crispyn.weighting_methods

Classes

AHP_WEIGHTING

Functions

`equal_weighting`(matrix)	Calculate criteria weights using objective Equal weighting method.
`entropy_weighting`(matrix)	Calculate criteria weights using objective Entropy weighting method.
`std_weighting`(matrix)	Calculate criteria weights using objective Standard deviation weighting method.
`critic_weighting`(matrix)	Calculate criteria weights using objective CRITIC weighting method.
`gini_weighting`(matrix)	Calculate criteria weights using objective Gini coefficient-based weighting method.
`merec_weighting`(matrix, types)	Calculate criteria weights using objective MEREC weighting method.
`stat_var_weighting`(matrix)	Calculate criteria weights using objective Statistical variance weighting method.
`cilos_weighting`(matrix, types)	Calculate criteria weights using objective CILOS weighting method.
`idocriw_weighting`(matrix, types)	Calculate criteria weights using objective IDOCRIW weighting method.
`angle_weighting`(matrix, types)	Calculate criteria weights using objective Angle weighting method.
`coeff_var_weighting`(matrix)	Calculate criteria weights using objective Coefficient of variation weighting method.
`swara_weighting`(criteria_indexes, s)	Calculation of criteria weights using SWARA subjective weighting method
`lbwa_weighting`(criteria_indexes, criteria_values_I)	Calculation of criteria weights using subjective LBWA weighting method.
`sapevo_weighting`(criteria_matrix)	Calculate criteria weights using SAPEVO subjective weighting method

Module Contents

crispyn.weighting_methods.equal_weighting(matrix)

Calculate criteria weights using objective Equal weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = equal_weighting(matrix)

crispyn.weighting_methods.entropy_weighting(matrix)

Calculate criteria weights using objective Entropy weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = entropy_weighting(matrix)

crispyn.weighting_methods.std_weighting(matrix)

Calculate criteria weights using objective Standard deviation weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = std_weighting(matrix)

crispyn.weighting_methods.critic_weighting(matrix)

Calculate criteria weights using objective CRITIC weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = critic_weighting(matrix)

crispyn.weighting_methods.gini_weighting(matrix)

Calculate criteria weights using objective Gini coefficient-based weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = gini_weighting(matrix)

crispyn.weighting_methods.merec_weighting(matrix, types)

Calculate criteria weights using objective MEREC weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

typesndarray
Vector with criteria types.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = merec_weighting(matrix, types)

crispyn.weighting_methods.stat_var_weighting(matrix)

Calculate criteria weights using objective Statistical variance weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = stat_var_weighting(matrix)

crispyn.weighting_methods.cilos_weighting(matrix, types)

Calculate criteria weights using objective CILOS weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

typesndarray
Vector with criteria types.

Returns

ndarray
Vector of criteria weights.

Examples >>> weights = cilos_weighting(matrix, types)

crispyn.weighting_methods.idocriw_weighting(matrix, types)

Calculate criteria weights using objective IDOCRIW weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

typesndarray
Vector with criteria types.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = idocriw_weighting(matrix, types)

crispyn.weighting_methods.angle_weighting(matrix, types)

Calculate criteria weights using objective Angle weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

typesndarray
Vector with criteria types.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = angle_weighting(matrix, types)

crispyn.weighting_methods.coeff_var_weighting(matrix)

Calculate criteria weights using objective Coefficient of variation weighting method.

Parameters

matrixndarray
Decision matrix with performance values of m alternatives and n criteria.

Returns

ndarray
Vector of criteria weights.

Examples

>>> weights = coeff_var_weighting(matrix)

class crispyn.weighting_methods.AHP_WEIGHTING

__call__(X, compute_priority_vector_method=None)

_check_consistency(X)

Consistency Check on the Pairwise Comparison Matrix of the Criteria or alternatives

Parameters

Xndarray
matrix of pairwise comparisons

Examples

>>> PCcriteria = np.array([[1, 1, 5, 3], [1, 1, 5, 3], [1/5, 1/5, 1, 1/3], [1/3, 1/3, 3, 1]])
>>> ahp_weighting = AHP_WEIGHTING()
>>> ahp_weighting._check_consistency(PCcriteria)

_eigenvector(X)

Compute the Priority Vector of Criteria (weights) or alternatives using Eigenvector method

Parameters

Xndarray
matrix of pairwise comparisons

Returns

ndarray
Eigenvector

Examples

>>> PCM1 = np.array([[1, 5, 1, 1, 1/3, 3],
[1/5, 1, 1/3, 1/5, 1/7, 1],
[1, 3, 1, 1/3, 1/5, 1],
[1, 5, 3, 1, 1/3, 3],
[3, 7, 5, 3, 1, 7],
[1/3, 1, 1, 1/3, 1/7, 1]])
>>> ahp = AHP()
>>> S = ahp._eigenvector(PCM1)

_normalized_column_sum(X)

Compute the Priority Vector of Criteria (weights) or alternatives using The normalized column sum method

