Metadata-Version: 2.4
Name: refs-mcc
Version: 1.0.1
Summary: Recursive Ensemble Feature Selection using Matthews Correlation Coefficient
Author: Alejandro Lopez-Rincon, Alberto Tonda, Brigitta Varga
Author-email: Alejandro Lopez-Rincon <alejandrolopezrn@gmail.com>
License: MIT
Project-URL: source, https://github.com/steppenwolf0/REFS-MCC/
Project-URL: tracker, https://github.com/steppenwolf0/REFS-MCC/issues
Keywords: feature-selection,matthews-correlation-coefficient,bioinformatics,machine-learning,ensemble-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: joblib>=1.5.3
Requires-Dist: matplotlib>=3.10.8
Requires-Dist: numpy>=2.4.1
Requires-Dist: pandas>=2.3.3
Requires-Dist: scikit-learn>=1.8.0
Requires-Dist: scipy>=1.17.0
Requires-Dist: statsmodels>=0.14.6
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"

# REFS-MCC
Recursive Ensemble Feauture Selection using Matthews Correlation Coefficient

--------------------------------------------------------------------
Installation

```bash
   pip install refs-mcc
```

After installation it can be used as the following (using the default parameters):
```python
    from refs_mcc import REFS_MCC
    REFS_MCC().run()
```

Or from CLI. For more information, run:
```bash
   refs-mcc --help
```
 
--------------------------------------------------------------------
Input

Next to the folder where the code is executed from, a `data` folder needs to be present with the following files:
- `data_0.csv`
- `features_0.csv`
- `ids.csv`
- `labels.csv`

--------------------------------------------------------------------
Output

The following folders and files will be created:
- run folders (`run0`, `run1`, ..., `run{n-1}`, where n is the selected number of total runs, 10 by default)
- `best` folder 
- `sumFig.pdf` & `sumFig.png`

--------------------------------------------------------------------
