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server.R
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server.R
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options(shiny.maxRequestSize = 9*1024^2)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(shiny,
shinydashboard,
tableHTML,
dplyr,
data.table,
Boruta,
h2o,
plotly,
ggplot2,
ggpubr,
GGally,
h2o,
lime)
h2o.init(nthreads = -1,
min_mem_size = "8g",
max_mem_size = "10g")
fetched.model <- h2o.loadModel(saved.model)
shinyServer(function(input, output){
my_modeling_data <- reactive({
my_modeling_data <- read.csv("modeling_data.csv",
header = T,
sep = ",",
stringsAsFactors = T)
my_modeling_data <- as.data.frame(my_modeling_data)
})
output$o.RawData <- renderDataTable({
if(is.null(my_modeling_data())){return()}
else
head(my_modeling_data(), 5)
})
output$o.featureGraphs_conti <- renderPlotly({
p1 <- ggdensity(my_modeling_data(),
x = "Pulse.rate",
add = "mean",
rug = TRUE,
xlab = "Pulse rate",
ylab = F,
color = "Valvular.heart.disease",
fill = "Valvular.heart.disease",
palette = c("#00AFBB", "#FC4E07"))
p1 <- ggplotly(p1)
p2 <- ggdensity(my_modeling_data(),
x = "EF.TTE",
add = "mean",
rug = TRUE,
xlab = "Ejection fraction",
ylab = F,
color = "Valvular.heart.disease",
fill = "Valvular.heart.disease",
palette = c("#00AFBB", "#FC4E07"))
p2 <- ggplotly(p2)
p.one <- subplot(p1,
p2,
nrows = 1,
titleX = T,
titleY = T,
shareY = F)
})
output$o.featureGraphs_discreet <- renderPlotly({
p4 <- ggplot(my_modeling_data(),
aes(Diastolic.Murmur,
group = Valvular.heart.disease)) +
geom_bar(aes(y = ..prop..,
fill = factor(..x..)),
stat="count") +
scale_y_continuous(labels=scales::percent) +
ylab("Percentage frequencies") +
facet_grid(~Valvular.heart.disease)+
theme(legend.title=element_blank())+
theme(legend.position="bottom")
p4 <- ggplotly(p4)
p5 <- ggplot(my_modeling_data(),
aes(Systolic.Murmur,
group = Valvular.heart.disease)) +
geom_bar(aes(y = ..prop..,
fill = factor(..x..)),
stat="count") +
scale_y_continuous(labels=scales::percent) +
ylab("Percentage frequencies") +
facet_grid(~Valvular.heart.disease)+
theme(legend.title=element_blank())+
theme(legend.position="bottom")
p5 <- ggplotly(p5)
p6 <- ggplot(my_modeling_data(),
aes(Lung.rales,
group = Valvular.heart.disease)) +
geom_bar(aes(y = ..prop..,
fill = factor(..x..)),
stat="count") +
scale_y_continuous(labels=scales::percent) +
ylab("Percentage frequencies") +
facet_grid(~Valvular.heart.disease)+
theme(legend.title=element_blank())+
theme(legend.position="bottom")
p6 <- ggplotly(p6)
p7 <- ggplot(my_modeling_data(),
aes(Dyspnea,
group = Valvular.heart.disease)) +
geom_bar(aes(y = ..prop..,
fill = factor(..x..)),
stat="count") +
scale_y_continuous(labels=scales::percent) +
ylab("Percentage frequencies") +
facet_grid(~Valvular.heart.disease)+
theme(legend.title=element_blank())+
theme(legend.position="bottom")
p7 <- ggplotly(p7)
p.two <- subplot(p4,
p5,
p6,
p7,
nrows = 1,
margin = c(0.05, 0.05, 0.05,0.05),
titleX = T,
titleY = T,
shareY = F)
})
output$o.para_coord <- renderPlotly({
p_pc <- ggparcoord(my_modeling_data(),
columns = c(1:8),
groupColumn = 9,
scale = "uniminmax",
