multimon_reg has different types of predictions

[topepo]

predict() produces a factor and multi_predict() is character:

library(tidymodels)
#> Registered S3 method overwritten by 'xts':
#>   method     from
#>   as.zoo.xts zoo
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.3 ──
#> ✔ broom     0.5.2          ✔ purrr     0.3.3     
#> ✔ dials     0.0.3.9001     ✔ recipes   0.1.7.9001
#> ✔ dplyr     0.8.3          ✔ rsample   0.0.5     
#> ✔ ggplot2   3.2.1          ✔ tibble    2.1.3     
#> ✔ infer     0.5.0          ✔ yardstick 0.0.4     
#> ✔ parsnip   0.0.3.9001
#> ── 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()
library(tune)
library(glmnet)
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
#> Loaded glmnet 3.0
library(mlbench)

data("Satellite")

mod <- multinom_reg() %>% 
  set_engine("glmnet")

fit <- mod %>% fit(classes ~ ., data = Satellite[-(1:10),])

predict(fit, new_data = Satellite[1:10, -37], penalty = .01)
#> # A tibble: 10 x 1
#>    .pred_class   
#>    <fct>         
#>  1 grey soil     
#>  2 grey soil     
#>  3 grey soil     
#>  4 grey soil     
#>  5 grey soil     
#>  6 grey soil     
#>  7 grey soil     
#>  8 grey soil     
#>  9 damp grey soil
#> 10 damp grey soil

multi_predict(fit, new_data = Satellite[1:10, -37], penalty = c(.1, 1))$.pred[[1]]
#> # A tibble: 2 x 2
#>   .pred_class penalty
#>   <chr>         <dbl>
#> 1 grey soil       0.1
#> 2 red soil        1

^{Created on 2019-10-23 by the reprex package (v0.3.0)}

[github-actions]

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