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#' cv_svm UI Function
#'
#' @description A shiny Module.
#'
#' @param id,input,output,session Internal parameters for {shiny}.
#'
#' @noRd
#'
#' @importFrom shiny NS tagList
mod_cv_svm_ui <- function(id){
ns <- NS(id)
tagList(
tabBoxPrmdt(
id = ns("Boxsvm"),
tabPanel(title = p(labelInput("seleParModel"),class = "wrapper-tag"), value = "tabCVsvmModelo",
div(col_6(radioSwitch(ns("scale_cvsvm"), "escal", c("si", "no"))),
col_6(
selectizeInput(
ns("sel_kernel_svm"), labelInput("selkernel"), multiple = T,
choices = c("linear", "polynomial", "radial", "sigmoid")))),
fluidRow(col_6(numericInput(ns("cvsvm_step"), labelInput("probC"), value = 0.5, width = "100%", min = 0, max = 1, step = 0.1)),
col_6(selectInput(ns("cvsvm_cat"), choices = "",label = labelInput("selectCat"), width = "100%"))),
div(id = ns("texto"),
style = "display:block",withLoader(verbatimTextOutput(ns("txtcvsvm")),
type = "html", loader = "loader4")),
hr(style = "border-top: 2px solid #cccccc;" ),
actionButton(ns("btn_cv_svm"), labelInput("generar"), width = "100%" ),br(),br()),
tabPanel(title = p(labelInput("indices"),class = "wrapper-tag"), value = "tabCVsvmIndices",
div(col_8(),
col_4(div(id = ns("row"), shiny::h5(style = "float:left;margin-top: 15px;", labelInput("tipoGrafico"),class = "wrapper-tag"),
tags$div(class="multiple-select-var",
selectInput(inputId = ns("plot_type_p"),label = NULL,
choices = c("barras", "lineas", "error"), width = "100%")))), hr()),
div(col_6(echarts4rOutput(ns("e_svm_glob"), width = "100%", height = "70vh")),
col_6(echarts4rOutput(ns("e_svm_error"), width = "100%", height = "70vh")))),
tabPanel(title = p(labelInput("indicesCat"),class = "wrapper-tag"), value = "tabCVsvmIndicesCat",
div(col_4(div(id = ns("row"), shiny::h5(style = "float:left;margin-top: 15px;", labelInput("selectCat"),class = "wrapper-tag"),
tags$div(class="multiple-select-var",
selectInput(inputId = ns("cv.cat.sel"),label = NULL,
choices = "", width = "100%")))),
col_4(),
col_4(div(id = ns("row"), shiny::h5(style = "float:left;margin-top: 15px;", labelInput("tipoGrafico"),class = "wrapper-tag"),
tags$div(class="multiple-select-var",
selectInput(inputId = ns("plot_type"),label = NULL,
choices = "", width = "100%"))))),hr(),
div(col_6(echarts4rOutput(ns("e_svm_category"), width = "100%", height = "70vh")),
col_6(echarts4rOutput(ns("e_svm_category_err"), width = "100%", height = "70vh"))))
)
)
}
#' cv_svm Server Functions
#'
#' @noRd
mod_cv_svm_server <- function(input, output, session, updateData, codedioma){
ns <- session$ns
M <- rv(MCs.svm = NULL, grafico = NULL, global = NULL, categories = NULL, times = 0)
observeEvent(codedioma$idioma, {
nombres <- list( "lineas", "barras","error")
names(nombres) <- tr(c("grafLineas", "grafBarras", "grafError"),codedioma$idioma)
updateSelectInput(session, "plot_type", choices = nombres, selected = "lineas")
updateSelectInput(session, "plot_type_p", choices = nombres, selected = "lineas")
})
observeEvent(c(updateData$datos, updateData$variable.predecir), {
M$MCs.svm <- NULL
M$grafico <- NULL
M$global <- NULL
M$categories <- NULL
datos <- updateData$datos
