## ---- echo=FALSE--------------------------------------------------------------
suppressPackageStartupMessages(library(smoof))
suppressPackageStartupMessages(library(CAISEr))
## ---- eval=FALSE--------------------------------------------------------------
# # Install if needed
# # devtools::install_github("fcampelo/MOEADr")
#
# suppressPackageStartupMessages(library(smoof))
# suppressPackageStartupMessages(library(MOEADr))
# suppressPackageStartupMessages(library(CAISEr))
#
# ### Build function names (instances: UF1 - UF7, dimensions 10 - 40)
# fname <- paste0("UF_", 1:7)
# dims <- c(10:40)
# allfuns <- expand.grid(fname, dims, stringsAsFactors = FALSE)
#
# # Assemble instances list
# instances <- vector(nrow(allfuns), mode = "list")
# for (i in 1:length(instances)){
# instances[[i]]$FUN <- paste0(allfuns[i,1], "_", allfuns[i,2])
# }
#
# ### Build the functions listed above (so that they can be properly used)
# for (i in 1:nrow(allfuns)){
# assign(x = instances[[i]]$FUN,
# value = MOEADr::make_vectorized_smoof(prob.name = "UF",
# dimensions = allfuns[i, 2],
# id = as.numeric(strsplit(allfuns[i, 1], "_")[[1]][2])))
# }
## ---- eval=FALSE--------------------------------------------------------------
# # Prepare algorithm function to be used in run_experiment():
# myalgo <- function(type, instance){
# # Input parameters:
# # - type (variant to use: "original", "original2", "moead.de" or "moead.de2")
# # - instance (instance to be solved, e.g., instance = instances[[i]])
# # All other parameters are set internally
#
# ## Extract instance information to build the MOEADr problem format
# fdef <- unlist(strsplit(instance$FUN, split = "_"))
# uffun <- smoof::makeUFFunction(dimensions = as.numeric(fdef[3]),
# id = as.numeric(fdef[2]))
# fattr <- attr(uffun, "par.set")
# prob.dim <- fattr$pars$x$len
#
# ## Build MOEADr problem list
# problem <- list(name = instance$FUN,
# xmin = fattr$pars$x$lower,
# xmax = fattr$pars$x$upper,
# m = attr(uffun, "n.objectives"))
#
# ## Load presets for the algorithm provided in input 'type' and
# ## modify whatever is needed for this particular experiment
# de2 <- FALSE
# if (type == "moead.de2"){
# de2 <- TRUE
# type <- "moead.de"
# }
# algo.preset <- MOEADr::preset_moead(type)
# algo.preset$decomp$H <- 99 # <-- set population size
# algo.preset$stopcrit[[1]]$name <- "maxeval" # <-- type of stop criterion
# algo.preset$stopcrit[[1]]$maxeval <- 2000 * prob.dim # stop crit.
# poly.ind <- which(sapply(algo.preset$variation,
# function(x){x$name == "polymut"}))
# algo.preset$variation[[poly.ind]]$pm <- 1 / prob.dim # <--- pm = 1/d
# if (de2){
# algo.preset$aggfun$name <- "pbi"
# algo.preset$aggfun$theta <- 5
# algo.preset$neighbors$name = "x"
# }
#
# ## Run algorithm on "instance"
# out <- MOEADr::moead(preset = algo.preset, problem = problem,
# showpars = list(show.iters = "none"))
#
# ## Read reference data to calculate the IGD
# Yref <- as.matrix(read.table(paste0("./inst/extdata/pf_data/",
# fdef[1], fdef[2], ".dat")))
# IGD = MOEADr::calcIGD(Y = out$Y, Yref = Yref)
#
# ## Return IGD as field "value" in the output list
# return(list(value = IGD))
# }
## ---- eval=FALSE--------------------------------------------------------------
# # Assemble Algorithm.list. Notice that we need to provide an alias for each
# # method, since both algorithms have the same '$FUN' argument.
