pyrepo_mcda.mcda_methods.daria

Classes

DARIA

Data vARIability Assessment (DARIA) method for analyzing variability

Module Contents

class pyrepo_mcda.mcda_methods.daria.DARIA

Data vARIability Assessment (DARIA) method for analyzing variability and temporal changes in alternative preferences.

_gini(R)

Calculate variability values measured by the Gini coefficient in scores obtained by each evaluated option.

Parameters

Rndarray
Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.

Returns

ndarray
Vector with Gini coefficient values for each alternative.

Examples

>>> daria = DARIA()
>>> variability = daria._gini(matrix)

_entropy(R)

Calculate variability values measured by the Entropy in scores obtained by each evaluated option.

Parameters

Rndarray
Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.

Returns

ndarray
Vector with Entropy values for each alternative.

Examples

>>> daria = DARIA()
>>> variability = daria._entropy(matrix)

_std(R)

Calculate variability values measured by the Standard Deviation in scores obtained by each evaluated option.

Parameters

Rndarray: Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.

Returns

ndarray: Vector with Standard Deviation values for each alternative.

Examples

>>> daria = DARIA()
>>> variability = daria._std(matrix)

_stat_var(X)

Calculate variability values measured by the Statistical Variance in scores obtained by each evaluated option.

Parameters

Rndarray: Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.

Returns

ndarray: Vector with Statistical Variance values for each alternative.

Examples

>>> daria = DARIA()
>>> variability = daria._stat_var(matrix)

_coeff_var(X)

Calculate variability values measured by the Coefficient of Variation in scores obtained by each evaluated option.

Parameters

Rndarray: Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.

Returns

ndarray: Vector with Coefficient of Variation values for each alternative.

Examples

>>> daria = DARIA()
>>> variability = daria._coeff_var(matrix)

_direction(R, preference_type=1)

Determine the direction of the variability of alternatives scores obtained in the following periods of time.

Parameters

Rndarray: Matrix with preference values obtained with MCDA method (for example, TOPSIS) with t periods of time in rows and m alternatives in columns.
preference_typeint: The variable represents the ordering of alternatives by the MCDA method. It can be equal to 1 or -1. 1 means that the MCDA method sorts options in descending order according to preference values (for example, the TOPSIS method). -1 means that the MCDA method sorts options in ascending order according to preference values (for example, the VIKOR method).

Returns

direction_listlist: List with strings representing the direction of variability in the form of the arrow up for improvement, arrow down for worsening, and = for stability. It is useful for results presentation.
dir_classndarray: Vector with numerical values representing the direction of variability. 1 represents increasing preference values, and -1 means decreasing preference values. It is used to calculate final aggregated preference values using DARIA method in next stage of DARIA method.

Examples

>>> daria = DARIA()
>>> dir_list, dir_class = daria._direction(matrix, preference_type)

_update_efficiency(scores, variability, direction)

Calculate final aggregated preference values of alternatives of DARIA method. Obtained preference values can be sorted according to chosen MCDA method rule to generate ranking of alternatives.

Parameters

scoresndarray: Vector with preference values of alternatives from the most recent year analyzed obtained by chosen MCDA method.
variabilityndarray: Vector with variability values of alternatives preferences obtained in investigated periods.
directionndarray: Vector with numerical values of the direction of variability in values of alternatives preferences obtained in investigated periods. 1 represents increasing in following preference values, and -1 means decreasing in following preference values.

Returns

ndarray: Final aggregated preference values of alternatives considering variability in preference values obtained in the following periods.

Examples

>>> updated_scores = daria._update_efficiency(scores, variability, direction)
>>> rank = rank_preferences(updated_scores, reverse = True)