Parameters

Xndarray
matrix of pairwise comparisons

Returns

ndarray
Vector with weights calculated with The normalized column sum method

Examples

>>> PCM1 = np.array([[1, 5, 1, 1, 1/3, 3],
[1/5, 1, 1/3, 1/5, 1/7, 1],
[1, 3, 1, 1/3, 1/5, 1],
[1, 5, 3, 1, 1/3, 3],
[3, 7, 5, 3, 1, 7],
[1/3, 1, 1, 1/3, 1/7, 1]])
>>> ahp = AHP()
>>> S = ahp._normalized_column_sum(PCM1)

_geometric_mean(X)

Compute the Priority Vector of Criteria (weights) or alternatives using The geometric mean method

Parameters

Xndarray
matrix of pairwise comparisons

Returns

ndarray
Vector with weights calculated with The geometric mean method

Examples

>>> PCM1 = np.array([[1, 5, 1, 1, 1/3, 3],
[1/5, 1, 1/3, 1/5, 1/7, 1],
[1, 3, 1, 1/3, 1/5, 1],
[1, 5, 3, 1, 1/3, 3],
[3, 7, 5, 3, 1, 7],
[1/3, 1, 1, 1/3, 1/7, 1]])
>>> ahp = AHP()
>>> S = ahp._geometric_mean(PCM1)

static _ahp_weighting(self, X, compute_priority_vector_method)

Calculate criteria weights using subjective AHP weighting method based on provided pairwise criteria comparison matrix

Parameters

Xndarray
pairwise criteria comparison matrix

compute_priority_vector_methodfunction
selected function for calculation priority vector eigenvector, _normalized_column_sum, _geometric_mean

Returns

ndarray
Vector of criteria weights.

Examples

>>> PCcriteria = np.array([[1, 1, 5, 3], [1, 1, 5, 3],
    [1/5, 1/5, 1, 1/3], [1/3, 1/3, 3, 1]])
>>> ahp_weighting = AHP_WEIGHTING()
>>> weights = ahp_weighting(X = PCcriteria, compute_priority_vector_method=ahp_weighting._normalized_column_sum)

crispyn.weighting_methods.swara_weighting(criteria_indexes, s)

Calculation of criteria weights using SWARA subjective weighting method

Parameters

criteria_indexesndarray
Vector with indexes of n criteria in accordance with given decision problem from C1 to Cn ordered in descending order beginning from the most important criterion (Vector with sorted evaluation criteria in descending order, based on their expected significances)

sndarray
The s vector containing n-1 values of criteria comparison generated in following way: Make the respondent express how much criterion j-1 is more significant than criterion j in percentage in range [0, 1]

Returns

ndarray
Vector with criteria weights

Examples

>>> criteria_indexes = np.array([0, 1, 2, 3, 4, 5, 6])
>>> s = np.array([0, 0.35, 0.2, 0.3, 0, 0.4])
>>> swara_weights = swara_weighting(criteria_indexes, s)

crispyn.weighting_methods.lbwa_weighting(criteria_indexes, criteria_values_I)

Calculation of criteria weights using subjective LBWA weighting method.

Parameters

criteria_indexeslist including sublists
A list including sublists containing grouped and ordered indexes of criteria in a given decision problem from C1 to Cn according to their significance, beginning from the most significant

criteria_values_Ilist including sublists
A list including sublists containing influence values of criteria within each subset provided in order beginning from the most significant

Returns

ndarray
Vector of criteria weights

Examples

>>> criteria_indexes = [
        [1, 4, 6, 5, 0, 2],
        [7, 3]
    ]
>>>  criteria_values_I = [
        [0, 2, 3, 4, 4, 5],
        [1, 2]
    ]
>>> weights = lbwa_weighting(criteria_indexes, criteria_values_I)

>>> criteria_indexes = [
        [4, 7, 8, 0],
        [2, 3],
        [],
        [5],
        [],
        [],
        [1, 6]
    ]

>>> criteria_values_I = [
        [0, 1, 2, 4],
        [1, 2],
        [],
        [2],
        [],
        [],
        [1, 3]
    ]

>>> weights = lbwa_weighting(criteria_indexes, criteria_values_I)

crispyn.weighting_methods.sapevo_weighting(criteria_matrix)

Calculate criteria weights using SAPEVO subjective weighting method

Parameters

criteria_matrixndarray
Matrix with degrees of pairwise criteria comparison in scale from -3 to 3

Returns

ndarray:
Vector of criteria weights

Examples

>>> criteria_matrix = np.array([
    [0, 0, 3, 3, 1, 3, 2, 1, 2],
    [0, 0, 3, 3, 1, 3, 2, 1, 2],
    [-3, -3, 0, 0, -1, -2, -2, -1, -2],
    [-3, -3, 0, 0, -2, 2, -2, -2, -2],
    [-1, -1, 1, 2, 0, 2, 0, -1, 1],
    [-3, -3, 2, -2, -2, 0, -2, -1, -2],
    [-3, -2, 2, 2, 0, 2, 0, 3, 0],
    [-1, -1, 1, 2, 1, 1, -3, 0, -1],
    [-2, -2, 2, 2, -1, 2, 0, 1, 0],
])

>>> weights = sapevo_weighting(criteria_matrix)