R/pcaKMeans.R
In SMLMS/pguXAI:
#' @title pca.KMeans
#'
#' @description
#' Performs KMeans clustering on a given pca dataset.
#'
#' @details
#' Performs KMeans clustering on a given pca dataset.
#'
#' @format [R6::R6Class] object.
#'
#' @import R6
#' @import tidyverse
#' @import reshape2
#' @import GGally
#' @import cluster
#' @import factoextra
#' @import flexclust
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @examples
#' library(tidyverse)
#' library(pguXAI)
#' library(FactoMineR)
#' library(caret)
#'
#' main = function(){
#' # load data set and remove class labels
#' df_data <- iris %>%
#' dplyr::select(-Species)
#'
#' # define true class labels
#' classes_true <- iris$Species
#'
#' # define nuber of components for pca and number of clusters for kmeans
#' nComponents <- 2
#' nCluster <- 10
#'
#' # pre-scale the data for pca
#' PreProcessor <- caret::preProcess(x=df_data, method=c("center", "scale"), pcaComp = nComponents)
#' df_scaled <- predict(PreProcessor, df_data)
#'
#' # reduce dimensions of sclaed dataset using pca
#' rslt_pca <- df_scaled %>%
#' FactoMineR::PCA(ncp = nComponents, scale.unit = FALSE, graph = FALSE)
#' df_pred <- as.data.frame(predict(rslt_pca, df_scaled)$coord)
#'
#' # run kmeans analysis
#' km <- pguXAI::pca.KMeans$new(n=nCluster, seed = 42, verbose = TRUE)
#' km$train(obj = df_pred)
#'
#' km$cluster_plot(obj = df_pred)
#'
#' km$silhouette_plot(obj = df_pred) %>%
#' plot()
#'
#' print("Result of silhouette analysis:")
#' km$df_silhouette %>%
#' print()
#'
#' print("Average silhouette width:")
#' km$av_sil_width %>%
#' print()
#'
#' print("Centers of clusters:")
#' km$df_centers %>%
#' print()
#'
#' print("Probability of the class label assignment:")
#' km$predProb %>%
#' print()
#'
#' print("Majority vote of the class label assignment:")
#' km$predClass %>%
#' print()
#'
#' print("Within cluster sum of squares analysis:")
#' km$df_withinss %>%
#' print()
#'
#' km$tot_withinss %>%
#' print()
#'
#' fin <- "done"
#' fin
#' }
#'
#' main()
#'
#' @export
#'
pca.KMeans <- R6::R6Class("pca.KMeans",
####################
# instance variables
####################
private = list(
.seed = "integer",
.nCenters = "integer",
.level = "character",
.predClass = "factor",
.df_centers = "tbl_df",
.df_silhouette = "tbl_df",
.av_sil_width = "numeric",
.av_withinss = "tbl_df",
.tot_withinss = "numeric",
.verbose = "logical"
), # private
##################
# accessor methods
##################
active = list(
#' @field seed
#' Returns the instance variable seed
#' (integer)
seed = function(){
return(as.integer(private$.seed))
},
#' @field setSeed
#' Sets the instance variable seed
#' (integer)
setSeed = function(value = "integer"){
if (!(is.numeric(value))){
warningMessage <- sprintf("Warning: Seed needs to be of type numeric. setSeed was given a value of type %s. Seed will be set to 42.", typeof(value))
print(warningMessage)
private$.seed <- as.integer(42)
} else if(length(value) > 1){
warningMessage <- sprintf("Warning: Seed needs to be of length 1. setSeed was given a value of length %i. Seed will be set to 42.", length(value))
print(warningMessage)
private$.seed <- as.integer(42)
}
else{
private$.seed <- as.integer(value)
}
},
#' @field nCenters
#' Returns the instance variable nCenters
#' (integer)
nCenters = function(){
return(private$.nCenters)
},
#' @field setNCenters
#' Sets the instance variable nCenters
#' (integer)
setNCenters = function(value = "integer"){
if (!(is.numeric(value))){
warningMessage <- sprintf("Warning: nCenters needs to be of type numeric. nCenters was given a value of type %s. nCenters will be set to 2.", typeof(value))
