inst/shiny-server/server/EAF_plots.R

In IOHprofiler/IOHanalyzer: Data Analysis Part of 'IOHprofiler'

### EAF for single functions

output$EAF.Single_Plot <- renderPlotly({
  req(length(DATA()) > 0)
  render_EAF_Plot()
})

get_data_EAF <- reactive({
  input$EAF.Single.Refresh
  dsList <- subset(DATA(), ID %in% input$EAF.Single.Algs)

  generate_data.EAF(dsList, n_sets = input$EAF.Single.levels,
                    subsampling = 50*input$EAF.Single.Subsampling,
                    scale_xlog = input$EAF.Single.Logx)
})


render_EAF_Plot <- reactive({
  withProgress({
    dt_eaf <- get_data_EAF()

    if (input$EAF.Single.Problines) {
      dsList <- subset(DATA(), ID %in% input$EAF.Single.Algs)
      dt_fv <- get_FV_sample(dsList, unique(dt_eaf$runtime), output='long')
      dt_lines <- dt_fv[, .(median = median(`f(x)`), per25 = quantile(`f(x)`, 0.25),
                            per75 = quantile(`f(x)`, 0.75), ID=`ID` ), by='runtime']

      dt_lines <- dt_lines %>% set_colnames(c('runtime', 'median', '25%', '75%', 'ID'))

    } else {
      dt_lines <- NULL
    }

    plot_eaf_data(dt_eaf, attr(DATA(), 'maximization'),
                  scale.xlog = input$EAF.Single.Logx, scale.ylog = input$EAF.Single.Logy,
                  xmin = input$EAF.Single.Min, xmax = input$EAF.Single.Max,
                  ymin = input$EAF.Single.yMin, ymax = input$EAF.Single.yMax,
                  subplot_attr = 'ID', x_title = "Funciton Evaluations",
                  y_title = "f(x)", show.colorbar = input$EAF.Single.Colorbar,
                  dt_overlay = dt_lines)
  },
  message = "Creating plot")
})

output$EAF.Single.Download <- downloadHandler(
  filename = function() {
    eval(FIG_NAME_EAF)
  },
  content = function(file) {
    save_plotly(render_EAF_Plot(), file)
  },
  contentType = paste0('image/', input$EAF.Single.Format)
)


### EAF-based ECDF
output$EAF.CDF_Plot <- renderPlotly({
  req(length(DATA()) > 0)
  render_EAFCDF_Plot()
})

get_data_EAFCDF <- reactive({
  dsList <- subset(DATA(), ID %in% input$EAF.CDF.Algs)
  dt_eaf <- generate_data.EAF(dsList,
                              subsampling = 50*input$EAF.CDF.Subsampling,
                              scale_xlog = input$EAF.CDF.Logx)

  dt_ecdf <- rbindlist(lapply(input$EAF.CDF.Algs, function(id) {
    dt_sub <- dt_eaf[ID == id, ]
    temp <- generate_data.ECDF_From_EAF(dt_sub, maximization = attr(dsList, 'maximization'),
                                        min_val = input$EAF.CDF.yMin,
                                        max_val = input$EAF.CDF.yMax, scale_log=input$EAF.CDF.Logy)
    temp$ID <- id
    temp
  }))
})

render_EAFCDF_Plot <- reactive({
  withProgress({
    plot_general_data(get_data_EAFCDF(), 'x', 'mean', 'line', 'ID',
                  scale.xlog = input$EAF.CDF.Logx, scale.ylog = F, x_title = "Function Evaluations",
                  y_title = "Fraction")
  },
  message = "Creating plot")
})

output$EAF.CDF.Download <- downloadHandler(
  filename = function() {
    eval(FIG_NAME_EAFCDF)
  },
  content = function(file) {
    save_plotly(render_EAFCDF_Plot(), file)
  },
  contentType = paste0('image/', input$EAF.CDF.Format)
)

### EAF-differences

output$EAF.Diff_Plot <- renderPlotly({
  req(length(DATA()) > 0)
  render_EAFDiff_Plot()
})

get_data_EAFDiff <- reactive({
  dsList <- subset(DATA(), ID %in% input$EAF.Diff.Algs)
  matrices <- generate_data.EAF_diff_Approximate(dsList, input$EAF.Diff.Min, input$EAF.Diff.Max,
                                                 input$EAF.Diff.yMin, input$EAF.Diff.yMax)

  matrices
})

render_EAFDiff_Plot <- reactive({
  withProgress({
    plot_eaf_differences(get_data_EAFDiff(), scale.xlog = input$EAF.Diff.Logx,
                         scale.ylog = input$EAF.Diff.Logy, zero_transparant = input$EAF.Diff.ZeroTransparant,
                         show_negatives = input$EAF.Diff.ShowNegatives)
  },
  message = "Creating plot")
})

output$EAF.Diff.Download <- downloadHandler(
  filename = function() {
    eval(FIG_NAME_EAFDiff)
  },
  content = function(file) {
    save_plotly(render_EAFDiff_Plot(), file)
  },
  contentType = paste0('image/', input$EAF.Diff.Format)
)


