inst/shinyapp/ui/ml_rf.R

In gongyh/RamanD2O: RamanD2O: an R/Shiny app designed for the analysis of cellular Ramanome data

# Random forest tab
tabItem(
  tabName = "rf",
  h2("Training, evaluation and testing"),
  br(),
  fluidRow(
    box(
      title = "Settings", status = "info",
      solidHeader = TRUE, collapsible = FALSE,
      column(
        12,
        fluidRow(
          column(3, uiOutput("hs_select_for_ml_train")),
          column(3, uiOutput("hs_select_for_ml_eval")),
          column(3, uiOutput("hs_select_for_ml_label")),
          column(3, uiOutput("hs_select_for_ml_test"))
        ),
        h4("Parameters for Random forest classifier:"),
        fluidRow(
          column(3,
                 numericInput("rf_ntree",
                              "Number of trees",
                              100, min = 1, step = 1)),
          column(3,
                 checkboxInput("rf_replace", "Replacement", value = TRUE),
                 class = "top25")
        ),
        hr(),
        fluidRow(
          column(3, withBusyIndicatorUI(actionButton("train",
                                                     "Train & Eval",
                                                     class = "btn-success"))),
          column(3, withBusyIndicatorUI(actionButton("eval",
                                                     "Eval",
                                                     class = "btn-success"))),
          column(3, withBusyIndicatorUI(actionButton("test",
                                                     "Test",
                                                     class = "btn-success")))
        ),
        hr(),
        fluidRow(
          column(4,
                 fileInput("upload_model_file",
                           "Select Existing Model",
                           accept = ".rds", placeholder = "model.rds")),
          column(2,
                 withBusyIndicatorUI(actionButton("upload_model",
                                                  "Upload Model",
                                                  class = "btn-success")),
                 class = "top25"),
          column(3,
                 withBusyIndicatorUI(downloadButton("download_model",
                                                    "Download Model",
                                                    class = "btn-success")),
                 class = "top25")
        )
      )
    ),
    box(
      title = "Notes", status = "info", solidHeader = TRUE, collapsible = FALSE,
      p("randomForest implements Breiman's random forest algorithm (based on
        Breiman and Cutler's original Fortran code) for classification
        and regression.
        The RandomForestClassifier is trained using bootstrap aggregation,
        where each new tree is fit from a bootstrap sample of
        the training observations.
        The out-of-bag (OOB) error is the average error for each observation
        calculated using predictions from the trees that do not contain this
        observation in their respective bootstrap sample.
        This allows the RandomForestClassifier to be fit and validated
        whilst being trained."),
      p("1. If you also want to evaluate your model, please change Evaluation
        dataset to anything except _ ."),
      p("2. Change Label to your categorical variable."),
      p("3. Number of trees should not be set to too small to ensure that every
        input row gets predicted at least a few times."),
      p("4. Sampling of cases (replicate) can be done with or
        without replacement.")
    )
  ),
  fluidRow(
    box(
      title = "Results", status = "warning",
      solidHeader = TRUE, collapsible = TRUE, width = 6,
      column(
        12,
        tabsetPanel(
          tabPanel(
            "Error rates",
            br(),
            plotOutput("rf_mse_plot") %>% withSpinner(),
            tags$div(
              style = "text-align: right; margin-right: 20px;",
              downloadButton("download_result1",
                             "Download",
                             class = "btn-primary")
            )
          ),
          tabPanel(
            "Importance",
            br(),
            plotOutput("rf_importance_plot") %>% withSpinner(),
            tags$div(
              style = "text-align: right; margin-right: 20px;",
              downloadButton("download_result2",
                             "Download",
                             class = "btn-primary")
            )
          ),
          tabPanel(
            "Confusion (OOB)",
            br(),
            DTOutput("rf_confusion_oob_plot") %>% withSpinner()
          ),
          tabPanel(
            "Confusion (Evaluation)",
            br(),
            DTOutput("rf_confusion_eval_plot") %>% withSpinner()
          ),
          tabPanel(
            "Eval/Test results",
            br(),
            DTOutput("rf_test_predicted_plot") %>% withSpinner()
          )
        )
      )
    ),
    box(
      title = "Raman spectra", status = "warning",
      solidHeader = TRUE, collapsible = FALSE,
      plotlyOutput("selected_predicted_plot") %>% withSpinner()
    )
  )
)