skcriteria.madm.electre.py
module¶
ELECTRE is a family of multicriteria decision analysis methods that originated in Europe in the mid1960s. The acronym ELECTRE stands for: ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing REality).
Usually the Electre Methods are used to discard some alternatives to the problem, which are unacceptable. After that we can use another MCDA to select the best one. The Advantage of using the Electre Methods before is that we can apply another MCDA with a restricted set of alternatives saving much time.

class
skcriteria.madm.electre.
ELECTRE1
(p=0.65, q=0.35, mnorm=u'sum', wnorm=u'sum', njobs=None)[source]¶ Bases:
skcriteria.madm._dmaker.DecisionMaker
The ELECTRE I model find the kernel solution in a situation where true criteria and restricted outranking relations are given.
That is, ELECTRE I cannot derive the ranking of alternatives but the kernel set. In ELECTRE I, two indices called the concordance index and the discordance index are used to measure the relations between objects.
Parameters: p : float, optional (default=0.65)
Concordance threshold. Threshold of how much one alternative is at least as good as another to be significative.
q : float, optional (default=0.35)
Discordance threshold. Threshold of how much the degree one alternative is strictly preferred to another to be significative.
mnorm : string, callable, optional (default=”sum”)
Normalization method for the alternative matrix.
wnorm : string, callable, optional (default=”sum”)
Normalization method for the weights array.
njobs : int, default=None
How many cores to use to solve the linear programs and the second method. By default all the availables cores are used.
Returns: Decision :
skcriteria.madm.Decision
With values:
 kernel_: Array with the indexes of the alternatives in he kernel.
 rank_: None
 best_alternative_: None
 alpha_solution_: False
 beta_solution_: True
 gamma_solution_: False
 e_: Particular data created by this method.
 e_.closeness: Array where the inth element represent the closenees of the inth alternative to ideal and worst solution.
 e_.outrank: numpy.ndarray of bool
The outranking matrix of superation. If the element[i][j] is True
The alternative
i
outrank the alternativej
.  e_.mtx_concordance: numpy.ndarray
The concordance indexes matrix where the element[i][j] measures how
much the alternative
i
is at least as good asj
.  e_.mtx_discordance: numpy.ndarray
The discordance indexes matrix where the element[i][j] measures the
degree to which the alternative
i
is strictly preferred toj
.  e_.p: float Concordance index threshold.
 e_.q: float Discordance index threshold.
References
[R4] Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Readings in multiple criteria decision aid (pp.155183). Springer, Berlin, Heidelberg. [R5] Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle, 2(8), 5775. [R6] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press. Attributes
mnorm
Normalization function for the alternative matrix. njobs
How many cores to use to solve the linear programs and the second method. p
Concordance threshold. q
Discordance threshold. wnorm
Normalization function for the weights vector. Methods
as_dict
(**kwargs)Create a simply dict
representation of the object.decide
(data[, criteria, weights])Execute the Solver over the given data. make_result
(data, kernel, rank, extra)Create a new skcriteria.madm.Decision
preprocess
(data)Normalize the alternative matrix and weight vector. solve
(**kwargs)Execute the multicriteria method. 
as_dict
(**kwargs)[source]¶ Create a simply
dict
representation of the object.Notes
x.as_dict != dict(x)

njobs
¶ How many cores to use to solve the linear programs and the second method. By default all the availables cores are used.

p
¶ Concordance threshold. Threshold of how much one alternative is at least as good as another to be significative.

q
¶ Discordance threshold. Threshold of how much the degree one alternative is strictly preferred to another to be significative.