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# RclusTool: clustering of items in datasets
#
# Copyright 2013 Guillaume Wacquet, Pierre-Alexandre Hebert, Emilie Poisson-Caillault
#
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#' function to create the 'supTab' for supervised classification
#' @title Supervised tab
#' @description Generate the supervised classification tab of the \code{\link{RclusToolGUI}}, in which the user can choose and configure the supervised method to apply.
#' @param RclusTool.env : environment in which data and intermediate results are stored.
#' @return None
#' @importFrom utils alarm
#' @import tcltk tcltk2
#' @keywords internal
#'
buildsupTab <- function(RclusTool.env) {
sup.env <- RclusTool.env$gui$tabs.env$sup
fontFrame <- tkfont.create(family = RclusTool.env$param$visu$font, weight = "bold", size = RclusTool.env$param$visu$size)
fontTitleFrame <- tkfont.create(family = RclusTool.env$param$visu$titlefont, weight = "bold", size = RclusTool.env$param$visu$titlesize)
padx = "6m"
pady = "1m"
sup.env$tcl.export.clustering <-tclVar("1")
sup.env$tcl.classif.imgsig <- tclVar("0")
sup.env$tcl.export.calcul <- tclVar("0")
sup.env$tcl.extract.protos <- tclVar("0")
sup.env$tcl.method.select <- tclVar("RF")
win1.nb <- RclusTool.env$gui$win1$env$nb
win2.nb <- RclusTool.env$gui$win2$env$nb
# Frames
method.title <- c("RF"="Random Forest (RF)",
"K-NN"="K-Nearest-Neighbor (K-NN)",
"MLP"="MultiLayer Perceptron (MLP)",
"SVM"="Support Vector Machine (SVM)")
method.description <- c("RF"="Method: Construction of a multitude of decision trees\nTechnique: majority vote on predictions from all\n\tclassification trees\nResults: partition of N items into K clusters\nAdvantage: reduction in overfitting\n\n",
"K-NN"="Method: non-parametric\nTechnique: item is classified by a majority vote\n\tof its neighbors, with the object being assigned\n\tto the class most common among its k nearest\n\tneighbors mean (randomly initialized)\nResults: partition of N items into K clusters\nAdvantage: among the simplest of supervised methods",
"MLP"="Method: feedforward artificial neural network\nTechnique: at least 3 layers of nodes for which each\n\tnode is a neuron that uses a nonlinear\n\tactivation function\nResults: partition of N items into K clusters\nAdvantage: processing of non-linearly separable data\n",
"SVM"="Method: Construction of a multitude of decision trees\nTechnique: majority vote on predictions from all\n\tclassification trees\nResults: partition of N items into K clusters\nAdvantage: reduction in overfitting\n\n")
MethodFrametext <- makeTitle("CLASSIFICATION")
MethodFrame <- tkwidget(win1.nb$env$sup, "labelframe", text = MethodFrametext, font = fontTitleFrame, padx = padx, pady = pady, relief = "flat")
AdviceFrame <- tkwidget(MethodFrame, "labelframe", text = "method name", font = fontFrame, padx = padx, pady = pady, relief = "groove")
tkgrid(AdviceFrame, columnspan = 3, rowspan = 5, column = 10, row = 2, padx = padx)
AdviceFrameText <- tk2label(AdviceFrame, text = "method description", width=50)
tkgrid(AdviceFrameText, sticky = "w")
MethodFrameExpert <- tkwidget(MethodFrame, "labelframe", font = fontFrame, padx = padx, relief = "flat")
MethodFrameStandard <- tkwidget(MethodFrame, "labelframe", font = fontFrame, padx = padx, relief = "flat")
sup.env$onMethodDescription <- function()
{
tkconfigure(AdviceFrame, text=method.title[tclvalue(sup.env$tcl.method.select)], font=fontFrame)
tkconfigure(AdviceFrameText, text=method.description[tclvalue(sup.env$tcl.method.select)], font=fontFrame)
}
# method selection buttons
rb_methods <- sapply(names(method.title), function(name) {
tkr <- tkradiobutton(MethodFrameExpert, variable=sup.env$tcl.method.select, value=name, text=method.title[name])
tkbind(tkr, "<ButtonRelease-1>", sup.env$onMethodDescription)
tkr
}, simplify=FALSE)
# prototypes importation
OnLoadDir <- function() {
protos.directory.default <- RclusTool.env$gui$protos.dir
if (is.null(protos.directory.default) || !dir.exists(protos.directory.default))
protos.directory.default <- getwd()
sup.env$protos.directory <- tk_choose.dir(default = protos.directory.default, caption = "Select training set base dir.")
