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.
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skcriteria.validate.
MIN
= -1¶ Int: Minimization criteria
-
skcriteria.validate.
MAX
= 1¶ Int: Maximization criteria
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exception
skcriteria.validate.
DataValidationError
[source]¶ Bases:
ValueError
Raised when some part of the multicriteria data (alternative matrix, criteria array or weights array) are not compatible with another part.
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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.
Returns: - numpy.ndarray :
Criteria array.
Raises: - DataValidationError :
if some value of the criteria array are not MIN (-1) or MAX (1)
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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.
Returns: - 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).
Raises: - DataValidationError :
If the data are incompatible.