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server.R
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source("ui.R")
source("models.R")
suppressPackageStartupMessages(require(shiny))
suppressPackageStartupMessages(require(DBI))
suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(openssl))
#sudo apt-get install libssl-dev
suppressPackageStartupMessages(require(shinydashboard))
suppressPackageStartupMessages(require(shinyWidgets))
suppressPackageStartupMessages(require(DT))
suppressPackageStartupMessages(require(shinyjs))
suppressPackageStartupMessages(require(sodium))
#sudo apt-get install libsodium-dev
suppressPackageStartupMessages(require(digest))
# sudo apt-get install libxml2-dev
# sudo apt-get install libcurl4-openssl-dev libxml2-dev
# sudo apt-get install libcurl4-openssl-dev
# sudo apt-get install libcurl4-openssl-dev libxml2-dev
suppressPackageStartupMessages(require(tidyverse))
suppressPackageStartupMessages(require(InformationValue))
suppressPackageStartupMessages(require(ggplot2))
suppressPackageStartupMessages(require(plotROC))
# sudo apt-get install r-base-dev default-jdk
# sudo R CMD javareconf
suppressPackageStartupMessages(require(xlsx))
suppressPackageStartupMessages(require(VIM))
suppressPackageStartupMessages(require(DMwR))
suppressPackageStartupMessages(require(mice))
suppressPackageStartupMessages(require(Hmisc))
suppressPackageStartupMessages(require(checkmate))
suppressPackageStartupMessages(require(RSQLite))
DB_NAME <- "data.sqlite"
TBL_USER_DATA <- "users"
DB_test_connect <- function(){
db <- dbConnect(RSQLite::SQLite(), DB_NAME)
print("#######################")
print("Connected to Database sqlite")
# If a user data table doesn't already exist, create one
if(!(TBL_USER_DATA %in% dbListTables(db))){
print("- Warning: No 'users' table found. Creating table...")
df <- data.frame(ID = as.numeric(character()),
USER = character(),
HASH = character(),
stringsAsFactors = FALSE)
dbWriteTable(db, TBL_USER_DATA, df)
}
print("- Table exists.")
print("#######################")
dbDisconnect(db)
}
DB_upload_csv <- function(filename, tblname){
db <- dbConnect(RSQLite::SQLite(), DB_NAME)
df <- read.csv(file = filename, header = T, row.names = F, stringsAsFactors = F)
dbWriteTable(db, tblname, df)
dbDisconnect(db)
}
DB_get_user <- function(user){
db <- dbConnect(RSQLite::SQLite(), DB_NAME)
users_data <- dbReadTable(db, TBL_USER_DATA)
hashusers_data <- filter(users_data, USER == user)
dbDisconnect(db)
return(hashusers_data)
}
DB_add_user <- function(usr, hsh){
db <- dbConnect(RSQLite::SQLite(), DB_NAME)
df <- dbReadTable(db, TBL_USER_DATA)
q <- paste("INSERT INTO", TBL_USER_DATA, "(ID, USER, HASH) VALUEs (", paste("", nrow(df), ",", usr, ",", hsh, "", sep="'"), ")")
dbSendQuery(db, q)
suppressWarnings({dbDisconnect(db)})
}
# Initialisation de la base de donnnées ------
DB_test_connect()
shinyServer(function(input, output, session) {
loggedIn <- reactiveVal(value = FALSE)
user <- reactiveVal(value = NULL)
login <- eventReactive(input$login, {
user_data <- DB_get_user(input$username)
if(nrow(user_data) > 0){
if(md5(input$password) == user_data[1, "HASH"]){
user(input$username)
loggedIn(TRUE)
return(TRUE)
}
}
return(FALSE)
})
register_user <- eventReactive(input$register_user, {
users_data <- DB_get_user(input$new_user)
if (nchar(input$new_user)<3)
return(span("username at least 3 characters please give a nother", style = "color:red"))
else if(nchar(input$new_pw)<3)
return(span("password at least 3 characters\nplease give a nother", style = "color:red"))
else if(nrow(users_data) > 0 )
return(span("User already exists", style = "color:red"))
new_hash <- md5(input$new_pw)
new_user <- input$new_user
DB_add_user(new_user, new_hash)
print("- New user added to database")
return(span("New user registered", style = "color:green"))
})
output$register_status <- renderUI({
if(input$register_user == 0){
return(NULL)
} else {
register_user()
}
})
output$login_status <- renderUI({
if(input$login == 0){
return(NULL)
} else {
if(!login()){
return(span("The Username or Password is Incorrect", style = "color:red"))
}
}
})
observeEvent(input$create_login, {
showModal(
modalDialog(title = "Create New Login", size = "m",
textInput(inputId = "new_user", label = tagList(icon("user"),"Username")),
