Nothing
R/pguOutliers.R
In pguIMP: 'pguIMP'
#' @title pgu.outliers
#'
#' @description
#' Detects and replaces possible outliers from data set.
#'
#' @details
#' Performes Grubb's test for outliers to detect outliers in the normalized and Z-score transfromed data set.
#' Replace missing values with substitutes by classical and AI-powerd substitution algorithms.
#' For this purpose outliers are handled as imputation sites.
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dbscan dbscan
#' @importFrom dplyr add_row count filter mutate mutate_if pull select select_if
#' @importFrom dplyr slice summarise_all sym transmute_all
#' @importFrom DT datatable formatStyle
#' @importFrom e1071 svm
#' @importFrom ggplot2 aes aes_string coord_flip element_blank element_rect element_text
#' @importFrom ggplot2 geom_bar geom_boxplot geom_col geom_jitter ggplot
#' @importFrom ggplot2 ggtitle layer_scales scale_x_continuous scale_y_continuous theme theme_linedraw
#' @importFrom ggplot2 xlab ylab
#' @importFrom gridExtra grid.arrange
#' @importFrom magrittr %>%
#' @importFrom outliers grubbs.test
#' @importFrom R6 R6Class
#' @importFrom shiny Progress
#' @importFrom tibble add_row is_tibble tibble
#' @importFrom tidyr drop_na gather
#' @importFrom tidyselect all_of
#'
#' @include pguOutlierDetection.R
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
#'
pgu.outliers <- R6::R6Class("pgu.outliers",
####################
# instance variables
####################
private = list(
.outliersParameter = "tbl_df",
.outliers = "tbl_df",
.one_hot_df = "tbl_df",
.outliersStatistics = "tbl_df",
.outliersAgentAlphabet = "character",
.outliersAgent = "factor",
.featureData = "numeric",
.alpha = "numeric",
.epsilon = "numeric",
.minSamples = "integer",
.gamma = "numeric",
.nu = "numeric",
.k = "integer",
.cutoff = "numeric",
.seed = "integer"
),
##################
# accessor methods
##################
active = list(
#' @field outliersParameter
#' Returns the instance variable outliersParameter.
#' (tibble::tibble)
outliersParameter = function(){
return(private$.outliersParameter)
},
#' @field outliers
#' Returns the instance variable outliers.
#' (tibble::tibble)
outliers = function(){
return(private$.outliers)
},
#' @field one_hot_df
#' Returns the positions of missings in one_hot encoding
#' (tibble::tibble)
one_hot_df = function(){
return(private$.one_hot_df)
},
#' @field outliersStatistics
#' Returns the instance variable outliersStatistics.
#' (tibble::tibble)
outliersStatistics = function(){
return(private$.outliersStatistics)
},
#' @field outliersAgentAlphabet
#' Returns the instance variable of outliersAgentAlphabet
#' (character)
outliersAgentAlphabet = function(){
return(private$.outliersAgentAlphabet)
},
#' @field outliersAgent
#' Returns the instance variable outliersAgent.
#' (character)
outliersAgent = function(){
return(as.character(private$.outliersAgent))
},
#' @field setOutliersAgent
#' Sets the instance variable outliersAgent.
#' (character)
setOutliersAgent = function(agent = "character") {
private$.outliersAgent <- factor(agent, levels = self$outliersAgentAlphabet)
},
#' @field featureData
#' Returns the instance variable featureData.
#' (numeric)
featureData = function(){
return(private$.featureData)
},
#' @field alpha
#' Returns the instance variable alpha.
#' (numeric)
alpha = function(){
return(private$.alpha)
},
#' @field setAlpha
#' Set the instance variable alpha.
#' (numeric)
setAlpha = function(value = "numeric"){
if(value < 0){
private$.alpha <- 0.001
}
else if(value > 0.999){
private$.alpha <- 0.999
}
else{
private$.alpha <- value
}
},
#' @field epsilon
#' Returns the instance variable epsilon.
#' (numeric)
epsilon = function(){
return(private$.epsilon)
},
#' @field setEpsilon
#' Set the instance variable epsilon.
#' (numeric)
setEpsilon = function(value = "numeric"){
if(value < 0.001){
private$.epsilon <- 0.001
}
else{
private$.epsilon <- value
}
},
#' @field minSamples
#' Returns the instance variable minSamples.
#' (integer)
minSamples = function(){
return(private$.minSamples)
},
#' @field setMinSamples
#' Set the instance variable minSamples.
#' (integer)
setMinSamples = function(value = "integer"){
if(value < 1){
private$.minSamples <- 1
}
else{
private$.minSamples <- value
}
},
#' @field gamma
#' Returns the instance variable gamma.
#' (numeric)
gamma = function(){
return(private$.gamma)
},
#' @field setGamma
#' Set the instance variable gamma.
#' (numeric)
setGamma = function(value = "numeric"){
if(value < 0.001){
private$.gamma <- 0.001
}
else if(value > 0.999){
private$.gamma <- 0.999
}
else{
private$.gamma <- value
}
},
#' @field nu
#' Returns the instance variable nu.
#' (numeric)
nu = function(){
return(private$.nu)
},
#' @field setNu
#' Set the instance variable nu.
#' (numeric)
setNu = function(value = "numeric"){
if(value < 0.001){
private$.nu <- 0.001
}
else if(value > 0.999){
private$.nu <- 0.999
}
else{
private$.nu <- value
}
},
#' @field k
#' Returns the instance variable k
#' (integer)
k = function(){
return(private$.k)
},
#' @field setK
#' Sets the instance variable k.
#' (integer)
setK = function(value = "integer"){
if(value < 1){
private$.k <- 1
}
else{
private$.k <- value
}
},
#' @field cutoff
#' Returns the instance variable cutoff.
#' (numeric)
cutoff = function(){
return(private$.cutoff)
},
#' @field setCutoff
#' Sets the instance variable cutoff.
#' (numeric)
setCutoff = function(value = "numeric"){
if(value < 0.01){
private$.cutoff <- 0.01
}
else if(value > 0.99){
private$.cutoff <- 0.99
}
else{
private$.cutoff <- value
}
},
#' @field seed
#' Returns the instance variable seed.
#' (integer)
seed = function(){
return(private$.seed)
},
#' @field setSeed
#' Set the instance variable seed.
#' (integer)
setSeed = function(value = "integer"){
if(value < 1){
private$.seed <- 1
}
else if (value > 100){
private$.seed <- 100
}
else{
private$.seed <- value
}
}
),#active
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.outliers` object.
#' @param data_df
#' The data to be cleaned.