variable <- updateData$variable.predecir
if(!is.null(datos)){
choices <- as.character(unique(datos[, variable]))
updateSelectizeInput(session, "sel_kernel_svm", selected = "")
updateSelectInput(session, "cv.cat.sel", choices = choices, selected = choices[1])
updateSelectInput(session, "cvsvm_cat", choices = choices, selected = choices[1])
if(length(choices) == 2){
shinyjs::show("cvsvm_cat", anim = TRUE, animType = "fade")
shinyjs::show("cvsvm_step", anim = TRUE, animType = "fade")
}else{
shinyjs::hide("cvsvm_cat", anim = TRUE, animType = "fade")
shinyjs::hide("cvsvm_step", anim = TRUE, animType = "fade")
}
}
})
output$txtcvsvm <- renderPrint({
input$btn_cv_svm
M$MCs.svm <- NULL
M$grafico <- NULL
M$global <- NULL
M$categories <- NULL
tryCatch({
kernels <- isolate(input$sel_kernel_svm) # Algoritmos seleccionados para CV (vector)
cant.vc <- isolate(updateData$numValC)# Obtiene cantidad de validaciones a realizar
MCs.svm <- vector(mode = "list")# Lista de listas que va a guardar todas las MCs
datos <- isolate(updateData$datos)# Obtiene los datos
numGrupos <- isolate(updateData$numGrupos)# Obtiene la cantidad de grupos
grupos <- isolate(updateData$grupos)# Obtiene los grupos de cada validación
scales <- isolate(input$scale_cvsvm) # Estandarizar o no
variable <- updateData$variable.predecir# Variable a predecir
var_ <- paste0(variable, "~.")
category <- isolate(levels(updateData$datos[,variable]))# Categorías de la variable a predecir
dim_v <- isolate(length(category))# Cantidad de categorías (para generar las matrices de confusión)
nombres <- vector(mode = "character", length = length(kernels))# Almacena el nombre de los modelos (vector en caso de varios kernels, uno solo en caso que no aplican los kernels)
Corte <- isolate(input$cvsvm_step)# Obtiene la probabilidad de corte para el modelo
cat_sel <- isolate(input$cvsvm_cat)# Obtiene la categoría de la variable a predecir seleccionada para aplicar probabilidad de corte
if(length(kernels)<1){
if(M$times != 0)
showNotification("Debe seleccionar al menos un kernel")
}
for (kernel in 1:length(kernels)){
# Llena la lista de listas de MCs con los nombres de cada modelo
MCs.svm[[paste0("MCs.",kernels[kernel])]] <- vector(mode = "list", length = cant.vc)
# Guarda los nombres para las matrices individuales
nombres[kernel] <- paste0("MC.",kernels[kernel])
}
for (i in 1:cant.vc){
# Lista de Matrices, se identifican con el nombre del modelo
MC.svm <- vector(mode = "list", length = length(kernels))
names(MC.svm) <- nombres
# Crea la matriz que almacena la MC de confusión
# Toma en cuenta las dimensiones de la variable a predecir con dim_v
for (kernel in 1:length(kernels)){
MC.svm[[kernel]] <- matrix(rep(0, dim_v * dim_v), nrow = dim_v)
}
for (k in 1:numGrupos){
muestra <- grupos[[i]][[k]]
ttraining <- datos[-muestra, ]
ttesting <- datos[muestra, ]
for (j in 1:length(kernels)){
modelo <- train.svm(as.formula(var_),
data = ttraining,
kernel = kernels[j],
scale = as.logical(scales))
if(length(category) == 2){
positive <- category[which(category == cat_sel)]
negative <- category[which(category != cat_sel)]
prediccion <- predict(modelo, ttesting, type = "prob")
Clase <- ttesting[,variable]
