pyrepo_mcda.mcda_methods.promethee

Classes

PROMETHEE_II

Preference Ranking Organization Method for Enrichment Evaluation II

Module Contents

class pyrepo_mcda.mcda_methods.promethee.PROMETHEE_II

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

Preference Ranking Organization Method for Enrichment Evaluation II (PROMETHEE II) for ranking alternatives based on positive, negative, and net preference flows.

__call__(matrix, weights, types, preference_functions=None, p=None, q=None)

Score alternatives from decision matrix matrix using criteria weights weights and criteria types types

Parameters

matrixndarray: decision matrix with performance values for m alternatives in rows and n criteria in columns
weightsndarray: matrix with criteria weights vectors with number of columns equal to number of columns n of matrix
typesndarray: vector with criteria types containing values of 1 for profit criteria and -1 for cost criteria with size equal to number of columns n of matrix

_usual_function(d, p, q)

_ushape_function(d, p, q)

_vshape_function(d, p, q)

_level_function(d, p, q)

_linear_function(d, p, q)

_gaussian_function(d, p, q)

static _promethee_II(self, matrix, weights, types, preference_functions, p, q)

Score alternatives provided in the decision matrix matrix using criteria weights and criteria types.

Parameters

matrixndarray
Decision matrix with m alternatives in rows and n criteria in columns.

weights: ndarray
Criteria weights. The sum of weights must be equal to 1.

types: ndarray
Criteria types. Profit criteria are represented by 1 and cost by -1.

preference_functionslist
List with methods containing preference functions for calculating the preference degree for each criterion. If None, default PROMETHEE preference function (usual) is applied.

pndarray
Vector with values representing the threshold of absolute preference. If None, default PROMETHEE preference thresholds are applied.

qndarray
Vector with values representing the threshold of indifference. If None, default PROMETHEE indifference thresholds are applied.

Returns

ndrarray
Preference values of each alternative. The best alternative has the highest preference value.

Examples

>>> promethee_II = PROMETHEE_II()
>>> preference_functions = [promethee_II._linear_function for pf in range(len(weights))]
>>> u = np.sqrt(np.sum(np.square(np.mean(matrix, axis = 0) - matrix), axis = 0) / matrix.shape[0])
>>> p = 2 * u
>>> q = 0.5 * u
>>> pref = promethee_II(matrix, weights, types, preference_functions, p = p, q = q)
>>> rank = rank_preferences(pref, reverse = True)