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Naive Bayes Classification"
In NBShiny2: Interactive Document for Working with Naive Bayes Classification
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(caret)
library(e1071)
library(rhandsontable)
library(datasets)
```r
sidebarPanel(
downloadButton("downloaddata", "Download Example Data(Titanic)"),
checkboxInput("ex","Uncheck for using your own file",value = TRUE),
fileInput("file", "Upload the *.csv file with headers"),
sliderInput("train_num", label = "Enter the proportion of training dataset:",
min = 0.6, max = 1, value = 0.6, step = 0.01),
#sliderInput("cut_offprob", label = "Enter cutoff probability",
# min = 0, max = 1, value = 0.5, step = 0.01),
uiOutput("vx"),
uiOutput("vy"),
# tableOutput("convertd"),
uiOutput("vxi"),
downloadButton("downloadPlot", "Download Plot"),
uiOutput("vyl")
)
mainPanel(
tabsetPanel(type = "tab",
tabPanel("Model Summary", verbatimTextOutput("AD") ),
tabPanel("Model Visualization", plotOutput("MV") ),
tabPanel("Model Evaluation", verbatimTextOutput("ME") ),
tabPanel("Model Deployment",verbatimTextOutput("MD"))
),
h6("Edit the test data record"),
rHandsontableOutput("testdata"),
h6("", tags$img(src ="K.JPG", height= 400, width=400))
)
output$AD<-renderPrint({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds =data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet)
print(model)
})
output$MV<-renderPlot({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
attach(dataframet)
assocplot(table(get(input$variablexi),get(input$variabley)),col = c("green","red"),xlab = input$variablexi, ylab = input$variabley)
})
output$ME<-renderPrint({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet)
if(prop <1 )
{ cat(sprintf("\nValidation data is used\n"))
prediction = predict(model,newdata = dataframev)
attach(dataframev)
}
else
{
cat(sprintf("\nTraining data is used\n"))
prediction = predict(model,newdata = dataframet)
attach(dataframet)
}
print(confusionMatrix(as.factor(prediction),as.factor(get(input$variabley)),positive = input$variableyl))
})
output$MD<-renderPrint({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet)
test_data = data.frame(hot_to_r(input$testdata))
prediction = predict(model,newdata = test_data)
prob = predict(model,newdata = test_data,type = "raw")
test_data$predictedvalue = prediction
test_data$prob = prob
print(test_data)
})
output$vx <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
data = select_if(data,is.factor)
checkboxGroupInput("variablex","Select the variables",choices = colnames(data),selected = colnames(data))
})
output$vxi <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,c(-input$variabley))
selectInput("variablexi","Select the variable x for the plot",choices = colnames(ds),selected = "")
})
output$vy <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds) ,selected = "")
})
output$testdata <- renderRHandsontable({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,c(-input$variabley))
row.names(ds)= 1:NROW(ds)
rhandsontable(data.frame(ds[1,]))
})
output$convertd <- renderTable({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,c(input$variabley))
df = ds
ds = model.matrix(~0 + get(input$variabley),data = ds)
dnew = data.frame(cbind(df,ds))
df = data.frame(dcat =unique(dnew))
row.names(df)= 1:NROW(df)
df[,3]= NULL
colnames(df)=c("ActualValues","BinaryValues")
print(data.frame(df))
})
output$downloadPlot<- downloadHandler(
filename = function() {
paste("NB_Associationplot", ".png", sep = "")
},
content = function(file) {
png(file)
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
attach(dataframet)
assocplot(table(get(input$variablexi),get(input$variabley)),col = c("green","red"),xlab = input$variablexi, ylab = input$variabley)
dev.off()
})
datasetInput <- reactive({
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
})
output$downloaddata <- downloadHandler(
filename = function() {
filetitle = paste("Exampledataset_Titanic")
paste(filetitle, ".csv", sep = "")
},
content = function(file) {
write.csv(datasetInput(), file, row.names = FALSE)
}
)
output$vyl <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic)
dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ]
row.names(dataset) = 1:nrow(dataset)
set.seed(1)
index = runif(nrow(dataset))
dataset = dataset[order(index),]
data = dataset
}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variabley)
selectInput("variableyl","Select the Positive Class",choices = levels(ds[,1]) ,selected = levels(ds[,1]))
})
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NBShiny2 documentation built on Oct. 30, 2019, 10:47 a.m.
