Augment fails in 0.1.6 with a character ID column

[cgoo4]

Augment handled character ID columns in version 0.1.5, but errors in 0.1.6 per this reprex:

library(tidyverse)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

# devtools::install_version("parsnip", "0.1.5")

mt_model <- boost_tree() %>%
  set_mode("classification") %>%
  set_engine("xgboost")

mtcars_tb <- as_tibble(mtcars %>% rownames_to_column()) %>% 
  mutate(vs = factor(vs))

mt_recipe <-
  mtcars_tb %>%
  recipe() %>%
  update_role(vs, new_role = "outcome") %>%
  update_role(rowname, new_role = "id") %>%
  update_role(-has_role("outcome"), -has_role("id"), new_role = "predictor")

mt_wflow <- workflow() %>%
  add_recipe(mt_recipe) %>%
  add_model(mt_model)

mt_fit <- mt_wflow %>%
  fit(mtcars_tb[1:25, ])
#> [12:54:49] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

# Fails for parsnip 0.1.6
mt_results <- mt_fit %>% augment(new_data = mtcars_tb[26:32, ])
#> Error in xgboost::xgb.DMatrix(data = newdata, missing = NA): 'data' has class 'character' and length 77.
#>   'data' accepts either a numeric matrix or a single filename.

# Work-around for parsnip 0.1.6
mt_results <-
  mtcars_tb[26:32, ] %>%
  mutate(
    .pred_class = predict(mt_fit, new_data = mtcars_tb[26:32, ]) %>% pull(.pred_class),
    .pred_0 = predict(mt_fit, new_data = mtcars_tb[26:32, ], type = "prob") %>% pull(.pred_0),
    .pred_1 = predict(mt_fit, new_data = mtcars_tb[26:32, ], type = "prob") %>% pull(.pred_1)
    )

^{Created on 2021-05-30 by the reprex package (v2.0.0)}

Session info

sessioninfo::session_info()
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#>  language (EN)                        
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#>  ctype    en_GB.UTF-8                 
#>  tz       Europe/London               
#>  date     2021-05-30                  
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#> 
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[juliasilge]

The problem here is that .pred_class is bound onto new_data:

parsnip/R/augment.R

Lines 74 to 77 in 2a8da60

	new_data <- dplyr::bind_cols(
	new_data,
	predict(x, new_data = new_data, type = "class")
	)

Which then gets used as input into predict() again:

parsnip/R/augment.R

Line 82 in 2a8da60

predict(x, new_data = new_data, type = "prob")

xgboost isn't happy about this new factor showing up in the input. Those new columns shouldn't be bound on until the end.

[juliasilge]

Thanks for the report @cgoo4! This bug is now fixed in the development version:

devtools::install_github("tidymodels/parsnip")

[github-actions]

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