knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(rlang_backtrace_on_error = "none")
library(hardhat) library(modeldata) data(penguins) penguins <- na.omit(penguins)
The counterpart to
mold() (which you can read all about in
vignette("mold", "hardhat")), is
mold() is used to preprocess your training data,
forge() is used to preprocess new data that you are going to use to generate predictions from your model.
forge() is not intended to be used interactively. Instead, it should be called from the
predict() method for your model. To learn more about using
forge() in a modeling package, see
vignette("package", "hardhat"). The rest of this vignette will be focused on the many features that
mold() is used, one of the returned objects is an
blueprint. This is the key to preprocessing new data with
forge(). For instance, assume you've called
mold() like so:
penguin_train <- penguins[1:300,] penguin_test <- penguins[-(1:300),]
penguin_form <- mold( log(body_mass_g) ~ species + bill_length_mm, penguin_train, blueprint = default_formula_blueprint(indicators = "none") ) formula_eng <- penguin_form$blueprint formula_eng
A formula blueprint is returned here, which knows about the predictors and outcomes that were used at training time, and knows that you don't want to expand
species into dummy variables by setting
indicators = "none".
When it is time to
predict() on new data, that data is passed on to
forge() along with the
blueprint we just created.
Note that in
species was not expanded because the
blueprint knew about the preprocessing options that were set when
mold() was called.
forge() always returns three things, and they should look familiar to you if you have used
predictors holds a tibble of the predictors.
outcomes is returned as
NULL by default, because most
predict() methods assume you only have access to the new predictors. Alternatively, as you will read in a moment, this can contain a tibble of the new outcomes.
extras varies per blueprint, but is a catch-all slot to hold the same kind of extra objects that were returned by the blueprint when
mold() was called.
Generally when generating predictions you only need to know about the new predictors. However, when performing resampling you will need to have the processed outcomes as well so you can compute cross validated performance statistics and decide between multiple models, or choose between hyperparameters.
You can easily request the outcomes as well with
outcomes = TRUE. Just like with the predictors, these get processed using the same steps as done to the outcomes at fit time.
forge(penguin_test, formula_eng, outcomes = TRUE)
One of the most useful things about
forge() is its robustness against malformed new data. It isn't unreasonable to enforce that the new data a user provides at prediction time should have the same type as the data used at fit time. Type is defined in the vctrs sense, and for our uses essentially means that a number of checks on the test data have to pass, including:
The column names of the testing data and training data must be the same.
The type of each column of the testing data must be the same as the columns found in the training data. This means:
The classes must be the same (e.g. if it was a factor in training, it must be a factor in testing).
The attributes must be the same (e.g. the levels of the factors must also be the same).
Almost all of this validation is possible through the use of
vctrs::vec_cast(), and is called for you by
The easiest example to demonstrate is missing columns in the testing data.
forge() won't let you continue until all of the required predictors used at training are also present in the new data.
test_missing_column <- subset(penguin_test, select = -species) forge(test_missing_column, formula_eng)
After an initial scan for the column names is done, a deeper scan of each column is performed, checking the type of that column. For instance, what happens if the new
species column was a double, not a factor?
test_species_double <- penguin_test test_species_double$species <- as.double(test_species_double$species) forge(test_species_double, formula_eng)
An error is thrown, indicating that a double can't be cast to a factor.
The error message above suggests that in some cases you can automatically cast from one type to another, and in fact that is true! Rather than being a double, what if
species was just a character?
test_species_character <- penguin_test test_species_character$species <- as.character(test_species_character$species) forged_char <- forge(test_species_character, formula_eng) forged_char$predictors class(forged_char$predictors$species) levels(forged_char$predictors$species)
Interesting, so in this case we can actually convert to a factor, and the class and even the levels are all restored. The key here is that this was a lossless conversion. We lost no information when converting the character
species to a factor because the unique character values were a subset of the original levels.
An example of a conversion that would be lossy is if the character
species column had a value that was not a level in the training data.
test_species_lossy <- penguin_test test_species_lossy$species <- as.character(test_species_lossy$species) test_species_lossy$species <- "im new!" forged_lossy <- forge(test_species_lossy, formula_eng) forged_lossy$predictors
In this case:
A lossy warning is thrown
species column is still converted to a factor with the right levels
The novel level is removed and its value is set to
Just like with the formula method, a recipe can be used as the preprocessor at fit and prediction time.
hardhat handles calling
bake() for you at the right times. For instance, say we have a recipe that just creates dummy variables out of
library(recipes) rec <- recipe(bill_length_mm ~ body_mass_g + species, penguin_train) %>% step_dummy(species) penguin_recipe <- mold(rec, penguin_train) penguin_recipe$predictors
The blueprint is a
recipe_eng <- penguin_recipe$blueprint recipe_eng
forge(), we can request
outcomes to have the predictors and outcomes separated like with the formula method.
forge(penguin_test, recipe_eng, outcomes = TRUE)
One complication with
recipes is that, in the
bake() step, the processing happens to the predictors and the outcomes all together. This means that you might run into the situation where the outcomes seem to be required to
forge(), even if you aren't requesting them.
rec2 <- recipe(bill_length_mm ~ body_mass_g + species, penguin_train) %>% step_dummy(species) %>% step_center(bill_length_mm) # Here we modify the outcome penguin_recipe2 <- mold(rec2, penguin_train) recipe_eng_log_outcome <- penguin_recipe2$blueprint
new_data doesn't have the outcome, baking this recipe will fail even if we don't request that the outcomes are returned by
penguin_test_no_outcome <- subset(penguin_test, select = -bill_length_mm) forge(penguin_test_no_outcome, recipe_eng_log_outcome)
The way around this is to use the built-in recipe argument,
skip, on the step containing the outcome. This skips the processing of that step at
rec3 <- recipe(bill_length_mm ~ body_mass_g + species, penguin_train) %>% step_dummy(species) %>% step_center(bill_length_mm, skip = TRUE) penguin_recipe3 <- mold(rec3, penguin_train) recipe_eng_skip_outcome <- penguin_recipe3$blueprint forge(penguin_test_no_outcome, recipe_eng_skip_outcome)
There is a tradeoff here that you need to be aware of.
If you are just interested in generating predictions on completely new data, you can safely use
skip = TRUE because you will almost never have access to the corresponding true outcomes to preprocess and compare against.
If you know you need to do resampling, you will likely have access to the outcomes during the resampling step so you can cross-validate the performance. In this case, you can't set
skip = TRUE because then the outcomes won't be processed, but since you have access to them, you shouldn't need to.
For example, if we used
penguin_test with the above recipe (which has the outcome),
bill_length_mm wouldn't get centered when
forge() is called. But we probably would not have skipped that step if we knew that our test data would have the outcome.
forge(penguin_test, recipe_eng_skip_outcome, outcomes = TRUE)$outcomes # Notice that the `outcome` values haven't been centered # and are the same as before head(penguin_test$bill_length_mm)
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