R/util_runnl_dyn.R

In nldoc/nlrx: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'

#' Simulated Annealing call simulations function
#'
#' @description Simulated Annealing call simulations function
#'
#' @param nl nl object
#' @param seed current model seed
#' @param cleanup.csv TRUE/FALSE, if TRUE temporary created csv output files will be deleted after gathering results.
#' @param cleanup.xml TRUE/FALSE, if TRUE temporary created xml output files will be deleted after gathering results.
#' @param cleanup.bat TRUE/FALSE, if TRUE temporary created bat/sh output files will be deleted after gathering results.
#' @aliases util_run_nl_dyn_GenSA
#' @rdname util_run_nl_dyn_GenSA
#' @keywords internal
util_run_nl_dyn_GenSA <- function(nl,
                                  seed,
                                  cleanup.csv,
                                  cleanup.xml,
                                  cleanup.bat) {

  # Get GenSA object from simdesign:
  gensa <- getsim(nl, "simobject")

  # Call the GenSA function from the GenSA package:
  results <- GenSA::GenSA(
    par = NULL,
    fn = function(par, ...) {
      util_run_nl_dyn_GenSA_fn(
        param = par,
        nl = nl,
        evalcrit = gensa$evalcrit,
        seed = seed,
        cleanup.csv = cleanup.csv,
        cleanup.xml = cleanup.xml,
        cleanup.bat = cleanup.bat,
        ...
      )
    },
    control = gensa$control,
    lower = as.vector(gensa$lower),
    upper = as.vector(gensa$upper)
  )

  return(results)
}


#' Simulated Annealing run simulation function
#'
#' @description Simulated Annealing run simulation function
#'
#' @param param vector of model parameters, generated by GenSA function
#' @param nl nl object
#' @param evalcrit evaluation criterion for simulated annealing
#' @param seed current model seed
#' @param cleanup.csv TRUE/FALSE, if TRUE temporary created csv output files will be deleted after gathering results.
#' @param cleanup.xml TRUE/FALSE, if TRUE temporary created xml output files will be deleted after gathering results.
#' @param cleanup.bat TRUE/FALSE, if TRUE temporary created bat/sh output files will be deleted after gathering results.
#' @aliases util_run_nl_dyn_GenSA_fn
#' @rdname util_run_nl_dyn_GenSA_fn
#' @keywords internal
util_run_nl_dyn_GenSA_fn <- function(param,
                                     nl,
                                     evalcrit,
                                     seed,
                                     cleanup.csv,
                                     cleanup.xml,
                                     cleanup.bat) {

  # Generate a parameterset:
  names(param) <- names(getexp(nl, "variables"))

  ## Generate parameterset
  gensa_param <- tibble::as_tibble(t(param))

  ## Add constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    gensa_param <- tibble::as_tibble(cbind(gensa_param,
                                           getexp(nl, "constants"),
                                           stringsAsFactors = FALSE))

  }

  # Attach current parameterisation to nl object:
  setsim(nl, "siminput") <- gensa_param
  # Call netlogo:
  results <- run_nl_one(
    nl = nl,
    siminputrow = 1,
    seed = seed,
    cleanup.csv = cleanup.csv,
    cleanup.xml = cleanup.xml,
    cleanup.bat = cleanup.bat
  )

  # Select metric for gensa:
  if(is.function(evalcrit)) {
    # Apply evalcrit function
    nl@simdesign@simoutput <- results
    results <- evalcrit(nl)
  } else {
    # Select evalcrit metric and calculate mean value over ticks:
    results <- results[[evalcrit]]
    # Calc mean and convert to numeric:
    if (length(results) > 1) {
      results <- mean(results)
    }
  }
  results <- as.numeric(results)

  return(results)
}


#' Genetic Algorithm call simulations function
#'
#' @description Genetic Algorithm call simulations function
#'
#' @param nl nl object
#' @param seed current model seed
#' @param cleanup.csv TRUE/FALSE, if TRUE temporary created csv output files will be deleted after gathering results.
#' @param cleanup.xml TRUE/FALSE, if TRUE temporary created xml output files will be deleted after gathering results.
#' @param cleanup.bat TRUE/FALSE, if TRUE temporary created bat/sh output files will be deleted after gathering results.
#' @aliases util_run_nl_dyn_GenAlg
#' @rdname util_run_nl_dyn_GenAlg
#' @keywords internal
util_run_nl_dyn_GenAlg <- function(nl,
                                   seed,
                                   cleanup.csv,
                                   cleanup.xml,
                                   cleanup.bat) {