passwordInput(inputId = "new_pw", label = tagList(icon("unlock-alt"),"Password")),
actionButton(inputId = "register_user", label = "Create",style = "color: white; background-color:#3c8dbc;
cursor: pointer;"),
uiOutput("register_status")
)
)
register_user()
})
observeEvent(input$logout, {
user(NULL)
loggedIn(FALSE)
print("- User: logged out")
})
observe({
skin = "blue"
dsnames <- reactiveValues()
data_set <- reactive({
req(input$file1)
inFile <- input$file1
baseName = basename(inFile$datapath)
baseNameSep = tail(strsplit(gsub('[.]', ' ', baseName),split=" ")[[1]],1)
if(baseNameSep == 'csv') data_set<-read.csv(inFile$datapath, header=input$header,sep=input$sep, quote=input$quote,dec = input$dec)
else if(baseNameSep == 'xlsx') data_set = read.xlsx(inFile$datapath,sheetIndex = 1,header = input$header)
else
return(showNotification(
h4("The file type cannot be read! Only CSV or SLSX files are accepted ",style = "color:red"),
duration = 3
))
})
if(loggedIn()){
nFolds = reactiveValues()
nFolds = 5
TyppeCross = reactiveValues()
TyppeCross = "cv"
mycols = c("red","blue")
colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
NewCat <- reactiveValues()
NewCat$df <- list()
Mcorr <- reactiveValues()
output$App_Panel <- renderUI({
fluidPage(
fluidRow(
strong(paste("username is", user(), "|")), actionLink(inputId = "logout", "Logout",icon = icon("fa fa-sign-out")), align = "right",
hr()
)
)
observe({
asDouble <- function(x) as.double(as.character(x))
factorsNumeric <- function(d) modifyList(d, lapply(d[, sapply(d, is.factor)],asDouble))
req(input$file1)
dsnames <- names(data_set())
cb_options <- list()
cb_options[dsnames] <- dsnames
if(is.null(dsnames))
return()
updateSelectInput(session, "inSelect",
choices = dsnames)
updatePickerInput(session = session, inputId = "choose_var_delete",
choices = dsnames)
updatePickerInput(
session = session, inputId = "choose_var_factor",
choices = dsnames
)
})
output$resSelect <- renderPrint({
input$createcategories
})
myValues <- reactiveValues()
myValuesdListVect = reactiveValues()
choose_label_var = reactiveValues()
choose_var_delete = reactiveValues()
choose_var_factor = reactiveValues()
y = reactiveValues()
X = reactiveValues()
df = reactiveValues()
selectedVra = reactiveValues()
selectedVra$var = as.character(c())
data_setTrNA = reactiveValues()
data_setTrNA$df = NULL
output$nameCatText <- renderText({sub(" ", "_",input$nameCat)})
########################################################
# Traitement des données manquantes #
########################################################
observe({
myValues$dList <- input$createcategories
myValuesdListVect$dList = dput(myValues$dList)
choose_label_var<- input$inSelect
choose_var_delete <- input$choose_var_delete
choose_var_factor <- input$choose_var_factor
dataset = reactiveValues()
dataset$df = data_set()
testsize <- reactiveValues()
preProc = reactiveValues()
val<-reactiveValues()
val$txt<-""
dataNoNa = reactiveValues()
dataNoNa$df = data_set()
choiceTraitNa = reactiveValues()
choiceTraitNa$choice = NULL
preProc = input$preprocessingUI
choose_label_var = input$inSelect
choose_var_delete = input$choose_var_delete
nFolds = input$foldsUI
TyppeCross = input$crossFoldTypeUI
observe({
req(input$goButton)
df = data_set()
df[choose_var_factor] <- lapply(df[choose_var_factor], factor)
df[colnames(df)[!colnames(df) %in% choose_var_factor]] <- lapply(df[!colnames(df) %in% choose_var_factor], as.double)
if(input$inSelect!='---')
if(is.factor(df[,input$inSelect]))
{
updateRadioButtons(session, "inCheckPositiveLabel",
choices = levels(df[,input$inSelect]),
selected = levels(df[,input$inSelect])[1])
}
updateSelectInput(session, "varExpl",
choices = names(dplyr::select_if(df[ , !Mcorr], is.numeric))
[!(names(dplyr::select_if(df[ , !Mcorr], is.numeric)) %in% c(input$inSelect))])
output$plotRelation <- renderPlot({
if(!is.null(input$inSelect) && !is.null(input$varExpl)){
label = as.factor(df[,input$inSelect])
ggplot(df, aes(df[,input$varExpl])) +
stat_ecdf(aes(color = label))+
scale_color_brewer(palette="Dark2")+
ggtitle("Empirical cumulative distribution") +
labs(fill = "Class")+
xlab(input$varExpl) +
ylab(input$inSelect)
}
})
})
Mcorr = names(dataset$df) %in% sub(" ", ",",choose_var_delete)
print(Mcorr)
if (input$PreData == 'delete') {
dataNoNa$df <- dataset$df[ , !Mcorr] %>%
filter(complete.cases(.))