sup.env$refreshTrainingSetName()
if (is.na(sup.env$protos.directory))
return()
RclusTool.env$gui$protos.dir <- sup.env$protos.directory
if (nchar(RclusTool.env$gui$protos.dir)) {
sup.env$prototypes <- readTrainSet(traindir = sup.env$protos.directory, operations=RclusTool.env$data.sample$config$operations, RclusTool.env=RclusTool.env)
if (any(grepl(RclusTool.env$data.sample$name, sup.env$prototypes$Id)))
sup.env$prototypes <- sup.env$prototypes[-grep(RclusTool.env$data.sample$name, sup.env$prototypes$Id), ]
sup.env$id.clean.proto <- 1:NROW(sup.env$prototypes)
}
}
## Export summary
summaryConfig <- function() {
summarytt <- tktoplevel()
tktitle(summarytt) <- "Summaries"
# Summaries frame
summaryFrame <- tkwidget(summarytt, "labelframe", font = fontFrame, text = "SUMMARIES", padx = padx, pady = pady, relief = "flat")
summaries <- c("Min", "Max", "Sum", "Average", "SD")
config.env <- new.env()
config.env$summariesList <- summaries %in% names(RclusTool.env$param$analysis$summary.functions)
names(config.env$summariesList) <- summaries
functionsList <- c("min", "max", "sum", "mean", "sd")
names(functionsList) <- summaries
# Export min(parameters) per cluster
OnMinCheck <- function() {
config.env$summariesList["Min"] <- tclvalue(tcl.min.check)=="1"
}
# Export max(parameters) per cluster
OnMaxCheck <- function() {
config.env$summariesList["Max"] <- tclvalue(tcl.max.check)=="1"
}
# Export sum(parameters) per cluster
OnSumCheck <- function() {
config.env$summariesList["Sum"] <- tclvalue(tcl.sum.check)=="1"
}
# Export mean(parameters) per cluster
OnMeanCheck <- function() {
config.env$summariesList["Average"] <- tclvalue(tcl.mean.check)=="1"
}
# Export std(parameters) per cluster
OnStdCheck <- function() {
config.env$summariesList["SD"] <- tclvalue(tcl.std.check)=="1"
}
tcl.min.check <- tclVar(as.character(as.numeric(config.env$summariesList["Min"])))
tk.min.check <- tkcheckbutton(summaryFrame, text="", variable=tcl.min.check,
command=OnMinCheck)
tcl.max.check <- tclVar(as.character(as.numeric(config.env$summariesList["Max"])))
tk.max.check <- tkcheckbutton(summaryFrame, text="", variable=tcl.max.check,
command=OnMaxCheck)
tcl.sum.check <- tclVar(as.character(as.numeric(config.env$summariesList["Sum"])))
tk.sum.check <- tkcheckbutton(summaryFrame, text="", variable=tcl.sum.check,
command=OnSumCheck)
tcl.mean.check <- tclVar(as.character(as.numeric(config.env$summariesList["Average"])))
tk.mean.check <- tkcheckbutton(summaryFrame, text="", variable=tcl.mean.check,
command=OnMeanCheck)
tcl.std.check <- tclVar(as.character(as.numeric(config.env$summariesList["SD"])))
tk.std.check <- tkcheckbutton(summaryFrame, text="", variable=tcl.std.check,
command=OnStdCheck)
tkgrid(tk2label(summarytt, text = " "), row=1, sticky = "w")
tkgrid(summaryFrame, row = 2, columnspan = 2, sticky = "w")
tkgrid(tk2label(summaryFrame, text="Minimum"), row=2, column=0, sticky="w")
tkgrid(tk.min.check, row=2, column=1, sticky="e")
tkgrid(tk2label(summaryFrame, text="Maximum"), row=3, column=0, sticky="w")
tkgrid(tk.max.check, row=3, column=1, sticky="e")
tkgrid(tk2label(summaryFrame, text="Sum"), row=4, column=0, sticky="w")