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knitr::opts_chunk$set(echo = TRUE) library(dplyr) library(caret) library(e1071) library(rhandsontable) library(datasets)
```r sidebarPanel(
downloadButton("downloaddata", "Download Example Data(Titanic)"), checkboxInput("ex","Uncheck for using your own file",value = TRUE), fileInput("file", "Upload the *.csv file with headers"), sliderInput("train_num", label = "Enter the proportion of training dataset:", min = 0.6, max = 1, value = 0.6, step = 0.01), #sliderInput("cut_offprob", label = "Enter cutoff probability", # min = 0, max = 1, value = 0.5, step = 0.01), uiOutput("vx"), uiOutput("vy"), # tableOutput("convertd"), uiOutput("vxi"), downloadButton("downloadPlot", "Download Plot"), uiOutput("vyl")
) mainPanel( tabsetPanel(type = "tab", tabPanel("Model Summary", verbatimTextOutput("AD") ), tabPanel("Model Visualization", plotOutput("MV") ), tabPanel("Model Evaluation", verbatimTextOutput("ME") ),
tabPanel("Model Deployment",verbatimTextOutput("MD"))
),
h6("Edit the test data record"),
rHandsontableOutput("testdata"), h6("", tags$img(src ="K.JPG", height= 400, width=400)) ) output$AD<-renderPrint({ if(input$ex == TRUE) {
data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()} }
ds =data
ds = select(ds,input$variablex) mod = paste(input$variabley,"~.") options(scipen = 999) prop = input$train_num set.seed(1) dataframe = ds train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop) dataframet = dataframe[train.rows,] valid.rows = setdiff(row.names(dataframe),train.rows) dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet) print(model)
})
output$MV<-renderPlot({ if(input$ex == TRUE) { data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
attach(dataframet)
assocplot(table(get(input$variablexi),get(input$variabley)),col = c("green","red"),xlab = input$variablexi, ylab = input$variabley)
}) output$ME<-renderPrint({ if(input$ex == TRUE) { data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset } else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet) if(prop <1 ) { cat(sprintf("\nValidation data is used\n")) prediction = predict(model,newdata = dataframev) attach(dataframev) } else { cat(sprintf("\nTraining data is used\n")) prediction = predict(model,newdata = dataframet) attach(dataframet) }
print(confusionMatrix(as.factor(prediction),as.factor(get(input$variabley)),positive = input$variableyl))
}) output$MD<-renderPrint({ if(input$ex == TRUE) { data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
model = naiveBayes(formula = as.formula(mod),data = dataframet)
test_data = data.frame(hot_to_r(input$testdata))
prediction = predict(model,newdata = test_data)
prob = predict(model,newdata = test_data,type = "raw")
test_data$predictedvalue = prediction
test_data$prob = prob
print(test_data)
})
output$vx <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
data = select_if(data,is.factor)
checkboxGroupInput("variablex","Select the variables",choices = colnames(data),selected = colnames(data))
})
output$vxi <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,c(-input$variabley))
selectInput("variablexi","Select the variable x for the plot",choices = colnames(ds),selected = "")
}) output$vy <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset } else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds) ,selected = "")
})
output$testdata <- renderRHandsontable({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset } else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,c(-input$variabley)) row.names(ds)= 1:NROW(ds) rhandsontable(data.frame(ds[1,]))
})
output$convertd <- renderTable({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,c(input$variabley))
df = ds ds = model.matrix(~0 + get(input$variabley),data = ds)
dnew = data.frame(cbind(df,ds))
df = data.frame(dcat =unique(dnew))
row.names(df)= 1:NROW(df) df[,3]= NULL colnames(df)=c("ActualValues","BinaryValues")
print(data.frame(df))
})
output$downloadPlot<- downloadHandler( filename = function() { paste("NB_Associationplot", ".png", sep = "") }, content = function(file) { png(file) if(input$ex == TRUE) { data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset
}
else{
file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
attach(dataframet)
assocplot(table(get(input$variablexi),get(input$variabley)),col = c("green","red"),xlab = input$variablexi, ylab = input$variabley)
dev.off()
})
datasetInput <- reactive({ data("Titanic") dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset }) output$downloaddata <- downloadHandler( filename = function() { filetitle = paste("Exampledataset_Titanic") paste(filetitle, ".csv", sep = "") }, content = function(file) {
write.csv(datasetInput(), file, row.names = FALSE) }
)
output$vyl <- renderUI({
if(input$ex == TRUE)
{
data("Titanic")
dataset = data.frame(Titanic) dataset = dataset[rep(seq_len(nrow(dataset)), dataset$Freq) ,1:4 ] row.names(dataset) = 1:nrow(dataset) set.seed(1) index = runif(nrow(dataset)) dataset = dataset[order(index),] data = dataset } else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variabley)
selectInput("variableyl","Select the Positive Class",choices = levels(ds[,1]) ,selected = levels(ds[,1]))
})
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