Failure on predicting class prob from `multinom_reg()` with penalty

[DavisVaughan]

library(parsnip)

mod <- multinom_reg(penalty = 0.01)
mod <- set_engine(mod, "glmnet")

fit <- fit(mod, Species ~ Sepal.Length + Sepal.Width, iris)

predict(fit, iris, type = "prob")
#> Error: `predict()` doesn't work with multiple penalties (i.e. lambdas). Please specify a single value using `penalty = some_value` or use `multi_predict()` to get multiple predictions per row of data.

^{Created on 2019-11-11 by the reprex package (v0.3.0.9000)}

The failure is in this part of parsnip:::predict_classprob.model_fit()

parsnip/R/predict_classprob.R

Line 25 in 74ff766

new_data <- object$spec$method$pred$prob$pre(new_data, object)

[topepo]

Done!

library(parsnip)

mod <- multinom_reg(penalty = 0.01)
mod <- set_engine(mod, "glmnet")

fit <- fit(mod, Species ~ Sepal.Length + Sepal.Width, iris)

predict(fit, iris, type = "prob")
#> # A tibble: 150 x 3
#>    .pred_setosa .pred_versicolor .pred_virginica
#>           <dbl>            <dbl>           <dbl>
#>  1        0.978          0.0181         0.00390 
#>  2        0.872          0.114          0.0136  
#>  3        0.984          0.0146         0.00140 
#>  4        0.983          0.0160         0.00124 
#>  5        0.993          0.00619        0.00120 
#>  6        0.989          0.00767        0.00335 
#>  7        0.997          0.00277        0.000250
#>  8        0.977          0.0198         0.00344 
#>  9        0.980          0.0192         0.000962
#> 10        0.925          0.0669         0.00843 
#> # … with 140 more rows

^{Created on 2019-12-01 by the reprex package (v0.3.0)}

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