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analysis.R
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analysis.R
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### ------------------------------------------------------------------------ ###
### analyse MSY GA runs ####
### ------------------------------------------------------------------------ ###
req_pckgs <- c("doParallel", "doRNG", "mse", "GA", "ggplot2", "cowplot",
"scales", "Cairo", "tidyr", "dplyr", "FLCore", "FLash", "FLBRP")
for (i in req_pckgs) library(package = i, character.only = TRUE)
### function for transforming y-axis origin in plots
trans_from <- function(from = 1) {
trans <- function(x) x - from
inv <- function(x) x + from
trans_new("from", trans, inv,
domain = c(from, Inf))
}
### load stock specifications
stocks <- read.csv("input/stocks.csv", stringsAsFactors = FALSE)
### ------------------------------------------------------------------------ ###
### plot fishing histories - pollack example ####
### ------------------------------------------------------------------------ ###
pol_ow <- readRDS("input/500_50/OM_1_hist/one-way/pol.rds")$stk
pol_rnd <- readRDS("input/500_50/OM_1_hist/random/pol.rds")$stk
pol_brp <- readRDS("input/brps.rds")$pol
refpts <- pol_brp@refpts
df_hist <- rbind(
cbind(
as.data.frame(FLQuants(Fbar = fbar(pol_ow)/c(refpts["msy", "harvest"]),
SSB = ssb(pol_ow)/c(refpts["msy", "ssb"]))),
fhist = "one-way"),
cbind(
as.data.frame(FLQuants(Fbar = fbar(pol_rnd)/c(refpts["msy", "harvest"]),
SSB = ssb(pol_rnd)/c(refpts["msy", "ssb"]))),
fhist = "random")
)
df_hist <- df_hist %>%
filter(year <= 100 & year > 0) %>%
mutate(data = ifelse(year %in% c(0, 1) & fhist == "one-way", NA, data)) %>%
mutate(year = year - 100) %>%
mutate(qname = as.character(qname)) %>%
mutate(qname = factor(qname, levels = c("Fbar", "SSB"),
labels = c("F/F[MSY]", "SSB/B[MSY]"))) %>%
mutate(fhist = as.character(fhist)) %>%
mutate(fhist = factor(fhist, levels = c("one-way", "random"),
labels = c("one-way", "random"))) %>%
mutate(type = "replicate")
df_hist <- df_hist %>% full_join(
df_hist %>%
group_by(year, qname, fhist) %>%
summarise(data = median(data, na.rm = TRUE)) %>%
mutate(type = "median")
) %>%
mutate(type = factor(type, levels = c("replicate", "median")))
p_f <- df_hist %>%
#filter(iter %in% c(1:50)) %>%
filter(qname == "F/F[MSY]") %>%
ggplot(aes(x = year, y = data, group = iter, colour = type, alpha = type,
linetype = type, size = type)) +
geom_line() +
scale_alpha_manual("", values = c(replicate = 0.075, median = 1), ) +
scale_colour_manual("", values = c(replicate = "black", median = "red")) +
scale_linetype_manual("", values = c(replicate = "solid", median = "dashed")) +
scale_size_manual("", values = c(replicate = 0.1, median = 0.5)) +
geom_hline(yintercept = 1, size = 0.1, colour = "black", linetype = "dashed") +
guides(alpha = FALSE) +
theme_bw(base_size = 8) +
facet_grid(~ fhist, scales = "free_y") +
scale_y_continuous(
sec.axis = dup_axis(trans = ~ . /
c(refpts["crash", "harvest"]/refpts["msy", "harvest"]),
name = expression(F/F[crash])),
name = expression(F/F[MSY])) +
theme(axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
plot.margin = unit(x = c(1, 3, 1, 3), units = "pt"),
legend.position = c(0.2, 0.7),
legend.background = element_blank(),
legend.key = element_blank(), legend.key.height = unit(0.3, "cm"))
p_ssb <- df_hist %>%
#filter(iter %in% c(1:50)) %>%
filter(qname == "SSB/B[MSY]") %>%
ggplot(aes(x = year, y = data, group = iter, colour = type, alpha = type,
linetype = type, size = type)) +
geom_line(show.legend = FALSE) +
scale_alpha_manual("", values = c(replicate = 0.075, median = 1), ) +
scale_colour_manual("", values = c(replicate = "black", median = "red")) +
scale_linetype_manual("", values = c(replicate = "solid", median = "dashed")) +
scale_size_manual("", values = c(replicate = 0.1, median = 0.5)) +
geom_hline(yintercept = 1, size = 0.1, colour = "black", linetype = "dashed") +
theme_bw(base_size = 8) +
facet_grid(~ fhist, scales = "free_y") +
scale_y_continuous(
sec.axis = dup_axis(trans = ~ . /
c(refpts["virgin", "ssb"]/refpts["msy", "ssb"]),
name = expression(SSB/B[0])),
name = expression(SSB/B[MSY])) +
theme(strip.text.x = element_blank(),
plot.margin = unit(x = c(1, 3, 1, 3), units = "pt"))
p_both <- plot_grid(p_f, p_ssb, ncol = 1, align = "v")
p_both
ggsave(filename = "output/plots/pol_fhist.png", plot = p_both,
width = 8.5, height = 6, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = "output/plots/pol_fhist.pdf", plot = p_both,
width = 8.5, height = 6, units = "cm")
### ------------------------------------------------------------------------ ###
### collate results - pollack objective function explorations ####
### ------------------------------------------------------------------------ ###
n_yrs <- 50