#' (tibble::tibble)
#' @param alpha
#' Initial definition of the instance variable alpha.
#' (numeric)
#' @param epsilon
#' Initial definition of the instance variable epsilon.
#' (numeric)
#' @param minSamples
#' Initial definition of the instance variable minSamples.
#' (integer)
#' @param gamma
#' Initial definition of the instance variable gamma.
#' (numeric)
#' @param nu
#' Initial definition of the instance variable nu.
#' (numeric)
#' @param cutoff
#' Initial definition of the instance variable cutoff.
#' (numeric)
#' @param k
#' Initial definition of the instance variable k.
#' (integer)
#' @param seed
#' Initial definition of the instance variable seed.
#' (integer)
#' @return
#' A new `pgu.outliers` object.
#' (pguIMP::pgu.outliers)
initialize = function(data_df = "tbl_df", alpha = 0.05, epsilon = 0.1, minSamples = 4, gamma = 0.05, nu=0.1, k=4, cutoff=0.99, seed = 42){
self$setAlpha <- alpha
self$setEpsilon <- epsilon
self$setMinSamples <- minSamples
self$setGamma <- gamma
self$setNu <- nu
self$setK <- k
self$setCutoff <- cutoff
self$setSeed <- seed
private$.outliersAgentAlphabet <-c("none", "Grubbs", "DBSCAN", "SVM", "knn")
self$setOutliersAgent <- "none"
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
}
self$resetOutliers(data_df)
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.outliers` is removed from heap.
finalize = function(){
print("Instance of pgu.outliers removed from heap")
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.outliers` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.outliers\n")
cat(rString)
uString <- sprintf("outliersAgent: %s\nseed: %i\n", self$outliersAgent,self$seed)
uString <- sprintf("%s\nGrubbs parameter:\nalpha: %.3e\n", uString, self$alpha)
uString <- sprintf("%s\nDBSCAN parameter\nepsilon: %.3e\nminSamples: %i\n", uString, self$epsilon, self$minSamples)
uString <- sprintf("%s\nSVM parameter\ngamma: %.3e\nnu: %.3e\n", uString, self$gamma, self$nu)
uString <- sprintf("%s\nKNN parameter\ncutoff: %.3e\nk: %i\n", uString, self$cutoff, self$k)
cat(uString)
print(self$outliers)
print(self$outliersParameter)
print(self$outliersStatistics)
cat("\n\n")
invisible(self)
}, #function
####################
# public functions #
####################
#' @description
#' Resets instance variables and
#' performes Grubb's test for outliers to detect outliers in the normalized and Z-score transfromed data set.
#' Progresse is indicated by the progress object passed to the function.
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
resetOutliers = function(data_df = "tbl_df"){
numericData <- data_df %>%
dplyr::select_if(is.numeric)
features <- numericData %>%
colnames()
measurements <- c(rep(0.0, length(features)))
existings <- c(rep(0.0, length(features)))
outliers <- c(rep(0.0, length(features)))
fractionOfOutliers <- c(rep(0.0, length(features)))
private$.outliersParameter <- tibble::tibble(features, measurements, existings, outliers, fractionOfOutliers)
private$.outliers <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0)) %>%
dplyr::mutate_if(is.numeric, round, 8)
self$one_hot(data_df)
}, #function
####################
# helper functions #
####################
#' @description
#' Filters attributes from the given dataframe that are known to the class.
#' @param data_df
#' Dataframe to be filtered.
#' (tibble::tibble)
#' @return
#' A filterd dataframe.
#' (tibble::tibble)
filterFeatures = function(data_df = "tbl_df"){
data_df %>%
dplyr::select(tidyselect::all_of(private$.outliersParameter[["features"]])) %>%
return()
}, #function
#' @description
#' Checks if the feature consists of a sufficient number of instances.
#' @param data_df
#' Dataframe to be analyzed
#' (tibble::tibble)
#' @param feature
#' The attribute to be analyzed.
#' (character)
checkFeatureValidity = function(data_df = "tbl_df", feature = "character"){
result <- FALSE
n <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na() %>%
nrow()
if(n > 4){
result <- TRUE
}
return(result)
}, #function
#' @description
#' determines the outliers parameter by analyzing the
#' tibble data_df and the instance variable outliers.
#' Results are stored to instance variable outliersParameter.
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
detectOutliersParameter = function(data_df="tbl_df"){
measurements <- rep(0, nrow(self$outliersParameter))
n_outliers <- rep(0, nrow(self$outliersParameter))
for (i in seq(nrow(self$outliersParameter))){
measurements[i] <- data_df %>%
dplyr::select(tidyselect::all_of(self$outliersParameter[["features"]][i])) %>%
tidyr::drop_na() %>%
dplyr::count() %>%
unlist() %>%
as.integer()
n_outliers[i] <- self$outliers %>%
dplyr::select(feature) %>%
dplyr::filter(grepl(self$outliersParameter[["features"]][i],feature)) %>%
dplyr::count() %>%
unlist() %>%
as.integer()
}
private$.outliersParameter <- tibble::tibble(features = self$outliersParameter[["features"]],
measurements = measurements,
outliers = n_outliers) %>%
dplyr::mutate(existings = measurements - outliers) %>%
dplyr::mutate(fractionOfOutliers = outliers/measurements) %>%
dplyr::select(tidyselect::all_of(c("features", "measurements", "existings", "outliers", "fractionOfOutliers")))
},
#' @description
#' Characterizes each row of the data frame as either `complete`
#' or indicates which attribute has been identified as an outlier within the row.
#' If multiple attributes' row entries were identified as outliers, the row is characterized by `multiple`.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @return
#' Vector of row characteristics.
#' (character)
outliersFeatureList = function(data_df = "tbl_df"){
outFeature <- c(rep("complete", nrow(data_df)))
if (nrow(self$outliers) > 0) {
for(i in seq(from = 1,to = nrow(self$outliers), by =1)){
if (grepl("complete", outFeature[self$outliers[[i,"measurement"]]])){
outFeature[self$outliers[[i,"measurement"]]] <- self$outliers[[i, "feature"]]
}#if
else{
outFeature[self$outliers[[i,"measurement"]]] <- "multiple"
}#else
}#for
}#if
return(outFeature)
},#function
#' @description
#' Returns the detected outliers of a given attribute.
#' @param feature
#' The attribute to be analyzed
#' (character)
#' @return
#' The attribute's outliers
#' (tibble::tibble)
featureOutlier = function(feature = "character"){
t <- NULL
tryCatch({
t <- self$outliers %>%
dplyr::filter(feature == !!feature)
},
error = function(e) {
print("error")
print(e)
}, #error
warning = function(w) {
print("warning")
print(w)
} #warning
)#tryCatch
return(t)
}, #function
#' @description
#' Gathers statistical information about missing values
#' in one hot format.
