#' Predict from a `brulee_multinomial_reg`
#'
#' @inheritParams predict.brulee_mlp
#' @param object A `brulee_multinomial_reg` object.
#' @param type A single character. The type of predictions to generate.
#' Valid options are:
#'
#' - `"class"` for hard class predictions
#' - `"prob"` for soft class predictions (i.e., class probabilities)
#'
#' @return
#'
#' A tibble of predictions. The number of rows in the tibble is guaranteed
#' to be the same as the number of rows in `new_data`.
#'
#' @examples
#' \donttest{
#' if (torch::torch_is_installed()) {
#'
#' library(recipes)
#' library(yardstick)
#'
#' data(penguins, package = "modeldata")
#'
#' penguins <- penguins %>% na.omit()
#'
#' set.seed(122)
#' in_train <- sample(1:nrow(penguins), 200)
#' penguins_train <- penguins[ in_train,]
#' penguins_test <- penguins[-in_train,]
#'
#' rec <- recipe(island ~ ., data = penguins_train) %>%
#' step_dummy(species, sex) %>%
#' step_normalize(all_numeric_predictors())
#'
#' set.seed(3)
#' fit <- brulee_multinomial_reg(rec, data = penguins_train, epochs = 5)
#' fit
#'
#' predict(fit, penguins_test) %>%
#' bind_cols(penguins_test) %>%
#' conf_mat(island, .pred_class)
#' }
#' }
#' @export
predict.brulee_multinomial_reg <- function(object, new_data, type = NULL, epoch = NULL, ...) {
forged <- hardhat::forge(new_data, object$blueprint)
type <- check_type(object, type)
if (is.null(epoch)) {
epoch <- object$best_epoch
}
predict_brulee_multinomial_reg_bridge(type, object, forged$predictors, epoch = epoch)
}
# ------------------------------------------------------------------------------
# Bridge
predict_brulee_multinomial_reg_bridge <- function(type, model, predictors, epoch) {
if (!is.matrix(predictors)) {
predictors <- as.matrix(predictors)
if (is.character(predictors)) {
cli::cli_abort(
paste(
"There were some non-numeric columns in the predictors.",
"Please use a formula or recipe to encode all of the predictors as numeric."
)
)
}
}
predict_function <- get_multinomial_reg_predict_function(type)
max_epoch <- length(model$estimates)
if (epoch > max_epoch) {
msg <- paste("The model fit only", max_epoch, "epochs; predictions cannot",
"be made at epoch", epoch, "so last epoch is used.")
cli::cli_warn(msg)
}
predictions <- predict_function(model, predictors, epoch)
hardhat::validate_prediction_size(predictions, predictors)
predictions
}
get_multinomial_reg_predict_function <- function(type) {
switch(
type,
prob = predict_brulee_multinomial_reg_prob,
class = predict_brulee_multinomial_reg_class
)
}
# ------------------------------------------------------------------------------
# Implementation
predict_brulee_multinomial_reg_raw <- function(model, predictors, epoch) {
# convert from raw format
module <- revive_model(model$model_obj)
# get current model parameters
estimates <- model$estimates[[epoch]]
# convert to torch representation
estimates <- lapply(estimates, torch::torch_tensor)
# stuff back into the model
module$load_state_dict(estimates)
# put the model in evaluation mode
module$eval()
predictions <- module(torch::torch_tensor(predictors))
predictions <- as.array(predictions)
# torch doesn't have a NA type so it returns NaN
predictions[is.nan(predictions)] <- NA
predictions
}
predict_brulee_multinomial_reg_prob <- function(model, predictors, epoch) {
predictions <- predict_brulee_multinomial_reg_raw(model, predictors, epoch)
lvs <- get_levels(model)
hardhat::spruce_prob(pred_levels = lvs, predictions)
}
predict_brulee_multinomial_reg_class <- function(model, predictors, epoch) {
predictions <- predict_brulee_multinomial_reg_raw(model, predictors, epoch)
predictions <- apply(predictions, 1, which.max2) # take the maximum value
lvs <- get_levels(model)
hardhat::spruce_class(factor(lvs[predictions], levels = lvs))
}
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