n_iter <- 500
### get optimised parameterisation
pol_obj <- foreach(optimised = c(TRUE, FALSE), .combine = rbind) %:%
foreach(scenario = "fitness_function", .combine = rbind) %:%
foreach(stock = "pol", .combine = rbind) %:%
foreach(fhist = c("one-way", "random"), .combine = rbind) %do% {
#browser()
### find files
fs <- list.files(path = paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/pol/"), pattern = "*_res.rds")
if (length(fs) < 1) return(NULL)
fs <- fs[grep(x = fs,
pattern = paste0("lag_idx-range_idx_1-range_idx_2-exp_r-exp_f",
"-exp_b-interval-multiplier--obj_"))]
trials <- data.frame(file = fs)
trials$obj_fun <- gsub(x = trials$file,
pattern = paste0("lag_idx-range_idx_1-range_idx_2-",
"exp_r-exp_f-exp_b-interval-",
"multiplier--obj_|_res\\.rds"),
replacement = "")
trials$fhist <- fhist
trials$stock <- stock
trials$scenario <- scenario
trials$optimised <- optimised
### load GA results
res_lst <- lapply(trials$file, function(x) {
readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario, "/", fhist,
"/pol/", x))
})
res_par <- lapply(res_lst, function(x) {
tmp <- x@solution[1,]
tmp[c(1:4, 8)] <- round(tmp[c(1:4, 8)])
tmp[5:7] <- round(tmp[5:7], 1)
tmp[9] <- round(tmp[9], 2)
tmp[10] <- ifelse(is.nan(tmp[10]), Inf, tmp[10])
return(tmp)
})
names(res_par) <- trials$obj_fun
### default (non-optimised?)
if (isFALSE(optimised)) {
res_par <- lapply(res_par, function(x) {
x[] <- c(1, 2, 3, 1, 1, 1, 1, 2, 1, Inf, 0)
x})
}
res <- as.data.frame(do.call(rbind, res_par))
res$file <- trials$file
res$solution <- sapply(res_par, paste0, collapse = "_")
res$fitness <- sapply(res_lst, slot, "fitnessValue")
res$iter <- sapply(res_lst, slot, "iter")
if (isFALSE(optimised)) {
res$fitness <- NA
res$iter <- NA
}
### combine
res <- merge(trials, res)
### load stats of solution
res_stats <- lapply(split(res, seq(nrow(res))), function(x) {
res_runs <- readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/pol/",
gsub(x = x$file, pattern = "res",
replacement = "runs")))
stats_i <- t(res_runs[[x$solution]]$stats)
as.data.frame(lapply(as.data.frame(stats_i), unlist))
})
res_stats <- do.call(rbind, res_stats)
res <- cbind(res, res_stats)
if (isFALSE(optimised)) {
for (i in 1:nrow(res)) {
res$fitness[i] <- switch(res$obj_fun[i],
"C" = -abs(res$Catch_rel[i] - 1),
"SSB" = -abs(res$SSB_rel[i] - 1),
"SSB_risk_ICV" = -sum(abs(res$Catch_rel[i] - 1),
res$ICV[i], res$risk_Blim[i]),
"SSB_C_risk_ICV" = -sum(abs(res$Catch_rel[i] - 1),
abs(res$SSB_rel[i] - 1),
res$ICV[i], res$risk_Blim[i]),
"SSB_F_C_risk_ICV" = -sum(abs(res$Catch_rel[i] - 1),
abs(res$SSB_rel[i] - 1),
abs(res$Fbar_rel[i] - 1),
res$ICV[i], res$risk_Blim[i]))
}
}
return(res)
}
### fitness improvement
pol_obj_improvement <- pol_obj %>%
select(obj_fun, fhist, optimised, fitness) %>%
pivot_wider(names_from = optimised, values_from = fitness) %>%
mutate(fitness_improvement = (`FALSE` - `TRUE`)/`FALSE` * 100,
optimised = TRUE, `TRUE` = NULL, `FALSE` = NULL)
pol_obj <- pol_obj %>%
left_join(pol_obj_improvement)
pol_obj_improvement %>%
arrange(fhist) %>%
mutate(fitness_improvement = round(fitness_improvement),
optimised = NULL) %>%
print(n = Inf)
saveRDS(pol_obj, file = "output/pol_obj_fun_explorations_stats.rds")
write.csv(pol_obj, file = "output/pol_obj_fun_explorations_stats.csv",
row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate results - all stocks with MSY fitness function ####
### ------------------------------------------------------------------------ ###
n_yrs <- 50
n_iter <- 500
### get optimised parameterisation
all_MSY <- foreach(stock = stocks$stock, .combine = rbind) %:%
foreach(optimised = c(TRUE, FALSE), .combine = rbind) %:%
foreach(scenario = "MSY", .combine = rbind) %:%
foreach(fhist = c("one-way", "random"), .combine = rbind) %do% {#browser()
#browser()
### find files
fs <- list.files(path = paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/", stock, "/"),
pattern = paste0("lag_idx-range_idx_1-range_idx_2-exp_r-",
"exp_f-exp_b-interval-multiplier--",
"obj_SSB_C_risk_ICV_res.rds"))
if (length(fs) < 1) return(NULL)
trials <- data.frame(file = fs)
trials$obj_fun <- gsub(x = trials$file,
pattern = paste0("lag_idx-range_idx_1-range_idx_2-",
"exp_r-exp_f-exp_b-interval-",
"multiplier--obj_|_res\\.rds"),
replacement = "")
trials$fhist <- fhist
trials$scenario <- scenario
trials$optimised <- optimised
trials$stock <- stock
### load GA results
res_lst <- lapply(trials$file, function(x) {
readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario, "/", fhist,
"/", stock, "/", x))
})
res_par <- lapply(res_lst, function(x) {
tmp <- x@solution[1,]
tmp[c(1:4, 8)] <- round(tmp[c(1:4, 8)])
tmp[5:7] <- round(tmp[5:7], 1)
tmp[9] <- round(tmp[9], 2)
tmp[10] <- ifelse(is.nan(tmp[10]), Inf, tmp[10])
return(tmp)
})
