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向我的 OMPR 模型添加约束时出现错误消息（它像这样正常工作）

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)

如果我添加一个/一些约束（我在评论中删除我的#），poste我会收到消息

Error in check_for_unknown_vars_impl(model, the_ast) : 
  The expression contains a variable that is not part of the model.

非常感谢 ：）

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2 回答 2

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感谢@cookesd 的回答，抱歉耽搁了。

终于找到办法了，但是不是很干净……

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

我手动添加的值results$constraints[[k]]$lhs

对于您的问题，我检查了打印值时一切正常......我不明白这个错误，如果您有任何其他想法，请不要犹豫。

于 2020-10-12T15:57:47.307 回答
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我最近遇到了类似的问题。我能够在索引中使用过滤器函数来修复它，而不是使用您在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"
于 2020-10-06T02:31:51.840 回答