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Setting the penalty for logistic and multinomial regression with glmnet #863

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Merged
merged 2 commits into from
Feb 6, 2023
Merged

Setting the penalty for logistic and multinomial regression with glmnet #863

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1b33a6e
setting penalty in `predict_raw()` method
hfrick Jan 25, 2023
b652e0d
sticking to the general pattern
hfrick Jan 25, 2023
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
Package: parsnip
Title: A Common API to Modeling and Analysis Functions
Version: 1.0.3.9000
Version: 1.0.3.9001
Authors@R: c(
person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre")),
person("Davis", "Vaughan", , "davis@rstudio.com", role = "aut"),
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7 changes: 4 additions & 3 deletions R/logistic_reg.R
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Expand Up @@ -271,8 +271,6 @@ multi_predict._lognet <-
}
}

dots$s <- penalty

if (is.null(type))
type <- "class"
if (!(type %in% c("class", "prob", "link", "raw"))) {
Expand All @@ -284,7 +282,9 @@ multi_predict._lognet <-
dots$type <- type

object$spec <- eval_args(object$spec)
pred <- predict.model_fit(object, new_data = new_data, type = "raw", opts = dots)
pred <- predict._lognet(object, new_data = new_data, type = "raw",
opts = dots, penalty = penalty, multi = TRUE)

param_key <- tibble(group = colnames(pred), penalty = penalty)
pred <- as_tibble(pred)
pred$.row <- 1:nrow(pred)
Expand Down Expand Up @@ -340,6 +340,7 @@ predict_raw._lognet <- function(object, new_data, opts = list(), ...) {
rlang::abort("Did you mean to use `new_data` instead of `newdata`?")

object$spec <- eval_args(object$spec)
opts$s <- object$spec$args$penalty
predict_raw.model_fit(object, new_data = new_data, opts = opts, ...)
}

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6 changes: 4 additions & 2 deletions R/multinom_reg.R
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Expand Up @@ -200,6 +200,7 @@ multi_predict._multnet <-
penalty <- eval_tidy(penalty)

dots <- list(...)

if (is.null(penalty)) {
# See discussion in https://github.com/tidymodels/parsnip/issues/195
if (!is.null(object$spec$args$penalty)) {
Expand All @@ -208,7 +209,6 @@ multi_predict._multnet <-
penalty <- object$fit$lambda
}
}
dots$s <- penalty

if (is.null(type))
type <- "class"
Expand All @@ -221,7 +221,8 @@ multi_predict._multnet <-
dots$type <- type

object$spec <- eval_args(object$spec)
pred <- predict.model_fit(object, new_data = new_data, type = "raw", opts = dots)
pred <- predict._multnet(object, new_data = new_data, type = "raw",
opts = dots, penalty = penalty, multi = TRUE)

format_probs <- function(x) {
x <- as_tibble(x)
Expand Down Expand Up @@ -268,5 +269,6 @@ predict_classprob._multnet <- function(object, new_data, ...) {
#' @export
predict_raw._multnet <- function(object, new_data, opts = list(), ...) {
object$spec <- eval_args(object$spec)
opts$s <- object$spec$args$penalty
predict_raw.model_fit(object, new_data = new_data, opts = opts, ...)
}