# skcriteria.validate module¶

This module core functionalities for validate the data used inside scikit criteria.

• Constants that represent minimization and mazimization criteria.
• Scikit-Criteria Criteria ndarray creation.
• Scikit-Criteria Data validation.
skcriteria.validate.MIN = -1

Int: Minimization criteria

skcriteria.validate.MAX = 1

Int: Maximization criteria

exception skcriteria.validate.DataValidationError[source]

Bases: exceptions.ValueError

Raised when some part of the multicriteria data (alternative matrix, criteria array or weights array) are not compatible with another part.

skcriteria.validate.criteriarr(criteria)[source]

Validate if the iterable only contains MIN (or any alias) and MAX (or any alias) values. And also always returns an ndarray representation of the iterable.

Parameters: criteria : Array-like Iterable containing all the values to be validated by the function. numpy.ndarray : Criteria array. DataValidationError : if some value of the criteria array are not MIN (-1) or MAX (1)
skcriteria.validate.validate_data(mtx, criteria, weights=None)[source]

Validate if the main components of the Data in scikit-criteria are compatible.

The function tests:

• The matrix (mtx) must be 2-dimensional.
• The criteria array must be a criteria array (criteriarr function).
• The number of criteria must be the same number of columns in mtx.
• The weight array must be None or an iterable with the same length of the criteria.
Parameters: mtx : 2D array-like 2D alternative matrix, where every column (axis 0) are a criteria, and every row (axis 1) is an alternative. criteria : Array-like The sense of optimality of every criteria. Must has only MIN (-1) and MAX (1) values. Must has the same elements as columns has mtx weights : array like or None The importance of every criteria. Must has the same elements as columns has mtx or None. mtx : numpy.ndarray mtx representations as 2d numpy.ndarray. criteria : numpy.ndarray A criteria as numpy.ndarray. weights : numpy.ndarray or None A weights as numpy.ndarray or None (if weights is None). DataValidationError : If the data are incompatible.