  # Get GenSA object from simdesign:
  galg <- getsim(nl, "simobject")

  # Call the GenSA function from the GenSA package:
  results <- genalg::rbga(
    stringMin = galg$lower,
    stringMax = galg$upper,
    popSize = galg$popSize,
    iters = galg$iters,
    elitism = galg$elitism,
    mutationChance = galg$mutationChance,
    evalFunc = function(par, ...) {
      util_run_nl_dyn_GenAlg_fn(
        param = par,
        nl = nl,
        evalcrit = galg$evalcrit,
        seed = seed,
        cleanup.csv = cleanup.csv,
        cleanup.xml = cleanup.xml,
        cleanup.bat = cleanup.bat,
        ...
      )
    }
  )

  return(results)
}


#' Genetic Algorithm run simulation function
#'
#' @description Genetic Algorithm run simulation function
#'
#' @param param vector of model parameters, generated by GenSA function
#' @param nl nl object
#' @param evalcrit evaluation criterion for simulated annealing
#' @param seed current model seed
#' @param cleanup.csv TRUE/FALSE, if TRUE temporary created csv output files will be deleted after gathering results.
#' @param cleanup.xml TRUE/FALSE, if TRUE temporary created xml output files will be deleted after gathering results.
#' @param cleanup.bat TRUE/FALSE, if TRUE temporary created bat/sh output files will be deleted after gathering results.
#' @aliases util_run_nl_dyn_GenAlg_fn
#' @rdname util_run_nl_dyn_GenAlg_fn
#' @keywords internal
util_run_nl_dyn_GenAlg_fn <- function(param,
                                      nl,
                                      evalcrit,
                                      seed,
                                      cleanup.csv,
                                      cleanup.xml,
                                      cleanup.bat) {

  # Generate a parameterset:
  names(param) <- names(getexp(nl, "variables"))

  ## Generate parameterset
  gensa_param <- tibble::as_tibble(t(param))

  ## Add constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    gensa_param <- tibble::as_tibble(cbind(gensa_param,
                                           getexp(nl, "constants"),
                                           stringsAsFactors = FALSE))

  }

  # Attach current parameterisation to nl object:
  setsim(nl, "siminput") <- gensa_param
  # Call netlogo:
  results <- run_nl_one(
    nl = nl,
    siminputrow = 1,
    seed = seed,
    cleanup.csv = cleanup.csv,
    cleanup.xml = cleanup.xml,
    cleanup.bat = cleanup.bat
  )

  # Select metric for gensa:
  if(is.function(evalcrit)) {
    # Apply evalcrit function
    nl@simdesign@simoutput <- results
    results <- evalcrit(nl)
  } else {
    # Select evalcrit metric and calculate mean value over ticks:
    results <- results[[evalcrit]]
    # Calc mean and convert to numeric:
    if (length(results) > 1) {
      results <- mean(results)
    }
  }
  results <- as.numeric(results)

  return(results)
}

#' ABCmcmc call simulations function
#'
#' @description ABCmcmc call simulations function
#'
#' @param nl nl object
#' @param seed current model seed
#' @param cleanup.csv TRUE/FALSE, if TRUE temporary created csv output files will be deleted after gathering results.
#' @param cleanup.xml TRUE/FALSE, if TRUE temporary created xml output files will be deleted after gathering results.
#' @param cleanup.bat TRUE/FALSE, if TRUE temporary created bat/sh output files will be deleted after gathering results.
#' @aliases util_run_nl_dyn_ABCmcmc
#' @rdname util_run_nl_dyn_ABCmcmc
#' @keywords internal
util_run_nl_dyn_ABCmcmc <- function(nl, seed, cleanup.csv=TRUE, cleanup.xml=TRUE, cleanup.bat=TRUE)
{
  abcmcmc <- getsim(nl, "simobject")

  globals <- list(nl=nl,
                  postpro_function=abcmcmc$postpro_function,
                  seed=seed,
                  cleanup.csv=cleanup.csv,
                  cleanup.xml=cleanup.xml,
                  cleanup.bat=cleanup.bat,
                  use_seed=abcmcmc$use_seed)