output$number_NA <- renderText({
anyNA(dataNoNa$df)
})
}
M <- stats::cor(dplyr::select_if(dataNoNa$df, is.numeric))
output$plotcorr <- renderPlot({
ggcorrplot(M,lab = TRUE,hc.order = TRUE)
})
output$summary <- renderPrint({
summary(dataNoNa$df)
})
output$rawdata <- renderTable({
if(input$nrows<nrow(dataset$df[ , !Mcorr])+1 && input$ncols<ncol(dataset$df[ , !Mcorr])+1)
dataset$df[ , !Mcorr][1:input$nrows,1:input$ncols]
else
dataset$df[ , !Mcorr]
},
options = list(pageLength = 20,searching = FALSE))
nameCat <- eventReactive(input$create_categorie, {
return(as.character(sub(" ", "_",input$nameCat)))
})
varList <- eventReactive(input$create_categorie, {
return(input$createcategories)
})
})
observe({
observeEvent(input$run, {
req(data_set())
data_setTrNA$df = data_set()
Mcorr2 = names(data_setTrNA$df) %in% sub(" ", ",",input$choose_var_delete)
data_setTrNA$df[input$choose_var_factor] <- lapply(data_setTrNA$df[input$choose_var_factor], factor)
data_setTrNA$df = data_setTrNA$df[ , !Mcorr2]
switch(input$PreData,
view={
print('view')
#############
# n_iter <- 50
#
# pb <- tkProgressBar(title = "Tk progress bar",
# label = "Percentage completed",
# min = 0,
# max = n_iter,
# initial = 0,
# width = 300)
#
# for(i in 1:n_iter) {
#
# Sys.sleep(0.1)
#
#
#
# pctg <- paste(round(i/n_iter *100, 0), "% completed")
# setTkProgressBar(pb, i, label = pctg)
# }
#
# close(pb)
##########
output$plotVarManq <- renderPlot({
matrixplot(data_set())
})
output$number_NA <- renderText({
paste("\nNumber NA are there in dataset",length(which(is.na(data_set()))))
paste("\nNumber rows contain NA",length(which(!complete.cases(data_set()))))
paste("\nPercentage of variables is NA",summary(aggr(data_set())))
dim(data_setTrNA$df)
})
},
delete={
print('delete')
data_setTrNA$df <- data_setTrNA$df[complete.cases(data_setTrNA$df), ]
output$plotVarManq <- renderPlot({
matrixplot(data_setTrNA$df)
})
output$number_NA <- renderText({
anyNA(data_setTrNA$df)
dim(data_setTrNA$df)
})
},
mean={
print('mean')
for(i in colnames(data_setTrNA$df)){
data_setTrNA$df[,i] = impute(data_setTrNA$df[,i], mean)
}
output$plotVarManq <- renderPlot({
matrixplot(data_setTrNA$df)
})
output$number_NA <- renderText({
anyNA(data_setTrNA$df)
dim(data_setTrNA$df)
})
},
median={
print('median')
for(i in colnames(data_setTrNA$df)){
data_setTrNA$df[,i] = impute(data_setTrNA$df[,i], median)
}
output$plotVarManq <- renderPlot({
matrixplot(data_setTrNA$df)
})
output$number_NA <- renderText({
anyNA(data_setTrNA$df)
dim(data_setTrNA$df)
})
},
kNN={
print('kNN')
data_setTrNA$df[colnames(dplyr::select_if(data_setTrNA$df, is.numeric))] <- knnImputation(dplyr::select_if(data_setTrNA$df, is.numeric))
output$plotVarManq <- renderPlot({
matrixplot(data_setTrNA$df)
})
output$number_NA <- renderText({
anyNA(data_setTrNA$df)
dim(data_setTrNA$df)
})
},
multiImpu={
print('multiImpu')
miceMod <- mice(dplyr::select_if(data_setTrNA$df, is.numeric), method="rf")
data_setTrNA$df <- complete(miceMod)
output$number_NA <- renderText({
anyNA(data_setTrNA$df)
})
output$plotVarManq <- renderPlot({
matrixplot(data_setTrNA$df)
dim(data_setTrNA$df)
})
},
{
print('default')
}
)
})
})
observe({output$createcategories = renderUI(
if(input$inSelect != '---')
multiInput(width = 1000,
inputId = "createcategories",
label = "Select all variables ",
choices = names(isolate(data_setTrNA$df[,!(names(data_setTrNA$df) %in% c(input$inSelect,input$choose_var_delete))])),
options = list(
enable_search = FALSE,
non_selected_header = "Choose between:",
selected_header = "You have selected:"
)
)
else multiInput(width = 1000,
inputId = "createcategories",
label = "Select all variables ",
choices = names(data_setTrNA$df),
options = list(
enable_search = FALSE,