# algorithms <- list(list(FUN = "myalgo",
# alias = "Original 1",
# type = "original"),
# list(FUN = "myalgo",
# alias = "Original 2",
# type = "original2"),
# list(FUN = "myalgo",
# alias = "MOEAD-DE",
# type = "moead.de"),
# list(FUN = "myalgo",
# alias = "MOEAD-DE2",
# type = "moead.de2"))
## ---- eval=FALSE--------------------------------------------------------------
# my.results <- run_experiment(instances = instances,
# algorithms = algorithms,
# power = 0.8, # Desired power: 80%
# power.target = "mean", # on average,
# d = 0.5, # to detect differences greater
# # than 0.5 standard deviations
# sig.level = 0.05, # at a 95% confidence level.
# se.max = 0.05, # Measurement error: 5%
# dif = "perc", # on the paired percent
# # differences of means,
# method = "param", # calculated using parametric
# # formula.
# comparisons = "all.vs.all", # Compare all algorithms
# # vs all others,
# nstart = 15, # start with 15 runs/algo/inst
# nmax = 200, # and do no more than 200 runs/inst.
# seed = 1234, # PRNG seed (for reproducibility)
# #
# # NOTICE: Using all but 1 cores. Change if needed
# ncpus = parallel::detectCores() - 1)
## ---- echo=FALSE--------------------------------------------------------------
load("../inst/extdata/vignette_results.RData")
## ---- fig.align="center", fig.width=8, fig.height=8---------------------------
plot(my.results)
## ---- fig.align="center", fig.width=6, fig.height=10--------------------------
suppressPackageStartupMessages(library(car))
algopairs <- paste(my.results$data.summary$Alg1,
my.results$data.summary$Alg2,
sep = " - ")
par(mfrow = c(3, 2))
for (i in seq_along(unique(algopairs))){
tmp <- my.results$data.summary[algopairs == unique(algopairs)[i], ]
car::qqPlot(tmp$Phi,
pch = 16, las = 1, main = unique(algopairs)[i],
ylab = "observed", xlab = "theoretical quantiles")
}
par(mfrow = c(1, 1))
## ---- fig.align="center", fig.width=6, fig.height=8---------------------------
par(mfrow = c(3, 2))
for (i in seq_along(unique(algopairs))){
tmp <- my.results$data.summary[algopairs == unique(algopairs)[i], ]
boot.means <- CAISEr::boot_sdm(tmp$Phi, boot.R = 999)
hist(boot.means, breaks = 30, main = unique(algopairs)[i], las = 1)
}
par(mfrow = c(1, 1))
## ---- fig.align="center", fig.width=6, fig.height=4---------------------------
df <- cbind(Comparison = algopairs, my.results$data.summary)
suppressPackageStartupMessages(library(ggplot2))
mp <- ggplot(df, aes(x = Comparison, y = Phi, fill = Comparison))
mp +
geom_violin(alpha = 0.6, show.legend = FALSE, scale = "width") +
geom_boxplot(alpha = 0, show.legend = FALSE,
outlier.shape = NA, width = .15) +
geom_point(shape = 16, col = "black", fill = "black", alpha = 0.6,
position = position_jitter(width = .15)) +
geom_abline(slope = 0, intercept = 0, col = "red", lty = 2) +
ylab("Percent difference in IGD") + xlab("") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## ---- fig.align="center", fig.width=6, fig.height=8---------------------------
ggplot(df, aes(x = Instance, y = Phi, colour = Comparison,
ymin = Phi - SE, ymax = Phi + SE)) +
geom_pointrange(show.legend = FALSE) +
geom_abline(slope = 0, intercept = 0, col = 1, lty = 2) +
facet_grid(Comparison ~ .) +
theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
xlab("")
## ---- fig.align="center", fig.width=6, fig.height=8---------------------------
summary(my.results, test = "wilcoxon")