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MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems

MetaCluster 최신버젼 다운로드

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---

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MetaCluster is the largest open-source nature-inspired optimization (Metaheuristic Algorithms) library for 
clustering problem in Python

* **Free software:** GNU General Public License (GPL) V3 license
* **Provided 3 classes: `MetaCluster`, `MhaKCentersClustering`, and `MhaKMeansTuner`**
* **Total nature-inspired metaheuristic optimizers (Metaheuristic Algorithms)**: > 200 optimizers
* **Total objective functions (as fitness)**: > 40 objectives
* **Total supported datasets**: 48 datasets from Scikit learn, UCI, ELKI, KEEL...
* **Total performance metrics**: > 40 metrics
* **Total different way of detecting the K value**: >= 10 methods
* **Documentation:** https://metacluster.readthedocs.io/en/latest/
* **Python versions:** >= 3.7.x
* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics, plotly, kaleido

# Citation Request

Please include these citations if you plan to use this library:

```code
@article{VanThieu2023,
  author = {Van Thieu,  Nguyen and Oliva,  Diego and Pérez-Cisneros,  Marco},
  title = {MetaCluster: An open-source Python library for metaheuristic-based clustering problems},
  journal = {SoftwareX},
  year = {2023},
  pages = {101597},
  volume = {24},
  DOI = {10.1016/j.softx.2023.101597},
}

@article{van2023mealpy,
  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal={Journal of Systems Architecture},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.sysarc.2023.102871}
}
```

# Installation

* Install the [current PyPI release](https://pypi.python.org/pypi/metacluster):
```bash
$ pip install metacluster
```

After installation, check the version:
```bash
$ python
>>> import metacluster
>>> metacluster.__version__
```

### Examples

We implement a dedicated Github repository for examples at [MetaCluster_examples](https://github.com/thieu1995/MetaCluster_examples)

Let's go through some basic examples from here:

#### 1. First, load dataset. You can use the available datasets from MetaCluster:

```python
# Load available dataset from MetaCluster
from metacluster import get_dataset

# Try unknown data
get_dataset("unknown")
# Enter: 1      -> This wil list all of avaialble dataset

data = get_dataset("Arrhythmia")
```

* Or you can load your own dataset

```python
import pandas as pd
from metacluster import Data

# load X and y
# NOTE MetaCluster accepts numpy arrays only, hence use the .values attribute
dataset = pd.read_csv('examples/dataset.csv', index_col=0).values
X, y = dataset[:, 0:-1], dataset[:, -1]
data = Data(X, y, name="my-dataset")
```

#### 2. Next, scale your features

**You should confirm that your dataset is scaled and normalized**

```python
# MinMaxScaler 
data.X, scaler = data.scale(data.X, method="MinMaxScaler", feature_range=(0, 1))

# StandardScaler 
data.X, scaler = data.scale(data.X, method="StandardScaler")

# MaxAbsScaler 
data.X, scaler = data.scale(data.X, method="MaxAbsScaler")

# RobustScaler 
data.X, scaler = data.scale(data.X, method="RobustScaler")

# Normalizer 
data.X, scaler = data.scale(data.X, method="Normalizer", norm="l2")   # "l1" or "l2" or "max"
```

#### 3. Next, select Metaheuristic Algorithm, Its parameters, list of objectives, and list of performance metrics

```python
list_optimizer = ["BaseFBIO", "OriginalGWO", "OriginalSMA"]
list_paras = [
    {"name": "FBIO", "epoch": 10, "pop_size": 30},
    {"name": "GWO", "epoch": 10, "pop_size": 30},
    {"name": "SMA", "epoch": 10, "pop_size": 30}
]
list_obj = ["SI", "RSI"]
list_metric = ["BHI", "DBI", "DI", "CHI", "SSEI", "NMIS", "HS", "CS", "VMS", "HGS"]
```

You can check all supported metaheuristic algorithms from: https://github.com/thieu1995/mealpy.
All supported clustering objectives and metrics from: https://github.com/thieu1995/permetrics.

If you don't want to read the documents, you can print out all supported information by:

```python
from metacluster import MetaCluster

# Get all supported methods and print them out
MetaCluster.get_support(name="all")
```

#### 4. Next, create an instance of MetaCluster class and run it.

```python
model = MetaCluster(list_optimizer=list_optimizer, list_paras=list_paras, list_obj=list_obj, n_trials=3, seed=10)

model.execute(data=data, cluster_finder="elbow", list_metric=list_metric, save_path="history", verbose=False)

model.save_boxplots()
model.save_convergences()
```

As you can see, you can define different datasets and using the same model to run it. 
Remember to set the name to your dataset, because the folder that hold your results is the name of your dataset.
More examples can be found [here](/examples)

# Support

### Official links (questions, problems)

* Official source code repo: https://github.com/thieu1995/metacluster
* Official document: https://metacluster.readthedocs.io/
* Download releases: https://pypi.org/project/metacluster/
* Issue tracker: https://github.com/thieu1995/metacluster/issues
* Notable changes log: https://github.com/thieu1995/metacluster/blob/master/ChangeLog.md
* Official chat group: https://t.me/+fRVCJGuGJg1mNDg1

* This project also related to our another projects which are optimization and machine learning. Check it here:
    * https://github.com/thieu1995/metaheuristics
    * https://github.com/thieu1995/mealpy
    * https://github.com/thieu1995/mafese
    * https://github.com/thieu1995/pfevaluator
    * https://github.com/thieu1995/opfunu
    * https://github.com/thieu1995/enoppy
    * https://github.com/thieu1995/permetrics
    * https://github.com/thieu1995/IntelELM
    * https://github.com/thieu1995/MetaPerceptron
    * https://github.com/thieu1995/GrafoRVFL
    * https://github.com/aiir-team

### Supported links

```code
1. https://jtemporal.com/kmeans-and-elbow-method/
2. https://medium.com/@masarudheena/4-best-ways-to-find-optimal-number-of-clusters-for-clustering-with-python-code-706199fa957c
3. https://github.com/minddrummer/gap/blob/master/gap/gap.py
4. https://www.tandfonline.com/doi/pdf/10.1080/03610927408827101
5. https://doi.org/10.1016/j.engappai.2018.03.013
6. https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Clustering_metrics.ipynb
7. https://elki-project.github.io/
8. https://sci2s.ugr.es/keel/index.php
9. https://archive.ics.uci.edu/datasets
10. https://python-charts.com/distribution/box-plot-plotly/
11. https://plotly.com/python/box-plots/?_ga=2.50659434.2126348639.1688086416-114197406.1688086416#box-plot-styling-mean--standard-deviation
```

MetaCluster

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