run_model

Define the parameter space still not yet run

This is defined in an R script that sets up the parameter space, and runs everything that has not already completed.

source("code/set_up_parameters.R")

To save you searching on Github, here is the entire contents of the file code/set_up_parameters.R:

setwd("/data/projects/punim0243/W_shredder")

#############################################
# Load all custom functions and packages
#############################################
source_rmd <- function(file){
  options(knitr.duplicate.label = "allow")
  tempR <- tempfile(tmpdir = ".", fileext = ".R")
  on.exit(unlink(tempR))
  knitr::purl(file, output = tempR, quiet = TRUE)
  source(tempR, local = globalenv())
}
source_rmd("analysis/model_functions.Rmd")
custom_functions <- ls()

#############################################
# Define the entire parameter space to be run
#############################################
print("Defining parameter space")

parameters <- expand.grid(
  release_size = 20,
  release_strategy = c("one_patch", "all_patches"),
  W_shredding_rate = c(0.50, 0.95, 1), # strength of gene drive in females
  Z_conversion_rate = c(0, 0.5, 0.95), # strength of gene drive in males
  Zr_creation_rate = c(0, 0.001, 0.01, 0.1), # frequency of NHEJ in males
  Zr_mutation_rate = c(0.0, 0.00001),
  Wr_mutation_rate = c(0.0, 0.00001),
  cost_Zdrive_female = c(0.01, 0.1, 0.5, 1), # Cost of Z* to female fecundity
  cost_Zdrive_male = c(0.01, 0.2),  # Cost of Z* to male mating success
  male_migration_prob = c(0.05, 0.5),
  female_migration_prob = c(0.05, 0.5),
  migration_type = c("local", "global"), # do migrants move to next door patch, or a random patch anywhere in the world?
  n_patches = c(2, 20),
  softness = c(0, 0.5, 1),
  male_weighting = c(0.5, 1, 1.5),
  density_dependence_shape = c(0.2, 1, 1.8),
  cost_Wr = 0,   # Assume resistance is not costly for now. Seems pretty obvious how this affects evolution
  cost_Zr = 0,
  cost_A = 0,
  cost_B = 0,
  max_fecundity = c(50, 100),
  carrying_capacity = 10000,
  initial_pop_size = 10000,
  initial_Zdrive = 0,
  initial_Zr = 0.00,
  initial_Wr = 0.00,
  initial_A = c(0, 0.05),
  initial_B = c(0, 0.05),
  realisations = 1, # change to e.g. 1:100 for replication
  generations = 1000,
  burn_in = 50
) %>% filter(!(W_shredding_rate == 0 & Z_conversion_rate == 0)) %>%
  mutate(migration_type = as.character(migration_type),
         release_strategy = as.character(release_strategy))

# Shuffle for even workload across all cores
set.seed(1)
parameters <- parameters[sample(nrow(parameters)), ]

# Set the initial frequency to be the same as the mutation rate for the resistant chromosomes
parameters$initial_Wr <- parameters$Wr_mutation_rate
parameters$initial_Zr <- parameters$Zr_mutation_rate

# No point doing lots of different W_shredding_rate values when cost_Zdrive_female == 1
parameters$W_shredding_rate[parameters$cost_Zdrive_female == 1] <- 1
parameters <- parameters %>% distinct()
num_parameter_spaces <- nrow(parameters)

#############################################################################
# Create a data frame of parameter spaces that have been completed already
# and remove rows from `parameters` that are already finished
#############################################################################

print("Checking previously-completed files...")

completed <- readRDS("data/all_results.rds") %>%
  select(!! names(parameters))
completed <- apply(completed, 1, paste0, collapse = "_")

to_do <- data.frame(row = 1:nrow(parameters),
                    pasted = apply(parameters, 1, paste0, collapse = "_"),
                    stringsAsFactors = FALSE) %>%
  filter(!(pasted %in% completed))

parameters <- parameters[to_do$row, ]
print(paste("Already completed", length(completed), "parameter spaces"))
print(paste("Queing up", nrow(parameters), "model runs"))
rm(to_do)

Now launch lots of SLURM jobs to run the remaining parameter spaces

chunk_size <- 4000
cpus <- 1
sopt <- list(time = '168:00:00',  # max run time per node in hours
             mem  = '32768')     # 32GB memory across all 8 cores

chunks <- split(1:nrow(parameters),
                ceiling(seq_along(1:nrow(parameters))/chunk_size))
number_of_chunks <- length(chunks)


sjob <- slurm_apply(
  f = function(i) {
    try(do_all_parameters(parameters[chunks[[i]],], 
                          over_write = FALSE, 
                          cores = cpus,
                          wd = working_directory))
  },
  params = data.frame(i = 1:length(chunks)),
  add_objects = c("do_all_parameters", 
                  "parameters", "cpus",
                  "working_directory",
                  "chunks", "number_of_chunks",
                  custom_functions),
  jobname = 'W_shredder',
  nodes = number_of_chunks, 
  cpus_per_node = cpus, 
  slurm_options = sopt)

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] tibble_2.0.99.9000 readr_1.1.1        rslurm_0.4.0      
[4] Rcpp_1.0.0         reshape2_1.4.3     stringr_1.3.1     
[7] tidyr_0.8.2        purrr_0.3.1        dplyr_0.8.0.1     

loaded via a namespace (and not attached):
 [1] knitr_1.20        whisker_0.3-2     magrittr_1.5     
 [4] workflowr_1.3.0   hms_0.4.2         tidyselect_0.2.5 
 [7] R6_2.4.0          rlang_0.3.1       plyr_1.8.4       
[10] tools_3.5.1       git2r_0.23.0      htmltools_0.3.6  
[13] yaml_2.2.0        rprojroot_1.3-2   digest_0.6.18    
[16] assertthat_0.2.0  crayon_1.3.4      fs_1.2.6         
[19] glue_1.3.0.9000   evaluate_0.11     rmarkdown_1.10   
[22] stringi_1.3.1     compiler_3.5.1    pillar_1.3.1.9000
[25] backports_1.1.2   pkgconfig_2.0.2