tkgrid(tk.sum.check, row=4, column=1, sticky="e")
tkgrid(tk2label(summaryFrame, text="Average"), row=5, column=0, sticky="w")
tkgrid(tk.mean.check, row=5, column=1, sticky="e")
tkgrid(tk2label(summaryFrame, text="Standard deviation"), row=6, column=0, sticky="w")
tkgrid(tk.std.check, row=6, column=1, sticky="e")
# Apply operation
onClose <- function() {
RclusTool.env$param$analysis$summary.functions <- functionsList[config.env$summariesList]
tkdestroy(summarytt)
}
butClose <- tk2button(summarytt, text = "Close", width = -6, command = onClose)
tkgrid(butClose, row = 7, columnspan = 2)
tkwait.window(summarytt)
}
# Compute the selected method
OnCompute <- function() {
if (RclusTool.env$gui$protos.dir==""){
utils::alarm()
tkmessageBox(message="Please first select a training set.")
OnLoadDir()
return()
}
method.select <- tclvalue(sup.env$tcl.method.select)
res <- computeSupervised(RclusTool.env$data.sample,
prototypes = sup.env$prototypes[sup.env$id.clean.proto, , drop=FALSE],
method.name = method.select, model=NULL, RclusTool.env=RclusTool.env)
if (is.null(res))
return()
messageConsole(paste("----- Supervised Classification -----\n",
"Training set:", basename(RclusTool.env$gui$protos.dir), "\n",
method.select, " computing\n\n", sep = ""), RclusTool.env=RclusTool.env)
RclusTool.env$data.sample$clustering[[method.select]] <- list(label=res$label, summary=res$summary, K=sum(table(res$label)>0))
prototypes <- res$prototypes # non global? useful?
sup.env$label <- RclusTool.env$data.sample$clustering[[method.select]]$label
sup.env$cluster.summary <- RclusTool.env$data.sample$clustering[[method.select]]$summary
# Plot abundances from different methods
tk2delete.notetab(win2.nb)
abdPlotTabsGUI(RclusTool.env)
#Keep only levels who are in clustering
sup.env$label=droplevels(sup.env$label)
#visualize prototypes obtained from the sample used
new.protos <- visualizeSampleClustering(RclusTool.env$data.sample, label=sup.env$label, clustering.name=method.select,
selection.mode = "prototypes", cluster.summary=sup.env$cluster.summary,
profile.mode="whole sample", wait.close=TRUE,
RclusTool.env=RclusTool.env, fontsize=RclusTool.env$param$visu$size)
new.protos$label <- new.protos$label[[method.select]]$label
sup.env$cluster.summary <- clusterSummary(RclusTool.env$data.sample, new.protos$label,
summary.functions=RclusTool.env$param$analysis$summary.functions)
# Save clustering and summary (csv files)
if (tclvalue(sup.env$tcl.export.clustering)=="1") {
fileClust.csv <- paste("clustering ", RclusTool.env$gui$user.name, " ",
method.select, ".csv", sep="")
saveClustering(fileClust.csv, new.protos$label, RclusTool.env$data.sample$files$results$clustering)
fileSum.csv <- paste("results ", RclusTool.env$gui$user.name, " ",
method.select, ".csv", sep="")
saveSummary(fileSum.csv, sup.env$cluster.summary, RclusTool.env$data.sample$files$results$clustering)
}
# Classify images and signals (if available)
if (tclvalue(sup.env$tcl.classif.imgsig)=="1") {
imgClassif(data.sample = RclusTool.env$data.sample,
imgdir = RclusTool.env$data.sample$files$images,
method = method.select, user.name=RclusTool.env$gui$user.name)
sigClassif(data.sample = RclusTool.env$data.sample,
