knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(rlang_backtrace_on_error = "none")
library(hardhat) library(modeldata) data(penguins) penguins <- na.omit(penguins)
For most modeling functions, data must be accepted from the user in some format where the outcomes and predictors are both specified. The next step is often to validate and preprocess that input in some way to prepare it for the actual modeling implementation function. For example, when a formula method is used, R provides some infrastructure for preprocessing the user input through the
But the formula method is not the only way to specify modeling terms. There is also an XY method, where
y are supplied directly, and, recently, a
recipe implementation can be used to preprocess data using a set of sequential steps.
As a developer, you likely won't want to care about the details of how each of these methods work, but (hopefully) still want to provide all three of these interfaces for your shiny new model.
mold() makes this easy on you, and takes care of the details of preprocessing user input from any of these methods.
The intended use of
mold() is to be called from your user facing modeling function. To see that in action, have a look at the vignette found here:
vignette("package", "hardhat"). The rest of this vignette will be focused on the various different ways to use
mold(), but keep in mind that generally it is not used as an interactive function like this.
The most familiar interface for R users is likely the formula interface. In this case, terms are specified using the formula notation:
outcomes ~ predictors. Generally, as a developer, you have to then call
model.matrix() on this result to coerce it into the right format for ingestion into your model.
mold() handles all of that for you.
penguin_form <- mold(body_mass_g ~ log(bill_length_mm), penguins) names(penguin_form)
mold() returns four things. Two of them are immediately useful, and are almost always applicable to the modeling implementation you have created. The first is the
predictors, returned as a tibble. All of the required processing has been done for you, so you just have to focus on the modeling implementation.
Second is the
outcomes, also returned as a tibble. While not used here, any processing on the outcome that was specified in the formula would also be done here.
Beyond these two elements,
mold() also returns a slot for any
extras that might have been generated during preprocessing, but aren't specifically predictors or outcomes. For example, an
offset() can be specified directly in the formula, but isn't technically a predictor.
mold(body_mass_g ~ log(bill_length_mm) + offset(bill_depth_mm), penguins)$extras
mold() returns a very important object, the
blueprint. This is responsible for knowing how to preprocess both the training data, and any new data at prediction time. As a developer, you should attach the
blueprint to your model object before returning it to the user. For more information about this, see the package development vignette,
As mentioned above, one of the objects that
mold() returns is an
blueprint responsible for controlling the preprocessing. There are multiple blueprints available in
hardhat, but when you call
mold() one is selected automatically for you. The following two calls generate the same result, using the default formula blueprint.
identical( mold(~ body_mass_g, penguins), mold(~ body_mass_g, penguins, blueprint = default_formula_blueprint()) )
Each blueprint can be tweaked to change how the processing for that interface occurs, and the options vary per blueprint. To understand why you'd ever want to do this, read on!
Now that you have a basic idea of how
mold() works, we can talk about some of the more interesting functionality.
One challenge with the standard formula interface is that, by default, intercepts are always implicitly present and are added to your data set automatically. This works great for the simple regression case. However, other models might either always require or never allow an intercept, but still use the formula interface because of its convenience (for example,
earth). This has led to many ad hoc solutions that prevent the user from removing or adding an intercept.
To get around this,
mold() will never add an intercept by default. Instead, the addition of an intercept is completely controlled by the formula blueprint argument,
no_intercept <- mold(~ body_mass_g, penguins) no_intercept$predictors
with_intercept <- mold( ~ body_mass_g, penguins, blueprint = default_formula_blueprint(intercept = TRUE) ) with_intercept$predictors
An error is thrown if an intercept removal term is specified:
mold(~ body_mass_g - 1, penguins) mold(~ body_mass_g + 0, penguins)
One of the nice things about the formula interface is that it expands factors into dummy variable columns for you. Like intercepts, this is great...until it isn't. For example,
ranger fits a random forest, which can take factors directly, but still uses the formula notation. In this case, it would be great if the factor columns specified as predictors weren't expanded. This is the job of the blueprint argument,
expanded_dummies <- mold(~ body_mass_g + species, penguins) expanded_dummies$predictors
non_expanded_dummies <- mold( ~ body_mass_g + species, penguins, blueprint = default_formula_blueprint(indicators = "none") ) non_expanded_dummies$predictors
Note: It's worth mentioning that when an intercept is not present, base R expands the first factor completely into
K indicator columns corresponding to the
K levels present in that factor (also known as one-hot encoding). Subsequent columns are expanded into the more traditional
K - 1 columns. When an intercept is present,
K - 1 columns are generated for all factor predictors.
k_cols <- mold(~ species, penguins) k_minus_one_cols <- mold( ~ species, penguins, blueprint = default_formula_blueprint(intercept = TRUE) ) colnames(k_cols$predictors) colnames(k_minus_one_cols$predictors)
One of the other frustrating things about working with the formula method is that multivariate outcomes are a bit clunky to specify.
.f <- cbind(body_mass_g, bill_length_mm) ~ bill_depth_mm frame <- model.frame(.f, penguins) head(frame)
This might look like 3 columns, but it is actually 2, where the first column is named
cbind(body_mass_g, bill_length_mm), and it is actually a matrix with 2 columns,
bill_length_mm inside it.
ncol(frame) class(frame$`cbind(body_mass_g, bill_length_mm)`) head(frame$`cbind(body_mass_g, bill_length_mm)`)
The default formula blueprint used with
mold() allows you to specify multiple outcomes like you specify multiple predictors. You can even do inline transformations of each outcome, although if you are doing very much of that, I'd advise using a recipe instead.
outcomes then holds the two outcomes columns.
multivariate <- mold(body_mass_g + log(bill_length_mm) ~ bill_depth_mm, penguins) multivariate$outcomes
The second interface is the XY interface, useful when the predictors and outcomes are specified separately.
x <- subset(penguins, select = -body_mass_g) y <- subset(penguins, select = body_mass_g) penguin_xy <- mold(x, y) penguin_xy$predictors penguin_xy$outcomes
This interface doesn't do too much in the way of preprocessing, but it does let you specify an
intercept in the blueprint specific arguments. Rather than
default_formula_blueprint(), this uses the
xy_with_intercept <- mold(x, y, blueprint = default_xy_blueprint(intercept = TRUE)) xy_with_intercept$predictors
y is a bit special in the XY interface, because in the univariate case users might expect to be able to pass a vector, a 1 column data frame, or a matrix.
mold() is prepared for all of those cases, but the vector case requires special attention. To be consistent with all of the other
mold() interfaces, the
outcomes slot of the return value should be a tibble. To achieve this when
y is supplied as a vector, a default column name is created,
The last of the three interfaces is the relatively new recipes interface. The
default_recipe_blueprint() knows how to
prep() your recipe, and
juice() it to extract the predictors and the outcomes. This is by far the most flexible way to preprocess your data.
library(recipes) rec <- recipe(bill_length_mm ~ species + bill_depth_mm, penguins) %>% step_log(bill_length_mm) %>% step_dummy(species) penguin_recipe <- mold(rec, penguins) penguin_recipe$predictors penguin_recipe$outcomes
The only special thing you can tweak with the recipe blueprint is whether or not an intercept is added.
recipe_with_intercept <- mold( rec, penguins, blueprint = default_recipe_blueprint(intercept = TRUE) ) recipe_with_intercept$predictors
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