Score <- prediccion$prediction[,positive]
Prediccion <- ifelse(Score > Corte, positive, negative)
MC <- table(Clase , Pred = factor(Prediccion, levels = category))
MC.svm[[j]] <- MC.svm[[j]] + MC
}else{
prediccion <- predict(modelo, ttesting)
MC <- confusion.matrix(ttesting, prediccion)
MC.svm[[j]] <- MC.svm[[j]] + MC
}
}
}
for (l in 1:length(MCs.svm)){
MCs.svm[[l]][[i]] <- MC.svm[[l]]
}
}
M$MCs.svm <- MCs.svm
resultados <- indices.cv(category, cant.vc, kernels, MCs.svm)
M$grafico <- resultados$grafico
M$global <- resultados$global
M$categories <- resultados$categories
M$times <- 1
isolate(codedioma$code <- append(codedioma$code, cv_svm_code(variable, dim_v, cant.vc, numGrupos)))
print(MCs.svm)
},error = function(e){
M$MCs.svm <- NULL
M$grafico <- NULL
M$global <- NULL
M$categories <- NULL
M$times <- 0
return(invisible(""))
})
})
output$e_svm_glob <- renderEcharts4r({
input$btn_cv_svm
type <- input$plot_type_p
grafico <- M$grafico
if(!is.null(grafico)){
idioma <- codedioma$idioma
switch (type,
"barras" = return( resumen.barras(grafico, labels = c(tr("precG",idioma), "Kernel" ))),
"error" = return( resumen.error(grafico, labels = c(tr("precG",idioma), "Kernel", tr("maximo", idioma),tr("minimo", idioma)))),
"lineas" = return( resumen.lineas(grafico, labels = c(tr("precG",idioma),tr("crossval",idioma) )))
)
}
else
return(NULL)
})
output$e_svm_error <- renderEcharts4r({
idioma <- codedioma$idioma
type <- input$plot_type_p
if(!is.null(M$grafico)){
err <- M$grafico
err$value <- 1 - M$global
switch (type,
"barras" = return( resumen.barras(err, labels = c(tr("errG",idioma), "Kernel" ))),
"error" = return( resumen.error(err, labels = c(tr("errG",idioma), "Kernel", tr("maximo", idioma),tr("minimo", idioma)))),
"lineas" = return( resumen.lineas(err, labels = c(tr("errG",idioma), tr("crossval",idioma) )))
)
}
else
return(NULL)
})
output$e_svm_category <- renderEcharts4r({
idioma <- codedioma$idioma
cat <- input$cv.cat.sel
type <- input$plot_type
if(!is.null(M$grafico)){
graf <- M$grafico
graf$value <- M$categories[[cat]]
switch (type,
"barras" = return( resumen.barras(graf, labels = c(paste0(tr("prec",idioma), " ",cat ), "Kernel" ))),
"error" = return( resumen.error(graf, labels = c(paste0(tr("prec",idioma), " ",cat ), "Kernel", tr("maximo", idioma),tr("minimo", idioma)))),
"lineas" = return( resumen.lineas(graf, labels = c(paste0(tr("prec",idioma), " ",cat ), tr("crossval",idioma) )))
)
}
else
return(NULL)
})
output$e_svm_category_err <- renderEcharts4r({
idioma <- codedioma$idioma
cat <- input$cv.cat.sel
type <- input$plot_type
if(!is.null(M$grafico)){
graf <- M$grafico
graf$value <- 1 - M$categories[[cat]]
switch (type,
"barras" = return( resumen.barras(graf, labels = c(paste0("Error ",cat ), "Kernel" ))),
"error" = return( resumen.error(graf, labels = c(paste0("Error ",cat ), "Kernel", tr("maximo", idioma),tr("minimo", idioma)))),
"lineas" = return( resumen.lineas(graf, labels = c(paste0("Error ",cat ), tr("crossval",idioma) )))
)
}
else
return(NULL)
})
}
## To be copied in the UI
# mod_cv_svm_ui("cv_svm_1")
## To be copied in the server
# mod_cv_svm_server("cv_svm_1")
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