Metadata-Version: 2.1
Name: advertion
Version: 0.1.0a1
Summary: A tiny framework to perform adversarial validation of your training and test data.
Home-page: https://pypi.org/project/advertion
License: Apache-2.0
Keywords: adversarial-validation,data-drift,machine-learning
Author: Ilias Antonopoulos
Author-email: ilias.antonopoulos@yahoo.gr
Requires-Python: >=3.9,<3.12
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: pandas (>=2.0.3,<2.1.0)
Requires-Dist: pydantic (>=2.0.3,<2.1.0)
Requires-Dist: scikit-learn (>=1.3.0,<1.4.0)
Requires-Dist: xgboost (>=1.7.6,<1.8.0)
Project-URL: Bug Tracker, https://github.com/ilias-ant/advertion/issues
Project-URL: Repository, https://github.com/ilias-ant/advertion
Description-Content-Type: text/markdown

# adversarial-validation

[![PyPI](https://img.shields.io/pypi/v/advertion?color=blue&label=PyPI&logo=PyPI&logoColor=white)](https://pypi.org/project/advertion/) 
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/advertion?logo=python&logoColor=white)](https://www.python.org/) 
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) 
[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/ilias-ant/advertion/ci.yml?branch=main)](https://github.com/ilias-ant/advertion/actions/workflows/ci.yml) 
[![PyPI - Wheel](https://img.shields.io/pypi/wheel/advertion?color=orange)](https://www.python.org/dev/peps/pep-0427/)

A tiny framework to perform adversarial validation of your training and test data.

## Install

The recommended installation is via `pip`:

```bash
pip install advertion
```

(*advertion stands for **adver**sarial valida**tion***)

## Usage

```python
from advertion import validate

train = pd.read_csv("...")
test = pd.read_csv("...")

are_similar = validate(
    train=train,
    test=test,
    target="label",
)
# are_similar = True: train and test are following the same underlying distribution.
# are_similar = False: test dataset exhibits a different underlying distribution than train dataset.
```
