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Auto prediction and forecasting added

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Asitav Sen
2022-03-02 21:10:36 +01:00
parent e73b19da49
commit bdb1f315df
4 changed files with 366 additions and 306 deletions
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330
.Rproj.user/178A6739/sources/session-99529da2/FE6BB309-contents
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@@ -30,30 +30,27 @@ source("modals.R")
# Define UI for application
ui <- navbarPage(
theme = shinytheme("cosmo"),
title = "LoanRisk",
panel1
)
ui <- navbarPage(theme = shinytheme("cosmo"),
title = "LoanRisk",
panel1)
# Define server logic required to draw a histogram
server <- function(input, output) {
# Data
# Some reactive values
collateral.dt<-reactiveVal()
transaction.dt<-reactiveVal()
collateral.dt <- reactiveVal()
transaction.dt <- reactiveVal()
# Adding initial data to reactive values
collateral.dt(read.csv("./data/collateral.csv") )
collateral.dt(read.csv("./data/collateral.csv"))
transaction.dt(read.csv("./data/transactions.csv") )
transaction.dt(read.csv("./data/transactions.csv"))
# Transforming Data
new.data <- reactive({
ndt<- transaction.dt()
ndt <- transaction.dt()
withProgress(message = "Trying to read",
detail = "Hope the handwriting is legible!",
value = 0,
@@ -68,12 +65,8 @@ server <- function(input, output) {
report_date = ymd(report_date)
) %>%
# Add age of loan, loan tenure in months, which are compulsory parameters
mutate(age_of_asset_months = round(as.numeric(
report_date - origination_date
) / 30)) %>%
mutate(loan_tenure_months = round(as.numeric(
maturity_date - origination_date
) / 30)) %>%
mutate(age_of_asset_months = round(as.numeric(report_date - origination_date) / 30)) %>%
mutate(loan_tenure_months = round(as.numeric(maturity_date - origination_date) / 30)) %>%
group_by(id) %>%
# Arranging to avoid mistakes in lag
arrange(report_date) %>%
@@ -104,7 +97,6 @@ server <- function(input, output) {
# Show uploaded data
output$up_data <- renderDataTable({
DT::datatable(
new.data(),
extensions = c("Buttons"),
@@ -114,15 +106,17 @@ server <- function(input, output) {
dom = 'Bfrtip',
filter = list(position = 'top', clear = FALSE),
buttons = list(
list(extend = "csv", text = "Download Visible", filename = "page",
exportOptions = list(
modifier = list(page = "current")
)
list(
extend = "csv",
text = "Download Visible",
filename = "page",
exportOptions = list(modifier = list(page = "current"))
),
list(extend = "csv", text = "Download All", filename = "data",
exportOptions = list(
modifier = list(page = "all")
)
list(
extend = "csv",
text = "Download All",
filename = "data",
exportOptions = list(modifier = list(page = "all"))
)
)
)
@@ -146,7 +140,7 @@ server <- function(input, output) {
databaseID <- "IFS"
startdate = min(new.data()$report_date)
enddate = max(new.data()$report_date)
country = countries[countries$Country == input$country,]$Alpha.2.code
country = countries[countries$Country == input$country, ]$Alpha.2.code
withProgress(message = "Extracting data from IMF",
detail = "Hope their server is up!",
value = 0,
@@ -154,7 +148,7 @@ server <- function(input, output) {
setProgress(value = 1, message = "Trying to reach..")
print(country)
imf.data <- tryCatch(
expr={
expr = {
imf_data(
databaseID,
c("NGDP_NSA_XDC",
@@ -166,21 +160,25 @@ server <- function(input, output) {
return_raw = FALSE,
print_url = T,
times = 3
)
)
},
error = function(e){ # Specifying error message
error = function(e) {
# Specifying error message
showModal(
modalDialog(
"Error in IMF database. Sorry for the inconvenience. Can you please try again later?"
)
)
message("Error with IMF database. This is usually temporary. Sorry for the inconvenience. Please try again later.")
message(
"Error with IMF database. This is usually temporary. Sorry for the inconvenience. Please try again later."
)
},
finally = { # Specifying final message
finally = {
# Specifying final message
message("Error with IMF database. Please try again later.")
}
)
setProgress(value = 2, message = "Done")
})
@@ -212,7 +210,7 @@ server <- function(input, output) {
# removing rows with no macroeconomic data
dataset_eco <-
dataset_with_eco[!is.na(dataset_with_eco$gdp_lag) &
!is.na(dataset_with_eco$prices_lag), ]
!is.na(dataset_with_eco$prices_lag),]
setProgress(value = 2, message = "working..")
