Account for possibility of custom objective function in XGBoost boost_tree()

[smingerson]

Currently, passing a custom objective function causes an error downstream when predicting. This happens in xgb_pred() when using switch() off of the objective (usually a character string) to modify the output of predict.xgb.Booster().

library(xgboost)
library(parsnip)
library(workflows)
mod <- boost_tree("regression") %>% 
  set_engine("xgboost",
             objective = function(preds, dtrain) {
               truth <- as.numeric(getinfo(dtrain, "label"))
               error <- truth - preds
               gradient <- -2 * error
               hess <- rep.int(2, length(preds))
               list(grad = gradient, hess = hess)
             }
             )

dt <- data.frame(x = rnorm(15))
dt$y <- dt$x + rnorm(15, 0, .05)

wf <- workflow() %>% 
  add_model(mod) %>% 
  add_formula(y~x)
fitted <- fit(wf, data = dt)
predict(fitted, new_data = dt)
#> Error in switch(object$params$objective, `binary:logitraw` = stats::binomial()$linkinv(res), : EXPR must be a length 1 vector

[jcpsantiago]

I also see this error when using parsnip::set_engine("xgboost", params = list(eval_metric = "aucpr")) without setting the objective argument. I came across this error after updating parsnip to 0.1.5 from 0.1.4, and tune::tune_grid started failing. (tidymodels and the other individual packages were also updated in that time i.e. {workflows}, {tune}).

this test is passing:

parsnip/tests/testthat/test_boost_tree_xgboost.R

Line 169 in cb08638

test_that('xgboost alternate objective', {

  spec <-
    boost_tree() %>%
    set_engine("xgboost", objective = "reg:pseudohubererror") %>%
    set_mode("regression")

  xgb_fit <- spec %>% fit(mpg ~ ., data = mtcars)

but if objective is not a string (I guess this is the reason for labelling this as a feature request instead of a bug) it fails like in OPs code. Additionally, if one adds anything else to set_engine it fails with the same error -- are the ... all added to the same vector?

library(xgboost)
library(parsnip)
library(workflows)

mod <- boost_tree("classification") %>% 
  set_engine(
    "xgboost", 
    objective = "binary:logistic",
    params = list(eval_metric = "aucpr") # <- added this and changed the data to be a classification problem
  )

dt <- data.frame(
  x = rnorm(15),
  y = rnorm(15) + rnorm(15, 0, .05),
  target = as.factor(rbinom(15, 1, 0.5))
)

wf <- workflow() %>% 
  add_model(mod) %>% 
  add_formula(target ~ x + y)

fitted <- fit(wf, data = dt)
predict(fitted, new_data = dt)
#> Error in switch(object$params$objective, `binary:logitraw` = stats::binomial()$linkinv(res), : EXPR must be a length 1 vector

^{Created on 2021-04-20 by the reprex package (v2.0.0)}

Session info

sessioninfo::session_info()
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#>  date     2021-04-20                  
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#>  P ── Loaded and on-disk path mismatch.

To fix it I had to change my code to:

mod <- boost_tree("classification") %>% 
  set_engine(
    "xgboost",
    params = list(
      eval_metric = "aucpr",
      objective = "binary:logistic" # <- MUST be present
    )
  )

the objective must be explicitly declared if params is used, otherwise object$params$objective is NULL. Not sure if this is expected behavior i.e. the default was dropped.

[amazongodman]

There are similar reports here as well.

tidymodels/butcher#214

[simonpcouch]

Related to #774.

[github-actions]

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