### ### Multi-function view ### ###

### EAF for multiple functions

output$EAF.Multi_Plot <- renderPlotly({
  req(length(DATA_RAW()) > 0)
  render_EAF_multi_Plot()
})

get_data_EAF_multi <- reactive({
  input$EAF.Multi.Refresh
  dsList <- subset(DATA_RAW(), ID %in% input$EAF.Multi.Algs, funcId %in% input$EAF.Multi.FuncIds, DIM == input$Overall.Dim )

  generate_data.EAF(dsList, n_sets = input$EAF.Multi.levels, subsampling = 50*input$EAF.Multi.Subsampling,
                    scale_xlog = input$EAF.Multi.Logx, xmin=input$EAF.Multi.Min, xmax=input$EAF.Multi.Max)
})

render_EAF_multi_Plot <- reactive({
  withProgress({
    dt_eaf <- get_data_EAF_multi()

    if (input$EAF.Multi.Problines) {
      dsList <- subset(DATA_RAW(), ID %in% input$EAF.Multi.Algs, funcId %in% input$EAF.Multi.FuncIds, DIM == input$Overall.Dim )

      dt_fv <- get_FV_sample(dsList, unique(dt_eaf$runtime), output='long')
      dt_lines <- dt_fv[, .(median = median(`f(x)`), per25 = quantile(`f(x)`, 0.25),
                            per75 = quantile(`f(x)`, 0.75), ID=`ID` ), by='runtime']

      dt_lines <- dt_lines %>% set_colnames(c('runtime', 'median', '25%', '75%', 'ID'))

    } else {
      dt_lines <- NULL
    }

    plot_eaf_data(dt_eaf, attr(DATA_RAW(), 'maximization'),
                  scale.xlog = input$EAF.Multi.Logx, scale.ylog = input$EAF.Multi.Logy,
                  xmin = input$EAF.Multi.Min, xmax = input$EAF.Multi.Min,
                  ymin = input$EAF.Multi.yMin, ymax = input$EAF.Multi.yMax,
                  subplot_attr = 'ID', x_title = "Function Evaluations",
                  y_title = "f(x)", show.colorbar = input$EAF.Multi.Colorbar,
                  dt_overlay = dt_lines)
  },
  message = "Creating plot")
})

output$EAF.Multi.Download <- downloadHandler(
  filename = function() {
    eval(FIG_NAME_EAF)
  },
  content = function(file) {
    save_plotly(render_EAF_multi_Plot(), file)
  },
  contentType = paste0('image/', input$EAF.Multi.Format)
)


### EAF-based ECDF
output$EAF.MultiCDF_Plot <- renderPlotly({
  req(length(DATA_RAW()) > 0)
  render_EAFMultiCDF_Plot()
})

get_data_EAFMultiCDF <- reactive({
  input$EAF.MultiCDF.Refresh
  dsList <- subset(DATA_RAW(), ID %in% isolate(input$EAF.MultiCDF.Algs),
                   funcId %in% isolate(input$EAF.MultiCDF.FuncIds),
                   DIM == isolate(input$Overall.Dim))
  dt_eaf <- generate_data.EAF(dsList, subsampling = 50*isolate(input$EAF.MultiCDF.Subsampling),
                              scale_xlog = isolate(input$EAF.MultiCDF.Logx),
                              xmin = isolate(input$EAF.MultiCDF.xMin),
                              xmax = isolate(input$EAF.MultiCDF.xMax), n_sets = 100)
  max_rt <- max(dt_eaf[,'runtime'])
  dt_eMultiCDF <- rbindlist(lapply(isolate(input$EAF.MultiCDF.Algs), function(id) {
    dt_sub <- dt_eaf[ID == id, ]
    temp <- generate_data.ECDF_From_EAF(dt_sub, maximization = attr(dsList, 'maximization'),
                                        min_val = isolate(input$EAF.MultiCDF.yMin),
                                        max_val = isolate(input$EAF.MultiCDF.yMax),
                                        scale_log=isolate(input$EAF.MultiCDF.Logy))
    if (max(temp[,'x']) < max_rt){
      extreme <- data.table("x" = max_rt,
                            "ID" = id,
                            "mean" = max(temp[,'mean']))
      temp <- rbind(temp, extreme)
    }
    temp$ID <- id
    temp
  }))
  dt_eMultiCDF
})

render_EAFMultiCDF_Plot <- reactive({
  withProgress({
    plot_general_data(get_data_EAFMultiCDF(), 'x', 'mean', 'line', 'ID',
                      scale.xlog = isolate(input$EAF.MultiCDF.Logx), scale.ylog = F, x_title = "Function Evaluations",
                      y_title = "Fraction", line.step = T, show.legend = T)
  },
  message = "Creating plot")
})

output$EAF.MultiCDF.Download <- downloadHandler(
  filename = function() {
    eval(FIG_NAME_EAFMultiCDF)
  },
  content = function(file) {
    save_plotly(render_EAFMultiCDF_Plot(), file)
  },
  contentType = paste0('image/', input$EAF.MultiCDF.Format)
)


output$EAF.AUC.Table.Download <- downloadHandler(
  filename = function() {
    eval(AUC_ECDFEAF_aggr_name)
  },
  content = function(file) {
    save_table(auc_eaf_grid_table(), file)
  }
)

auc_eaf_grid_table <- reactive({
  dt_ecdf <- get_data_EAFMultiCDF()
  df <- generate_data.AUC(NULL, NULL, dt_ecdf = dt_ecdf, normalize = input$EAF.MultiCDF.Normalize_AUC)
  if (input$EAF.MultiCDF.Normalize_AUC)
    df$aoc <- 1-df$auc
  else
    df$aoc <- df$x-df$auc
  colnames(df) <- c("ID", "Runtime", "AUC", "AOC")
  df
})

output$AUC_EAF_GRID_GENERATED <- DT::renderDataTable({
  req(length(DATA_RAW()) > 0)
  auc_eaf_grid_table()
},
editable = FALSE,
rownames = FALSE,
options = list(
  pageLength = 10,
  lengthMenu = c(5, 10, 25, -1),
  scrollX = T,
  server = T,
  columnDefs = list(
    list(
      className = 'dt-right', targets = "_all"
    )
  )
),
filter = 'top'
)