EmilHvitfeldt
commented
7 months ago Hello @SantiagoD999 👋
At prediction time in {workflows} only variables marked as "predictor" will be added to the preprocessor, such as recipes. If you really want to scale the outcome, you can scale it separately beforehand.
library(tidymodels)
set.seed(1)
n<-1000
y<-numeric(n)
x1<-rnorm(n)
x2<-rnorm(n)
x3<-rnorm(n)
x4<-rnorm(n)
e<-rnorm(n)
y<-2000+3*x1+8*x2+9*x3+8*x4+e
DATA<-tibble(y,x1,x2,x3,x4)
TRAIN<-DATA[1:800,]
TEST<-DATA[801:n,]
recipe_outcome <- recipe(y~., data=TRAIN) %>%
step_normalize(all_outcomes()) %>%
prep()
TRAIN <- bake(recipe_outcome, TRAIN)
# This is only needed because we know TEST has an outcome.
# On future data this will not be needed
TEST <- bake(recipe_outcome, TEST)
recipe_norm<-recipe(y~.,data=TRAIN) %>%
step_normalize(all_predictors())
mlp_norm<-workflow() %>%
add_model(mlp(epochs=tune()) %>% set_engine("nnet") %>% set_mode("regression")) %>%
add_recipe(recipe_norm)
mlp_resample<-vfold_cv(TRAIN, v = 5)
mlp_tune <- tune_grid(mlp_norm, mlp_resample, grid = 5,control=control_grid(save_pred = TRUE),metrics = metric_set(rmse))
mlp_norm_fit<-mlp_norm |>
finalize_workflow(select_best(mlp_tune, "rmse")) |>
fit(TRAIN)
augment(mlp_norm_fit,new_data=TEST)
#> # A tibble: 200 × 6
#> x1 x2 x3 x4 y .pred
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -1.09 0.714 -2.10 -0.728 -1.49 -1.38
#> 2 -1.83 0.581 -0.0844 -0.247 -0.149 -0.219
#> 3 0.995 -0.147 0.756 -0.614 0.208 0.227
#> 4 -0.0119 1.51 -1.58 0.104 0.0199 -0.0561
#> 5 -0.600 -0.280 0.707 -0.801 -0.277 -0.257
#> 6 -0.178 2.03 -1.05 1.32 1.08 1.07
#> 7 -0.426 -1.20 0.259 0.0312 -0.474 -0.518
#> 8 0.997 1.31 -0.00168 -0.824 0.499 0.436
#> 9 0.728 -0.524 -1.18 -0.867 -1.16 -1.24
#> 10 -1.73 0.354 1.74 -1.25 0.230 0.188
#> # ℹ 190 more rows
SantiagoD999
github-actions[bot]
Good morning, I am using your excellent tidymodels package and have came across an issue. Thank you.
The problem
I'm having trouble with using recipes when the objective is to scale both the features and the target, choosing a model after tuning a parameter. I want to use the scaled target both for the tuning and subsequent prediction. I have used
skip=TRUEbut this then makes the tuning process ignore that I want the target to be scaled.Reproducible example