Unused Arguments in R

I am working with R. I am learning about optimization and trying to follow the instructions from the following references: psoptim function - RDocumentation and RPubs - Introduction to Particle Swarm Optimization

For this example, I first generate some random data:

#load libraries
library(dplyr)


# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)

From here, I define the function that I want to optimize ("fitness"). This function takes 7 inputs and calculates a "total" mean (a single scalar value). The inputs required for this function are:

  • "random_1" (between 80 and 120)
  • "random_2" (between "random_1" and 120)
  • "random_3" (between 85 and 120)
  • "random_4" (between random_2 and 120)
  • "split_1" (between 0 and 1)
  • "split_2" (between 0 and 1)
  • split_3" (between 0 and 1 )

The function to optimize ("fitness") is defined as follows (takes these 7 numbers and returns a single scalar "total" - the goal is to get the largest value of "total" for these 7 numbers):

fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {

    #bin data according to random criteria
    train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_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 = quantile(c1, prob = split_1)))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_2)))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_3)))
    
    
    
    
    #create a new variable ("diff") that measures if the quantile is bigger than the value of "c1"
    table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
    table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
    table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized
    mean = mean(final_table$diff)
    
    
}

From here, I am interested in using the "ps_optim" function to optimize the function I just defined:

library(psoptim)
set.seed(90)
psoptim(rep(NA,3), fn = fitness, lower = c(80, random_1, 85, random_2, 0,0,0), upper = c(120,120,120,120,1,1,1))

But this returns the following error, suggesting that there are some "unused arguments":

Error in psoptim(rep(NA, 3), fn = fitness, lower = c(80, random_1, 85,  : 
  unused arguments (fn = fitness, lower = c(80, random_1, 85, random_2, 0, 0, 0), upper = c(120, 120, 120, 120, 1, 1, 1))

Can someone please show me why this error is being produced?
Thanks

Those arguments don't match the ones in the function in the package you have used:

https://www.rdocumentation.org/packages/psoptim/versions/1.0/topics/psoptim

They are a little closer to the ones in the psoptim() function in the pso package:

https://www.rdocumentation.org/packages/pso/versions/1.0.3/topics/psoptim

Thank you for your reply! I switched to the "pso" library but I am still getting an error:

fitness <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {

#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_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)
    
    split_1 =  runif(1,0, 1)
    split_2 =  runif(1, 0, 1)
    split_3 =  runif(1, 0, 1)
    
    #calculate  quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_1)))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_2)))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_3)))
    
    
    
    
    #create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
    table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
    table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
    table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    # calculate the total mean : this is what needs to be optimized
    mean = mean(final_table$diff)
    
    
}

library(pso)
set.seed(90)
psoptim(rep(NA,3), fn = fitness, lower = c(80, random_1, 85, random_2, 0,0,0), upper = c(120,120,120,120,1,1,1))

Error: Problem with mutate() column cat.
i cat = ifelse(...).
x argument "random_3" is missing, with no default
Run rlang::last_error() to see where the error occurred.
In addition: Warning message:
Error: Problem with mutate() column cat.
i cat = ifelse(...).
x argument "random_3" is missing, with no default
Run rlang::last_error() to see where the error occurred.

Do you know why this is happening?
Thanks

Did you use the packages that are in section 1.3?

library(tidyverse)
library(ranger)
library(tidymodels)
library(caret)
library(pso)
library(GA)
library(lubridate)
library(scales)

options(scipen = 999)

Yes, I would believe so

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