print(warningMessage)
private$.nCenters <- as.integer(2)
} else if(length(value) > 1){
warningMessage <- sprintf("Warning: nCenters needs to be of length 1. nCenters was given a value of length %i. nCenters will be set to 2.", length(value))
print(warningMessage)
private$.nCenters <- as.integer(2)
}
else{
private$.nCenters <- as.integer(value)
}
level_idx <- seq(self$nCenters)
private$.level <- sapply(level_idx, function(x) sprintf("Class_%i", x))
},
#' @field level
#' Returns the instancs variable level
#' (character)
level = function(){
return(private$.level)
},
#' @field predClass
#' Returns the instance variable predClass
#' (factor)
predClass = function(){
return(private$.predClass)
},
#' @field df_centers
#' Returns the instance variable df_centers
#' (tbl_df)
df_centers = function(){
return(private$.df_centers)
},
#' @field df_silhouette
#' Returns the instance variable df_silhouette
#' (tbl_df)
df_silhouette = function(){
return(private$.df_silhouette)
},
#' @field av_sil_width
#' Returns the instance variable av_sil_width
#' (numeric)
av_sil_width = function(){
return(private$.av_sil_width)
},
#' @field av_withinss
#' Returns the instance variable av_withinss
#' (tbl_df)
av_withinss = function(){
return(private$.av_withinss)
},
#' @field tot_withinss
#' Returns the instance variable tot_withinss
#' (numeric)
tot_withinss = function(){
return(private$.tot_withinss)
},
#' @field verbose
#' Returns the instance variable verbose
#' (logical)
verbose = function(){
return(private$.verbose)
}
), #active
public =list(
###################
# memory management
###################
#' @description
#' Creates and returns a new pca.KMeans object.
#' @param n
#' Initial number of cluster
#' (integer)
#' @param seed
#' An initial seed.
#' Default is 42
#' (integer)
#' @param verbose
#' Makes the class chatty.
#' Default is FALSE.
#' (logical)
#' @return
#' A new R6 object of type pca.KMeans.
#' (pguXAI::pca.KMeans)
initialize = function(n=2, seed = 42, verbose = FALSE){
self$setNCenters <- n
self$setSeed <- seed
private$.verbose <- verbose
level_idx <- seq(self$nCenters)
private$.level <- sapply(level_idx, function(x) sprintf("Class_%i", x))
}, #initialize
#' @description
#' Clears the heap and
#' indicates that instance of pca.KMeans is removed from heap.
finalize = function() {
if(self$verbose){
print("Instance of pca.KMeans removed from heap")
}
}, #finalize
#############
# print class
#############
#' @description
#' Prints instance variables of a pca.KMeans object.
#' @return
#' string
print = function(){
rString <- sprintf("\npca.KMeans\n")
rString <- sprintf("%s\nseed: %i\n", rString, self$seed)
rString <- sprintf("%s\nnCenters: %i\n", rString, self$nCenters)
rString <- sprintf("%s\nverbose: %s\n", rString, self$verbose)
rString <- sprintf("%s\n\n", rString)
cat(rString)
invisible(self)
}, #function
#' @description
#' trains the model
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
train = function(obj = "tbl_df"){
set.seed(self$seed)
cluster_obj <- flexclust::kcca(obj, k=self$nCenters, family=kccaFamily("kmeans"))
cluster_rslt <- cluster_obj %>%
flexclust::clusters()
private$.predClass <- factor(self$level[cluster_rslt], levels = self$level)
self$cluster_statistics(obj)
self$silhouette_analysis(obj)
private$.tot_withinss <- cluster_obj %>%
flexclust::info("distsum")
private$.av_withinss <- cluster_obj %>%
flexclust::info("av_dist") %>%
as.data.frame()
}, #train
#' @description
#' Performs cluster analysis step..
