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Ensure that predictions have the correct number of rows

Source: R/validation.R
validate_prediction_size.Rd

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)

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.

Value

validate_prediction_size() returns pred invisibly.

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

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.

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
#> # A tibble: 5 × 1
#>   .pred
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
#> 5     5

# Use this check to ensure that
# the number of rows or pred match new_data
check_prediction_size(pred, new_data)
#> $ok
#> [1] TRUE
#> 
#> $size_new_data
#> [1] 5
#> 
#> $size_pred
#> [1] 5
#> 

# An informative error message is thrown
# if the rows are different
try(validate_prediction_size(spruce_numeric(1:4), new_data))
#> Error in validate_prediction_size(spruce_numeric(1:4), new_data) : 
#>   The size of `new_data` (5) must match the size of `pred` (4).