I'm trying to use workflow_set() function in tidymodels to evaluate a batch of models. I've understand that is possible to modify some model specification in order to change the search range so, for example, given this specification:
spec_lin <- linear_reg( penalty = tune(),
mixture = tune() ) %>%
set_engine('glmnet')
I can modify the range using:
rec_base <- recipe( price ~ feat_1) %>%
step_novel(feat_1) %>%
step_other(feat_1,threshold=.2 ) %>%
step_dummy(feat_1)
rec_adv_param <- rec_base %>%
parameters() %>%
update ( mixture = mixture(c(0.1,0.01)) )
My attempt is to do the same but with the parameters in the recipe. For example:
rec_tuned <- recipe( price ~ feat_1) %>%
step_novel(feat_1) %>%
step_other(feat_1,threshold=tune() ) %>%
step_dummy(feat_1)
followed by
rec_adv_param <- rec_tuned %>%
parameters() %>%
update ( threshold = threshold(c(0.1,0.2)) )
However when I try to use it in the workflow_set() definition if I use something like
wf_set <- workflow_set(recipes, models, cross = TRUE )
option_add(param_info = rec_adv_param, id = "rec_tuned_spec_lin")
The finale "wf_set" lost his original tuning parameters the has been changed with the
threshold = threshold(c(0.1,0.2)
Is there a way to add the parameters specification for the recipe in all workflow_set models?
Thanks
You can add the parameters for a recipe via option_add()
, either for a single workflow by id
for all workflows if you leave id = NULL
. When you go to tune or fit on resampled data, these options will be used.
For example, if we want to try 0 to 20 PCA components (instead of the default):
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
data(Chicago)
data("chi_features_set")
time_val_split <-
sliding_period(
Chicago,
date,
"month",
lookback = 38,
assess_stop = 1
)
## notice that there are no options; defaults will be used
chi_features_set
#> # A workflow set/tibble: 3 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 date_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 plus_holidays_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 plus_pca_lm <tibble [1 × 4]> <opts[0]> <list [0]>
## make new params
pca_param <-
parameters(num_comp()) %>%
update(num_comp = num_comp(c(0, 20)))
## add new params to workflowset like this:
chi_features_set %>%
option_add(param_info = pca_param, id = "plus_pca_lm")
#> # A workflow set/tibble: 3 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 date_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 2 plus_holidays_lm <tibble [1 × 4]> <opts[0]> <list [0]>
#> 3 plus_pca_lm <tibble [1 × 4]> <opts[1]> <list [0]>
## now these new parameters can be used by `workflow_map()`:
chi_features_set %>%
option_add(param_info = pca_param, id = "plus_pca_lm") %>%
workflow_map(resamples = time_val_split, grid = 21, seed = 1)
#> # A workflow set/tibble: 3 × 4
#> wflow_id info option result
#> <chr> <list> <list> <list>
#> 1 date_lm <tibble [1 × 4]> <opts[2]> <rsmp[+]>
#> 2 plus_holidays_lm <tibble [1 × 4]> <opts[2]> <rsmp[+]>
#> 3 plus_pca_lm <tibble [1 × 4]> <opts[3]> <tune[+]>
Created on 2021-07-30 by the reprex package (v2.0.0)