I really don't like splitting data into 'train' and 'test'. I don't mean that I'm against the idea of it, though you could say it's a waste of data that could be used to better your model, but I mean that actual assignment in R of 'train' and 'test'. I always liked destructuring in Python, and I like it a lot in 2018 JavaScript, so when I remembered that the zeallot package has it, I thought it would be a good opportunity to see how that could fit in a tidy modelling pipeline. Much to my delight, one little helper function later, it works perfectly.
Loading packages and data:
library(recipes); library(rsample); library(tidyverse); library(zeallot)
# load some data:
data("credit_data")Next comes our little helper function:
m_bake <- function(recipe_object, data){
cd <- initial_split(credit_data)
tr <- training(cd)
te <- testing(cd)
x1 <- bake(recipe_object, tr)
x2 <- bake(recipe_object, te)
return(list(x1, x2))
}Now you get a nice clean pipeline with a train/test split as a result, using zeallot's great %->% operator:
recipe(Status ~ ., data = credit_data) %>%
step_knnimpute(all_predictors()) %>%
step_center(all_numeric()) %>%
step_dummy(-all_numeric()) %>%
prep() %>%
m_bake(data = credit_data) %->% c(train, test)
ls()## [1] "credit_data" "m_bake" "test" "train"head(train); head(test)## # A tibble: 6 x 23
## Seniority Time Age Expenses Income Assets Debt Amount Price
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.01 13.6 -7.08 17.4 -12.6 -5429. -343. -239. -617.
## 2 9.01 13.6 20.9 -7.57 -10.6 -5429. -343. -38.9 195.
## 3 -7.99 13.6 -13.1 7.43 40.4 -2929. -343. -139. -138.
## 4 -7.99 -10.4 -11.1 -9.57 -34.6 -5429. -343. -729. -553.
## 5 -6.99 13.6 -1.08 19.4 72.4 -1929. -343. -389. 182.
## 6 21.0 13.6 6.92 19.4 -16.6 4571. -343. 561. 337.
## # … with 14 more variables: Home_other <dbl>, Home_owner <dbl>,
## # Home_parents <dbl>, Home_priv <dbl>, Home_rent <dbl>,
## # Marital_married <dbl>, Marital_separated <dbl>, Marital_single <dbl>,
## # Marital_widow <dbl>, Records_yes <dbl>, Job_freelance <dbl>,
## # Job_others <dbl>, Job_partime <dbl>, Status_good <dbl>## # A tibble: 6 x 23
## Seniority Time Age Expenses Income Assets Debt Amount Price
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.01 -10.4 8.92 34.4 58.4 -2429. -343. 961. 1522.
## 2 -1.99 1.56 -3.08 4.43 -16.6 -1429. -343. 111. 114.
## 3 25.0 -22.4 30.9 9.43 58.4 -429. 1657. -439. -113.
## 4 -6.99 13.6 -6.08 -20.6 -4.65 -5429. -343. 211. -210.
## 5 -7.99 1.56 -0.0804 -20.6 -23.8 -4629. -343. 461. 387.
## 6 6.01 -22.4 13.9 19.4 56.4 -4429. -343. -589. -813.
## # … with 14 more variables: Home_other <dbl>, Home_owner <dbl>,
## # Home_parents <dbl>, Home_priv <dbl>, Home_rent <dbl>,
## # Marital_married <dbl>, Marital_separated <dbl>, Marital_single <dbl>,
## # Marital_widow <dbl>, Records_yes <dbl>, Job_freelance <dbl>,
## # Job_others <dbl>, Job_partime <dbl>, Status_good <dbl>Lovely.
(Ok, I still had to split it (!!), but once you write this function, you can just call this.)