validate_prediction_size: Ensure that predictions have the correct number of rows

In tidymodels/hardhat: Construct Modeling Packages

Ensure that predictions have the correct number of rows

Description

validate - asserts the following:

• The size of pred must be the same as the size of new_data.

check - returns the following:

• ok A logical. Does the check pass?

• size_new_data A single numeric. The size of new_data.

• size_pred A single numeric. The size of pred.

Usage

validate_prediction_size(pred, new_data)

check_prediction_size(pred, new_data, ..., call = caller_env())

Arguments

pred	A tibble. The predictions to return from any prediction type. This is often created using one of the spruce functions, like spruce_numeric().
new_data	A data frame of new predictors and possibly outcomes.
...	These dots are for future extensions and must be empty.
call	The call used for errors and warnings.

Details

This validation function is one that is more developer focused rather than user focused. It is a final check to be used right before a value is returned from your specific predict() method, and is mainly a "good practice" sanity check to ensure that your prediction blueprint always returns the same number of rows as new_data, which is one of the modeling conventions this package tries to promote.

Value

validate_prediction_size() returns pred invisibly.

check_prediction_size() returns a named list of three components, ok, size_new_data, and size_pred.

Validation

hardhat provides validation functions at two levels.

• ⁠check_*()⁠: check a condition, and return a list. The list always contains at least one element, ok, a logical that specifies if the check passed. Each check also has check specific elements in the returned list that can be used to construct meaningful error messages.

• ⁠validate_*()⁠: check a condition, and error if it does not pass. These functions call their corresponding check function, and then provide a default error message. If you, as a developer, want a different error message, then call the ⁠check_*()⁠ function yourself, and provide your own validation function.

See Also

Other validation functions: validate_column_names(), validate_no_formula_duplication(), validate_outcomes_are_binary(), validate_outcomes_are_factors(), validate_outcomes_are_numeric(), validate_outcomes_are_univariate(), validate_predictors_are_numeric()

Examples

# Say new_data has 5 rows
new_data <- mtcars[1:5, ]

# And somehow you generate predictions
# for those 5 rows
pred_vec <- 1:5

# Then you use `spruce_numeric()` to clean
# up these numeric predictions
pred <- spruce_numeric(pred_vec)

pred

# Use this check to ensure that
# the number of rows or pred match new_data
check_prediction_size(pred, new_data)

# An informative error message is thrown
# if the rows are different
try(validate_prediction_size(spruce_numeric(1:4), new_data))

tidymodels/hardhat documentation built on Feb. 3, 2025, 12:35 a.m.