#' The result is stored in the instance variable one_hot_df.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
one_hot = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
print("Warning: data_df needs to by of type tibble.")
private$.one_hot_df <- tibble::tibble()
}
private$.one_hot_df <- data_df %>%
dplyr::select_if(is.numeric) %>%
dplyr::transmute_all(list(miss = ~ as.integer(is.na(.))))
}, #function
###################
# detect outliers #
###################
#' @description
#' Chooses a method for identification of anomalies based upon the instance variable `outliersAgent`
#' Detects anomalies in a data frame by one-dimensional analysis of each feature.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectOutliers = function(data_df = "tbl_df", progress = "Progress"){
set.seed(self$seed)
switch(self$outliersAgent,
"none"=self$resetOutliers(data_df),
"Grubbs"=self$detectByGrubbs(data_df, progress),
"DBSCAN"= self$detectByDbscan(data_df, progress),
"SVM"= self$detectBySvm(data_df, progress),
"knn" = self$detectByKnn(data_df, progress),
stop("unkown outliersAgent"))
self$detectOutliersParameter(data_df)
self$calcOutliersStatistics(data_df)
},
#' @description
#' Identifies anomalies in the data frame based on Grubb's test.
#' Iterates over the whole data frame. Calls the object's public function
#' `grubbs_numeric` until no more anomalies are identified.
#' The threshold for anomaly detection is defined in the instance variable `alpha`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByGrubbs = function(data_df = "tbl_df", progress = "Progress"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
private$.featureData <- data_df[[feature]]
foundOutlier <- TRUE
while (foundOutlier) {
foundOutlier <- self$grubbs_numeric(data_df, feature)
}#while
} #if
}#for
},#function
#' @description
#' Performs Grubb's test for anomalies to detect a single outlier in the provided attributes data.
#' If an outlier is found, it is added to the instance variable `outliers`.
#' The threshold for anomaly detection is difined in the instance variable `alpha`.
#' The function indicates a find by a positive feedback.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' The attribute within the data frame to be analyzed.
#' @return
#' Feedback if an outlier was found.
#' (logical)
grubbs_numeric = function(data_df = "tbl_df", feature = "character"){
result <- outliers::grubbs.test(self$featureData, opposite = FALSE)
if(result$p.value < self$alpha){
if (grepl("lowest", result$alternative)){
col <- "blue"
t <- "min"
idx <- which.min(self$featureData)
}#if
else{
col <- "firebrick"
t <- "max"
idx <- which.max(self$featureData)
}#else
value <- data_df[[idx, feature]]
private$.outliers <- tibble::add_row(self$outliers,
measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(value),
type = as.character(t),
color = as.character(col))
private$.featureData[idx] <- NA
return (TRUE)
}#if
else{
return (FALSE)
}#else
},#function
#' @description
#' Identifies anomalies in the data frame based on DBSCAN.
#' Iterates over the whole data frame. Calls the object's public function
#' `dbscan_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `epsilon` and `minSamples`.
#' The results of the `dbscan_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByDbscan = function(data_df = "tbl_df", progress = "Progress"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$dbscan_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
} #if
} #for
}, #function
#' @description
#' Identifies anomalies in a single feature of a data frame based on DBSCAN.
#' The cluster hyperparameter are defined in the instance variables `epsilon` and `minSamples`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
dbscan_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
dbscan_model <- feature_df %>%
dbscan::dbscan(eps = self$epsilon, minPts = self$minSamples)
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
if(sum(dbscan_model$cluster==0)>0){
values <- feature_df[[feature]][dbscan_model$cluster==0]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values)) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
#' @description
#' Identifies anomalies in the data frame based on one class SVM.
#' Iterates over the whole data frame. Calls the object's public function
#' `svm_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `gamma` and `nu`.
#' The results of the `svm_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectBySvm = function(data_df = "tbl_df", progress = "Process"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$svm_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
} #if
}#for
},
#' @description
#' Identifies anomalies in a single feature of a data frame based on one class SVM.
#' The cluster hyperparameter are defined in the instance variables `gamma` and `nu`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
svm_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
feature_idx <- integer(0)
tryCatch({
svm_model <- feature_df %>%
e1071::svm(type='one-classification', kernel = "radial", gamma=self$gamma, nu=self$nu)
result <- predict(svm_model, data_df[feature]) %>%
as.logical()
feature_idx <- which(!result)
},
error = function(e) {
sprintf("Warning in pguOutlier's function svm_numeric while analyzing %s:\n %s" , feature, e) %>%
cat()
}
)
if(length(feature_idx)>0){
values <- feature_df[[feature]][feature_idx]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values) ) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
#' @description
#' Identifies anomalies in the data frame based on knnO.
#' Iterates over the whole data frame. Calls the object's public function
#' `svm_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `alpha` and `minSamples`.
#' The results of the `knn_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByKnn = function(data_df = "tbl_df", progress = "Process"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$knn_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
}#if
}#for
},
#' @description
#' Identifies anomalies in a single feature of a data frame based on knnO.
#' The cluster hyperparameter are defined in the instance variables `alpha` and `minSmaples`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
knn_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
feature_idx <- integer(0)
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
tryCatch({
knn_model <- feature_df %>%
pguIMP::nnk(k=self$k, cutoff = self$cutoff)
feature_idx <- knn_model$`Location of Outlier`
},
error = function(e) {
sprintf("Warning in pguOutlier's function knn_numeric while analyzing %s:\n %s" , feature, e) %>%
cat()
}
)
if(length(feature_idx)>0){
values <- feature_df[[feature]][feature_idx]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values) ) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
########################
# set imputation sites #
########################
#' @description
#' Replaces the detected anomalies of a user provided data frame with `NA` for further imputation routines.
#' @param data_df
#' Data frame to be mutated.
#' (tibble::tibble)
#' @return
#' A data frame with anomalies replaced by `NA`.