non_selected_header = "Choose between:",
selected_header = "You have selected:"
)
)
)})
nameCat <- eventReactive(input$create_categorie, {
return(as.character(sub(" ", "_",input$nameCat)))
})
varList <- eventReactive(input$create_categorie, {
return(input$createcategories)
})
observeEvent(input$create_categorie, {
if(nchar(as.character(sub(" ", "_",input$nameCat)))>2 && length(input$createcategories)>1)
{
text = input$createcategories
showModal( modalDialog(title = paste(paste(paste(paste("Create categorie :",
as.character(sub(" ", "_",input$nameCat))),"{"),
paste(text, collapse=', ' )),"}"), size = "m",
actionButton(inputId = "Create_categorie_variable", label = "Create",
style = "color: white; background-color:#3c8dbc;
cursor: pointer;"),
uiOutput("addCat")
))
}
else if(nchar(as.character(sub(" ", "_",input$nameCat)))>2 && length(input$createcategories)<2)
return(showNotification(
h4("At minimum two variables ",style = "color:red"),
duration = 3
))
else if(nchar(as.character(sub(" ", "_",input$nameCat)))<3 && length(input$createcategories)>1)return(showNotification(
h4("Name categorie contains less than two characters",style = "color:red"),
duration = 3
))
else return(showNotification(
h4("At minimum two variables and name categorie contains less than two characters",style = "color:red"),
duration = 5
))
})
val<-reactiveValues()
val$txt<-NULL
create_cat <- eventReactive(input$Create_categorie_variable, {
new<-paste("---", paste(paste(paste(paste("categorie :", as.character(sub(" ", "_",input$nameCat))),"{"),paste(varList(), collapse=', ' )),"}"))
val$txt<-paste( val$txt,new,sep='\n')
NewCat$df[[sub(" ", "_",input$nameCat)]] = c(varList())
output$valCat <- renderText({
req(input$Create_categorie_variable)
str1 <- val$txt
})
reset("choiceReset")
return(span("Create new categories", style = "color:green"))
})
output$addCat <- renderUI({
create_cat()
})
observeEvent(input$resetCat, {
val$txt<-NULL
NewCat$df = list()
output$valCat <- renderText({
str1 <- "--- All categories is delete"
})
})
observeEvent(input$all_single_selected, {
updateSelectizeInput(session, "selectionsEnter", choices = names(isolate(data_set()[,!(names(data_set()) %in% c(input$inSelect,input$choose_var_delete))])),
selected = input$selectionsEnter, server = TRUE)
showModal(modalDialog(selectInput("selectionsEnter", "Parameters Selted", multiple = TRUE, choices = character(0))))
})
output$selections <- renderText(input$selectionsEnter)
observeEvent(input$btn_launchModel, {
if(input$crossFoldTypeUI == 'repeatedcv')
trCtr <- trainControl(method = as.character(input$crossFoldTypeUI),
number = 10 ,
repeats = as.integer(input$repeatUI),
classProbs = TRUE,
savePredictions = TRUE)
else trCtr <- trainControl(method = as.character(input$crossFoldTypeUI),
number = as.integer(nFolds),
savePredictions = TRUE, returnResamp='none', summaryFunction = twoClassSummary, classProbs = TRUE)
if(is.null(input$inSelect)||input$inSelect=='---')
return(NULL)
y <- isolate(data_setTrNA$df[,input$inSelect])
X <- isolate(data_setTrNA$df[,!(names(data_setTrNA$df) %in% c(input$inSelect))])
yi <- !is.na(y)
Xi <- complete.cases(X)
df2 <- cbind(y,X)[yi&Xi,]
print(dim(df2))
c <- class(df2$y)
lvls <- length(unique(df2$y))
if(lvls<10|(c!='numeric'&c!='integer')){
modelType <<-'Classification'
df2$y <- factor(df2$y)
}
set.seed(123)
if (is.na(input$num) || input$num >100) {
testsize = 0.75
}else testsize <- input$num/100
trainIndex <- createDataPartition(df2$y,
p = testsize,
list = FALSE,
times = 1)
if (length(df2)>150)
isolate({
dataTrain <<- df2[ trainIndex,]
dataTest <<- df2[-trainIndex,]
})
else
isolate({
dataTrain <<- df2
dataTest <<- df2
})
# Creation de model
popupModal <- function(failed = FALSE) {