names(res_par) <- trials$obj_fun
### default (non-optimised?)
if (isFALSE(optimised)) {
trials <- trials[1, ]
res_lst <- res_lst[1]
res_par <- res_par[1]
res_par[[1]][] <- c(1, 2, 3, 1, 1, 1, 1, 2, 1, Inf, 0)
}
res <- as.data.frame(do.call(rbind, res_par))
res$file <- trials$file
res$solution <- sapply(res_par, paste0, collapse = "_")
res$fitness <- sapply(res_lst, slot, "fitnessValue")
res$iter <- sapply(res_lst, slot, "iter")
### combine
res <- merge(trials, res)
### load stats of solution
res_stats <- lapply(split(res, seq(nrow(res))), function(x) {
res_runs <- readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/", stock, "/",
gsub(x = x$file, pattern = "res",
replacement = "runs")))
stats_i <- t(res_runs[[x$solution]]$stats)
as.data.frame(lapply(as.data.frame(stats_i), unlist))
})
res_stats <- do.call(rbind, res_stats)
res <- cbind(res, res_stats)
### calculate fitness value for non-optimised rule
if (isFALSE(optimised)) {
res$fitness <- sapply(split(res, seq(nrow(res))), function(x) {
-sum(abs(x$SSB_rel - 1), abs(x$Catch_rel - 1), x$risk_Blim, x$ICV)
})
}
return(res)
}
### fitness improvement
all_MSY_improvement <- all_MSY %>%
select(stock, fhist, optimised, fitness) %>%
pivot_wider(names_from = optimised, values_from = fitness) %>%
mutate(fitness_improvement = (`FALSE` - `TRUE`)/`FALSE` * 100,
optimised = TRUE, `TRUE` = NULL, `FALSE` = NULL)
all_MSY <- all_MSY %>%
left_join(all_MSY_improvement)
all_MSY_improvement %>%
arrange(fhist) %>%
mutate(fitness_improvement = round(fitness_improvement),
optimised = NULL) %>%
print(n = Inf)
saveRDS(all_MSY, file = "output/all_stocks_MSY_stats.rds")
write.csv(all_MSY, file = "output/all_stocks_MSY_stats.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### collate results - stock groups with MSY fitness function ####
### ------------------------------------------------------------------------ ###
n_yrs <- 50
n_iter <- 500
### get optimised parameterisation
groups_MSY <- foreach(group = c("low", "medium", "high"), .combine = rbind) %:%
foreach(optimised = c(TRUE, FALSE), .combine = rbind) %:%
foreach(scenario = "MSY", .combine = rbind) %:%
foreach(fhist = c("one-way"), .combine = rbind) %do% {#browser()
#browser()
stocks_i <- switch(group,
"low" = stocks$stock[1:12],
"medium" = stocks$stock[13:20],
"high" = stocks$stock[21:29])
stock <- paste0(stocks_i, collapse = "_")
### find files
fs <- list.files(path = paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/", stock, "/"),
pattern = paste0("lag_idx-range_idx_1-range_idx_2-exp_r-",
"exp_f-exp_b-interval-multiplier--",
"obj_SSB_C_risk_ICV_res.rds"))
if (length(fs) < 1) return(NULL)
trials <- data.frame(file = fs)
trials$obj_fun <- gsub(x = trials$file,
pattern = paste0("lag_idx-range_idx_1-range_idx_2-",
"exp_r-exp_f-exp_b-interval-",
"multiplier--obj_|_res\\.rds"),
replacement = "")
trials$fhist <- fhist
trials$scenario <- scenario
trials$optimised <- optimised
trials$group <- group
trials <- trials[rep(1, length(stocks_i)), ]
trials$stock <- stocks_i
### load GA results
res_lst <- lapply(trials$file, function(x) {
readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario, "/", fhist,
"/", stock, "/", x))
})
res_par <- lapply(res_lst, function(x) {
tmp <- x@solution[1,]
tmp[c(1:4, 8)] <- round(tmp[c(1:4, 8)])
tmp[5:7] <- round(tmp[5:7], 1)
tmp[9] <- round(tmp[9], 2)
tmp[10] <- ifelse(is.nan(tmp[10]), Inf, tmp[10])
return(tmp)
})
