Setting objective in set_engine not respected for xgboost

[lorenzwalthert]

I wanted to use one of the many objectives in xgboost described here.
So I went on and wrote this code to set the objective in set_engine(), as ... are passed to the engine as described in the docs for ... in help(set_engine, package = 'parsnip'):

Any optional arguments associated with the chosen computational engine. [...]

However, I think the objective is not respected, as it is always derived from mode, which can only be regression or classification.

library(parsnip)
library(magrittr)
# boost_tree
model <- parsnip::boost_tree(mtry = NULL, trees = 5, mode = 'regression', learn_rate = .1)
model <- parsnip::set_engine(model, "xgboost", objective = "reg:squaredlogerror")
set.seed(4)
data <- matrix(rnorm(1000), ncol = 4) %>%
  tibble::as_tibble() %>%
  dplyr::mutate(y = sample(1000/4))
fit <- model %>%
  parsnip::fit(y ~., data = data)

predict(fit, data)

This problem actually did come up in curso-r/treesnip#24, where I wanted to use the quantile objective for {lightgbm} and could not and the answer was: "We just copied that code from parsnip". In the interest of a consistent solution to this problem, I am creating an issue here. As described in curso-r/treesnip#24, the maintainers of {treesnip} are happy to let the user override the objective, but I thought maybe this topic merits a discussion here, as the same problem applies to {xgboost} and other engines too.

I think it should be possible to override the objective if it is consistent with the mode argument, because I it can in some instances be orthogonal to the mode (e.g. in my example, i want to use the squared log error) or specify the mode more precisely, e.g. survival:cox is a Cox regression, which is consistent with mode = 'regression' conceptually I think.

[topepo]

Fixed in #411

library(parsnip)
library(magrittr)
# boost_tree
model <- parsnip::boost_tree(mtry = NULL, trees = 5, mode = 'regression', learn_rate = .1)
model <- parsnip::set_engine(model, "xgboost", objective = "reg:squaredlogerror")
set.seed(4)
data <- matrix(rnorm(1000), ncol = 4) %>%
  as.data.frame() %>%
  dplyr::mutate(y = sample(1000/4))
fit <- model %>%
  parsnip::fit(y ~., data = data)

predict(fit, data)
#> # A tibble: 250 x 1
#>    .pred
#>    <dbl>
#>  1  1.20
#>  2  1.20
#>  3  1.20
#>  4  1.20
#>  5  1.20
#>  6  1.20
#>  7  1.20
#>  8  1.20
#>  9  1.20
#> 10  1.20
#> # … with 240 more rows
fit$fit$params$objective
#> [1] "reg:squaredlogerror"

^{Created on 2021-01-14 by the reprex package (v0.3.0)}

[lorenzwalthert]

Thanks Max. Will head over to treesnip and try (or make them try) to implement the same there. 😄

[topepo]

I have on my list a few PRs to submit over there but have not had time. If you want I can include this (if you don't need it right away).

[lorenzwalthert]

If you want to do it, I'd surely prefer that 😄. There is already an issue for it as mentioned above just to make sure: curso-r/treesnip#24. Thanks a lot.

[Athospd]

I went ahead and replicate the logic for catboost and lightgbm to spare @topepo’s time.
curso-r/treesnip@62fe946 and curso-r/treesnip@c15533a

[github-actions]

This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.