  # The ABC_mcmc function does not allow to pass additional arguments for the simulation function
  # Instead, we write globals to tempfile and store the filename as a global option
  globals.file <- tempfile(pattern="nlrxglobals", fileext = ".rds")
  saveRDS(globals, globals.file)
  options(nlrx.globals = globals.file)

  ## Add model function to abcmcmc and remove postpro_function:
  abcmcmc$model <- util_run_nl_dyn_ABCmcmc_fn
  abcmcmc$postpro_function <- NULL

  # perform simulations:
  results <- do.call(EasyABC::ABC_mcmc, abcmcmc)

  # Convert result in nested tibble format:
  param <- results$param
  colnames(param) <- names(getexp(nl, "variables"))

  stats <- results$stats
  colnames(stats) <- getexp(nl, "metrics")

  ## Preapre output, depending on method:
  if(abcmcmc$method == "Wegmann")
  {
    results <- tibble::tibble(param=list(tibble::as_tibble(param)),
                              stats=list(tibble::as_tibble(stats)),
                              dist=list(results$dist),
                              epsilon=list(results$epsilon),
                              nsim=list(results$nsim),
                              n_between_sampling=list(results$n_between_sampling),
                              computime=list(results$computime),
                              min_stats=list(results$min_stats),
                              max_stats=list(results$max_stats),
                              lambda=list(results$lambda),
                              geometric_mean=list(results$geometric_mean),
                              boxcox_mean=list(results$boxcox_mean),
                              boxcox_sd=list(results$boxcox_sd),
                              pls_transform=list(results$pls_transform),
                              numcomp=list(results$numcomp))
  } else
  {
    results <- tibble::tibble(param=list(tibble::as_tibble(param)),
                              stats=list(tibble::as_tibble(stats)),
                              dist=list(results$dist),
                              stats_normalization=list(results$stats_normalization),
                              epsilon=list(results$epsilon),
                              nsim=list(results$nsim),
                              n_between_sampling=list(results$n_between_sampling),
                              computime=list(results$computime))
  }

  return(results)
}

#' Genetic Algorithm run simulation function
#'
#' @description Genetic Algorithm run simulation function
#'
#' @param param vector of model parameters passed from ABC_mcmc function. If use_seeds = TRUE, the first element of this vector is a random seed
#' @aliases util_run_nl_dyn_GenAlg_fn
#' @rdname util_run_nl_dyn_GenAlg_fn
#' @keywords internal
util_run_nl_dyn_ABCmcmc_fn <- function(param)
{
  # Get globals path from global option and read variables:
  globals.file <- getOption("nlrx.globals")
  globals <- readRDS(globals.file)
  # Restore variables from the globals file
  nl <- globals$nl
  postpro_function <- globals$postpro_function
  seed <- globals$seed
  cleanup.csv <- globals$cleanup.csv
  cleanup.xml <- globals$cleanup.xml
  cleanup.bat <- globals$cleanup.bat
  use_seed <- globals$use_seed

  # Check if use_seed was used and overwrite the global random seed
  if(use_seed == TRUE)
  {
    seed <- param[1]
    param <- param[-1]
  }

  # Generate a parameterset:
  names(param) <- names(getexp(nl, "variables"))
  abcmcmc_param <- tibble::as_tibble(t(param))

  ## Add constants if any:
  if(length(getexp(nl, "constants")) > 0)
  {
    abcmcmc_param <- tibble::as_tibble(cbind(abcmcmc_param,
                                             getexp(nl, "constants"),
                                             stringsAsFactors = FALSE))
  }

  # Attach current parameterisation to nl object:
  setsim(nl, "siminput") <- abcmcmc_param
  # Call netlogo:
  results <- run_nl_one(
    nl = nl,
    siminputrow = 1,
    seed = seed,
    cleanup.csv = cleanup.csv,
    cleanup.xml = cleanup.xml,
    cleanup.bat = cleanup.bat
  )

  # Check if a postpro function is provided
  if(is.function(postpro_function)) {
    # Apply evalcrit function
    nl@simdesign@simoutput <- results
    results <- postpro_function(nl)
  } else {
    # If no function is provided, the defined metrics columns are selected
    results <- results %>%
      dplyr::select(getexp(nl, "metrics"))
    # If there is more than one value per metric, we automatically calculated colMeans:
    results <- colMeans(results)
  }
  return(results)
}