names(res_par) <- trials$obj_fun
### default (non-optimised?)
if (isFALSE(optimised)) {
#trials <- trials[1, ]
#res_lst <- res_lst[1]
#res_par <- res_par[1]
res_par <- lapply(res_par, function(x) {
x[] <- c(1, 2, 3, 1, 1, 1, 1, 2, 1, Inf, 0)
return(x)
})
}
res <- as.data.frame(do.call(rbind, res_par))
#res$file <- trials$file
res$solution <- sapply(res_par, paste0, collapse = "_")
res$fitness <- sapply(res_lst, slot, "fitnessValue")
res$iter <- sapply(res_lst, slot, "iter")
### combine
res <- cbind(trials, res)
### load stats of solution
res_runs <- readRDS(paste0("output/", n_iter, "_", n_yrs, "/", scenario,
"/", fhist, "/", stock, "/",
gsub(x = res$file[1], pattern = "res",
replacement = "runs")))
stats_i <- t(res_runs[[res$solution[1]]]$stats)
res_stats <- as.data.frame(lapply(as.data.frame(stats_i), unlist))
#res_stats <- do.call(rbind, res_stats)
res <- cbind(res, res_stats)
### add separate group results
res_group <- res[1, ]
res_group$stock <- "group"
res_group[, c("risk_Blim", "risk_Bmsy", "risk_halfBmsy", "risk_collapse",
"SSB", "Fbar", "Catch", "SSB_rel", "Fbar_rel", "Catch_rel",
"ICV")] <- NA
### calculate fitness value for non-optimised rule
if (isFALSE(optimised)) {
res$fitness <- sapply(split(res, seq(nrow(res))), function(x) {
-sum(abs(x$SSB_rel - 1), abs(x$Catch_rel - 1), x$risk_Blim, x$ICV)
})
res_group$fitness <- sum(res$fitness)
}
res$fitness <- NA
res <- rbind(res_group, res)
return(res)
}
### fitness improvement
groups_MSY_improvement <- groups_MSY %>%
select(stock, group, fhist, optimised, fitness) %>%
pivot_wider(names_from = optimised, values_from = fitness) %>%
mutate(fitness_improvement = (`FALSE` - `TRUE`)/`FALSE` * 100,
optimised = TRUE, `TRUE` = NULL, `FALSE` = NULL)
groups_MSY <- groups_MSY %>%
left_join(groups_MSY_improvement)
groups_MSY_improvement %>%
arrange(fhist) %>%
mutate(fitness_improvement = round(fitness_improvement),
optimised = NULL) %>%
print(n = Inf)
saveRDS(groups_MSY, file = "output/groups_MSY_stats.rds")
write.csv(groups_MSY, file = "output/groups_MSY_stats.csv", row.names = FALSE)
### ------------------------------------------------------------------------ ###
### recreate MSE projections for pollack explorations ####
### ------------------------------------------------------------------------ ###
### run optimised solution
library(doParallel)
n_cores <- ifelse(parallel::detectCores() > 20, 20, 10)
cl <- makeCluster(n_cores)
registerDoParallel(cl)
cl_length <- length(cl)
req_pckgs <- c("FLCore", "FLash", "mse", "GA", "doParallel", "doRNG", "FLBRP")
for (i in req_pckgs) library(package = i, character.only = TRUE)
source("funs.R"); source("funs_GA.R")
. <- foreach(i = seq(cl_length)) %dopar% {
for (i in req_pckgs) library(package = i, character.only = TRUE)
source("funs.R"); source("funs_GA.R")
}
### load stocks
stocks <- read.csv("input/stocks.csv")
stocks <- "pol"
### optimised parameters
pol_obj <- readRDS("output/pol_obj_fun_explorations_stats.rds")
for (obj_fun in seq(nrow(pol_obj))) {
rm(res_mp)
input <- readRDS(paste0("input/500_50/OM_2_mp_input/", pol_obj$fhist[obj_fun],
"/", "pol.rds"))
### prepare input object for MSE
input$args$nblocks <- cl_length
input$cut_hist <- FALSE ### retain full history
### optimised parameters
input$ctrl$est@args$idxB_lag <- pol_obj$lag_idx[obj_fun]
input$ctrl$est@args$idxB_range_1 <- pol_obj$range_idx_1[obj_fun]
input$ctrl$est@args$idxB_range_2 <- pol_obj$range_idx_2[obj_fun]
input$ctrl$est@args$catch_range <- pol_obj$range_catch[obj_fun]
input$ctrl$est@args$comp_m <- pol_obj$multiplier[obj_fun]
input$ctrl$phcr@args$exp_r <- pol_obj$exp_r[obj_fun]
input$ctrl$phcr@args$exp_f <- pol_obj$exp_f[obj_fun]
input$ctrl$phcr@args$exp_b <- pol_obj$exp_b[obj_fun]
input$ctrl$hcr@args$interval <- pol_obj$interval[obj_fun]
input$ctrl$isys@args$interval <- pol_obj$interval[obj_fun]
input$ctrl$isys@args$upper_constraint <- pol_obj$upper_constraint[obj_fun]
input$ctrl$isys@args$lower_constraint <- pol_obj$lower_constraint[obj_fun]
### run MSE with optimised parameters
res_mp <- do.call(mp, input)
path_out <- paste0("output/500_50/fitness_function/",
pol_obj$fhist[obj_fun], "/pol/")
saveRDS(res_mp, file = paste0(path_out, "mp_", pol_obj[obj_fun,]$solution, ".rds"))
}
### ------------------------------------------------------------------------ ###
### pollack: objective function explorations - plots ####
### ------------------------------------------------------------------------ ###
pol_obj <- readRDS("output/pol_obj_fun_explorations_stats.rds")
