inst/shinyPdM/ui/train_and_predict.R

In svcuong/PdM: R package for predictive maintenance

tabpanel.train = fluidPage(theme = shinytheme("united"),
  sidebarLayout(
    sidebarPanel(
      div(align = "center",
        uiOutput("train.learner.sel"),
        br(),
        hr(),
        uiOutput("model_overview")
      )
    ),
    mainPanel(
      fluidRow(uiOutput("model.params"))
    )
  )
)

tabpanel.predict = fluidPage(theme = shinytheme("united"),
  sidebarLayout(
    sidebarPanel(
      sidebarMenu(
        menuItem("Predict on:"),
        selectInput("newdatatype", "", choices = c("task", "new data"),
          selected = "task"),
        uiOutput("import.pred.ui"),
        div(align = "center",
          actionButton("predict.run", label = "Predict"),
          br(),
          hr(),
          downloadButton("predict.download", "download predictions")
        )
      )
    ),
    mainPanel(
      tabBox(id = "predict.tab", selected = "test.set", side = "right", width = 12,
        tabPanel("Predictions", value = "pred.res",
          DT::dataTableOutput("predoverview")
        ),
        tabPanel("Test Set", value = "test.set",
          DT::dataTableOutput("import.pred.preview")
        )
      )
    )
  )
)

tabpanel.performance = fluidPage(theme = shinytheme("united"),
  tabBox(id = "performance.tab", selected = "Performance", width = 12,
    tabPanel("Performance",
      fluidRow(
        htmlOutput("performance.text"),
        column(width = 12, align = "center",
          uiOutput("perf.measures.sel"),
          actionButton("performance.run", label = "Measure Performance"),
          br(),
          br(),
          uiOutput("performance.overview", align = "left")
        )
      )
    ),
    tabPanel("Visualisations",
      htmlOutput("visualisation.text"),
      fluidRow(
        column(width = 12,
          uiOutput("visualisation.selection"),
          uiOutput("predictionplot.settings")
        )
      ),
      fluidRow(
        column(width = 12,
          verbatimTextOutput("confusion.matrix")
        )
      ),
      fluidRow(
        column(width = 12,
          plotOutput("prediction.plot")
        ),
        column(width = 12,
          plotOutput("prediction.plot.validation")
        )
      )
    )
  )
)








#  tabpanel.modelling = fluidRow(
#   tabBox(width = 12,
#     tabPanel(title = "Train",
#       # fluidRow(
#       fluidRow(
#         htmlOutput("train.text"),
#         column(width = 4, align = "center",
#           makeSidebar(
#             uiOutput("train.learner.sel"),
#             br(),
#             hr(),
#             uiOutput("model.overview")
#           )
#         ),
#         column(width = 8, align = "center",
#           fluidRow(uiOutput("model.params"))
#         )
#       )
#     ),
#     tabPanel(title = "Predict",
#       htmlOutput("prediction.text"),
#       fluidRow(
#         column(width = 3, align = "center",
#           makeSidebar(
#             selectInput("newdatatype", "Predict on:", choices = c("task", "new data"),
#               selected = "task"),
#             conditionalPanel("input.newdatatype == 'new data'",
#               selectInput("import.pred.type", "Type", selected = "mlr",
#                 choices = c("mlr", "OpenML", "CSV", "ARFF"))
#             ),
#             uiOutput("import.pred.ui"),
#             actionButton("predict.run", label = "Predict"),
#             br(),
#             br()
#           )
#         ),
#         column(width = 9, align = "center",

#       )
#     ),
#     tabPanel(title = "Performance",
#       htmlOutput("performance.text"),
#       fluidRow(
#         column(width = 12, align = "center",
#           uiOutput("perf.measures.sel"),
#           actionButton("performance.run", label = "Measure Performance"),
#           br(),
#           br(),
#           uiOutput("performance.overview", align = "left")
#         )
#       )
#     ),
#     tabPanel("Visualisations",
#       htmlOutput("visualisation.text"),
#       fluidRow(
#         column(width = 12,
#           uiOutput("visualisation.selection"),
#           uiOutput("predictionplot.settings")
#         )
#       ),
#       fluidRow(
#         column(width = 12,
#           verbatimTextOutput("confusion.matrix")
#         )
#       ),
#       fluidRow(
#         column(width = 12,
#           plotOutput("prediction.plot")
#         )
#       )
#     )
#   )
# )