#' Not to run by the user.
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
cluster_statistics = function(obj = "tbl_df"){
df_stat <- tibble::tibble(class = self$level)
df_data <- obj %>%
dplyr::mutate(class = self$predClass)
df_statistics <- NULL
for (className in self$level){
df_temp <- df_data %>%
dplyr::filter(class == className) %>%
dplyr::select(-c("class"))
df_statistics_temp <- do.call(cbind, lapply(df_temp, summary)) %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column("component") %>%
tibble::as_tibble() %>%
dplyr::mutate(class = className)
class_vector <- c()
comp_vector <- c()
low_bound_vector <- c()
high_bound_vector <- c()
if(nrow(df_temp) > 1){
for (compName in colnames(df_temp)){
test_rslt <- df_temp %>%
dplyr::select(compName) %>%
stats::t.test()
class_vector <- append(class_vector,className)
comp_vector <- append(comp_vector, compName)
low_bound_vector <- append(low_bound_vector, test_rslt$conf.int[1])
high_bound_vector <- append(high_bound_vector, test_rslt$conf.int[2])
}
} else {
for (compName in colnames(df_temp)){
class_vector <- append(class_vector,className)
comp_vector <- append(comp_vector, compName)
low_bound_vector <- append(low_bound_vector, NA)
high_bound_vector <- append(high_bound_vector, NA)
}
}
df_conf <- tibble::tibble(class = class_vector,
component = comp_vector,
lBound_95 = low_bound_vector,
hBound_95 = high_bound_vector)
df_statistics_temp <- df_statistics_temp %>%
merge(df_conf, by = c("class", "component"))
df_statistics <- df_statistics %>%
dplyr::bind_rows(df_statistics_temp)
}
private$.df_centers <- df_statistics
}, #cluster_statistics
#' @description
#' Performs a silouette analysis.
#' Not to run by the user.
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
silhouette_analysis = function(obj = "tbl_df"){
dist_mat <- stats::dist(obj, method = "euclidean")
sil <- self$predClass %>%
match(self$level) %>%
cluster::silhouette(dist_mat) %>%
abs()
sil_sum <- summary(sil)
class_names <- dimnames(sil_sum$clus.avg.widths) %>%
lapply( function(x) sprintf("Class_%s", x)) %>%
unlist()
private$.df_silhouette <- tibble::tibble(class = class_names) %>%
dplyr::mutate(size_abs = as.integer(sil_sum$clus.sizes)) %>%
dplyr::mutate(size_rel = size_abs / sum(size_abs)) %>%
dplyr::mutate(sil_width = sil_sum$clus.avg.widths)
private$.av_sil_width <- sil_sum$avg.width
},
################
# plot functions
################
#' @description
#' Plots Clustering Result in all pca dimensions
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
#' @return
#' (list)
cluster_plot = function(obj = "tbl_df"){
p <- obj %>%
dplyr::mutate(class = self$predClass) %>%
GGally::ggpairs(mapping=ggplot2::aes(colour = class), title = "Cluster structure (kMeans)") %>%
GGally::print_if_interactive()
return(p)
}, #cluster_plot
#' @description
#' Plots Silhouette analysis
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
#' @return
#' (list)
silhouette_plot = function(obj = "tbl_df"){
dist_mat <- stats::dist(obj, method = "euclidean")
sil <- self$predClass %>%
match(self$level) %>%
cluster::silhouette(dist_mat) %>%
abs()
factoextra::fviz_silhouette(sil) +
#ggplot2::scale_fill_discrete(name = "Cluster", labels = self$level) +
ggplot2::guides(col=FALSE) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.title = element_text(size=18),
axis.text.y=element_text(size=10),
axis.title.y=element_text(size=12),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
}
)#public
)#pca.KMeans
SMLMS/pguXAI documentation built on Aug. 15, 2020, 7:09 a.m.
