validate_predictors_are_numeric: Ensure predictors are all numeric

In tidymodels/hardhat: Construct Modeling Packages

Ensure predictors are all numeric

Description

validate - asserts the following:

• predictors must have numeric columns.

check - returns the following:

• ok A logical. Does the check pass?

• bad_classes A named list. The names are the names of problematic columns, and the values are the classes of the matching column.

Usage

validate_predictors_are_numeric(predictors)

check_predictors_are_numeric(predictors, ..., call = caller_env())

Arguments

predictors	An object to check.
...	These dots are for future extensions and must be empty.
call	The call used for errors and warnings.

Details

The expected way to use this validation function is to supply it the ⁠$predictors⁠ element of the result of a call to mold().

Value

validate_predictors_are_numeric() returns predictors invisibly.

check_predictors_are_numeric() returns a named list of two components, ok, and bad_classes.

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_prediction_size()

Examples

# All good
check_predictors_are_numeric(mtcars)

# Species is not numeric
check_predictors_are_numeric(iris)

# This gives an intelligent error message
try(validate_predictors_are_numeric(iris))

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