method = method.select, user.name=RclusTool.env$gui$user.name)
}
# Automatically extract protos
if (tclvalue(sup.env$tcl.extract.protos)=="1") {
extractProtos(data.sample = RclusTool.env$data.sample, method = method.select, K.max=RclusTool.env$param$classif$unsup$K.max, kmeans.variance.min=RclusTool.env$param$classif$unsup$kmeans.variance.min, user.name=RclusTool.env$gui$user.name)
}
# Give new names to clusters and summaries
RclusTool.env$data.sample$clustering[[method.select]]$label <- new.protos$label
sup.env$cluster.summary <- RclusTool.env$data.sample$clustering[[method.select]]$summary
# Update labels with renamed clusters
RclusTool.env$data.sample <- updateClustersNames(RclusTool.env$data.sample, new.protos$prototypes)
# Update clusters names in plots (if necessary)
tk2delete.notetab(win2.nb)
abdPlotTabsGUI(RclusTool.env)
# Save prototypes (csv + image files in 'prototypes' directory)
if (length(new.protos$prototypes[[method.select]]>0)){
saveManualProtos(RclusTool.env$data.sample, new.protos$prototypes)
}
}
ProtoFrametext <- makeTitle("TRAINING SET")
ProtoFrame <- tkwidget(win1.nb$env$sup, "labelframe", text = ProtoFrametext, font = fontTitleFrame, padx = padx, pady = pady, relief = "flat")
tk.folder.but <- tk2button(ProtoFrame,text="Training set", image = "folder", compound = "left", width = 20, command=OnLoadDir)
TrainingSetName <- tktext(ProtoFrame, bg="white", font="courier", width=7*RclusTool.env$param$visu$size, height=2, font = fontFrame, state="disabled")
sup.env$refreshTrainingSetName <- function()
{
tkconfigure(TrainingSetName, state="normal")
tkdelete(TrainingSetName, "1.0", "end")
tkinsert(TrainingSetName,"end", sup.env$protos.directory)
tkconfigure(TrainingSetName, state="disabled")
}
# Positioning
tkEmptyLine(win1.nb$env$sup, row=1)
tkgrid(ProtoFrame, columnspan = 4, row = 2, sticky = "we", pady = pady)
tkgrid(tk2label(ProtoFrame, text=" "), row = 1,column = 0)
tkgrid(tk2label(ProtoFrame, text="Selection of required files and folder\n Format : separator = ',' decimal= '.'"), row = 2, column = 0)
tkgrid(tk.folder.but, row = 3, column = 0)
tkgrid(TrainingSetName, row = 3, column = 1)
# Output frames
OutputsFrametext <- makeTitle("OUTPUTS SELECTION")
OutputsFrame <- tkwidget(win1.nb$env$sup, "labelframe", text = OutputsFrametext, font = fontTitleFrame, padx = padx, pady = pady, relief = "flat")
tk.export.clustering <- tkcheckbutton(OutputsFrame, text="", variable=sup.env$tcl.export.clustering)
tk.classif.imgsig <- tkcheckbutton(OutputsFrame, text="", variable=sup.env$tcl.classif.imgsig)
tk.extract.protos <- tkcheckbutton(OutputsFrame, text="", variable=sup.env$tcl.extract.protos)
butSummary <- tk2button(OutputsFrame, text = "Summary settings", width = 20, command = summaryConfig)
tk.compute.but <- tkbutton(win1.nb$env$sup, text="COMPUTE", width = 10, command=OnCompute)
# expert method frame layout
#positioning radiobutton methods
sapply(1:length(rb_methods), function(i) tkgrid(rb_methods[[i]], row=i+1, column=0, padx=padx, sticky="w"))
# layout OutputsFrame
tkgrid(tk2label(OutputsFrame, text="Export classification results"), row=9, column=0, sticky="w")
