#' Performes a square root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformSquareRoot = function(value = "numeric"){
return(sqrt(value))
}, #function
#' @description
#' Performes a cube root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformCubeRoot = function(value = "numeric"){
return((value)^(1/3))
}, #function
#' @description
#' Performes an arcsine transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Normalization factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformArcsine = function(value = "numeric", lambda="numeric"){
return(asin(sqrt((value)/lambda)))
}, #function
#' @description
#' Performes an inverse transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformInverse = function(value = "numeric"){
return(1.0/(value))
}, #function
#' @description
#' Performes a tukeyLOP transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformTukeyLadderOfPowers = function(value = "numeric", lambda="numeric"){
if(lambda > 0){
return(value^lambda)
}#if
else if(lambda < 0){
return(-1.0*((value)^lambda))
}#else if
else {
return(self$transformLogModulus(value, base=exp(1)))
}#else
}, #function
#' @description
#' Performes a boxcox transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
transformBoxCox = function(value = "numeric", lambda="numeric"){
if (lambda == 0){
return(self$transformLogModulus(value, base=exp(1)))
}#if
else{
return(((value)^lambda -1) / lambda)
}#else
}, #function
####################################
# inverse transfromation functions #
####################################
#' @description
#' Inverse transforms the given numeric values,
#' that are assigned to a user defined attribute.
#' @param value
#' A numeric or a vector of numerics to be tranformed.
#' (numeric)
#' @param feature
#' The attribute which the given values are assigned to.
#' (character)
#' @return
#' An inverse transfromed version of the given numeric or vector of numerics.
#' (numeric)
inverseTransformNumeric = function(value = "numeric", feature = "character"){
tf <- numeric()
idx <- self$featureIdx(feature)
if(!is.na(idx)){
method <- self$trafoParameter[[idx, "trafoType"]]
switch(as.character(method),
none = {tf <- value},
log2 = {tf <- self$inverseTransformLogModulus(value, base=2)},
logNorm = {tf <- self$inverseTransformLogModulus(value, base=exp(1))},
log10 = {tf <- self$inverseTransformLogModulus(value, base=10)},
squareRoot = {tf <- self$inverseTransformSquareRoot(value)},
cubeRoot = {tf <- self$inverseTransformCubeRoot(value)},
arcsine = {tf <- self$inverseTransformArcsine(value, lambda=self$trafoParameter[[idx,"lambda_as"]])},
inverse = {tf <- self$inverseTransformInverse(value)},
tukeyLOP = {tf <- self$inverseTransformTukeyLadderOfPowers(value, lambda=self$trafoParameter[[idx,"lambda_lop"]])},
boxCox = {tf <- self$inverseTransformBoxCox(value, lambda=self$trafoParameter[[idx,"lambda_bc"]])},
{private$.trafoParameter[idx, "trafoType"] <- "none"
tf <- value
rString <- sprintf("\nWarning in pgu.transformator: trafoType %s is not known\n",as.character(method))
cat(rString)}
)#switch
}#if
return (tf)
}, #function
#' @description
#' Inverse transforms each attribute of the assigned data frame,
#' by calling the class' tranformNumeric function.
#' The respective lambda values of the individual attributes of the data frame
#' are read from the lambda attribute of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
inverseTransformData = function(data = "tbl_df"){
for (feature in self$trafoParameter[["features"]]){
idx <- self$featureIdx(feature)
if(!is.na(idx)){
data[feature] <- self$inverseTransformNumeric(data[[feature]], feature)
}#if
}#for
return(data)
}, #function
#' @description
#' Performes an inverse log transformation for the given values,
#' based on a user defined base value.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param base
#' Logarithmic base.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformLogModulus = function(value = "numeric", base="numeric"){
return (base^value)
}, #function
#' @description
#' Performes an inverse square root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformSquareRoot = function(value = "numeric"){
return(value^2)
}, #function
#' @description
#' Performes an inverse cube root transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformCubeRoot = function(value = "numeric"){
return(value^3)
}, #function
#' @description
#' Performes an inverse arcsine transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Normalization factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformArcsine = function(value = "numeric", lambda="numeric"){
return(((sin(value))^2.0)*lambda)
}, #function
#' @description
#' Performes an inverse inverse-transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformInverse = function(value = "numeric"){
return(1.0/value)
}, #function
#' @description
#' Performes an inverse tukeyLOP transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformTukeyLadderOfPowers = function(value = "numeric", lambda="numeric"){
if(lambda > 0){
return(value^(1/lambda))
}#if
else if(lambda == 0){
return(self$inverseTransformLogModulus(value, base=exp(1)))
}#else if
else {
return(((-1.0*value)^(1/lambda)))
}#else
}, #function
#' @description
#' Performes an inverse boxcox transformation for the given values.
#' @param value
#' A numeric or a vector of numerics to be analyzed.
#' (numeric)
#' @param lambda
#' Lambda factor.
#' (numeric)
#' @return
#' The transformed values.
#' (numeric)
inverseTransformBoxCox = function(value = "numeric", lambda="numeric"){
if (lambda == 0){
return(self$inverseTransformLogModulus(value, base=exp(1)))
}#if
else{
return(((value*lambda)+1)^(1/lambda))
}#else
}, #function
####################
# compound methods #
####################
#' @description
#' Estimate all transformation parameters(lambda, addConst,...)
#' for each attribute of a given data frame.
#' The function calls the class' functions calculateAddConst and estimateLambda.
#' The results are stored in the respective attributes of the instance variable trafoParameter.
#' @param data
#' A data frame.
#' (tibble:tibble)
fit = function(data = "tbl_df"){
data %>%
self$calculateAddConst()
data %>%
self$estimateLambda()
}, #function
#' @description
#' Mutates the values of each attribute of a given data frame.
#' Here, mutation is defined as the cesecutive sequence of the class'
#' functions mirrorData, tranlsateData and transfromData.
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A mutated data frame.
#' (tibble::tibble)
mutateData = function(data = "tbl_df"){
data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$mirrorData() %>%
self$translateData() %>%
self$transformData() %>%
return()
}, #function
#' @description
#' Re-mutates the values of each attribute of a given data frame.
#' Here, re-mutation is defined as the cesecutive sequence of the class'
#' functions inverseTransformData, backTranslateData, mirrorData
#' @param data
#' A data frame.
#' (tibble:tibble)
#' @return
#' A mutated data frame.
#' (tibble::tibble)
reverseMutateData = function(data = "tbl_df"){
data %>%
dplyr::select(self$trafoParameter[["features"]]) %>%
self$inverseTransformData() %>%
self$backTranslateData() %>%
self$mirrorData() %>%
return()
}#function
)#public
)#class
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