pol_obj$seq <- seq(nrow(pol_obj))
pol_obj
### get time series
res <- lapply(pol_obj$seq, function(x) {
res <- readRDS(paste0("output/500_50/fitness_function//", pol_obj$fhist[x],
"/pol/", "mp_", pol_obj$solution[x], ".rds"))
### stock metrics
SSBs <- FLCore::ssb(res@stock)
Fs <- FLCore::fbar(res@stock)
Cs <- FLCore::catch(res@stock)
#yrs <- dim(SSBs)[2]
its <- dim(SSBs)[6]
yrs_hist <- ac(50:100)
yrs_proj <- ac(101:150)
yrs <- length(yrs_proj)
### collapse correction
if (isTRUE(TRUE)) {
### find collapses
cd <- sapply(seq(its), function(x) {
min_yr <- min(which(SSBs[, yrs_proj,,,, x] < 1))
if (is.finite(min_yr)) {
all_yrs <- min_yr:yrs
} else {
all_yrs <- NA
}
all_yrs + (x - 1)*yrs
})
cd <- unlist(cd)
cd <- cd[which(!is.na(cd))]
### remove values
SSBs[, yrs_proj]@.Data[cd] <- 0
Cs[, yrs_proj]@.Data[cd] <- 0
Fs[, yrs_proj]@.Data[cd] <- 0
}
input <- readRDS(paste0("input/500_50/OM_2_mp_input/", pol_obj$fhist[x],
"/pol.rds"))
Bmsy <- c(input$refpts["msy", "ssb"])
Fmsy <- c(input$refpts["msy", "harvest"])
Cmsy <- c(input$refpts["msy", "yield"])
Blim <- input$Blim
### summarise
qnts <- FLQuants(SSB = SSBs/Bmsy, F = Fs/Fmsy, Catch = Cs/Cmsy)
qnts <- lapply(qnts, quantile, probs = c(0.05, 0.25, 0.5, 0.75, 0.95))
df_qnts <- as.data.frame(qnts)[, c("year", "iter", "data", "qname")]
df_qnts <- bind_cols(df_qnts, pol_obj[rep(x, nrow(df_qnts)), ])
df_qnts <- df_qnts %>% tidyr::spread(key = iter, value = data)
return(df_qnts)
})
res_df <- do.call(rbind, res)
res_df <- res_df %>%
mutate(year = year - 100,
group = ifelse(year < 1, "history", "projection"))
res_df <- res_df %>% bind_rows(res_df %>% filter(year == 0) %>%
mutate(group = "projection"))
res_df <- res_df %>%
mutate(group = factor(group, levels = c("history", "projection")))
res_df <- res_df %>%
filter(year >= -25)
res_df$obj_fun[res_df$optimised == FALSE] <- "not optimised"
res_df$obj_fun[res_df$obj_fun == "C"] <- "Catch"
res_df$obj_fun[res_df$obj_fun == "SSB"] <- "SSB"
res_df$obj_fun[res_df$obj_fun == "SSB_C_risk_ICV"] <- "SSB+Catch+\nrisk+ICV"
res_df$obj_fun[res_df$obj_fun == "SSB_F_C_risk_ICV"] <- "SSB+F+Catch+\nrisk+ICV"
res_df$obj_fun[res_df$obj_fun == "SSB_risk_ICV"] <- "SSB+risk+ICV"
res_df$obj_fun <- as.factor(res_df$obj_fun)
res_df$obj_fun <- factor(res_df$obj_fun, levels = levels(res_df$obj_fun)[c(2, 1, 3, 6, 4, 5)])
res_df$`50%` <- ifelse(res_df$group == "history" & res_df$optimised == TRUE,
NA, res_df$`50%`)
saveRDS(res_df, file = "output/plots/data_pol_fitness_trajectories.rds")
res_df <- readRDS("output/plots/data_pol_fitness_trajectories.rds")
plot_ssb_rel <- res_df %>% filter(qname == "SSB") %>%
ggplot(aes(x = year, y = `50%`, colour = obj_fun, linetype = obj_fun)) +
geom_ribbon(data = res_df %>% filter(qname == "SSB" & optimised == FALSE),
aes(x = year, ymin = `5%`, ymax = `95%`, fill = "90%"),
linetype = 0, colour = 0, show.legend = FALSE, alpha = 0.5) +
geom_ribbon(data = res_df %>% filter(qname == "SSB" & optimised == FALSE),
aes(x = year, ymin = `25%`, ymax = `75%`, fill = "50%"),
linetype = 0, colour = 0, show.legend = FALSE, alpha = 0.5) +
scale_fill_manual("confidence\ninterval",
values = c("50%" = "grey50", "90%" = "grey80")) +
geom_line(size = 0.3, show.legend = FALSE) +
scale_colour_manual("fitness function",
values = setNames(c("black", "black", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
scale_linetype_manual("fitness function",
values = setNames(c("solid", 11, 22, 1121, 31, 33),#c(1, 2, 3:6),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
facet_grid(fhist ~ group, scales = "free_x", space = "free_x") +
theme_bw(base_size = 8, base_family = "sans") +
scale_x_continuous(breaks = seq(-20, 50, 10), expand = c(0, 0)) +
ylim(c(0, 4.1)) +
labs(y = expression(SSB/B[MSY]), x = "") +
theme(panel.spacing.x = unit(0, units = "cm"),
panel.spacing.y = unit(1, units = "pt"),
axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
plot.margin = unit(x = c(1, 3, 1, 3), units = "pt"))
plot_fbar_rel <- res_df %>% filter(qname == "F") %>%
ggplot(aes(x = year, y = `50%`, colour = obj_fun, linetype = obj_fun)) +
geom_ribbon(data = res_df %>% filter(qname == "F" & optimised == FALSE),
aes(x = year, ymin = `5%`, ymax = `95%`, fill = "90%"),
linetype = 0, colour = 0, show.legend = FALSE, alpha = 0.5) +
geom_ribbon(data = res_df %>% filter(qname == "F" & optimised == FALSE),
aes(x = year, ymin = `25%`, ymax = `75%`, fill = "50%"),
linetype = 0, colour = 0, show.legend = FALSE, alpha = 0.5) +
scale_fill_manual("confidence\ninterval",
