aorsf - engine: model fit fails if mtry is specified

[MasterLuke84]

Hi,

the model fit fails if mtry is specified for the aorsf-engine. If it is not specified, it works with the default engine values.

library(bonsai)
#> Loading required package: parsnip

# This works with default mtry value
rf_mod <- 
  rand_forest() %>%
  set_engine(engine = "aorsf") %>%
  set_mode(mode = "regression") %>% 
  set_args(min_n = 1, trees = 2, importance = "permute") %>% 
  fit(
    formula = mpg  ~ . , 
    data = mtcars 
  )


rf_mod
#> parsnip model object
#> 
#> ---------- Oblique random regression forest
#> 
#>      Linear combinations: Accelerated Linear regression
#>           N observations: 32
#>                  N trees: 2
#>       N predictors total: 10
#>    N predictors per node: 4
#>  Average leaves per tree: 7.5
#> Min observations in leaf: 1
#>           OOB stat value: 0.27
#>            OOB stat type: RSQ
#>      Variable importance: permute
#> 
#> -----------------------------------------



# Error occurs...
rf_mod_w_mtry <- 
  rand_forest() %>%
  set_engine(engine = "aorsf") %>%
  set_mode(mode = "regression") %>% 
  set_args(mtry = 3, min_n = 1, trees = 2, importance = "permute") %>% 
  fit(
    formula = mpg  ~ . , 
    data = mtcars 
  )
#> Error in ncol(source): object 'x' not found

^{Created on 2024-08-08 with reprex v2.0.2}

Thank you in advance and best regards

[simonpcouch]

Thanks for the issue! Just confirming that I can reproduce this and 1) it does seem to be aorsf-specific (i.e. xgboost is not an issue) and 2) it doesn't seem to be due to any changes in parsnip (issue persists with parsnip v1.0.0). min_cols() seems to be evaluated in a different environment than its usual.

[bcjaeger]

Just saw this and thought I'd check it out. The issue appears to occur in parsnip:::make_form_call:

If we modify:

  if (object$engine == "spark") {
    env$x <- env$data
  }

to

  if (object$engine %in% c("spark", "aorsf")) {
    env$x <- env$data
  }

the x object will be recognized when you specify mtry.

@simonpcouch - would this solution be too hacky? A more general approach might change the call to aorsf::orsf, making it so that instead of mtry = min_cols(~3, x) we use mtry = min_cols(~3, data)