jakob-r / randomsearch Goto Github PK

#load library : https://cran.r-project.org/web/packages/randomsearch/index.html 

library(randomsearch)

 # create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,10)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)


#define function (4 inputs x[1], x[2], x[3], x[4] and 4 outputs f1, f2, f3, f4)

fn <- function(i) {
   x1 <- x[i,1]; x2 <- x[i,2]; x3 <- x[i,3] ; x4 <- x[i,4]
   f <- numeric(4)
   #bin data according to random criteria
   train_data <- train_data %>%
       mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
                           ifelse(a1 <= x2 & b1 <= x4, "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)
   
   #add some constraints
   if((x3 - x1) < 0. | (x4 - x2) < 0.) {
       f[1] <- NaN
       f[2] <- NaN
       f[3] <- NaN
       f[4] <- NaN
       
   }
   
   return (f)
}

#run algorithm
res = randomsearch(fn, lower = c(80, 80, 80, 80), upper = c(100,120,100,120), minimize = c(TRUE, TRUE, TRUE, TRUE), max.evals = 30)
rs = summary(res)