missing = "exclude") +
theme(axis.text.x = element_text(angle = 90),
text = element_text(size=12))
p_pc
})
#Prediction on test data
output$o.prediction_on_test <- renderPrint({
h2o.no_progress()
final.df <- as.h2o(my_modeling_data())
split_h2o <- h2o.splitFrame(final.df,
c(0.7,
0.1),
seed = 1234 )
train_h2o <- h2o.assign(split_h2o[[1]],
"train" )
valid_h2o <- h2o.assign(split_h2o[[2]],
"valid" )
test_h2o <- h2o.assign(split_h2o[[3]],
"test" )
target <- "Valvular.heart.disease"
predictors <- setdiff(names(train_h2o),
target)
predictors
fetched.model <- h2o.loadModel(saved.model)
# h2o.varimp_plot(automl_leader)
prediction_on_test <- h2o.predict(object = fetched.model,
newdata = test_h2o)
model_performance_on_test <- h2o.performance(automl_leader,
test_h2o)
model_performance_on_test
})
#Globally significant variables
output$o.global_plots <- renderPlot({
fetched.model <- h2o.loadModel(saved.model)
p8 <- h2o.varimp_plot(fetched.model)
p8
})
#Final prediction on new user input data
user.inputs <- reactive({
user.inputs <- data.frame(as.factor(input$Family.history),
as.integer(input$Pulse.rate),
as.factor(input$Lung.rales),
as.factor(input$Systolic.Murmur),
as.factor(input$Diastolic.Murmur),
as.factor(input$Dyspnea),
as.factor(input$Function.Class),
as.integer(input$EF.TTE))
predictors <- c("Family.history",
"Pulse.rate",
"Lung.rales",
"Systolic.Murmur",
"Diastolic.Murmur",
"Dyspnea",
"Function.Class",
"EF.TTE")
user.inputs <- as.h2o(user.inputs)
names(user.inputs)[1] = c("Family.history")
names(user.inputs)[2] = c("Pulse.rate")
names(user.inputs)[3] = c("Lung.rales")
names(user.inputs)[4] = c("Systolic.Murmur")
names(user.inputs)[5] = c("Diastolic.Murmur")
names(user.inputs)[6] = c("Dyspnea")
names(user.inputs)[7] = c("Function.Class")
names(user.inputs)[8] = c("EF.TTE")
user.inputs
})
output$jib1 <- renderPrint({
str(user.inputs())
})
user.input.predictions <- reactive({
fetched.model <- h2o.loadModel(saved.model)
predictions <- h2o.predict(object = fetched.model,
newdata = user.inputs())
predictions
})
output$o.final.pred <- renderPrint({
user.input.predictions()
})
#Local interpretation of the new data
output$o.local.plot <- renderPlot({
h2o.no_progress()
fetched.model <- h2o.loadModel(saved.model)
final.df <- as.h2o(my_modeling_data())
split_h2o <- h2o.splitFrame(final.df,
c(0.8,
0.1),
seed = 1234 )
train_h2o <- h2o.assign(split_h2o[[1]],
"train" )
test_h2o <- h2o.assign(split_h2o[[3]],
"test" )
train_org <- as.data.frame(train_h2o)
train_org$Family.history <- as.factor(train_org$Family.history)
train_org$Function.Class <- as.factor(train_org$Function.Class)
# str(train_org)
test_df <- as.data.frame(test_h2o)
test_df$Family.history <- as.factor(test_df$Family.history)
test_df$Function.Class <- as.factor(test_df$Function.Class)
# str(test_df)
test_sample <- as.data.frame(user.inputs())
# str(test_sample)
explain1 <- lime(train_org,
fetched.model,
bin_continuous = FALSE,
n_bins = 5,
n_permutations = 1000)
# explain1
#
explanation1 <- explain(test_sample,
explain1,
n_permutations = 500,
feature_select = "auto",
n_labels = 1,
n_features = 8)
# explanation1
#
p <- plot_features(explanation1)
p
})
})