CODEWITHSUNDEEP
tidymodelsbroom
mugdi
mugdi

Reputation: 415

tidy() function cant process last_fit() obejcts

Functions like last_fit() from the tune package produces last_fit objects which are large nested lists containing the fit results. I tried to transform them into data.frames using the tidy() function from the broom package but this resulted in the following error:

MRE :

library(tidymodels)
library(tidyverse)

data <- mtcars

model_default<-
parsnip::boost_tree(
    mode = "regression"
) %>%
set_engine('xgboost',objective = 'reg:squarederror')

wf <- workflow() %>%
add_model(model_default) %>%
add_recipe(recipe(mpg~.,data))

lf <- last_fit(wf,split)
tidy_lf <- tidy(lf)

Error in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm) : 
is.atomic(x) is not TRUE
In addition: Warning messages:
1: Data frame tidiers are deprecated and will be removed in an upcoming release of broom. 
2: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA
3: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA
4: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA
5: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA
6: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA
7: In mean.default(X[[i]], ...) :
  argument is not numeric or logical: returning NA

Question : How can I use tidy() with an last_fit() output?

Upvotes: 0

Views: 266

Answers (1)

Julia Silge
Julia Silge

Reputation: 11623

The object that last_fit() creates is a tibble (containing metrics, predictions, etc), not a model that can be tidied. You can use extract_workflow() to extract out the fitted workflow from the object created by last_fit(), and this object can be tidied:

library(tidymodels)

car_split <- initial_split(mtcars)

wf <- workflow() %>%
    add_model(linear_reg()) %>%
    add_recipe(recipe(mpg ~ ., mtcars))

lf <- last_fit(wf, car_split)
lf
#> # Resampling results
#> # Manual resampling 
#> # A tibble: 1 × 6
#>   splits         id               .metrics .notes   .predictions     .workflow 
#>   <list>         <chr>            <list>   <list>   <list>           <list>    
#> 1 <split [24/8]> train/test split <tibble> <tibble> <tibble [8 × 4]> <workflow>

lf %>%
    extract_workflow() %>%
    tidy()
#> # A tibble: 11 × 5
#>    term         estimate std.error statistic p.value
#>    <chr>           <dbl>     <dbl>     <dbl>   <dbl>
#>  1 (Intercept) -33.6       36.0      -0.935   0.367 
#>  2 cyl          -0.0296     1.34     -0.0221  0.983 
#>  3 disp          0.0252     0.0269    0.934   0.367 
#>  4 hp           -0.00539    0.0319   -0.169   0.868 
#>  5 drat         -0.167      2.54     -0.0659  0.948 
#>  6 wt           -5.69       2.79     -2.04    0.0623
#>  7 qsec          3.32       1.76      1.89    0.0820
#>  8 vs           -4.40       3.80     -1.16    0.268 
#>  9 am            2.54       2.67      0.950   0.360 
#> 10 gear          2.69       2.28      1.18    0.259 
#> 11 carb         -0.0486     1.11     -0.0439  0.966

Created on 2022-03-23 by the reprex package (v2.0.1)

Upvotes: 1

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