check_scalar#

sklearn.utils.check_scalar(x, name, target_type, *, min_val=None, max_val=None, include_boundaries='both')[source]#

Validate scalar parameters type and value.

Parameters:
xobject

The scalar parameter to validate.

namestr

The name of the parameter to be printed in error messages.

target_typetype or tuple

Acceptable data types for the parameter.

min_valfloat or int, default=None

The minimum valid value the parameter can take. If None (default) it is implied that the parameter does not have a lower bound.

max_valfloat or int, default=None

The maximum valid value the parameter can take. If None (default) it is implied that the parameter does not have an upper bound.

include_boundaries{“left”, “right”, “both”, “neither”}, default=”both”

Whether the interval defined by min_val and max_val should include the boundaries. Possible choices are:

  • "left": only min_val is included in the valid interval. It is equivalent to the interval [ min_val, max_val ).

  • "right": only max_val is included in the valid interval. It is equivalent to the interval ( min_val, max_val ].

  • "both": min_val and max_val are included in the valid interval. It is equivalent to the interval [ min_val, max_val ].

  • "neither": neither min_val nor max_val are included in the valid interval. It is equivalent to the interval ( min_val, max_val ).

Returns:
xnumbers.Number

The validated number.

Raises:
TypeError

If the parameter’s type does not match the desired type.

ValueError

If the parameter’s value violates the given bounds. If min_val, max_val and include_boundaries are inconsistent.

Examples

>>> from sklearn.utils.validation import check_scalar
>>> check_scalar(10, "x", int, min_val=1, max_val=20)
10