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#' @title pca.KMeans
#'
#' @description
#' Performs KMeans clustering on a given pca dataset.
#'
#' @details
#' Performs KMeans clustering on a given pca dataset.
#'
#' @format [R6::R6Class] object.
#'
#' @import R6
#' @import tidyverse
#' @import reshape2
#' @import GGally
#' @import cluster
#' @import factoextra
#' @import flexclust
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @examples
#' library(tidyverse)
#' library(pguXAI)
#' library(FactoMineR)
#' library(caret)
#'
#' main = function(){
#' # load data set and remove class labels
#' df_data <- iris %>%
#' dplyr::select(-Species)
#'
#' # define true class labels
#' classes_true <- iris$Species
#'
#' # define nuber of components for pca and number of clusters for kmeans
#' nComponents <- 2
#' nCluster <- 10
#'
#' # pre-scale the data for pca
#' PreProcessor <- caret::preProcess(x=df_data, method=c("center", "scale"), pcaComp = nComponents)
#' df_scaled <- predict(PreProcessor, df_data)
#'
#' # reduce dimensions of sclaed dataset using pca
#' rslt_pca <- df_scaled %>%
#' FactoMineR::PCA(ncp = nComponents, scale.unit = FALSE, graph = FALSE)
#' df_pred <- as.data.frame(predict(rslt_pca, df_scaled)$coord)
#'
#' # run kmeans analysis
#' km <- pguXAI::pca.KMeans$new(n=nCluster, seed = 42, verbose = TRUE)
#' km$train(obj = df_pred)
#'
#' km$cluster_plot(obj = df_pred)
#'
#' km$silhouette_plot(obj = df_pred) %>%
#' plot()
#'
#' print("Result of silhouette analysis:")
#' km$df_silhouette %>%
#' print()
#'
#' print("Average silhouette width:")
#' km$av_sil_width %>%
#' print()
#'
#' print("Centers of clusters:")
#' km$df_centers %>%
#' print()
#'
#' print("Probability of the class label assignment:")
#' km$predProb %>%
#' print()
#'
#' print("Majority vote of the class label assignment:")
#' km$predClass %>%
#' print()
#'
#' print("Within cluster sum of squares analysis:")
#' km$df_withinss %>%
#' print()
#'
#' km$tot_withinss %>%
#' print()
#'
#' fin <- "done"
#' fin
#' }
#'
#' main()
#'
#' @export
#'
pca.KMeans <- R6::R6Class("pca.KMeans",
####################
# instance variables
####################
private = list(
.seed = "integer",
.nCenters = "integer",
.level = "character",
.predClass = "factor",
.df_centers = "tbl_df",
.df_silhouette = "tbl_df",
.av_sil_width = "numeric",
.av_withinss = "tbl_df",
.tot_withinss = "numeric",
.verbose = "logical"
), # private
##################
# accessor methods
##################
active = list(
#' @field seed
#' Returns the instance variable seed
#' (integer)
seed = function(){
return(as.integer(private$.seed))
},
#' @field setSeed
#' Sets the instance variable seed
#' (integer)
setSeed = function(value = "integer"){
if (!(is.numeric(value))){
warningMessage <- sprintf("Warning: Seed needs to be of type numeric. setSeed was given a value of type %s. Seed will be set to 42.", typeof(value))
print(warningMessage)
private$.seed <- as.integer(42)
} else if(length(value) > 1){
warningMessage <- sprintf("Warning: Seed needs to be of length 1. setSeed was given a value of length %i. Seed will be set to 42.", length(value))
print(warningMessage)
private$.seed <- as.integer(42)
}
else{
private$.seed <- as.integer(value)
}
},
#' @field nCenters
#' Returns the instance variable nCenters
#' (integer)
nCenters = function(){
return(private$.nCenters)
},
#' @field setNCenters
#' Sets the instance variable nCenters
#' (integer)
setNCenters = function(value = "integer"){
if (!(is.numeric(value))){
