cmf_test/server.R

In conmolfields: Continuous molecular fields

#
library(shiny)
require(conmolfields)

# Define a server for the Shiny app
function(input, output) {
 
  # Fill in the spot we created for a plot
    ###
    # Activity file name
    act_train_fname <- "activity-train.txt"

    # Activity column number
    act_colnum <- 1

    # Separator
    sep <- ","

    # Kernels file name
    kernels_train_fname <- "ligands-kernels-train.RData"

    # Model file name
    model_fname <- "ligands-model.RData"

    # Types of molecular fields
    mfields <- c("q","vdw","logp","abra","abrb")

    # Whether to perform grid search for attenuation factor alpha (TRUE/FALSE, 1/0).
    alpha_grid_search <- TRUE

    # Whether to perform grid search for Tikhonov regularization coefficient gamma (TRUE/FALSE, 1/0).
    gamma_grid_search <- FALSE

    # Whether to form tconic kernel combination
    conic_kernel_combination <- FALSE

    # Whether to optimize h (TRUE/FALSE, 1/0)
    optimize_h <- FALSE

    # Whether to set b=0 (TRUE/FALSE, 1/0)
    set_b_0 <- FALSE

    # Print intermediate results in the course of optimiization (TRUE/FALSE, 1/0)
    print_interm_icv <- TRUE

    # Produce intermediate scatter plots in the course of optimization  (TRUE/FALSE, 1/0)
    plot_interm_icv <- TRUE

    # Print final results on interna; cross-validation (TRUE/FALSE, 1/0)
    print_final_icv <- TRUE

    # Produce final scatter plot for internal cross-valudation (TRUE/FALSE, 1/0)
    plot_final_icv <- TRUE
    
    #cat(sprintf("Hey %s", "Yo"))
  m13 <- eventReactive(input$goButton, {
      input$act_colnum
    #})
    #m <- reactive({
     
      ####
      act_train_fname = input$activity
      cat(sprintf("%s\n", act_train_fname))
      act_colnum = input$act_colnum
      cat(sprintf("%d\n", act_colnum))
    #
    if(FALSE)
    {
    output$text <- renderText({  
      paste("You have selected:",input$activity)
      #paste("You have selected:",m())
    })
    
    output$text1 <- renderText({  
      paste("You have selected:",as.character(input$act_colnum))
      #paste("You have selected:", "")
    })
    #
    }
    cat(sprintf("Hey %s", "WASSUP?"))
      cmf_krr_train_test(
      act_train_fname,
      act_colnum,
      sep = sep,
      kernels_train_fname = kernels_train_fname,
      model_fname = model_fname,
      mfields = mfields,
      alpha_grid_search = alpha_grid_search,
      gamma_grid_search = gamma_grid_search,
      conic_kernel_combination = conic_kernel_combination,
      optimize_h = optimize_h,
      set_b_0 = set_b_0,
      print_interm_icv = print_interm_icv,
      plot_interm_icv = plot_interm_icv,
      print_final_icv = print_final_icv,
      plot_final_icv = plot_final_icv
    )
    
      ##Yo
      load(model_fname)
      cat(sprintf("Hey %s", "WASSUP YO?"))
      
      #
 
      output$myPlot <- renderPlot({
        ##
        margin=0.5
        x = model$y_pred_cv
        y = model$y_exp
        xmin <- min(x)
        xmax <- max(x)
        ymin <- min(y)
        ymax <- max(y)
        xymin <- min(xmin, ymin) - margin
        xymax <- max(xmax, ymax) + margin
        xlab="Predicted" 
        ylab="Experimental" 
        ##
      plot(model$y_pred_cv, model$y_exp)
      plot(x, y, xlim=c(xymin, xymax), ylim=c(xymin, xymax), xlab=xlab, ylab=ylab)
        abline(coef=c(0,1))
      })
      
      })#end of reactive     
   
    output$text1 <- renderText({
      m13()
})
 
}