Metadata-Version: 2.1
Name: cutoml
Version: 0.0.10
Summary: A lightweight automl library
Home-page: https://github.com/omkarudawant/CutoML
Author: Omkar Udawant
Author-email: omkarudawant97@gmail.com
License: gpl-3.0
Download-URL: https://github.com/omkarudawant/CutoML/archive/refs/tags/0.0.10.tar.gz
Keywords: pipeline optimization,automated hyperparameter optimization,data science,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

<img src="https://user-images.githubusercontent.com/51676294/105076115-ac83e100-5ab0-11eb-8743-9a6dcc21cf45.png" width="90%"></img>


[![PyPI version](https://badge.fury.io/py/cutoml.svg)](https://pypi.org/project/cutoml/)
[![Downloads](https://pepy.tech/badge/cutoml)](https://pepy.tech/project/cutoml)
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[![Python 3.6](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/)
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[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/omkarudawant/CutoML)

CutoML is a lightweight automl library, highly optimized to give you the best possible model depending on your datasets very quickly.


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

    pip install -U cutoml

Usage Example
-------------


For classification,

```python

from cutoml.cutoml import CutoClassifier
from sklearn.model_selection import train_test_split
from sklearn import datasets

dataset = datasets.load_digits()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
                                                    dataset.target,
                                                    test_size=0.25)

ctc = CutoClassifier(k_folds=5, n_jobs=2)
ctc.fit(X=X_train, y=y_train)
```

For regression,

```python

from cutoml.cutoml import CutoRegressor
from sklearn.model_selection import train_test_split
from sklearn import datasets

dataset = datasets.load_boston()
X_train, X_test, y_train, y_test = train_test_split(dataset.data,
                                                    dataset.target,
                                                    test_size=0.25)

ctr = CutoRegressor(k_folds=5, n_jobs=2)
ctr.fit(X=X_train, y=y_train)
```


