EVO-SPOTIS

This is Python 3 library for multi-criteria decision analysis with decision-maker preference identification based on historical datasets.

Installation

Downloading and installation of evo_spotis package can be done using pip

pip install evo-spotis

Methods provided

mcda_methods module includes:

• spotis with SPOTIS method (the Stable Preference Ordering Towards Ideal Solution method)

stochastic_algorithms module includes:

• DE algorithm DE_algorithm (the Differential Evolution algorithm)

The DE algorithm is applied for the identification of criteria weights (decision-maker preferences) based on a training dataset with evaluated alternatives, including alternatives performances (training features) and their ranking (target variable). The goal (fitness) function uses the correlation coefficient of predicted ranking with real ranking. The predicted ranking is generated using the SPOTIS method and weights calculated by the DE algorithm in each DE iteration. It is a profit function. Therefore, higher values denote better results. Examples of use of evo_spotis are included on GitHub in examples

Other modules:

• additions including rank_preference method for ranking alternatives according to MCDA score.

• correlations containing:

  • Spearman rank correlation coefficient spearman,
  • Weighted Spearman rank correlation coefficient weighted_spearman,
  • Pearson correlation coefficient pearson_coeff.

• normalizations with methods for decision matrix normalization:

  • linear_normalization - Linear normalization,
  • minmax_normalization - Minimum- Maximum normalization,
  • max_normalization - Maximum normalization,
  • sum_normalization - Sum normalization,
  • vector_normalization - Vector normalization.

• weighting_methods containing:

  • entropy_weighting - Entropy objective weighting method.

License

The evo-spotis library is licensed under the terms of the MIT license.

Documentation

Documentation of this library with instruction for installation and usage is provided here