#' (tibble::tibble)
setImputationSites = function(data_df = "tbl_df"){
for (i in seq(nrow(self$outliersParameter))){
temp_feature <- self$outliersParameter[["features"]][i]
idx <- self$outliers %>%
dplyr::select(tidyselect::all_of(c("measurement", "feature"))) %>%
dplyr::filter(grepl(temp_feature, feature)) %>%
dplyr::select("measurement") %>%
unlist() %>%
as.integer()
data_df <- data_df %>%
dplyr::mutate(!!temp_feature := data_df %>%
dplyr::select(temp_feature) %>%
unlist() %>%
as.numeric() %>%
replace(idx, NA))
}#for
return(data_df)
}, #function
######################
# outlier statistics #
######################
#' @description
#' Calculates the statistics on the previously performed outlier detection analysis
#' and stores the results in the instance variable `outliersStatistcs`.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
calcOutliersStatistics = function(data_df = "tbl_df"){
absCount <- data_df %>%
self$filterFeatures() %>%
dplyr::summarise_all(~sum(!is.na(.)))
private$.outliersStatistics <- tibble::tibble(features = colnames(absCount),
absCount = absCount %>%
unlist() %>%
as.numeric())
outlierCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
minCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
maxCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
if(nrow(self$outliers) > 0){
for (i in seq(from = 1,to = length(self$outliersParameter[["features"]]), by =1)) {
outlierCount[i] <- sum(self$outliers["feature"] == self$outliersParameter[[i, "features"]])
minCount[i] <- self$outliers %>%
dplyr::filter(type == "min" & feature == self$outliersParameter[[i, "features"]]) %>%
dplyr::select("feature") %>%
dplyr::count() %>%
unlist() %>%
as.numeric()
maxCount[i] <- self$outliers %>%
dplyr::filter(type == "max" & feature == self$outliersParameter[[i, "features"]]) %>%
dplyr::select("feature") %>%
dplyr::count() %>%
unlist() %>%
as.numeric()
}#for
}#if
private$.outliersStatistics <- self$outliersStatistics %>%
dplyr::mutate(outlierCount = outlierCount,
low = minCount,
high = maxCount,
outlierFraction = 100* outlierCount/absCount) %>%
dplyr::mutate(minFraction = 100 * low/absCount,
maxFraction = 100 * high/absCount)
},#function
####################
# output functions #
####################
#' @description
#' Creates a datatable with substituted outliers highlightes by colored background.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @return
#' A colored datatable
#' (DT::datatable)
outlierTable = function(data_df = "tbl_df"){
idx <- self$outliers[["measurement"]][!duplicated(self$outliers[["measurement"]])]
t <- data_df %>%
dplyr::slice(idx) %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE)
)
for (featureName in self$outliersParameter[["features"]]){
featureOutlier <- self$outliers %>%
dplyr::filter(grepl(featureName, feature)) %>%
dplyr::mutate_if(is.numeric, round, 3)
if (nrow(featureOutlier)>0){
t <- DT::formatStyle(t,
featureName,
backgroundColor = styleEqual(data_df %>%
dplyr::select(!!featureName) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
as.numeric() %>%
round(digits = 3),
featureOutlier[["color"]])
)
}#if
}#for
return(t)
},#function
##################
# plot functions #
##################
#' @description
#' Displays the occurrence of outlier candidates per attribute as bar plot.
#' @return
#' A bar plot.
#' (ggplot2::ggplot)
plotOutliersDistribution = function(){
p <- self$outliersStatistics %>%
tidyr::gather('low', 'high', key = "type", value="typeCount") %>%
dplyr::mutate(fraction = typeCount/absCount) %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x = "features", y = "fraction", fill = "type"), na.rm=TRUE)+
ggplot2::geom_col()+
ggplot2::ggtitle("Outliers Distribution") +
ggplot2::ylab("fraction") +
ggplot2::xlab("feature") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent"),
axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)
)
return(p)
},#function
#' @description
#' Displays the distribution of an attribute's values as histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A histogram.
#' (ggplot2::ggplot)
featureBarPlot = function(data_df = "tbl_df", feature = "character"){
feature <- dplyr::sym(feature)
p <- data_df %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x=feature), na.rm=TRUE) +
ggplot2::geom_bar(stat = "bin", bins = 30) +
ggplot2::xlab("value") +
ggplot2::ylab("counts") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
},#function
#' @description
#' Displays the distribution of an attribute's vlues as box plot.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A box plot.
#' (ggplot2::ggplot)
featureBoxPlotWithSubset = function(data_df = "tbl_df", feature = "character"){
outlier_idx <- self$outliers %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select(measurement) %>%
dplyr::pull()
data_type <- rep("regular", nrow(data_df))
data_type[outlier_idx] <- "outlier"
# outFeature <- self$outliersFeatureList(data_df)
p <- data_df %>%
dplyr::select(feature) %>%
dplyr::mutate(type = data_type ) %>%
tidyr::gather(key = "feature", value = "value", -type) %>%
ggplot2::ggplot(mapping=ggplot2::aes_string(x = "feature", y="value"), na.rm=TRUE)+
ggplot2::geom_boxplot(outlier.shape = NA, na.rm=TRUE) +
ggplot2::geom_jitter(ggplot2::aes(colour=type), na.rm=TRUE) +
ggplot2::xlab("feature") +
ggplot2::ylab("value") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as a composition of a box plot and a histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A composite plot.
#' (ggplot2::ggplot)
featurePlot = function(data_df = "tbl_df", feature = "character"){
p1 <- self$featureBoxPlotWithSubset(data_df, feature) +
ggplot2::theme(legend.position = c(0.9, 0.9),
legend.key = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
p2 <- self$featureBarPlot(data_df, feature)
limits <- ggplot2::layer_scales(p2)$x$range$range
p1 <- p1 +
ggplot2::scale_y_continuous(limits=limits)
p2 <- p2 +
ggplot2::scale_x_continuous(position = "top") +
ggplot2::coord_flip()
p <- gridExtra::grid.arrange(p1,p2, layout_matrix = rbind(c(1,1,2),c(1,1,2)),
top = textGrob(label = sprintf("Distribution of %s", feature)))
return(p)
}#function
)#public
)#class
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#' @title pgu.outliers
#'
#' @description
#' Detects and replaces possible outliers from data set.
#'
#' @details
#' Performes Grubb's test for outliers to detect outliers in the normalized and Z-score transfromed data set.
#' Replace missing values with substitutes by classical and AI-powerd substitution algorithms.
#' For this purpose outliers are handled as imputation sites.
