R6Sim_simulate_posterior.R

#------------------------------------------------------------------------------#
# R6Sim: R6-based Simulation Modeling Toolkit
# Copyright (C) 2024 by The RAND Corporation
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# See LICENSE.md and README.md for more information on usage and licensing
#
# Author: Pedro Nascimento de Lima
#------------------------------------------------------------------------------#

# loading parallel and doSNOW package and creating cluster ----------------

#' Simulates Posterior Distribution for a single model
#'
#'
#' This function is most useful to simulate the posterior distribution for a single model in parallel in one machine. This function is not used when calibrating the model and not useful for parallelization across multiple nodes.
#'
#' @param self model object
#' @param n_cores number of cores to use
#' @param parallel whether to evaluate run in parallel
#' @param cluster_eval_script path to script that instantiates necessary functions. this will often mean sourcing functions external to the package and loading dependencies for the model. needed if parallel = T
#'
#' @return results data.frame from all simulations in parallel
#'
#' import parallel
#' import foreach
#' import progress
# R6Sim_simulate_posterior <- function(self, n_cores, parallel, cluster_eval_script) {
#   if (parallel) {
#     # PNL note: Decide whether to use snow:: or parallel:: makeCluster
#     cl <- parallel::makeCluster(n_cores)
#     clusterEvalQ(cl, source(cluster_eval_script))
#   }
#
#   # progress bar ------------------------------------------------------------
#   # Progress bar setup adapted from:
#   # https://stackoverflow.com/questions/5423760/how-do-you-create-a-progress-bar-when-using-the-foreach-function-in-r
#   pb <- progress_bar$new(
#     format = "R6Sim: simulating parm set = :ode_sim [:bar] :elapsed | eta: :eta",
#     total = nrow(self$posterior_params), # 100
#     width = 80
#   )
#
#   # allowing progress bar to be used in foreach -----------------------------
#   progress <- function(n) {
#     pb$tick(tokens = list(ode_sim = n))
#   }
#
#   opts <- list(progress = progress)
#
#   # foreach loop ------------------------------------------------------------
#   results <- foreach(i = 1:nrow(self$posterior_params), .combine = rbind, .options.snow = opts) %dopar% {
#     self$inputs$params <- as.numeric(self$posterior_params[i, self$inputs$priors$parameter_name])
#     names(self$inputs$params) <- self$inputs$priors$parameter_name
#     res <- self$simulate()
#     res$param.id <- i
#     return(res)
#   }
#
#   if (parallel) {
#     stopCluster(cl)
#   }
#
#   return(results)
# }