Predictors dropped with a recipe step

[ttrodrigz]

After doing feature selection - specifically, feature dropping - I am wondering if there is a way for a trained workflow to ignore predictors that were dropped from a recipe step.

Such as...

library(tidymodels)

# adding in a predictor that will be dropped
mt2 <-
    mtcars |> 
    mutate(zv = 1)

# build recipe, showing that it is correctly getting dropped
rec <-
    mt2 |> 
    recipe(mpg ~ hp + zv) |> 
    step_zv(all_predictors())

prep(rec)
#> 
#> -- Recipe ----------------------------------------------------------------------
#> 
#> -- Inputs
#> Number of variables by role
#> outcome:   1
#> predictor: 2
#> 
#> -- Training information
#> Training data contained 32 data points and no incomplete rows.
#> 
#> -- Operations
#> * Zero variance filter removed: zv | Trained


# build/fit a workflow
spec <- linear_reg()

wf <- workflow(rec, spec)

wf.fitted <- fit(wf, data = mt2)

# showing that the `zv` predictor was not used in the model fitting
extract_fit_engine(wf.fitted)
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)           hp  
#>    30.09886     -0.06823

# trying to predict on data that does not have the `zv` column
predict(wf.fitted, new_data = mtcars)
#> Error in `validate_column_names()`:
#> ! The following required columns are missing: 'zv'.

^{Created on 2024-03-27 with reprex v2.0.2}

My questions are:

1. I'm sure this was an intentional decision and a there is a good reason for it, but am wondering what it is?
2. Is there any way of telling a trained workflow to ignore variables that are dropped in a recipe step?

Thanks!

[Max]

@ttrodrigz

I'm sure this was an intentional decision and a there is a good reason for it, but am wondering what it is?

It's very hard to retrospectively change the data given as input. That's why R's model formula apparatus doesn't do the same.

You can put in a feature request; we might be able to make an api for that. I added an issue.

@ttrodrigz

Is there any way of telling a trained workflow to ignore variables that are dropped in a recipe step?

Not yet.

For this model (and no other preprocessing), you could use tidypredict to get the prediction equation and use that.

[ttrodrigz]

Thanks, Max. This is particularly useful in the event that feature selection was done and it is determined that one or more of the dropped predictors isn't worth collecting/storing in the future. Feels like it could help "future proof" a trained workflow. Certainly many ways around this but would be a useful feature nonetheless.