Ensure that predictions have the correct number of rows
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.
validate_prediction_size(pred, new_data) check_prediction_size(pred, new_data)
pred |
A tibble. The predictions to return from any prediction
|
new_data |
A data frame of new predictors and possibly outcomes. |
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.
validate_prediction_size() returns pred invisibly.
check_prediction_size() returns a named list of three components,
ok, size_new_data, and size_pred.
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.
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()
# 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))
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