#' Read files and calculate model predictions
#' Function to read in model results and generate figures
#'
#' @param the_args Argument List
#' @param data_in Data in for sampling
#' @param return_filenames Option to only return the filenames and not sample
#' @export
read_files_predict <- function(the_args, data_in = tows_clust,
return_filenames = FALSE){
#First generate choices based on ports and focus_year
combs <- expand.grid(unlist(the_args$port), 2011:2014, stringsAsFactors = F,
KEEP.OUT.ATTRS = F)
names(combs) <- c('port', "year")
pps <- lapply(1:nrow(combs), FUN = function(xx){
tp <- combs[xx, "port"]
ty <- combs[xx, "year"]
ff <- get_filenames(the_args = a_l, years = ty, ports = tp)
ff1 <- paste0("model_runs//", ff, ".Rdata")
if(return_filenames == TRUE) return(ff)
choices1 <- sampled_rums(data_in = data_in, the_port = tp,
min_year = the_args$m_y, max_year = ty, risk_coefficient = the_args$r_c,
ndays = the_args$dyz, focus_year = ty, nhauls_sampled = the_args$nhauls_sampled,
seed = the_args$seed, ncores = 6, rev_scale = the_args$r_s,
model_type = "no_bycatch", net_cost = the_args$n_c,
habit_distance = the_args$h_d, return_hauls = T)
#Read in model
load(ff1)
mod1 <- mod
rm(mod)
preds <- pred_metrics(choices = choices1, mod = mod1)
args_out <- the_args
args_out$preds <- preds
args_out$filename <- ff
#Format the output
rm_inds <- which(names(args_out) %in% c("ports", "quota_species", "preds"))
args_out <- args_out[-rm_inds]
names(args_out)
aodf <- data.frame(matrix(unlist(args_out), nrow = 1), stringsAsFactors = F)
names(aodf) <- names(args_out)
aodf$score1 <- preds[1]
aodf$score2 <- preds[2]
aodf$score3 <- preds[3]
aodf$score4 <- preds[4]
aodf$f_y <- ty
return(aodf)
})
pps <- ldply(pps)
return(pps)
}
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