tkgrid(tk.export.clustering, row=9, column=1, sticky="e")
tkgrid(tk2label(OutputsFrame, text="Classify images/signals (if available)"), row=11, column=0, sticky="w")
tkgrid(tk.classif.imgsig, row=11, column=1, sticky="e")
tkgrid(tk2label(OutputsFrame, text="Extract prototypes automatically"), row=12, column=0, sticky="w")
tkgrid(tk.extract.protos, row=12, column=1, sticky="e")
tkgrid(tk2label(OutputsFrame, text=" "))
tkgrid(butSummary, column = 0)
# Standard method frame layout
tkgrid(tk2label(MethodFrameStandard, text="Standard parameters:\n - Random Forest\n - Number of trees: 500"), row = 2, column = 0)
tkgrid(tk2label(MethodFrameStandard, text=" "), row = 6, column = 0)
if (RclusTool.env$gui$user.type=="expert")
{
# method selection frame
tkgrid(MethodFrameExpert, row=2, column=0)
# secondary frames
tkgrid(OutputsFrame, row = 7, columnspan = 4, sticky = "we", pady = pady)
} else {
tkgrid(MethodFrameStandard, row=2, column=0)
}
# Reset Sup Tab
onReset <- function() {
initSupTab(RclusTool.env = RclusTool.env, reset=TRUE)
}
butReset <- tk2button(win1.nb$env$sup, text = "Reset", image = "reset", compound = "left", width = -6, command = onReset)
#positioning
tkEmptyLine(win1.nb$env$sup, row=3)
tkgrid(MethodFrame, columnspan = 4, row = 4, column = 0, sticky = "we", pady = pady)
tkEmptyLine(win1.nb$env$sup, row=5)
tkgrid(tk.compute.but, row=9, column = 0)
tkgrid(butReset, row = 9, column = 2)
invisible(list(label=sup.env$label, cluster.summary=sup.env$cluster.summary)) #value(s)
}
#' function to initialize (and to create) the 'supTab' for supervised classification
#' @title supervised tab
#' @description This function generates the supervised classification tab of the \code{\link{RclusToolGUI}}, in which the user can choose and configure the classification method to apply.
#' @param RclusTool.env : environment in which data and intermediate results are stored.
#' @param reset : if TRUE the whole tab is reset, with default options
#' @return None
#' @import tcltk tcltk2
#' @keywords internal
#'
initSupTab <- function(RclusTool.env, reset=FALSE)
{
if (is.null(RclusTool.env$gui$tabs.env$sup) || !length(RclusTool.env$gui$tabs.env$sup))
{
RclusTool.env$gui$tabs.env$sup <- new.env()
buildsupTab(RclusTool.env)
reset <- TRUE
}
sup.env <- RclusTool.env$gui$tabs.env$sup
if (reset)
{
tclvalue(sup.env$tcl.export.clustering) <- "1"
tclvalue(sup.env$tcl.classif.imgsig) <- "0"
tclvalue(sup.env$tcl.extract.protos) <- "0"
tclvalue(sup.env$tcl.method.select) <- "RF"
sup.env$onMethodDescription()
sup.env$prototypes <- NULL
sup.env$id.clean.proto <- NULL
sup.env$export.clustering <- TRUE
sup.env$export.calcul <- FALSE
sup.env$classif.imgsig <- FALSE
sup.env$extract.protos <- FALSE
sup.env$selectedvar <- NULL
sup.env$protoclean <- NULL
sup.env$datenames <- NULL
sup.env$protodatelist <- NULL
sup.env$classnames <- NULL
sup.env$protoclasslist <- NULL
sup.env$tcl.lowerthreshold <- NULL
sup.env$tk.lowerthreshold <- NULL
sup.env$tcl.upperthreshold <- NULL
sup.env$tk.upperthreshold <- NULL
sup.env$label <- NULL
sup.env$cluster.summary <- NULL
sup.env$protos.directory <- ""
sup.env$refreshTrainingSetName()
}
}
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