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dbscan dbscan
#' @importFrom dplyr add_row count filter mutate mutate_if pull select select_if
#' @importFrom dplyr slice summarise_all sym transmute_all
#' @importFrom DT datatable formatStyle
#' @importFrom e1071 svm
#' @importFrom ggplot2 aes aes_string coord_flip element_blank element_rect element_text
#' @importFrom ggplot2 geom_bar geom_boxplot geom_col geom_jitter ggplot
#' @importFrom ggplot2 ggtitle layer_scales scale_x_continuous scale_y_continuous theme theme_linedraw
#' @importFrom ggplot2 xlab ylab
#' @importFrom gridExtra grid.arrange
#' @importFrom magrittr %>%
#' @importFrom outliers grubbs.test
#' @importFrom R6 R6Class
#' @importFrom shiny Progress
#' @importFrom tibble add_row is_tibble tibble
#' @importFrom tidyr drop_na gather
#' @importFrom tidyselect all_of
#'
#' @include pguOutlierDetection.R
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
#'
pgu.outliers <- R6::R6Class("pgu.outliers",
####################
# instance variables
####################
private = list(
.outliersParameter = "tbl_df",
.outliers = "tbl_df",
.one_hot_df = "tbl_df",
.outliersStatistics = "tbl_df",
.outliersAgentAlphabet = "character",
.outliersAgent = "factor",
.featureData = "numeric",
.alpha = "numeric",
.epsilon = "numeric",
.minSamples = "integer",
.gamma = "numeric",
.nu = "numeric",
.k = "integer",
.cutoff = "numeric",
.seed = "integer"
),
##################
# accessor methods
##################
active = list(
#' @field outliersParameter
#' Returns the instance variable outliersParameter.
#' (tibble::tibble)
outliersParameter = function(){
return(private$.outliersParameter)
},
#' @field outliers
#' Returns the instance variable outliers.
#' (tibble::tibble)
outliers = function(){
return(private$.outliers)
},
#' @field one_hot_df
#' Returns the positions of missings in one_hot encoding
#' (tibble::tibble)
one_hot_df = function(){
return(private$.one_hot_df)
},
#' @field outliersStatistics
#' Returns the instance variable outliersStatistics.
#' (tibble::tibble)
outliersStatistics = function(){
return(private$.outliersStatistics)
},
#' @field outliersAgentAlphabet
#' Returns the instance variable of outliersAgentAlphabet
#' (character)
outliersAgentAlphabet = function(){
return(private$.outliersAgentAlphabet)
},
#' @field outliersAgent
#' Returns the instance variable outliersAgent.
#' (character)
outliersAgent = function(){
return(as.character(private$.outliersAgent))
},
#' @field setOutliersAgent
#' Sets the instance variable outliersAgent.
#' (character)
setOutliersAgent = function(agent = "character") {
private$.outliersAgent <- factor(agent, levels = self$outliersAgentAlphabet)
},
#' @field featureData
#' Returns the instance variable featureData.
#' (numeric)
featureData = function(){
return(private$.featureData)
},
#' @field alpha
#' Returns the instance variable alpha.
#' (numeric)
alpha = function(){
return(private$.alpha)
},
#' @field setAlpha
#' Set the instance variable alpha.
#' (numeric)
setAlpha = function(value = "numeric"){
if(value < 0){
private$.alpha <- 0.001
}
else if(value > 0.999){
private$.alpha <- 0.999
}
else{
private$.alpha <- value
}
},
#' @field epsilon
#' Returns the instance variable epsilon.
#' (numeric)
epsilon = function(){
return(private$.epsilon)
},
#' @field setEpsilon
#' Set the instance variable epsilon.
#' (numeric)
setEpsilon = function(value = "numeric"){
if(value < 0.001){
private$.epsilon <- 0.001
}
else{
private$.epsilon <- value
}
},
#' @field minSamples
#' Returns the instance variable minSamples.
#' (integer)
minSamples = function(){
return(private$.minSamples)
},
#' @field setMinSamples
#' Set the instance variable minSamples.
#' (integer)
setMinSamples = function(value = "integer"){
if(value < 1){
private$.minSamples <- 1
}
else{
private$.minSamples <- value
}
},
#' @field gamma
#' Returns the instance variable gamma.
#' (numeric)
gamma = function(){
return(private$.gamma)
},
#' @field setGamma
#' Set the instance variable gamma.
#' (numeric)
setGamma = function(value = "numeric"){
if(value < 0.001){
private$.gamma <- 0.001
}
else if(value > 0.999){
private$.gamma <- 0.999
}
else{
private$.gamma <- value
}
},
#' @field nu
#' Returns the instance variable nu.
#' (numeric)
nu = function(){
return(private$.nu)
},
#' @field setNu
#' Set the instance variable nu.
#' (numeric)
setNu = function(value = "numeric"){
if(value < 0.001){
private$.nu <- 0.001
}
else if(value > 0.999){
private$.nu <- 0.999
}
else{
private$.nu <- value
}
},
#' @field k
#' Returns the instance variable k
#' (integer)
k = function(){
return(private$.k)
},
#' @field setK
#' Sets the instance variable k.
#' (integer)
setK = function(value = "integer"){
if(value < 1){
private$.k <- 1
}
else{
private$.k <- value
}
},
#' @field cutoff
#' Returns the instance variable cutoff.
#' (numeric)
cutoff = function(){
return(private$.cutoff)
},
#' @field setCutoff
#' Sets the instance variable cutoff.
#' (numeric)
setCutoff = function(value = "numeric"){
if(value < 0.01){
private$.cutoff <- 0.01
}
else if(value > 0.99){
private$.cutoff <- 0.99
}
else{
private$.cutoff <- value
}
},
#' @field seed
#' Returns the instance variable seed.
#' (integer)
seed = function(){
return(private$.seed)
},
#' @field setSeed
#' Set the instance variable seed.
#' (integer)
setSeed = function(value = "integer"){
if(value < 1){
private$.seed <- 1
}
else if (value > 100){
private$.seed <- 100
}
else{
private$.seed <- value
}
}
),#active
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.outliers` object.
#' @param data_df
#' The data to be cleaned.
#' (tibble::tibble)
#' @param alpha
#' Initial definition of the instance variable alpha.
#' (numeric)
#' @param epsilon
#' Initial definition of the instance variable epsilon.
#' (numeric)
#' @param minSamples
#' Initial definition of the instance variable minSamples.
#' (integer)
#' @param gamma
#' Initial definition of the instance variable gamma.
#' (numeric)
#' @param nu
#' Initial definition of the instance variable nu.
#' (numeric)
#' @param cutoff
#' Initial definition of the instance variable cutoff.
#' (numeric)
#' @param k
#' Initial definition of the instance variable k.
#' (integer)
#' @param seed
#' Initial definition of the instance variable seed.
#' (integer)
#' @return
#' A new `pgu.outliers` object.