modalDialog(
textInput("txt", "Write something"),
if (failed)
div(tags$b("You did not input anything", style = "color: red;")),
footer = tagList(
modalButton("Cancel"),
actionButton("ok", "OK")
)
)
}
"
Dans cette partie du code nous définissons les paramètres inclus dans les differents modèles
- Dans un premier temps nous prennons en compte l'exécution sur les seules variables sélectionnées par l'utilisateurs
ainsi que toutes les combinaisons possibles de variables s'il en existe.
- Dans la deuximee option on prnd en compte que les singletons de variables
- Dans la troisième option on considère que les combinaison de variables s'il ne désire que les modèles soient exécurés
sur les seules combinaisons crées.
- Et en dernière possibilité tous les singletons associés aux variables combinées
"
selectedVra$var = append(selectedVra$var,input$selectionsEnter)
if(length(selectedVra$var)>0){
if(length(names(NewCat$df))>0) results <- data.frame(Parms = c(selectedVra$var,names(NewCat$df)))
else {results <- data.frame(Parms = selectedVra$var)
}
}
else if(input$Id044 == 'all_single')
results <- data.frame( Parms = names(X))
else if (input$Id044 == 'only_combinate')
results <- data.frame( Parms = names(NewCat$df))
else if (input$Id044 == 'all_single_all_combinate')
results <- data.frame( Parms = c(names(X),names(NewCat$df)))
for (i_model in input$modelSelect) {
#input$inCheckPositiveLabel = 1
if (i_model == 'NoMod'){
print(input$inCheckPositiveLabel)
#print( levels(dataTrain$y)[levels(dataTrain$y)!=input$inCheckPositiveLabel])
ktest = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,
negative_label = 0)
output$NMtrainResultsUI <- renderTable(ktest$res)
if(ncol(ktest$ROCVALs)>2){
output$NMfinalPlotUI <- renderPlot({
ggplot(ktest$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$NMfinalPlotUI <- renderPlot({
ggplot(ktest$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
#resultsAUCPlot
output$NMfinalPlotCIAUC <- renderPlot({
ggplot(ktest$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold", angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
scale_color_viridis_d()+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
#
if (!is.null(ktest$df.Impts)) {
output$NMfinalPlotImpUI <- renderPlot({
ggplot(ktest$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
}
}
# logistic regression
else if (i_model == 'glm'){
ktest2 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,negative_label = 0)
#NEGA = levels(dataTrain$y)[levels(dataTrain$y)!=input$inCheckPositiveLabel]
output$LRtrainResultsUI <- renderTable(ktest2$res)
if(ncol(ktest2$ROCVALs)>2){
output$LRfinalPlotUI <- renderPlot({
ggplot(ktest2$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$LRfinalPlotUI <- renderPlot({
ggplot(ktest2$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$LRfinalPlotCIAUC <- renderPlot({
ggplot(ktest2$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold", angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
if (!is.null(ktest2$df.Impts)) {
output$LRfinalPlotImpUI <- renderPlot({
ggplot(ktest2$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
}
}
# Naive Bayes algorithm
else if (i_model == 'nb'){
ktest3 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,negative_label = 0)
output$NBtrainResultsUI <- renderTable(ktest3$res)
if(ncol(ktest3$ROCVALs)>2){
output$NBfinalPlotUI <- renderPlot({
ggplot(ktest3$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$NBfinalPlotUI <- renderPlot({
ggplot(ktest3$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$NBfinalPlotCIAUC <- renderPlot({
ggplot(ktest3$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold", angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