values = c("50%" = "grey50", "90%" = "grey80")) +
geom_line(size = 0.3, show.legend = FALSE) +
scale_colour_manual("fitness function",
values = setNames(c("black", "black", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
scale_linetype_manual("fitness function",
values = setNames(c("solid", 11, 22, 1121, 31, 33),#c(1, 2, 3:6),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
facet_grid(fhist ~ group, scales = "free_x", space = "free_x") +
theme_bw(base_size = 8, base_family = "sans") +
scale_x_continuous(breaks = seq(-20, 50, 10), expand = c(0, 0)) +
labs(y = expression(F/F[MSY]), x = "year") +
theme(panel.spacing.x = unit(0, units = "cm"),
panel.spacing.y = unit(1, units = "pt"),
axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
strip.text.x = element_blank(),
plot.margin = unit(x = c(1, 3, 1, 3), units = "pt"))
plot_catch_rel <- res_df %>% filter(qname == "Catch") %>%
ggplot(aes(x = year, y = `50%`, colour = obj_fun, linetype = obj_fun)) +
geom_ribbon(data = res_df %>% filter(qname == "Catch" & optimised == FALSE),
aes(x = year, ymin = `5%`, ymax = `95%`, fill = "90%"),
linetype = 0, colour = 0, alpha = 0.5) +
geom_ribbon(data = res_df %>% filter(qname == "Catch" & optimised == FALSE),
aes(x = year, ymin = `25%`, ymax = `75%`, fill = "50%"),
linetype = 0, colour = 0, alpha = 0.5) +
scale_fill_manual("confidence\nintervals",
values = c("50%" = "grey50", "90%" = "grey80")) +
geom_line(size = 0.3) +
scale_colour_manual("fitness\nfunction",
values = setNames(c("black", "black", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
scale_linetype_manual("fitness\nfunction",
values = setNames(c("solid", 11, 22, 1121, 31, 33),#c(1, 2, 3:6),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
facet_grid(fhist ~ group, scales = "free_x", space = "free_x") +
theme_bw(base_size = 8, base_family = "sans") +
scale_x_continuous(breaks = seq(-20, 50, 10), expand = c(0, 0)) +
ylim(0, 3.1) +
labs(y = expression(Catch/MSY), x = "year") +
theme(panel.spacing.x = unit(0, units = "cm"),
panel.spacing.y = unit(1, units = "pt"),
legend.position = "right",
legend.key.height = unit(1, "lines"),
legend.key.width = unit(1, "lines"),
strip.text.x = element_blank(),
plot.margin = unit(x = c(1, 3, 1, 3), units = "pt"))
p_pol_trajectories <- plot_grid(
plot_grid(plot_ssb_rel, plot_fbar_rel,
plot_catch_rel + theme(legend.position = "none"),
ncol = 1, align = "v", rel_heights = c(1.1, 1, 1.2)),
get_legend(plot_catch_rel), rel_widths = c(1, 0.4), ncol = 2)
ggsave(filename = "output/plots/pol_trajectories.png",
plot = p_pol_trajectories,
width = 8.5, height = 10, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = "output/plots/pol_trajectories.pdf",
plot = p_pol_trajectories,
width = 8.5, height = 10, units = "cm", dpi = 600)
### add GA progress to same plot
pol_ga <- readRDS(paste0("output/500_50/fitness_function/one-way/pol/",
"lag_idx-range_idx_1-range_idx_2-exp_r-exp_f-exp_b",
"-interval-multiplier--obj_SSB_C_risk_ICV_res.rds"))
ga_df <- as.data.frame(pol_ga@summary)
ga_df$generation <- seq(nrow(ga_df))
saveRDS(ga_df, file = "output/plots/data_pol_fitness_GA_progress.rds")
ga_df <- readRDS("output/plots/data_pol_fitness_GA_progress.rds")
p_ga <- ga_df %>%
mutate(best = max) %>%
pivot_longer(c(best, median), names_to = "key", values_to = "value") %>%
ggplot(aes(x = generation, y = value, linetype = key, shape = key,
ymin = min, ymax = max)) +
geom_ribbon(alpha = 0.5, show.legend = FALSE, fill = "grey80") +
geom_line(size = 0.3) +
geom_point(size = 0.7) +
facet_grid("one-way" ~ NA) +
scale_linetype_discrete("") + scale_shape_discrete("") +
theme_bw(base_size = 8, base_family = "sans") +
ylim(c(NA, 0)) +
scale_x_continuous(expand = expand_scale(add = c(1, 1))) +
scale_y_continuous(expand = expand_scale(add = c(0.4, abs(max(ga_df$max))))) +
labs(x = "generations", y = "fitness value") +
theme(legend.position = "right", legend.background = element_blank(),
legend.key = element_blank(),
legend.key.size = unit(0.5, "lines"), legend.key.width = unit(1, "lines"),
strip.text.x = element_blank(), legend.title = element_blank())
ggsave(filename = "output/plots/pol_ga.png",
plot = p_ga,
width = 8.5, height = 4, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = "output/plots/pol_ga.pdf",
plot = p_ga,
width = 8.5, height = 4, units = "cm", dpi = 600)
### plot stats
pol_obj
### format labels for plotting
pol_obj_plot <- pol_obj
pol_obj_plot$obj_label <- as.character(pol_obj_plot$obj_fun)
