Extract elements of a parsnip model object

These functions extract various elements from a parsnip object. If they do not exist yet, an error is thrown.

• extract_spec_parsnip() returns the parsnip model specification.

• extract_fit_engine() returns the engine specific fit embedded within a parsnip model fit. For example, when using linear_reg() with the "lm" engine, this returns the underlying lm object.

• extract_parameter_dials() returns a single dials parameter object.

• extract_parameter_set_dials() returns a set of dials parameter objects.

• extract_fit_time() returns a tibble with fit times. The fit times correspond to the time for the parsnip engine to fit and do not include other portions of the elapsed time in fit.model_spec().

Usage

# S3 method for class 'model_fit'
extract_spec_parsnip(x, ...)

# S3 method for class 'model_fit'
extract_fit_engine(x, ...)

# S3 method for class 'model_spec'
extract_parameter_set_dials(x, ...)

# S3 method for class 'model_spec'
extract_parameter_dials(x, parameter, ...)

# S3 method for class 'model_fit'
extract_fit_time(x, summarize = TRUE, ...)

Arguments

x

A parsnip model_fit object or a parsnip model_spec object.

...

Not currently used.

parameter

A single string for the parameter ID.

summarize

A logical for whether the elapsed fit time should be returned as a single row or multiple rows. Doesn't support FALSE for parsnip models.

Value

The extracted value from the parsnip object, x, as described in the description section.

Details

Extracting the underlying engine fit can be helpful for describing the model (via print(), summary(), plot(), etc.) or for variable importance/explainers.

However, users should not invoke the predict() method on an extracted model. There may be preprocessing operations that parsnip has executed on the data prior to giving it to the model. Bypassing these can lead to errors or silently generating incorrect predictions.

Good:

   parsnip_fit %>% predict(new_data)

Bad:

   parsnip_fit %>% extract_fit_engine() %>% predict(new_data)

Examples

lm_spec <- linear_reg() %>% set_engine("lm")
lm_fit <- fit(lm_spec, mpg ~ ., data = mtcars)

lm_spec
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm 
#> 
extract_spec_parsnip(lm_fit)
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm 
#> 
#> Model fit template:
#> stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

extract_fit_engine(lm_fit)
#> 
#> Call:
#> stats::lm(formula = mpg ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat  
#>    12.30337     -0.11144      0.01334     -0.02148      0.78711  
#>          wt         qsec           vs           am         gear  
#>    -3.71530      0.82104      0.31776      2.52023      0.65541  
#>        carb  
#>    -0.19942  
#> 
lm(mpg ~ ., data = mtcars)
#> 
#> Call:
#> lm(formula = mpg ~ ., data = mtcars)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat  
#>    12.30337     -0.11144      0.01334     -0.02148      0.78711  
#>          wt         qsec           vs           am         gear  
#>    -3.71530      0.82104      0.31776      2.52023      0.65541  
#>        carb  
#>    -0.19942  
#>