Results Outside of the Specified Ranges

[swaheera]

I am trying to use the mlrMBO library in R to perform optimization on the following function:

library(dplyr)
library(mlrMBO)
a1 = rnorm(1000,100,10)
    b1 = rnorm(1000,100,9)
    c1 = sample.int(1000, 1000, replace = TRUE)
    train_data = data.frame(a1,b1,c1)


    obj.fun = makeSingleObjectiveFunction(
    name = "Some function",
    fn = function(x) {
    #bin data according to random criteria
    train_data <- train_data %>%
        mutate(cat = ifelse(a1 <= x[1] & b1 <= x[3], "a",
                            ifelse(a1 <= x[2] & b1 <= x[4], "b", "c")))
   
    train_data$cat = as.factor(train_data$cat)
   
    #new splits
    a_table = train_data %>%
        filter(cat == "a") %>%
        select(a1, b1, c1, cat)
   
    b_table = train_data %>%
        filter(cat == "b") %>%
        select(a1, b1, c1, cat)
   
    c_table = train_data %>%
        filter(cat == "c") %>%
        select(a1, b1, c1, cat)
   
   
    #calculate  quantile ("quant") for each bin
   
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > 150,1,0 )))
   
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > 300,1,0 )))
   
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > 400,1,0 )))
   
    f1 = mean(table_a$quant)
    f2 = mean(table_b$quant)
    f3 = mean(table_c$quant)
   
   
    #group all tables
   
    final_table = rbind(table_a, table_b, table_c)
    # calculate the total mean : this is what needs to be optimized
   
    f4 = mean(final_table$quant)
   
   
    return (f4);
},
    par.set = makeParamSet(
              makeNumericParam("x[1]", lower = 80, upper = 90),
              makeNumericParam("x[2]", lower = 95, upper = 110),
              makeNumericParam("x[3]", lower = 80, upper = 90),
              makeNumericParam("x[4]", lower = 95, upper = 110)
),
     minimize = TRUE
)

ctrl = makeMBOControl()
ctrl = setMBOControlTermination(ctrl, iters = 20L)

# we can basically do an exhaustive search in 3 values
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI())
  # opt.restarts = 1L, opt.focussearch.points = 3L, opt.focussearch.maxit = 1L)

#design = generateDesign(20L, getParamSet(obj.fun), fun = lhs::maximinLHS)

lrn = makeMBOLearner(ctrl, obj.fun)

#run optimization
res = mbo(obj.fun, design = NULL, learner = lrn, control = ctrl, show.info = TRUE)

When I look at the final results of the optimization:

Solution Fitness Value: 1.784275e+02

Parameters at the Solution (parameter, gradient):

 X[ 1] :	1.991896e+01	G[ 1] :	7.069061e-01
 X[ 2] :	9.580277e+00	G[ 2] :	-3.680317e-01
 X[ 3] :	1.998836e+01	G[ 3] :	6.662898e-01
 X[ 4] :	1.612573e+01	G[ 4] :	-2.797171e-01
 X[ 5] :	8.869973e-05	G[ 5] :	-5.665712e-03

Solution Found Generation 5
Number of Generations Run 5

These do not seem to match the ranges that I specified:

    par.set = makeParamSet(
              makeNumericParam("x[1]", lower = 80, upper = 90),
              makeNumericParam("x[2]", lower = 95, upper = 110),
              makeNumericParam("x[3]", lower = 80, upper = 90),
              makeNumericParam("x[4]", lower = 95, upper = 110)

Does anyone know why this is happening?

Thanks