#' (pguIMP::pgu.outliers)
initialize = function(data_df = "tbl_df", alpha = 0.05, epsilon = 0.1, minSamples = 4, gamma = 0.05, nu=0.1, k=4, cutoff=0.99, seed = 42){
self$setAlpha <- alpha
self$setEpsilon <- epsilon
self$setMinSamples <- minSamples
self$setGamma <- gamma
self$setNu <- nu
self$setK <- k
self$setCutoff <- cutoff
self$setSeed <- seed
private$.outliersAgentAlphabet <-c("none", "Grubbs", "DBSCAN", "SVM", "knn")
self$setOutliersAgent <- "none"
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
}
self$resetOutliers(data_df)
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.outliers` is removed from heap.
finalize = function(){
print("Instance of pgu.outliers removed from heap")
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.outliers` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.outliers\n")
cat(rString)
uString <- sprintf("outliersAgent: %s\nseed: %i\n", self$outliersAgent,self$seed)
uString <- sprintf("%s\nGrubbs parameter:\nalpha: %.3e\n", uString, self$alpha)
uString <- sprintf("%s\nDBSCAN parameter\nepsilon: %.3e\nminSamples: %i\n", uString, self$epsilon, self$minSamples)
uString <- sprintf("%s\nSVM parameter\ngamma: %.3e\nnu: %.3e\n", uString, self$gamma, self$nu)
uString <- sprintf("%s\nKNN parameter\ncutoff: %.3e\nk: %i\n", uString, self$cutoff, self$k)
cat(uString)
print(self$outliers)
print(self$outliersParameter)
print(self$outliersStatistics)
cat("\n\n")
invisible(self)
}, #function
####################
# public functions #
####################
#' @description
#' Resets instance variables and
#' performes Grubb's test for outliers to detect outliers in the normalized and Z-score transfromed data set.
#' Progresse is indicated by the progress object passed to the function.
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
resetOutliers = function(data_df = "tbl_df"){
numericData <- data_df %>%
dplyr::select_if(is.numeric)
features <- numericData %>%
colnames()
measurements <- c(rep(0.0, length(features)))
existings <- c(rep(0.0, length(features)))
outliers <- c(rep(0.0, length(features)))
fractionOfOutliers <- c(rep(0.0, length(features)))
private$.outliersParameter <- tibble::tibble(features, measurements, existings, outliers, fractionOfOutliers)
private$.outliers <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0)) %>%
dplyr::mutate_if(is.numeric, round, 8)
self$one_hot(data_df)
}, #function
####################
# helper functions #
####################
#' @description
#' Filters attributes from the given dataframe that are known to the class.
#' @param data_df
#' Dataframe to be filtered.
#' (tibble::tibble)
#' @return
#' A filterd dataframe.
#' (tibble::tibble)
filterFeatures = function(data_df = "tbl_df"){
data_df %>%
dplyr::select(tidyselect::all_of(private$.outliersParameter[["features"]])) %>%
return()
}, #function
#' @description
#' Checks if the feature consists of a sufficient number of instances.
#' @param data_df
#' Dataframe to be analyzed
#' (tibble::tibble)
#' @param feature
#' The attribute to be analyzed.
#' (character)
checkFeatureValidity = function(data_df = "tbl_df", feature = "character"){
result <- FALSE
n <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na() %>%
nrow()
if(n > 4){
result <- TRUE
}
return(result)
}, #function
#' @description
#' determines the outliers parameter by analyzing the
#' tibble data_df and the instance variable outliers.
#' Results are stored to instance variable outliersParameter.
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
detectOutliersParameter = function(data_df="tbl_df"){
measurements <- rep(0, nrow(self$outliersParameter))
n_outliers <- rep(0, nrow(self$outliersParameter))
for (i in seq(nrow(self$outliersParameter))){
measurements[i] <- data_df %>%
dplyr::select(tidyselect::all_of(self$outliersParameter[["features"]][i])) %>%
tidyr::drop_na() %>%
dplyr::count() %>%
unlist() %>%
as.integer()
n_outliers[i] <- self$outliers %>%
dplyr::select(feature) %>%
dplyr::filter(grepl(self$outliersParameter[["features"]][i],feature)) %>%
dplyr::count() %>%
unlist() %>%
as.integer()
}
private$.outliersParameter <- tibble::tibble(features = self$outliersParameter[["features"]],
measurements = measurements,
outliers = n_outliers) %>%
dplyr::mutate(existings = measurements - outliers) %>%
dplyr::mutate(fractionOfOutliers = outliers/measurements) %>%
dplyr::select(tidyselect::all_of(c("features", "measurements", "existings", "outliers", "fractionOfOutliers")))
},
#' @description
#' Characterizes each row of the data frame as either `complete`
#' or indicates which attribute has been identified as an outlier within the row.
#' If multiple attributes' row entries were identified as outliers, the row is characterized by `multiple`.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @return
#' Vector of row characteristics.
#' (character)
outliersFeatureList = function(data_df = "tbl_df"){
outFeature <- c(rep("complete", nrow(data_df)))
if (nrow(self$outliers) > 0) {
for(i in seq(from = 1,to = nrow(self$outliers), by =1)){
if (grepl("complete", outFeature[self$outliers[[i,"measurement"]]])){
outFeature[self$outliers[[i,"measurement"]]] <- self$outliers[[i, "feature"]]
}#if
else{
outFeature[self$outliers[[i,"measurement"]]] <- "multiple"
}#else
}#for
}#if
return(outFeature)
},#function
#' @description
#' Returns the detected outliers of a given attribute.
#' @param feature
#' The attribute to be analyzed
#' (character)
#' @return
#' The attribute's outliers
#' (tibble::tibble)
featureOutlier = function(feature = "character"){
t <- NULL
tryCatch({
t <- self$outliers %>%
dplyr::filter(feature == !!feature)
},
error = function(e) {
print("error")
print(e)
}, #error
warning = function(w) {
print("warning")
print(w)
} #warning
)#tryCatch
return(t)
}, #function
#' @description
#' Gathers statistical information about missing values
#' in one hot format.
#' The result is stored in the instance variable one_hot_df.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
one_hot = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
print("Warning: data_df needs to by of type tibble.")
private$.one_hot_df <- tibble::tibble()
}
private$.one_hot_df <- data_df %>%
dplyr::select_if(is.numeric) %>%
dplyr::transmute_all(list(miss = ~ as.integer(is.na(.))))
}, #function
###################
# detect outliers #
###################
#' @description
#' Chooses a method for identification of anomalies based upon the instance variable `outliersAgent`
#' Detects anomalies in a data frame by one-dimensional analysis of each feature.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectOutliers = function(data_df = "tbl_df", progress = "Progress"){
set.seed(self$seed)
switch(self$outliersAgent,
"none"=self$resetOutliers(data_df),
"Grubbs"=self$detectByGrubbs(data_df, progress),
"DBSCAN"= self$detectByDbscan(data_df, progress),
"SVM"= self$detectBySvm(data_df, progress),
"knn" = self$detectByKnn(data_df, progress),
stop("unkown outliersAgent"))
self$detectOutliersParameter(data_df)
self$calcOutliersStatistics(data_df)
},
#' @description
#' Identifies anomalies in the data frame based on Grubb's test.
