Metadata-Version: 2.1
Name: keel_ds
Version: 0.1.11
Summary: KeelDS is a package to load some datasets from KEEL repository with some normalizations and with split and discretization options.
Author: Maicondalllg
Author-email: maicon.dallagnol@unesp.br
Requires-Python: >=3.8,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: pandas (>=1.2.4,<2.0.0)
Description-Content-Type: text/markdown

# KeelDS


## KeelDS: A Python package for loading datasets from KEEL repository

KEEL [https://sci2s.ugr.es/keel/datasets.php] is a popular repository for machine learning datasets. This package provides a simple way to load datasets from the KEEL repository. The package also provides a simple way to load the datasets in a cross-validation setting.

### Features

- Load datasets available in the KEEL repository with a single line of code.
- Load datasets already split into train and test sets and with discretization (with Fayyad algorithm - MDLP[https://github.com/hlin117/mdlp-discretization]).

### Installation
----------------

Dependencies

- Python (>= 3.8)
- Pandas (>= 1.2.4)

You can install KeelDS using pip:

```bash
pip install keel-ds
```

### Usage


```python
from keel_ds import load_data
import numpy as np
from catboost import CatBoostClassifier

file_name = 'iris'
folds = load_data(file_name)

evaluations = []
for x_train, y_train, x_test, y_test in folds:
    model = CatBoostClassifier(verbose=False)
    model.fit(x_train, y_train)
    evaluation = model.score(x_test, y_test)
    evaluations.append(evaluation)
    
print(np.mean(evaluations)) # Output = 0.933333333333

```
