Metadata-Version: 2.1
Name: automl-tools
Version: 0.1.0
Summary: automl_tools
Home-page: https://github.com/jonaqp/automl_tools/
Author: Jonathan Quiza
Author-email: jony327@gmail.com
License: UNKNOWN
Download-URL: https://github.com/jonaqp/automl_tools/archive/main.zip
Keywords: banking
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: certifi (==2020.4.5.2)
Requires-Dist: numpy (==1.19.5)
Requires-Dist: pandas (==1.1.5)
Requires-Dist: prettytable (==2.0.0)
Requires-Dist: scikit-learn (==0.23.2)
Requires-Dist: optbinning (==0.8.0)
Requires-Dist: psutil (==5.8.0)
Requires-Dist: xgboost (==1.3.3)
Requires-Dist: lightgbm (==3.1.1)
Requires-Dist: catboost (==0.24.4)
Requires-Dist: category-encoders (==2.2.2)
Requires-Dist: ngboost (==0.3.7)
Requires-Dist: hyperopt (==0.2.5)
Requires-Dist: seaborn (==0.11.1)
Requires-Dist: colorama (==0.4.4)
Requires-Dist: tabulate (==0.8.7)

# Automl_tools: automl binary classification

[![Github License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)


Automl_tools is a Python library that implements Gradient Boosting
## Installation

```sh
pip install automl-tools
```

## Usage

Probabilistic binary example on the Boston housing dataset:

```python
import pandas as pd
from automl_tools.main import automl_run

train = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/train.csv?token=AAN2ZBGCYYR7PATAMC6NIKDABSDCQ", sep=";")
test = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/test.csv?token=AAN2ZBBD63PDQLGJNUWVHOLABSC4O", sep=";")

automl_run(train=train,
           test=test,
           target_col="Survived",
           imp_num="knn",
           imp_cat="knn",
           processing="binding",
           mutual_information=False,
           correlation_drop=False,
           model_feature_selection=None,
           model_run="LR",
           augmentation=True,
           Stratified=True)

```


## License

[Apache License 2.0](https://github.com/stanfordmlgroup/ngboost/blob/master/LICENSE).


## New features v2.1
 * multiclass
 * regression

## Reference
Jonathan Quiza binary automl.



