Metadata-Version: 2.1
Name: mloptimizer
Version: 0.5.2
Summary: Genetic hyper-parameter selection for machine learning algorithms
Home-page: https://github.com/Caparrini/mloptimizer
Author: Antonio Caparrini
Author-email: a.caparrini@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: deap (==1.2.2)
Requires-Dist: joblib (==1.0.1)
Requires-Dist: numpy (==1.19.5)
Requires-Dist: pandas (==1.2.4)
Requires-Dist: python-dateutil (==2.8.1)
Requires-Dist: pytz (==2021.1)
Requires-Dist: scikit-learn (==0.24.2)
Requires-Dist: scipy (==1.6.3)
Requires-Dist: six (==1.15.0)
Requires-Dist: xgboost (==1.4.2)

# mloptimizer

**mloptimizer** is a Python module for hyper-parameters optimization in machine learning using genetic algorithms.


### Installation

```bash
pip install mloptimizer
```
### Quickstart

A simple example of use optimizing hyper-parameters in a decision tree classifier using the iris dataset:

```python
from mloptimizer.genoptimizer import TreeOptimizer
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
opt = TreeOptimizer(X, y, "output_log_file.log")
clf = opt.optimize_clf(10, 10)
```

## Modules used

* [Deap](https://github.com/DEAP/deap) - Genetic Algorithms
* [XGBoost](https://github.com/dmlc/xgboost) - Gradient boosting classifier
* [sklearn](https://github.com/scikit-learn/scikit-learn) - Usado para generar RSS

## Wiki

 TODO [Wiki](DOCUMENTATION TODO)

## Authors

* **Antonio Caparrini** - *Owner* - [caparrini](https://github.com/caparrini)

## License

This project is under the [LICENSE](LICENSE) for more details.


