Unused Arguments in R

General
1

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

2

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

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

3

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

4

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)
5

Yes, I would believe so

6

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.