Setting the penalty for logistic and multinomial regression with glmnet

[hfrick]

closes #858

This PR moves where we set s, glmnet's argument for the penalty value, from inside the relevant multi_predict() method into the relevant predict_raw() method.

This means that now the default penalty value specified in the parsnip spec will be used when penalty = NULL, also for for type = "raw" (aka the original issue).

This also means that the call stack for multi_predict() for logistic and multinomial regression now follows that of linear regression and what happens in the code is what was laid out in the comments, e.g.,

parsnip/R/logistic_reg.R

Lines 209 to 234 in 2249cbb

	# ------------------------------------------------------------------------------
	# glmnet call stack for logistic regression using `predict` when object has
	# classes "_lognet" and "model_fit" (for class predictions):
	#
	# predict()
	# predict._lognet(penalty = NULL) <-- checks and sets penalty
	# predict.model_fit() <-- checks for extra vars in ...
	# predict_class()
	# predict_class._lognet()
	# predict_class.model_fit()
	# predict.lognet()
	# glmnet call stack for logistic regression using `multi_predict` when object has
	# classes "_lognet" and "model_fit" (for class predictions):
	#
	# multi_predict()
	# multi_predict._lognet(penalty = NULL)
	# predict._lognet(multi = TRUE) <-- checks and sets penalty
	# predict.model_fit() <-- checks for extra vars in ...
	# predict_raw()
	# predict_raw._lognet()
	# predict_raw.model_fit(opts = list(s = penalty))
	# predict.lognet()
	# ------------------------------------------------------------------------------

The tests are in extratests: tidymodels/extratests#72

library(parsnip)

data(lending_club, package = "modeldata")

lr_spec <- logistic_reg(penalty = 0.123) %>% set_engine("glmnet")
f_fit <- fit(lr_spec, Class ~ log(funded_amnt) + int_rate + term,
             data = lending_club)
predict(f_fit, lending_club[1:5, ], type = "raw")
#>         s1
#> 1 2.894019
#> 2 2.894019
#> 3 2.894019
#> 4 2.894019
#> 5 2.894019

data("penguins", package = "modeldata")
penguins <- tidyr::drop_na(penguins)

mr_spec <- multinom_reg(penalty = 0.123) %>% set_engine("glmnet")
f_fit <- fit(mr_spec, species ~ island + bill_length_mm + bill_depth_mm,
             data = penguins)

predict(f_fit, penguins[1:5,], type = "raw")
#> , , 1
#> 
#>      Adelie Chinstrap    Gentoo
#> 1 -5.131312 -7.544642 -7.542310
#> 2 -5.229012 -7.544642 -6.800678
#> 3 -5.424412 -7.544642 -7.142970
#> 4 -4.545112 -7.544642 -7.884602
#> 5 -5.180162 -7.544642 -8.626234

^{Created on 2023-01-25 with reprex v2.0.2}

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