Grf qr engine

[dajmcdon]

Checklist

Please:

• Make sure this PR is against "dev", not "main".
• Request a review from one of the current epipredict main reviewers:
  dajmcdon.
• Make sure to bump the version number in DESCRIPTION and NEWS.md.
  Always increment the patch version number (the third number), unless you are
  making a release PR from dev to main, in which case increment the minor
  version number (the second number).
• Describe changes made in NEWS.md, making sure breaking changes
  (backwards-incompatible changes to the documented interface) are noted.
  Collect the changes under the next release number (e.g. if you are on
  0.7.2, then write your changes under the 0.8 heading).

[dsweber2]

Comparing make_quantile_reg and make_grf_quantiles, it looks like they're both using the same model/engine/mode settings. I remember some discussion about maybe switching so that quantile_regression was a separate mode? I guess if that's happening it's happening some later version?

Also, I'm assuming all parameters not here are just set to their defaults, so sample.fraction=0.5; is there some reason for this particular trio of parameters, and not including say sample.fraction, honesty, honesty.fraction, honesty.prune.leaves, etc?

Overall, mostly just some clarification stuff and some bike-shedding w.r.t which parameters we include. I think it's good to go.

[dsweber2]

Suggested change

	y = rnorm(100), x = rnorm(100), z = rnorm(100),
	+ y = rnorm(100), x = rnorm(100), z = y-x+ .01 * rnorm(100)+1,

if we make it so that there's something other than noise, the fit models are the same regardless of the state of the seed.
So I tried this, and got different results still, unfortunately.

[dsweber2]

My guess is that this is because fseed specifies the c++ seed, whereas set_engine(...seed=fseed) is specifying an R seed, which isn't getting passed down. We may just want to set the tolerances large on expect_equal or something along those lines

[dsweber2]

I ran the manual twice and got something roughly at the same size. Given that this is just a wrapper I'm not sure we need to do too extensive of testing

[dajmcdon]

My understanding is that these two should result in the same thing. So, I'm a bit miffed it doesn't.

library(epipredict)
library(grf)
tib <- tibble::tibble(
  y = rnorm(100), x = rnorm(100), z = rnorm(100),
  f = factor(sample(letters[1:3], 100, replace = TRUE))
)
fseed <- 12345
spec_seed <- rand_forest(mode = "regression", mtry = 2L, min_n = 10) %>%
  set_engine("grf_quantiles", seed = fseed)

manual <- quantile_forest(
  as.matrix(tib[, 2:3]), tib$y,
  quantiles = c(0.1, 0.5, 0.9), seed = fseed,
  mtry = 2L, min.node.size = 10
)
translate(spec_seed)
#> Random Forest Model Specification (regression)
#> 
#> Main Arguments:
#>   mtry = 2
#>   min_n = 10
#> 
#> Engine-Specific Arguments:
#>   seed = fseed
#> 
#> Computational engine: grf_quantiles 
#> 
#> Model fit template:
#> grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~2L, 
#>     x), min.node.size = min_rows(~10, x), seed = fseed, quantiles = c(0.1, 
#> 0.5, 0.9), num.threads = 1L)

^{Created on 2024-08-13 with reprex v2.1.1}

The function is being called with the same args, and passing the seed. I'm not concerned, but I wish the were the same.

[dajmcdon]

As for the overall comment, yes, the plan is for {parsnip} to add a mode = "quantile_regression" setting. At that point, we'll probably pivot all of the engines here to that setup, and potentially remove them from here.