warningMessage <- sprintf("Warning: nCenters needs to be of type numeric. nCenters was given a value of type %s. nCenters will be set to 2.", typeof(value))
print(warningMessage)
private$.nCenters <- as.integer(2)
} else if(length(value) > 1){
warningMessage <- sprintf("Warning: nCenters needs to be of length 1. nCenters was given a value of length %i. nCenters will be set to 2.", length(value))
print(warningMessage)
private$.nCenters <- as.integer(2)
}
else{
private$.nCenters <- as.integer(value)
}
level_idx <- seq(self$nCenters)
private$.level <- sapply(level_idx, function(x) sprintf("Class_%i", x))
},
#' @field level
#' Returns the instancs variable level
#' (character)
level = function(){
return(private$.level)
},
#' @field predClass
#' Returns the instance variable predClass
#' (factor)
predClass = function(){
return(private$.predClass)
},
#' @field df_centers
#' Returns the instance variable df_centers
#' (tbl_df)
df_centers = function(){
return(private$.df_centers)
},
#' @field df_silhouette
#' Returns the instance variable df_silhouette
#' (tbl_df)
df_silhouette = function(){
return(private$.df_silhouette)
},
#' @field av_sil_width
#' Returns the instance variable av_sil_width
#' (numeric)
av_sil_width = function(){
return(private$.av_sil_width)
},
#' @field av_withinss
#' Returns the instance variable av_withinss
#' (tbl_df)
av_withinss = function(){
return(private$.av_withinss)
},
#' @field tot_withinss
#' Returns the instance variable tot_withinss
#' (numeric)
tot_withinss = function(){
return(private$.tot_withinss)
},
#' @field verbose
#' Returns the instance variable verbose
#' (logical)
verbose = function(){
return(private$.verbose)
}
), #active
public =list(
###################
# memory management
###################
#' @description
#' Creates and returns a new pca.KMeans object.
#' @param n
#' Initial number of cluster
#' (integer)
#' @param seed
#' An initial seed.
#' Default is 42
#' (integer)
#' @param verbose
#' Makes the class chatty.
#' Default is FALSE.
#' (logical)
#' @return
#' A new R6 object of type pca.KMeans.
#' (pguXAI::pca.KMeans)
initialize = function(n=2, seed = 42, verbose = FALSE){
self$setNCenters <- n
self$setSeed <- seed
private$.verbose <- verbose
level_idx <- seq(self$nCenters)
private$.level <- sapply(level_idx, function(x) sprintf("Class_%i", x))
}, #initialize
#' @description
#' Clears the heap and
#' indicates that instance of pca.KMeans is removed from heap.
finalize = function() {
if(self$verbose){
print("Instance of pca.KMeans removed from heap")
}
}, #finalize
#############
# print class
#############
#' @description
#' Prints instance variables of a pca.KMeans object.
#' @return
#' string
print = function(){
rString <- sprintf("\npca.KMeans\n")
rString <- sprintf("%s\nseed: %i\n", rString, self$seed)
rString <- sprintf("%s\nnCenters: %i\n", rString, self$nCenters)
rString <- sprintf("%s\nverbose: %s\n", rString, self$verbose)
rString <- sprintf("%s\n\n", rString)
cat(rString)
invisible(self)
}, #function
#' @description
#' trains the model
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
train = function(obj = "tbl_df"){
set.seed(self$seed)
cluster_obj <- flexclust::kcca(obj, k=self$nCenters, family=kccaFamily("kmeans"))
cluster_rslt <- cluster_obj %>%
flexclust::clusters()
private$.predClass <- factor(self$level[cluster_rslt], levels = self$level)
self$cluster_statistics(obj)
self$silhouette_analysis(obj)
private$.tot_withinss <- cluster_obj %>%
flexclust::info("distsum")
private$.av_withinss <- cluster_obj %>%
flexclust::info("av_dist") %>%
as.data.frame()
}, #train
#' @description
#' Performs cluster analysis step..
