Package: pfevaluator
pfevaluator: A library for evaluating performance metrics of Pareto fronts in multiple/many objective optimization problems"Knowledge is power, sharing it is the premise of progress in life. It seems like a burden to someone, but it is the only way to achieve immortality."
---Thieu NguyenIntroductionDependenciesPython (>= 3.6)Numpy (>= 1.18.1)pygmo (>= 2.13.0)User installationInstall thecurrent PyPI release:pipinstallpfevaluatorOr install the development version from GitHub:pipinstallgit+https://github.com/thieu1995/pfevaluatorPareto front Performance MetricsCloseness: Metrics Measuring the Closeness of the Solutions to the True Pareto FrontGD: Generational DistanceIGD: Inverted Generational DistanceMPFE: Maximum Pareto Front ErrorCloseness - Diversity: Metrics Measuring the Closeness of the Solutions to the True Pareto FrontHV: Hyper Volume (Using Different Library)HAR: Hyper Area Ratio (Using Different Library)Distribution: Metrics Focusing on Distribution of the SolutionsUD: Uniform DistributionS: SpacingSTE: Spacing To ExtendNDC: Number of Distinct Choices (Not Implemented Yet)Ratio: Metrics Assessing the Number of Pareto Optimal Solutions in the SetRNI: Ratio of Non-dominated IndividualsER: Error RatioONVG: Overall Non-dominated Vector GenerationPDI: Pareto Dominance Indicator (Not Implemented Yet)Spread: Metrics Concerning Spread of the SolutionsMS: Maximum SpreadExamples+ front: the file contains class Metric for evaluating all posible solution (population of obtained fronts).
+ pfront (Pareto front): the file contains class Metric for evaluating the obtained front from each test case.
+ tpfront: (True pareto front): the file contains class Metric for evaluating the obtained front and True pareto front
 (Reference front). Means, you need to pass the Reference front in this class.

+ True pareto front (Reference front) can be obtained by:
    1) You provide it (If you know the True Pareto front for your problem)
    2) Calculate from all possible fronts obtained from all test case.
        + Assumption you have N1 algorithms to test. 
        + Each algorithm give you a Obtained front. 
        + Each algorithm you run N2 independent trials --> Number of all possible fronts: N1 * N2 
        + Pass all N1*N2 front in our function to calculate the Non-donminated Solutions (Reference front
 - Approximate Pareto front - True Pareto front)


import pfevaluator

## Some avaiable performance metrics for evaluate each type of Pareto front.
pfront_metrics = ["UD", "NDC"]
tpfront_metrics = ["ER", "ONVG", "MS", "GD", "IDG", "MPFE", "S", "STE"]
volume_metrics = ["HV", "HAR"]

pm = pfevaluator.metric_pfront(obtained_front, pfront_metrics)              # Evaluate for each algorithm in each trial
tm = pfevaluator.metric_tpfront(obtained_front, reference_front, tpfront_metrics)        # Same above
vm = pfevaluator.metric_volume(obtained_front, reference_front, volume_metrics, None, all_fronts=matrix_fitness)

## obtained_front: is your front you found in each test case (each trial of each algorithm)
## reference_front (True Pareto front): is your True Pareto front of your problem.
##      If you don't know your True Pareto front, do the above step to get it from population of obtained fronts.
##      Using this function: reference_front = pfevaluator.find_reference_front(matrix_fitness)
##          matrix_fitness is all of your fronts in all test cases.

## The results is dict such as:     pm = { "UD": 0.2, "NDC": 0.1 }The full test case in the file: examples/full.pyImportant linksOfficial source code repo:https://github.com/thieu1995/pfevaluatorDownload releases:https://pypi.org/project/pfevaluator/Issue tracker:https://github.com/thieu1995/pfevaluator/issuesChange log:https://github.com/thieu1995/pfevaluator/blob/master/ChangeLog.mdThis project also related to my another projects which are "meta-heuristics" and "neural-network", check it herehttps://github.com/thieu1995/opfunuhttps://github.com/thieu1995/metaheuristicshttps://github.com/thieu1995/mealpyhttps://github.com/thieu1995/permetricshttps://github.com/chasebkContributionsCitationIf you use pfevaluator in your project, please cite my works:@article{nguyen2019efficient,
  title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
  author={Nguyen, Thieu and Nguyen, Tu and Nguyen, Binh Minh and Nguyen, Giang},
  journal={International Journal of Computational Intelligence Systems},
  volume={12},
  number={2},
  pages={1144--1161},
  year={2019},
  publisher={Atlantis Press}
}Documents:Yen, G. G., & He, Z. (2013). Performance metric ensemble for multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 18(1), 131-144.Panagant, N., Pholdee, N., Bureerat, S., Yildiz, A. R., & Mirjalili, S. (2021). A Comparative Study of Recent Multi-objective Metaheuristics for Solving Constrained Truss Optimisation Problems. Archives of Computational Methods in Engineering, 1-17.Knowles, J., & Corne, D. (2002, May). On metrics for comparing nondominated sets. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 1, pp. 711-716). IEEE.Yen, G. G., & He, Z. (2013). Performance metric ensemble for multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 18(1), 131-144.Guerreiro, A. P., Fonseca, C. M., & Paquete, L. (2020). The hypervolume indicator: Problems and algorithms. arXiv preprint arXiv:2005.00515.