pol_obj_plot$obj_label[pol_obj_plot$optimised == FALSE] <- "not optimised"
pol_obj_plot$obj_label[pol_obj_plot$obj_label == "C"] <- "Catch"
pol_obj_plot$obj_label[pol_obj_plot$obj_label == "SSB"] <- "SSB"
pol_obj_plot$obj_label[pol_obj_plot$obj_label == "SSB_C_risk_ICV"] <- "SSB+Catch+\nrisk+ICV"
pol_obj_plot$obj_label[pol_obj_plot$obj_label == "SSB_F_C_risk_ICV"] <- "SSB+F+Catch+\nrisk+ICV"
pol_obj_plot$obj_label[pol_obj_plot$obj_label == "SSB_risk_ICV"] <- "SSB+risk+ICV"
pol_obj_plot$obj_label <- as.factor(pol_obj_plot$obj_label)
pol_obj_plot$obj_label <- factor(pol_obj_plot$obj_label,
levels = levels(pol_obj_plot$obj_label)[c(2, 1, 3, 6, 4, 5)])
pol_obj_plot <- pol_obj_plot %>%
pivot_longer(c(SSB_rel, Fbar_rel, Catch_rel, risk_Blim, ICV, fitness),
names_to = "key", values_to = "value") %>%
mutate(stat = factor(key, levels = c("SSB_rel", "Fbar_rel", "Catch_rel", "risk_Blim",
"ICV", "fitness"),
labels = c("SSB/B[MSY]", "F/F[MSY]", "Catch/MSY",
"B[lim]~risk", "ICV", "fitness~value")))
stats_targets <- data.frame(stat = c("SSB/B[MSY]", "F/F[MSY]", "Catch/MSY",
"B[lim]~risk", "ICV", "fitness~value"),
target = c(1, 1, 1, 0, 0, NA))
saveRDS(pol_obj_plot, file = "output/plots/data_pol_fitness_stats.rds")
pol_obj_plot <- readRDS("output/plots/data_pol_fitness_stats.rds")
saveRDS(stats_targets, file = "output/plots/data_pol_fitness_targets.rds")
stats_targets <- readRDS("output/plots/data_pol_fitness_targets.rds")
### individual plots
y_max <- 1.4
p_pol_stats_SSB <- pol_obj_plot %>%
filter(stat %in% c("SSB/B[MSY]")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 1, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = position_dodge2(preserve = "single"), width = 0.8,
show.legend = FALSE, colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
plot.margin = unit(x = c(1, 3, 0, 3), units = "pt"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()) +
scale_y_continuous(trans = trans_from(), limits = c(0, y_max))
p_pol_stats_F <- pol_obj_plot %>%
filter(stat %in% c("F/F[MSY]")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 1, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = "dodge", show.legend = FALSE, width = 0.8,
colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.text.x = element_blank(),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
plot.margin = unit(x = c(0, 3, 0, 3), units = "pt"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()) +
scale_y_continuous(trans = trans_from(), limits = c(0, y_max))
p_pol_stats_C <- pol_obj_plot %>%
filter(stat %in% c("Catch/MSY")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 1, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = "dodge", show.legend = FALSE, width = 0.8,
colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.text.x = element_blank(),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
plot.margin = unit(x = c(0, 3, 0, 3), units = "pt"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()) +
scale_y_continuous(trans = trans_from(), limits = c(0, y_max))
p_pol_stats_risk <- pol_obj_plot %>%
filter(stat %in% c("B[lim]~risk")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 0, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = "dodge", show.legend = FALSE, width = 0.8,
colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.text.x = element_blank(),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
plot.margin = unit(x = c(0, 3, 0, 3), units = "pt"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()) +
scale_y_continuous(trans = trans_from(0), limits = c(0, y_max))
p_pol_stats_ICV <- pol_obj_plot %>%
filter(stat %in% c("ICV")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 0, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = "dodge", show.legend = FALSE, width = 0.8,
colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.text.x = element_blank(),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
plot.margin = unit(x = c(0, 3, 0, 3), units = "pt"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()) +
scale_y_continuous(trans = trans_from(0), limits = c(0, y_max))
p_pol_stats_fitness <- pol_obj_plot %>%
filter(stat %in% c("fitness~value")) %>%
ggplot(aes(x = obj_label, y = value, fill = obj_label,
colour = obj_label)) +
geom_hline(yintercept = 0, linetype = "solid", size = 0.5, colour = "grey") +
geom_col(position = "dodge", show.legend = FALSE, width = 0.8,
colour = "black", size = 0.1) +
facet_grid(stat ~ fhist, scales = "free", space = "free_x", switch = "y",
labeller = "label_parsed") +
labs(y = "", x = "fitness function") +