#' Iterates over the whole data frame. Calls the object's public function
#' `grubbs_numeric` until no more anomalies are identified.
#' The threshold for anomaly detection is defined in the instance variable `alpha`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByGrubbs = function(data_df = "tbl_df", progress = "Progress"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
private$.featureData <- data_df[[feature]]
foundOutlier <- TRUE
while (foundOutlier) {
foundOutlier <- self$grubbs_numeric(data_df, feature)
}#while
} #if
}#for
},#function
#' @description
#' Performs Grubb's test for anomalies to detect a single outlier in the provided attributes data.
#' If an outlier is found, it is added to the instance variable `outliers`.
#' The threshold for anomaly detection is difined in the instance variable `alpha`.
#' The function indicates a find by a positive feedback.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' The attribute within the data frame to be analyzed.
#' @return
#' Feedback if an outlier was found.
#' (logical)
grubbs_numeric = function(data_df = "tbl_df", feature = "character"){
result <- outliers::grubbs.test(self$featureData, opposite = FALSE)
if(result$p.value < self$alpha){
if (grepl("lowest", result$alternative)){
col <- "blue"
t <- "min"
idx <- which.min(self$featureData)
}#if
else{
col <- "firebrick"
t <- "max"
idx <- which.max(self$featureData)
}#else
value <- data_df[[idx, feature]]
private$.outliers <- tibble::add_row(self$outliers,
measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(value),
type = as.character(t),
color = as.character(col))
private$.featureData[idx] <- NA
return (TRUE)
}#if
else{
return (FALSE)
}#else
},#function
#' @description
#' Identifies anomalies in the data frame based on DBSCAN.
#' Iterates over the whole data frame. Calls the object's public function
#' `dbscan_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `epsilon` and `minSamples`.
#' The results of the `dbscan_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByDbscan = function(data_df = "tbl_df", progress = "Progress"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$dbscan_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
} #if
} #for
}, #function
#' @description
#' Identifies anomalies in a single feature of a data frame based on DBSCAN.
#' The cluster hyperparameter are defined in the instance variables `epsilon` and `minSamples`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
dbscan_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
dbscan_model <- feature_df %>%
dbscan::dbscan(eps = self$epsilon, minPts = self$minSamples)
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
if(sum(dbscan_model$cluster==0)>0){
values <- feature_df[[feature]][dbscan_model$cluster==0]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values)) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
#' @description
#' Identifies anomalies in the data frame based on one class SVM.
#' Iterates over the whole data frame. Calls the object's public function
#' `svm_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `gamma` and `nu`.
#' The results of the `svm_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectBySvm = function(data_df = "tbl_df", progress = "Process"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$svm_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
} #if
}#for
},
#' @description
#' Identifies anomalies in a single feature of a data frame based on one class SVM.
#' The cluster hyperparameter are defined in the instance variables `gamma` and `nu`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
svm_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
feature_idx <- integer(0)
tryCatch({
svm_model <- feature_df %>%
e1071::svm(type='one-classification', kernel = "radial", gamma=self$gamma, nu=self$nu)
result <- predict(svm_model, data_df[feature]) %>%
as.logical()
feature_idx <- which(!result)
},
error = function(e) {
sprintf("Warning in pguOutlier's function svm_numeric while analyzing %s:\n %s" , feature, e) %>%
cat()
}
)
if(length(feature_idx)>0){
values <- feature_df[[feature]][feature_idx]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values) ) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
#' @description
#' Identifies anomalies in the data frame based on knnO.
#' Iterates over the whole data frame. Calls the object's public function
#' `svm_numeric` until all features are analyzed.
#' The cluster hyper parameter are defined in the instance variables `alpha` and `minSamples`.
#' The results of the `knn_numeric` routine are added to the instance variable `outliers`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
detectByKnn = function(data_df = "tbl_df", progress = "Process"){
self$resetOutliers(data_df)
for (feature in self$outliersParameter[["features"]]){
if(("shiny" %in% (.packages())) & (class(progress)[1] == "Progress")){
progress$inc(1)
}#if
if (self$checkFeatureValidity(data_df, feature)){
outliers_df <- self$knn_numeric(data_df, feature)
if(nrow(outliers_df)>0){
private$.outliers <- self$outliers %>%
dplyr::add_row(outliers_df)
} #if
}#if
}#for
},
#' @description
#' Identifies anomalies in a single feature of a data frame based on knnO.
#' The cluster hyperparameter are defined in the instance variables `alpha` and `minSmaples`.
#' Display the progress if shiny is loaded.
#' @param data_df
#' Data frame to be analyzed.
#' (tibble::tibble)
#' @param feature
#' Feature to be analyzed
#' (character)
#' @param progress
#' If shiny is loaded, the analysis' progress is stored in this instance of the shiny Progress class.
#' (shiny::Progress)
#' @return
#' A data frame comprising the information about detected anomalies of the feature.
#' (tibble::tibble)
knn_numeric = function(data_df = "tbl_df", feature = "character"){
feature_df <- data_df %>%
dplyr::select(feature) %>%
tidyr::drop_na()
mean_val <- feature_df %>%
unlist() %>%
as.numeric() %>%
mean()
feature_idx <- integer(0)
outliers_df <- tibble::tibble(measurement = numeric(0),
feature = character(0),
values = numeric(0),
type = character(0),
color = character(0))
tryCatch({
knn_model <- feature_df %>%
pguIMP::nnk(k=self$k, cutoff = self$cutoff)
feature_idx <- knn_model$`Location of Outlier`
},
error = function(e) {
sprintf("Warning in pguOutlier's function knn_numeric while analyzing %s:\n %s" , feature, e) %>%
cat()
}
)
if(length(feature_idx)>0){
values <- feature_df[[feature]][feature_idx]
idx <- which(data_df[[feature]] %in% values)
feature <- rep(feature, length(values))
outliers_df <- tibble::tibble(measurement = as.numeric(idx),
feature = as.character(feature),
values = as.numeric(values) ) %>%
dplyr::mutate(type = ifelse(values > mean_val, "max", "min")) %>%
dplyr::mutate(color = ifelse(values > mean_val, "firebrick", "blue"))
}#if
return(outliers_df)
},
########################
# set imputation sites #
########################
#' @description
#' Replaces the detected anomalies of a user provided data frame with `NA` for further imputation routines.