if (!is.null(ktest3$df.Impts)) {
output$NBfinalPlotImpUI <- renderPlot({
ggplot(ktest3$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
}
}
# Latent Dirichlet allocation
else if (i_model == 'lda'){
ktest4 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,negative_label = 0)
output$LDAtrainResultsUI <- renderTable(ktest4$res)
if(ncol(ktest4$ROCVALs)>2){
output$LDAfinalPlotUI <- renderPlot({
ggplot(ktest4$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$LDAfinalPlotUI <- renderPlot({
ggplot(ktest4$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$LDAfinalPlotCIAUC <- renderPlot({
ggplot(ktest4$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold", angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
if (!is.null(ktest4$df.Impts)) {
output$LDAfinalPlotImpUI <- renderPlot({
ggplot(ktest4$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
}
}
# k-nearest neighbors algorithme
if (i_model == 'knn'){
ktest5 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,negative_label = 0)
output$KNNtrainResultsUI <- renderTable(ktest5$res)
if(ncol(ktest5$ROCVALs)>2){
output$KNNfinalPlotUI <- renderPlot({
ggplot(ktest5$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$KNNfinalPlotUI <- renderPlot({
ggplot(ktest5$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$KNNfinalPlotCIAUC <- renderPlot({
ggplot(ktest5$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold", angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
if (!is.null(ktest5$df.Impts)) {
output$KNNfinalPlotImpUI <- renderPlot({
ggplot(ktest5$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
}
}
# Logistic model tree - arbre de décision
else if (i_model == 'LMT'){
ktest6 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel = 1,trCtr = trCtr,negative_label = 0)
output$DTtrainResultsUI <- renderTable(ktest6$res)
if(ncol(ktest6$ROCVALs)>2){
output$DTfinalPlotUI <- renderPlot({
ggplot(ktest6$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$DTfinalPlotUI <- renderPlot({
ggplot(ktest6$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$DTfinalPlotCIAUC <- renderPlot({
ggplot(ktest6$resultsAUCPlot, aes(Parms, AUC)) +
geom_line(aes(group = 1),size=1) +
geom_errorbar( aes(ymin = min_ci_auc, ymax = max_ci_auc),width = 0.2) +
geom_point(size = 2)+theme(axis.text.y = element_text(colour = 'black', size = 12,face = "bold"),
axis.title.y = element_text(size = 12,
hjust = 0.5, vjust = 0.2,face = "bold"),
axis.text.x = element_text(colour = 'black', size = 12,face = "bold",angle = 90),
axis.title.x = element_text(size = 12,face = "bold")) +
theme(strip.text.y = element_text(size = 11, hjust = 0.5,
vjust = 0.5, face = 'bold'))
})
if (!is.null(ktest6$df.Impts)) {
output$DTfinalPlotImpUI <- renderPlot({
ggplot(ktest6$df.Impts, aes(x = Parameters, y = Contribution)) +
geom_segment(
aes(x = Parameters, xend = Parameters, y = 0, yend = Contribution),
color = "lightgray"
) +
geom_point(aes(color = Categories), size = 3) +
scale_color_viridis_d() +
theme_pubclean() +
rotate_x_text(45)+
theme(strip.text.y = element_text(size = 11, hjust = 0.5, vjust = 0.5, face = 'bold'))
})
}
}
# Gradient boosting
else if (i_model == 'gbm'){
ktest7 = testModel(data = dataTrain,dataT = dataTest,i_model = i_model,grpeParams = results,
grpeNewCat = NewCat$df,positiveLabel =1,trCtr = trCtr,negative_label = 0)
output$GBMtrainResultsUI <- renderTable(ktest7$res)
if(ncol(ktest7$ROCVALs)>2){
output$GBMfinalPlotUI <- renderPlot({
ggplot(ktest7$ROCVALs, aes(d = D, m = M, color = name)) + geom_roc(n.cuts = 0) + style_roc()
})
}else{
output$GBMfinalPlotUI <- renderPlot({
ggplot(ktest7$ROCVALs, aes(d = D, m = M1)) + geom_roc()
})
}
output$GBMfinalPlotCIAUC <- renderPlot({