I have an error message when I add a constraint to my OMPR model (it works properly like this)
n = dim(note_mpg)[1]
nb_joueurs = 18
perf = scale(note_mpg$performance_beta)
cote = note_mpg$cote_alpha
poste = note_mpg$Poste
note_mpg$Buts[is.na(note_mpg$Buts)] <- 0
buts = scale(note_mpg$Buts)
results = MIPModel() %>%
add_variable(z[i], i = 1:n, type = "binary") %>%
set_objective(sum_expr((perf[i] + buts[i]) * z[i], i = 1:n), "max") %>%
add_constraint(sum_expr(z[i], i = 1:n) == nb_joueurs) %>%
# add_constraint(sum_expr( (poste[i] == "G") * z[i], i = 1:n) == 2) %>%
# add_constraint(sum_expr( (poste[i] == "D") * z[i], i = 1:n) == 6) %>%
# add_constraint(sum_expr( (poste[i] == "M") * z[i], i = 1:n) == 6) %>%
# add_constraint(sum_expr( (poste[i] == "A") * z[i], i = 1:n) == 4) %>%
add_constraint(sum_expr(cote[i] * z[i], i = 1:n) <= 500) %>%
solve_model(with_ROI(solver = "glpk")) %>%
get_solution(z[i]) %>%
filter(value > 0)
If I add a/some constraint(s) (I remove my # on a comment) on the poste
I get the message
Error in check_for_unknown_vars_impl(model, the_ast) :
The expression contains a variable that is not part of the model.
Many thanks :)
I've run into a similar problem recently. I was able to fix it using a filter function in the indexing instead of using the comparisons you are in the sum_expr
.
# Example to replicate your poste variable
poste = rep(LETTERS[1:5],2)
print(poste)
# [1] "A" "B" "C" "D" "E" "A" "B" "C" "D" "E"
# function that accepts the indices and the letter you want to filter poste to
# returns a vector of T/F (one for each index in i_indices)
filter_function <- function(i_indices,letter){
# A list of indices that align to each of the letters in poste
# Change this for your actual data
index_list = lapply(unique(poste),function(letter) which(poste==letter))
names(index_list) = unique(poste)
# Get the T/F value for each index in i_indices
# T if poste[index] == the provided letter
# F otherwise
return(sapply(i_indices,function(index) index %in% index_list[[letter]]))
}
# Build the model
m = MIPModel() %>%
add_variable(z[i],i=1:10,type='binary') %>%
# Call the filter function after your indices
# Passing the index and the letter you want to limit the indices to
add_constraint(sum_expr(z[i], i = 1:10,
filter_function(i,'B')) == 2)
m$constraints
# Only sums the indices of z where poste == 'B'
# (i = 2 and i = 7)
# [[1]]
# $lhs
# expression(z[2L] + z[7L])
#
# $sense
# [1] "=="
#
# $rhs
# expression(2)
#
# attr(,"class")
# [1] "model_constraint"
thanks @cookesd for your answer, and sorry for the delay.
I finally find a way, but it is not very clean...
results= MIPModel() %>%
add_variable(z[i], i = 1:n, type = "binary") %>%
set_objective(sum_expr((perf[i] + buts[i]) * z[i], i = 1:n), "max") %>%
add_constraint(sum_expr(z[i], i = 1:n) == nb_joueurs) %>%
add_constraint(sum_expr(cote[i] * z[i], i = 1:n) <= 500) %>%
add_constraint( sum_expr(z[i], i = 1:n, poste[i] == "G") == as.numeric(input$gardiens)) %>%
add_constraint( sum_expr(z[i], i = 1:n, poste[i] == "D") == as.numeric(input$def)) %>%
add_constraint( sum_expr(z[i], i = 1:n, poste[i] == "M") == as.numeric(input$mil)) %>%
add_constraint( sum_expr(z[i], i = 1:n, poste[i] == "A") == as.numeric(input$att))
contraint3 = as.expression(sum_expr(z[i], i = 1:n, poste[i] == "G"))
contraint4 = as.expression(sum_expr(z[i], i = 1:n, poste[i] == "D"))
contraint5 = as.expression(sum_expr(z[i], i = 1:n, poste[i] == "M"))
contraint6 = as.expression(sum_expr(z[i], i = 1:n, poste[i] == "A"))
results$constraints[[3]]$lhs =contraint3
results$constraints[[4]]$lhs =contraint4
results$constraints[[5]]$lhs =contraint5
results$constraints[[6]]$lhs =contraint6
I manually add the value of results$constraints[[k]]$lhs
For your question, I checked and everything is ok when I print the value... i don't understand the bug, Do not hesitate if you have any other idea.
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.