control outcome transformations #8
Description
The extract_* functions do different things when there is an outcome transformation in a formula:
library(tidymodels)
library(important)tidymodels_prefer()
theme_set(theme_bw())
options(pillar.advice = FALSE, pillar.min_title_chars = Inf)# In-line function in preprocessor formula
ames <- ames %>% select(Sale_Price, Lot_Area, Neighborhood)
lm_wflow <- workflow(log(Sale_Price) ~ . , linear_reg())
lm_fit <- fit(lm_wflow, ames)
# Different variable names and units
important:::extract_data_original(lm_fit, head(ames)) %>%
select(contains("Sale"))
#> # A tibble: 6 × 1
#> Sale_Price
#> <int>
#> 1 215000
#> 2 105000
#> 3 172000
#> 4 244000
#> 5 189900
#> 6 195500
important:::extract_data_derived(lm_fit, head(ames)) %>%
select(contains("Sale"))
#> # A tibble: 6 × 1
#> `log(Sale_Price)`
#> <dbl>
#> 1 12.3
#> 2 11.6
#> 3 12.1
#> 4 12.4
#> 5 12.2
#> 6 12.2# In-line function in extra formula from add_model()
lm_form_wflow <-
workflow() %>%
add_formula(Sale_Price~ . ) %>%
add_model(linear_reg(), formula = log(Sale_Price) ~ .)
lm_form_fit <- fit(lm_form_wflow, ames)
# Same variable names and units
important:::extract_data_original(lm_form_fit, head(ames)) %>%
select(contains("Sale"))
#> # A tibble: 6 × 1
#> Sale_Price
#> <int>
#> 1 215000
#> 2 105000
#> 3 172000
#> 4 244000
#> 5 189900
#> 6 195500
important:::extract_data_derived(lm_form_fit, head(ames)) %>%
select(contains("Sale"))
#> # A tibble: 6 × 1
#> Sale_Price
#> <int>
#> 1 215000
#> 2 105000
#> 3 172000
#> 4 244000
#> 5 189900
#> 6 195500This may be intended/helpful in some cases but definitely not in others. We should perhaps make an option for this.