MOOVaR: MOOVaR
In alex-conanec/optisure:

Description Usage Arguments Examples

View source: R/MOOVaR.R

MOOVaR

MOOVaR(
  X,
  Y,
  sens = rep("max", NCOL(Y)),
  quantile_utility_idx = rep(TRUE, NCOL(Y)),
  optim_method = c("real_ind", "nsga", "none"),
  g = NULL,
  X_space_csrt = FALSE,
  tau = rep(0.5, NCOL(Y)),
  globale_tau = rep(F, NCOL(Y)),
  alpha = 0.15,
  updateProgress = NULL,
  path_tracking = NULL,
  mutation_method = "simple",
  allowed_dependence = matrix(TRUE, nrow = NCOL(X) - 1, ncol = NCOL(Y)),
  seed_R2 = NULL,
  ...
)

`X`	matrix or data.frame of n observation of the decision variable of size d.
`Y`	matrix or data.frame of n observation of the p objectifs.
`sens`	vector of size p containing either "max" (by default) or "min" to choose how to optimize each objectif.
`quantile_utility_idx`	vector of size p containing boolean to choose the utility method. TRUE and therefore quantile method is used by default.
`g`	list of constraint given as function of X. NULL by default
`X_space_csrt`	boolean to choose either a constraint should be added to check if each solution belongs to the decision space.
`tau`	vector of size p containing the risk in ]0,1[ of each objectif.
`globale_tau`	vector of size p boolean indicating either the objectif risk must ne manage with other.
`alpha`	probability to deal with the trade-off false positive false negatif. Only if X_space_csrt=TRUE.
`updateProgress`	function to follow the progression of the running function
`path_tracking`	path where to write the step of the running function.

library(tidyverse)
library(MOOVaR)
set.seed(12)
n <- 300

# X quanti ----
# X
d1 = 3
mini <- rep(x = -2, times = d1)
maxi <- rep(x = 2, times = d1)
X1 <- sapply(seq_len(d1), function(k){
  runif(n = n, min = mini[k], max = maxi[k])
}) %>% as.data.frame()
colnames(X1) = paste0("X", seq_len(d1))
X = X1

#calcul des Y selon une relation lineaire + du bruit
p = 4
fn = lapply(seq_len(p), function(j){
  beta = runif(n = d1, min = -2, max = 2)
  function(X) {
    as.matrix(X) %*% beta
  }
})
names(fn) = paste0("Y", seq_len(p))

Y = lapply(fn, function(f){
  f(X) + rnorm(n)
}) %>% as.data.frame(row.names = seq_len(n))


g_cstr = list(
  function(x){
    x = as.data.frame(x)
    x[,1] + x[,2] + x[,3] < 2
  }
)


res = MOOVaR(X, Y,
             sens = rep("max", NCOL(Y)),
             quantile_utility_idx = c(T,T,T,F),
             tau = rep(0.2, p),
             globale_tau = c(F, T, T, F),
             g = g_cstr,
             X_space_csrt=T, TT = 10)


plot(res)