Я создаю приложение с простой моделью ML, рабочий процесс выглядит следующим образом:
1) Чтение файла.2) Создайте модель 3) Составьте прогноз и значение переменной
Локально, приложение работает нормально:
Но когдаЯ пытаюсь опубликовать приложение, я получаю следующую ошибку:
Error in value[[3L]](cond) : app.R did not return a shiny.appobj object.
Calls: local ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Ejecución interrumpida
Ошибка не говорит мне, это полный код:
library(shiny)
library(readxl)
library(tidyverse)
library(xgboost)
library(caret)
library(iml)
#### UI
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
fileInput("file1", "Choose CSV File",
accept = c(
"text/csv",
"text/comma-separated-values,text/plain",
".csv")
),
tags$hr(),
checkboxInput("header", "Header", TRUE)
),
mainPanel(
plotOutput("plot2", click = "plot_brush"),
plotOutput("plot1", click = "plot_brush")
)
)
)
server <- function(input, output) {
# create mydata as a reactiveVal so that it can be edited everywhere
mydata = reactiveVal()
model <- reactiveValues()
# reactive block is changed with an observe that allows mydata to be updated
# on change of data
observe({
req(input$file1, input$header, file.exists(input$file1$datapath))
data = read.csv(input$file1$datapath, header = input$header)
mydata(data)
})
output$contents <- renderTable({
req(mydata())
#mydata()
})
### test
xgb_trcontrol = trainControl(
method = "cv",
number = 5,
allowParallel = TRUE,
verboseIter = FALSE,
returnData = FALSE
)
xgbGrid <- expand.grid(nrounds = c(10,14), # this is n_estimators in the python code above
max_depth = c(10, 15, 20, 25),
colsample_bytree = seq(0.5, 0.9, length.out = 5),
## The values below are default values in the sklearn-api.
eta = 0.1,
gamma=0,
min_child_weight = 1,
subsample = 1
)
observe({
if ('data.frame' %in% class(mydata()) & !'predicted' %in% names(mydata())){
set.seed(0)
xgb_model = train(
select(mydata(),"LotArea","YrSold"), as.vector(t(mydata()["SalePrice"])),
trControl = xgb_trcontrol,
tuneGrid = xgbGrid,
method = "xgbTree"
)
predicted = predict(xgb_model, select(mydata(),"LotArea","YrSold"))
data = mydata()
data["predicted"] = predicted
mydata(data)
}
#xgb_model
})
output$plot1 <- renderPlot({
data = mydata()
# this is here to prevent premature triggering of this ggplot.
# otherwise you'll get the "object not found" error
if('predicted' %in% names(data)){
ggplot(mydata(), aes(x=predicted, y=SalePrice)) + geom_point()
}
})
output$plot2 <- renderPlot({
data = mydata()
# this is here to prevent premature triggering of this ggplot.
# otherwise you'll get the "object not found" error
if('predicted' %in% names(data)){
xgb_model = train(
select(mydata(),"LotArea","YrSold"), as.vector(t(mydata()["SalePrice"])),
trControl = xgb_trcontrol,
tuneGrid = xgbGrid,
method = "xgbTree"
)
predictor = Predictor$new(xgb_model, data = select(mydata(),"LotArea","YrSold"), y = mydata()["SalePrice"])
shapley = Shapley$new(predictor, x.interest = select(mydata(),"LotArea","YrSold")[1,])
shapley$plot()
}
})
}
shinyApp(ui, server)
И образец входных данных:
https://drive.google.com/file/d/1R8GA0fW0pOgG8Cpykc8mAThvKOCRCVl0/view?usp=sharing