multi_predict column names

[topepo]

For some some models, the wrong column name was generated for type = "class":

library(tidymodels)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom     0.5.2          ✔ purrr     0.3.2     
#> ✔ dials     0.0.2.9001     ✔ recipes   0.1.6.9000
#> ✔ dplyr     0.8.3          ✔ rsample   0.0.5     
#> ✔ ggplot2   3.2.1          ✔ tibble    2.1.3     
#> ✔ infer     0.4.0.1        ✔ yardstick 0.0.3.9000
#> ✔ parsnip   0.0.3.1
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard()  masks scales::discard()
#> ✖ dplyr::filter()   masks stats::filter()
#> ✖ dplyr::lag()      masks stats::lag()
#> ✖ ggplot2::margin() masks dials::margin()
#> ✖ dials::offset()   masks stats::offset()
#> ✖ recipes::step()   masks stats::step()

lr_mod <- 
  logistic_reg() %>% 
  set_engine("glmnet") %>% 
  fit(Class ~ ., data = two_class_dat)

test_class <- 
  multi_predict(lr_mod, new_data = two_class_dat[, -3], penalty = 0:3)
test_class$.pred[[1]]
#> # A tibble: 4 x 2
#>   penalty .pred 
#>     <int> <fct> 
#> 1       0 Class1
#> 2       1 Class1
#> 3       2 Class1
#> 4       3 Class1

^{Created on 2019-09-02 by the reprex package (v0.2.1)}

The new results are:

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom     0.5.2          ✔ purrr     0.3.2     
#> ✔ dials     0.0.2.9001     ✔ recipes   0.1.6.9000
#> ✔ dplyr     0.8.3          ✔ rsample   0.0.5     
#> ✔ ggplot2   3.2.1          ✔ tibble    2.1.3     
#> ✔ infer     0.4.0.1        ✔ yardstick 0.0.3.9000
#> ✔ parsnip   0.0.3.9000
#> ── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard()  masks scales::discard()
#> ✖ dplyr::filter()   masks stats::filter()
#> ✖ dplyr::lag()      masks stats::lag()
#> ✖ ggplot2::margin() masks dials::margin()
#> ✖ dials::offset()   masks stats::offset()
#> ✖ recipes::step()   masks stats::step()

lr_mod <- 
  logistic_reg() %>% 
  set_engine("glmnet") %>% 
  fit(Class ~ ., data = two_class_dat)

test_class <- 
  multi_predict(lr_mod, new_data = two_class_dat[, -3], penalty = 0:3)
test_class$.pred[[1]]
#> # A tibble: 4 x 2
#>   penalty .pred_class
#>     <int> <fct>      
#> 1       0 Class1     
#> 2       1 Class1     
#> 3       2 Class1     
#> 4       3 Class1

^{Created on 2019-09-02 by the reprex package (v0.2.1)}

[github-actions]

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