})
@@ -284,15 +282,17 @@ server <- function(input, output) {
dom = 'Bfrtip',
filter = list(position = 'top', clear = FALSE),
buttons = list(
list(extend = "csv", text = "Download Visible", filename = "page",
exportOptions = list(
modifier = list(page = "current")
)
list(
extend = "csv",
text = "Download Visible",
filename = "page",
exportOptions = list(modifier = list(page = "current"))
),
list(extend = "csv", text = "Download All", filename = "data",
exportOptions = list(
modifier = list(page = "all")
)
list(
extend = "csv",
text = "Download All",
filename = "data",
exportOptions = list(modifier = list(page = "all"))
)
)
)
@@ -325,8 +325,9 @@ server <- function(input, output) {
# Model Selection. Using functions. In production these are to be converted to APIs
selected_model <- reactive({
req(input$start_model_selection,!is.null(dataset_with_eco()))
model_sel(dff=dataset_with_eco())
req(input$start_model_selection,
!is.null(dataset_with_eco()))
model_sel(dff = dataset_with_eco())
})
# Preparing tables with predictions. Using functions. In production these are to be converted to APIs
@@ -334,11 +335,13 @@ server <- function(input, output) {
input$start_model_selection
req(!is.null(selected_model()))
z<-predic_t(dff=dataset_with_eco(),
gdpfor=gdp.forecast(),
prfor=pats.forecast(),
maxdate=maxdate(),
final.model= selected_model())
z <- predic_t(
dff = dataset_with_eco(),
gdpfor = gdp.forecast(),
prfor = pats.forecast(),
maxdate = maxdate(),
final.model = selected_model()
)
z
})
@@ -368,11 +371,9 @@ server <- function(input, output) {
)
)) %>%
filter(!is.na(name)) %>%
ggplot(aes(
x = name,
y = value,
fill = name
)) +
ggplot(aes(x = name,
y = value,
fill = name)) +
geom_violin() +
geom_boxplot(width = 0.1,
color = "black",
@@ -459,7 +460,7 @@ server <- function(input, output) {
output$exposure_on_default <- renderPlot({
input$update
req(nrow(predicted_table())>0)
req(nrow(predicted_table()) > 0)
discount_rate_pa <- input$discount_rate
withProgress(message = "Plotting",
@@ -550,9 +551,7 @@ server <- function(input, output) {
"Please click an drag to check the probabilities between ranges of possible loss."
),
plotOutput("simres",
brush = brushOpts(
id = "sim_res_sel", direction = "x"
)),
brush = brushOpts(id = "sim_res_sel", direction = "x")),
verbatimTextOutput("cumprob")
),
column(
@@ -571,10 +570,10 @@ server <- function(input, output) {
# })
simdata <- reactive({
req(nrow(predicted_table())>0)
req(nrow(predicted_table()) > 0)
req(!is.null(input$discount_rate))
input$update
collateral<-collateral.dt()
collateral <- collateral.dt()
discount_rate_pa <- input$discount_rate
withProgress(message = "Monte Carlo Simulation",
detail = "1000 simulations",
@@ -647,7 +646,7 @@ server <- function(input, output) {
})
output$cumprob <- renderText({
req(!is.null(input$sim_res_sel), !is.null(simresdata()))
req(!is.null(input$sim_res_sel),!is.null(simresdata()))
hist.pro <- simresdata()
pro_dens <- data.frame(hist.pro$x, hist.pro$y)
res <- brushedPoints(pro_dens, input$sim_res_sel)
@@ -751,29 +750,28 @@ server <- function(input, output) {
# File uploading Module
observeEvent(input$uploadnew, {
showModal(
modal1
)
showModal(modal1)
})
observeEvent(input$closemodal1,{
observeEvent(input$closemodal1, {
removeModal()
})
observeEvent(input$closemodal2,{
observeEvent(input$closemodal2, {
removeModal()
})
# Uploading temp file and collecting info about the columns
observeEvent(input$uploadfiles,{
observeEvent(input$uploadfiles, {
df.tr <- reactive({
inFile <- input$transaction
if (is.null(inFile))
return(NULL)
df <- read.csv(inFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
df <- read.csv(
inFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote
)
df
})
@@ -781,98 +779,130 @@ server <- function(input, output) {
inFile <- input$collaterals
if (is.null(inFile))
return(NULL)
df <- read.csv(inFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
df <- read.csv(
inFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote
)
df
})
updateVarSelectInput("collateralid","Select id column", df.c(),session = getDefaultReactiveDomain())
updateVarSelectInput("collateralvalue","Select Collateral value column", df.c(),session = getDefaultReactiveDomain())