#' Not to run by the user.
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
cluster_statistics = function(obj = "tbl_df"){
df_stat <- tibble::tibble(class = self$level)
df_data <- obj %>%
dplyr::mutate(class = self$predClass)
df_statistics <- NULL
for (className in self$level){
df_temp <- df_data %>%
dplyr::filter(class == className) %>%
dplyr::select(-c("class"))
df_statistics_temp <- do.call(cbind, lapply(df_temp, summary)) %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column("component") %>%
tibble::as_tibble() %>%
dplyr::mutate(class = className)
class_vector <- c()
comp_vector <- c()
low_bound_vector <- c()
high_bound_vector <- c()
if(nrow(df_temp) > 1){
for (compName in colnames(df_temp)){
test_rslt <- df_temp %>%
dplyr::select(compName) %>%
stats::t.test()
class_vector <- append(class_vector,className)
comp_vector <- append(comp_vector, compName)
low_bound_vector <- append(low_bound_vector, test_rslt$conf.int[1])
high_bound_vector <- append(high_bound_vector, test_rslt$conf.int[2])
}
} else {
for (compName in colnames(df_temp)){
class_vector <- append(class_vector,className)
comp_vector <- append(comp_vector, compName)
low_bound_vector <- append(low_bound_vector, NA)
high_bound_vector <- append(high_bound_vector, NA)
}
}
df_conf <- tibble::tibble(class = class_vector,
component = comp_vector,
lBound_95 = low_bound_vector,
hBound_95 = high_bound_vector)
df_statistics_temp <- df_statistics_temp %>%
merge(df_conf, by = c("class", "component"))
df_statistics <- df_statistics %>%
dplyr::bind_rows(df_statistics_temp)
}
private$.df_centers <- df_statistics
}, #cluster_statistics
#' @description
#' Performs a silouette analysis.
#' Not to run by the user.
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
silhouette_analysis = function(obj = "tbl_df"){
dist_mat <- stats::dist(obj, method = "euclidean")
sil <- self$predClass %>%
match(self$level) %>%
cluster::silhouette(dist_mat) %>%
abs()
sil_sum <- summary(sil)
class_names <- dimnames(sil_sum$clus.avg.widths) %>%
lapply( function(x) sprintf("Class_%s", x)) %>%
unlist()
private$.df_silhouette <- tibble::tibble(class = class_names) %>%
dplyr::mutate(size_abs = as.integer(sil_sum$clus.sizes)) %>%
dplyr::mutate(size_rel = size_abs / sum(size_abs)) %>%
dplyr::mutate(sil_width = sil_sum$clus.avg.widths)
private$.av_sil_width <- sil_sum$avg.width
},
################
# plot functions
################
#' @description
#' Plots Clustering Result in all pca dimensions
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
#' @return
#' (list)
cluster_plot = function(obj = "tbl_df"){
p <- obj %>%
dplyr::mutate(class = self$predClass) %>%
GGally::ggpairs(mapping=ggplot2::aes(colour = class), title = "Cluster structure (kMeans)") %>%
GGally::print_if_interactive()
return(p)
}, #cluster_plot
#' @description
#' Plots Silhouette analysis
#' @param obj
#' The data to be analyzed.
#' Needs to be the result of a pca analysis.
#' (tibble::tibble)
#' @return
#' (list)
silhouette_plot = function(obj = "tbl_df"){
dist_mat <- stats::dist(obj, method = "euclidean")
sil <- self$predClass %>%
match(self$level) %>%
cluster::silhouette(dist_mat) %>%
abs()
factoextra::fviz_silhouette(sil) +
#ggplot2::scale_fill_discrete(name = "Cluster", labels = self$level) +
ggplot2::guides(col=FALSE) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.title = element_text(size=18),
axis.text.y=element_text(size=10),
axis.title.y=element_text(size=12),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
}
)#public
)#pca.KMeans
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