pyrepo_mcda.mcda_methods.temporal_promethee

Classes

Temporal_PROMETHEE_II

Temporal extension of the PROMETHEE II method that incorporates

Module Contents

class pyrepo_mcda.mcda_methods.temporal_promethee.Temporal_PROMETHEE_II

Temporal extension of the PROMETHEE II method that incorporates variability of preferences over multiple time periods using DARIA.

__call__(matrices: dict[str, numpy.ndarray], weights: numpy.ndarray, types: numpy.ndarray, variability_function: Callable[[numpy.ndarray], numpy.ndarray] | None = None, preference_functions: list[Callable] | None = None, ps: list[float | int] | None = None, qs: list[float | int] | None = None, alt_names: list[str] | None = None) → tuple[numpy.ndarray, tuple[numpy.ndarray, numpy.ndarray, pandas.DataFrame]]

Calculate Temporal PROMETHEE II scores considering variability of alternative preferences over multiple time periods.

The method first calculates PROMETHEE II net flows for each decision matrix representing a separate time period. Next, variability and direction of changes in preferences are determined using selected DARIA measures. Finally, the PROMETHEE II scores from the most recent period are updated using variability information to obtain temporal preference scores.

Decision matrices must contain the same number of alternatives and criteria in all periods.

Parameters

matricesdict[str, np.ndarray]

Dictionary containing decision matrices for subsequent time periods. Keys represent time periods (for example years) and values are decision matrices. All matrices must have the same shape, where rows represent alternatives and columns represent criteria.

weightsnp.ndarray

Criteria weights.

typesnp.ndarray

Criteria types. Use 1 for profit criteria and -1 for cost criteria.

variability_functionCallable[[np.ndarray], np.ndarray], optional

Function used to calculate variability values for alternatives based on preference values obtained in consecutive periods. The function should accept a matrix with periods in rows and alternatives in columns and return a one-dimensional array of variability values.

If None, standard deviation from the DARIA method is used.

Examples include:

DARIA()._std
DARIA()._gini
DARIA()._entropy
DARIA()._stat_var
DARIA()._coeff_var

preference_functionslist[Callable], optional

Preference functions used by the PROMETHEE II method for each criterion. If None, default PROMETHEE preference function (usual) is applied.

pslist[float | int], optional

Preference thresholds used by selected PROMETHEE preference functions. If None, default PROMETHEE preference thresholds are applied.

qslist[float | int], optional

Indifference thresholds used by selected PROMETHEE preference functions. If None, default PROMETHEE indifference thresholds are applied.

alt_nameslist[str], optional

Names of alternatives. If provided, they are assigned as row labels in the returned DataFrame containing PROMETHEE II net flows.

Returns

tuple[np.ndarray, tuple[np.ndarray, np.ndarray, pd.DataFrame]]

updated_scoresnp.ndarray

Final temporal preference scores obtained after updating the most recent PROMETHEE II net flows using DARIA variability information.

tuple[np.ndarray, np.ndarray, pd.DataFrame]

variabilitynp.ndarray: Variability values for alternatives calculated with the selected variability function.
directionnp.ndarray: Numerical direction of preference changes over time. Values equal to 1 indicate improvement, -1 indicate deterioration, and 0 indicate stability.
all_net_flows_dfpd.DataFrame: DataFrame containing PROMETHEE II net flows for all analyzed periods. Rows correspond to alternatives and columns correspond to time periods.

Raises

ValueError: If no decision matrices are provided.
ValueError: If decision matrices do not have identical shapes.
ValueError: If the number of alternative names does not match the number of alternatives.
TypeError: If variability_function is not callable.

Examples

>>> import numpy as np
>>> from pyrepo_mcda.mcda_methods import Temporal_PROMETHEE_II, DARIA
>>> matrices = {
...     "2022": np.array([
...         [10, 5],
...         [12, 7],
...         [8, 9]
...     ]),
...     "2023": np.array([
...         [11, 6],
...         [13, 7],
...         [9, 8]
...     ]),
...     "2024": np.array([
...         [12, 7],
...         [14, 8],
...         [10, 9]
...     ])
... }
>>> weights = np.array([0.6, 0.4])
>>> types = np.array([1, -1])
>>> tp = Temporal_PROMETHEE_II()
>>> daria = DARIA()
>>> scores, (variability, direction, net_flows) = tp(
...     matrices=matrices,
...     weights=weights,
...     types=types,
...     variability_function=daria._gini
... )
>>> scores