updateVarSelectInput("reportdate","Select report date column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("origindate","Select origin date column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("maturitydate","Select maturity date column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("assettype","Select asset classifier column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("customertype","Select customer classifier column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("otherfact","Select any other classifier column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("bureauscore","Select bureau score column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("balance","Select asset balance column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("status","Select loan status", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("defaultflag","Select default flag column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("transid","Select id column", df.tr(),session = getDefaultReactiveDomain())
updateVarSelectInput("collateralid", "Select id column", df.c(), session = getDefaultReactiveDomain())
updateVarSelectInput("collateralvalue",
"Select Collateral value column",
df.c(),
session = getDefaultReactiveDomain())
updateVarSelectInput("reportdate",
"Select report date column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("origindate",
"Select origin date column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("maturitydate",
"Select maturity date column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("assettype",
"Select asset classifier column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("customertype",
"Select customer classifier column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("otherfact",
"Select any other classifier column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("bureauscore",
"Select bureau score column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("balance",
"Select asset balance column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("status", "Select loan status", df.tr(), session = getDefaultReactiveDomain())
updateVarSelectInput("defaultflag",
"Select default flag column",
df.tr(),
session = getDefaultReactiveDomain())
updateVarSelectInput("transid", "Select id column", df.tr(), session = getDefaultReactiveDomain())
})
# Modal 2
observeEvent(input$uploadfiles, {
showModal(
modal2
)
showModal(modal2)
})
observeEvent(input$confirmupload,{
transaction.dt(
{
df <- read.csv(input$transaction$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
head(df)
df<-
df %>%
rename(report_date=input$reportdate,
origination_date=input$origindate,
maturity_date=input$maturitydate,
asset_type=input$assettype,
customer_type=input$customertype,
bureau_score_orig=input$bureauscore,
balance=input$balance,
loan_status=input$status,
default_flag=input$defaultflag,
id=input$transid)
if(input$dateformat=="ymd"){
df$report_date<-ymd(df$report_date)
df$origination_date<-ymd(df$origination_date)
df$maturity_date<-ymd(df$maturity_date)
} else {
df$report_date<-dmy(df$report_date)
df$origination_date<-dmy(df$origination_date)
df$maturity_date<-dmy(df$maturity_date)
}
df$loan_status<-as.integer(df$loan_status)
# Covert to factors
df$asset_type<-as.factor(df$asset_type)
df$customer_type<-as.factor(df$customer_type)
if(!is.null(input$otherfact)){
for(i in 1:length(input$otherfact)){
df$input$otherfact[i]<-as.factor(df$input$otherfact[i])
}
}
df
observeEvent(input$confirmupload, {
transaction.dt({
df <- read.csv(
input$transaction$datapath,
header = input$header,
sep = input$sep,
quote = input$quote
)
head(df)
df <-
df %>%
rename(
report_date = input$reportdate,
origination_date = input$origindate,
maturity_date = input$maturitydate,
asset_type = input$assettype,
customer_type = input$customertype,
bureau_score_orig = input$bureauscore,
balance = input$balance,
loan_status = input$status,
default_flag = input$defaultflag,
id = input$transid
)
if (input$dateformat == "ymd") {
df$report_date <- ymd(df$report_date)
df$origination_date <- ymd(df$origination_date)
df$maturity_date <- ymd(df$maturity_date)
} else {
df$report_date <- dmy(df$report_date)
df$origination_date <- dmy(df$origination_date)
df$maturity_date <- dmy(df$maturity_date)
}
)
df$loan_status <- as.integer(df$loan_status)
# Covert to factors
df$asset_type <- as.factor(df$asset_type)
df$customer_type <- as.factor(df$customer_type)
if (!is.null(input$otherfact)) {
for (i in 1:length(input$otherfact)) {
df$input$otherfact[i] <- as.factor(df$input$otherfact[i])
}
}
df
})
collateral.dt(
{
df <- read.csv(input$collaterals$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
df<-
df %>%
rename(id=input$collateralid,
collateral=input$collateralvalue)
df
}
)
collateral.dt({
df <- read.csv(
input$collaterals$datapath,
header = input$header,
sep = input$sep,
quote = input$quote
)
df <-
df %>%
rename(id = input$collateralid,
collateral = input$collateralvalue)
df
})
removeModal()
})