EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired meta heuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which canbe utilized by experienced and non-experienced users for different applications.The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoClustercan be extended by designing other optimizers, including more objective func-tions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheristic optimizers, thirtydatasets, five objective functions, and twelve evaluation measures. The source code of EvoCluster is publicly available at (http://evo-ml.com/2019/10/25/evocluster/).
The full list of implemented optimizers is available here https://github.com/7ossam81/EvoloPy/wiki/List-of-optimizers
- Ten nature-inspired metaheuristic optimizers are implemented.
- The implimentation uses the fast array manipulation using [
NumPy] (http://www.numpy.org/). - Matrix support using [
SciPy's] (https://www.scipy.org/) package. - More optimizers are comming soon.
- Python 3.xx is required.
Run
pip3 install -r requirements.txt
(possibly with sudo)
That command above will install NumPy, SciPy, sklearn, pandas, and matplotlib for you.
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If you are installing EvoCluster onto Windows, please Install Anaconda from here https://www.continuum.io/downloads, which is the leading open data science platform powered by Python.
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If you are installing onto Ubuntu or Debian and using Python 3 then this will pull in all the dependencies from the repositories:
sudo apt-get install python3-numpy python3-scipy liblapack-dev libatlas-base-dev libgsl0-dev fftw-dev libglpk-dev libdsdp-dev
Clone the Git repository from GitHub
git clone https://github.com/RaneemQaddoura/EvoCluster.git
EvoCluster Framework contains more than thirty datasets (Obtainied from UCI repository, scikit learn, School of Computing at University of Eastern Finland, ELKI, KEEL, and Naftali Harris). The main file is the main.py, which considered the interface of the framewok. In the main.py you can setup your experiment by selecting the optmizers, the datasets, number of runs, number of iterations, number of neurons and population size. The following is a sample example to use the EvoCluster framework. To choose PSO optimizer for your experiment, change the PSO flag to true and others to false.
Select optimizers from the list of available ones: "SSA","PSO","GA","BAT","FFA","GWO","WOA","MVO","MFO","CS"
optimizer=["SSA","PSO","GA"]
Select objective function from the list of available ones:"SSE","TWCV","SC","DB","DI"
objectivefunc=["SSE","TWCV"]
Select data sets from the list of available ones The folder datasets in the repositoriy contains 30 datasets (All of them are obtained from Scikit learn, UCI, School of Computing at University of Eastern Finland, ELKI, KEEL, and Naftali Harris Blog).
To add new dataset:
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Put your dataset in a csv format (No header is required)
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Place the new datset files in the datasets folder.
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Add the dataset to the datasets list in the optimizer.py (Line 19).
For example, if the dastaset name is seed, the new line will be like this:
datasets=["aggregation", "seeds"]
Select number of repetitions for each experiment. To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm. NumOfRuns=3
Select general parameters for all optimizers (population size, number of iterations) ....
params = {'PopulationSize' : 20, 'Iterations' : 20}
run the framework
run(optimizer, objectivefunc, dataset_List, NumOfRuns, params, export_flags)
Now your experiment is ready to go. Enjoy!
The results will be automaticly generated ina folder which is concatnated with the date and time of the experiment. this folder consists of 3 csv file and 2 plots:
- experiment.csv
- experiment_details.csv
- experiment_details_Labels.csv
- Convergence plot
- Box plot
The experiment and the experiment_details files contain the following measures:
Optimizer: The name of the used optimizer
Dataset: The name of the dataset.
objfname: The objective function/ Fitness function
Experiment: Experiment ID/ Run ID.
startTime: Experiment's starting time
EndTime: Experiment's ending time
ExecutionTime : Experiment's executionTime (in seconds)
SSE
Purity
Entropy
HS
CS
VM
AMI
ARI
Fmeasure
TWCV
SC
Accuracy
DI
DB
STDev
Iter1 Iter2 Iter3... : Convergence values (The bjective function values after every iteration).
- Issue Tracker: https://github.com/RaneemQaddoura/EvoCluster/issues
- Source Code: https://github.com/RaneemQaddoura/EvoCluster
Use the issue tracker.
Please include these citations if you plan to use this Framework:
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Qaddoura, Raneem, Hossam Faris, Ibrahim Aljarah, and Pedro A. Castillo. "EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python." In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 20-36. Springer, Cham, 2020.
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Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, and Seyedali Mirjalili. Evolopy-fs: An open-source nature-inspired optimization framework in python for feature selection. In Evolutionary Machine Learning Techniques, pages 131–173. Springer, 2020
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Hossam Faris, Ibrahim Aljarah, Sayedali Mirjalili, Pedro Castillo, and J.J Merelo. "EvoloPy: An Open-source Nature-inspired Optimization Framework in Python". In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: ECTA,ISBN 978-989-758-201-1, pages 171-177.
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Raneem Qaddoura, Hossam Faris, and Ibrahim Aljarah*. An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. International Journal of Machine Learning and Cybernetics, pages 1–40, 2019.
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Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, and Seyedali Mirjalili. Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowledge and Information Systems, pages 1–33, 2019.
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Sarah Shukri, Hossam Faris, Ibrahim Aljarah*, Seyedali Mirjalili, and Ajith Abraham. Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Engineering Applications of Artificial Intelligence, 72:54–66, 2018.
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Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, and Seyedali Mirjalili. Multiverse optimizer: Theory, literature review, and application in a data clustering. In Nature-Inspired Optimizers: Theories, Literature Reviews and Applications, pages 123–141. Springer International Publishing, Cham, 2020