#' @param data_df
#' Data frame to be mutated.
#' (tibble::tibble)
#' @return
#' A data frame with anomalies replaced by `NA`.
#' (tibble::tibble)
setImputationSites = function(data_df = "tbl_df"){
for (i in seq(nrow(self$outliersParameter))){
temp_feature <- self$outliersParameter[["features"]][i]
idx <- self$outliers %>%
dplyr::select(tidyselect::all_of(c("measurement", "feature"))) %>%
dplyr::filter(grepl(temp_feature, feature)) %>%
dplyr::select("measurement") %>%
unlist() %>%
as.integer()
data_df <- data_df %>%
dplyr::mutate(!!temp_feature := data_df %>%
dplyr::select(temp_feature) %>%
unlist() %>%
as.numeric() %>%
replace(idx, NA))
}#for
return(data_df)
}, #function
######################
# outlier statistics #
######################
#' @description
#' Calculates the statistics on the previously performed outlier detection analysis
#' and stores the results in the instance variable `outliersStatistcs`.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
calcOutliersStatistics = function(data_df = "tbl_df"){
absCount <- data_df %>%
self$filterFeatures() %>%
dplyr::summarise_all(~sum(!is.na(.)))
private$.outliersStatistics <- tibble::tibble(features = colnames(absCount),
absCount = absCount %>%
unlist() %>%
as.numeric())
outlierCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
minCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
maxCount <- c(rep(0.0, length(self$outliersParameter[["features"]])))
if(nrow(self$outliers) > 0){
for (i in seq(from = 1,to = length(self$outliersParameter[["features"]]), by =1)) {
outlierCount[i] <- sum(self$outliers["feature"] == self$outliersParameter[[i, "features"]])
minCount[i] <- self$outliers %>%
dplyr::filter(type == "min" & feature == self$outliersParameter[[i, "features"]]) %>%
dplyr::select("feature") %>%
dplyr::count() %>%
unlist() %>%
as.numeric()
maxCount[i] <- self$outliers %>%
dplyr::filter(type == "max" & feature == self$outliersParameter[[i, "features"]]) %>%
dplyr::select("feature") %>%
dplyr::count() %>%
unlist() %>%
as.numeric()
}#for
}#if
private$.outliersStatistics <- self$outliersStatistics %>%
dplyr::mutate(outlierCount = outlierCount,
low = minCount,
high = maxCount,
outlierFraction = 100* outlierCount/absCount) %>%
dplyr::mutate(minFraction = 100 * low/absCount,
maxFraction = 100 * high/absCount)
},#function
####################
# output functions #
####################
#' @description
#' Creates a datatable with substituted outliers highlightes by colored background.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @return
#' A colored datatable
#' (DT::datatable)
outlierTable = function(data_df = "tbl_df"){
idx <- self$outliers[["measurement"]][!duplicated(self$outliers[["measurement"]])]
t <- data_df %>%
dplyr::slice(idx) %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE)
)
for (featureName in self$outliersParameter[["features"]]){
featureOutlier <- self$outliers %>%
dplyr::filter(grepl(featureName, feature)) %>%
dplyr::mutate_if(is.numeric, round, 3)
if (nrow(featureOutlier)>0){
t <- DT::formatStyle(t,
featureName,
backgroundColor = styleEqual(data_df %>%
dplyr::select(!!featureName) %>%
dplyr::slice(featureOutlier[["measurement"]]) %>%
unlist() %>%
as.numeric() %>%
round(digits = 3),
featureOutlier[["color"]])
)
}#if
}#for
return(t)
},#function
##################
# plot functions #
##################
#' @description
#' Displays the occurrence of outlier candidates per attribute as bar plot.
#' @return
#' A bar plot.
#' (ggplot2::ggplot)
plotOutliersDistribution = function(){
p <- self$outliersStatistics %>%
tidyr::gather('low', 'high', key = "type", value="typeCount") %>%
dplyr::mutate(fraction = typeCount/absCount) %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x = "features", y = "fraction", fill = "type"), na.rm=TRUE)+
ggplot2::geom_col()+
ggplot2::ggtitle("Outliers Distribution") +
ggplot2::ylab("fraction") +
ggplot2::xlab("feature") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent"),
axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)
)
return(p)
},#function
#' @description
#' Displays the distribution of an attribute's values as histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A histogram.
#' (ggplot2::ggplot)
featureBarPlot = function(data_df = "tbl_df", feature = "character"){
feature <- dplyr::sym(feature)
p <- data_df %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x=feature), na.rm=TRUE) +
ggplot2::geom_bar(stat = "bin", bins = 30) +
ggplot2::xlab("value") +
ggplot2::ylab("counts") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
},#function
#' @description
#' Displays the distribution of an attribute's vlues as box plot.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A box plot.
#' (ggplot2::ggplot)
featureBoxPlotWithSubset = function(data_df = "tbl_df", feature = "character"){
outlier_idx <- self$outliers %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select(measurement) %>%
dplyr::pull()
data_type <- rep("regular", nrow(data_df))
data_type[outlier_idx] <- "outlier"
# outFeature <- self$outliersFeatureList(data_df)
p <- data_df %>%
dplyr::select(feature) %>%
dplyr::mutate(type = data_type ) %>%
tidyr::gather(key = "feature", value = "value", -type) %>%
ggplot2::ggplot(mapping=ggplot2::aes_string(x = "feature", y="value"), na.rm=TRUE)+
ggplot2::geom_boxplot(outlier.shape = NA, na.rm=TRUE) +
ggplot2::geom_jitter(ggplot2::aes(colour=type), na.rm=TRUE) +
ggplot2::xlab("feature") +
ggplot2::ylab("value") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as a composition of a box plot and a histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A composite plot.
#' (ggplot2::ggplot)
featurePlot = function(data_df = "tbl_df", feature = "character"){
p1 <- self$featureBoxPlotWithSubset(data_df, feature) +
ggplot2::theme(legend.position = c(0.9, 0.9),
legend.key = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
p2 <- self$featureBarPlot(data_df, feature)
limits <- ggplot2::layer_scales(p2)$x$range$range
p1 <- p1 +
ggplot2::scale_y_continuous(limits=limits)
p2 <- p2 +
ggplot2::scale_x_continuous(position = "top") +
ggplot2::coord_flip()
p <- gridExtra::grid.arrange(p1,p2, layout_matrix = rbind(c(1,1,2),c(1,1,2)),
top = textGrob(label = sprintf("Distribution of %s", feature)))
return(p)
}#function
)#public
)#class
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