scale_fill_manual("fitness\nfunction",
values = setNames(c("black", "grey", scales::hue_pal()(4)),
c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
"SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
# scale_colour_manual("fitness\nfunction",
# values = setNames(c("black", "grey", scales::hue_pal()(4)),
# c("not optimised", "SSB+Catch+\nrisk+ICV", "Catch", "SSB",
# "SSB+risk+ICV", "SSB+F+Catch+\nrisk+ICV"))) +
theme_bw(base_size = 8, base_family = "sans") +
theme(panel.spacing.x = unit(0, units = "cm"),
strip.text.x = element_blank(),
strip.placement.y = "outside",
strip.background.y = element_blank(),
strip.text.y = element_text(size = 8),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
plot.margin = unit(x = c(0, 3, 3, 3), units = "pt")) +
scale_y_continuous(trans = trans_from(0), limits = c(-y_max, NA),
breaks = c(0, -0.5, -1),
minor_breaks = c(-0.25, -0.75, -1.25))
p_pol_stats_comb <- plot_grid(p_pol_stats_SSB, p_pol_stats_F, p_pol_stats_C,
p_pol_stats_risk, p_pol_stats_ICV,
p_pol_stats_fitness,
ncol = 1, align = "v",
rel_heights = c(1.25, 1, 1, 1, 1, 2.1))
ggsave(filename = "output/plots/pol_stats.png",
plot = p_pol_stats_comb,
width = 8.5, height = 13, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = "output/plots/pol_stats.pdf",
plot = p_pol_stats_comb,
width = 8.5, height = 13, units = "cm", dpi = 600)
### combine all plots
p <- plot_grid(
plot_grid(p_pol_trajectories,
plot_grid(plot_grid(NULL, p_ga + theme(legend.position = "none"),
rel_widths = c(0.07, 1), nrow = 1),
get_legend(p_ga),
nrow = 1, rel_widths = c(1, 0.39)),
ncol = 1, rel_heights = c(4, 1),
labels = c("(a)", "(b)"), label_size = 10, hjust = -0.1, vjust = 1.1),
p_pol_stats_comb, ncol = 2, rel_widths = c(1.2, 1),
labels = c("", "(c)"), label_size = 10, hjust = -0.1, vjust = 1.1)
ggsave(filename = "output/plots/pol_combined.png", plot = p,
width = 17, height = 13, units = "cm", dpi = 600, type = "cairo")
ggsave(filename = "output/plots/pol_combined.pdf", plot = p,
width = 17, height = 13, units = "cm", dpi = 600)
### ------------------------------------------------------------------------ ###
### pollack: check impact of fixing TAC interval ####
### ------------------------------------------------------------------------ ###
### get optimised parameterisation
pol_interval <- foreach(stock = "pol", .combine = rbind) %:%
foreach(optimised = c(TRUE, FALSE), .combine = rbind) %:%
foreach(scenario = "fitness_function", .combine = rbind) %:%
foreach(interval = 1:3, .combine = rbind) %:%
foreach(fhist = c("one-way"), .combine = rbind) %do% {#browser()
res_ga <- readRDS(paste0("output/500_50/", scenario, "/", fhist, "/", stock,
"/lag_idx-range_idx_1-range_idx_2-exp_r-exp_f-exp_b",
"-interval", interval, "-multiplier--obj_SSB_C_risk",
"_ICV_res.rds"))
trials <- data.frame(file = NA)
trials$obj_fun <- "SSB_Catch_risk_ICV"
trials$fhist <- fhist
trials$scenario <- scenario
trials$optimised <- optimised
trials$stock <- stock
### load GA results
res_par <- res_ga@solution[1,]
res_par[c(1:4, 8)] <- round(res_par[c(1:4, 8)])
res_par[5:7] <- round(res_par[5:7], 1)
res_par[9] <- round(res_par[9], 2)
res_par[10] <- ifelse(is.nan(res_par[10]), Inf, res_par[10])
### default (non-optimised?)
if (isFALSE(optimised)) {
res_par[] <- c(1, 2, 3, 1, 1, 1, 1, interval, 1, Inf, 0)
}
res <- cbind(trials, as.data.frame(t(res_par)))
res$solution <- paste0(res_par, collapse = "_")
res$fitness <- res_ga@fitnessValue
res$iter <- res_ga@iter
### load stats of solution
runs <- readRDS(paste0("output/500_50/", scenario, "/", fhist, "/", stock,
"/lag_idx-range_idx_1-range_idx_2-exp_r-exp_f-exp_b",
"-interval", interval, "-multiplier--obj_SSB_C_risk",
"_ICV_runs.rds"))
stats <- t(runs[[res$solution]]$stats)
stats <- as.data.frame(lapply(as.data.frame(stats), unlist))
res <- cbind(res, stats)
### calculate fitness value for non-optimised rule
if (isFALSE(optimised)) {
res$fitness <- -sum(abs(res$SSB_rel - 1),
abs(res$Catch_rel - 1),
res$risk_Blim, res$ICV)
}
return(res)
}
saveRDS(pol_interval, file = "output/pol_interval_MSY_stats.rds")
write.csv(pol_interval, file = "output/pol_interval_MSY_stats.csv",
row.names = FALSE)
pol_interval <- readRDS("output/pol_interval_MSY_stats.rds")
### check values
pol_interval %>%
filter(optimised == TRUE) %>%
select(stock, interval, fitness) %>%
filter(interval == 2) %>%
transmute(fitness2 = fitness, stock = stock) %>%
full_join(pol_interval %>%
filter(optimised == TRUE) %>%
select(stock, interval, fitness)) %>%