Metadata-Version: 2.1
Name: advertion
Version: 0.1.0b0
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: >=3.8.6,<3.12
Classifier: Development Status :: 4 - Beta
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: scipy (>=1.10.1,<1.11.0)
Requires-Dist: xgboost (>=1.7.6,<1.8.0)
Project-URL: Bug Tracker, https://github.com/ilias-ant/adversarial-validation/issues
Project-URL: Repository, https://github.com/ilias-ant/adversarial-validation
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/) 
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[![Documentation Status](https://readthedocs.org/projects/advertion/badge/?version=latest)](https://advertion.readthedocs.io/en/latest/?badge=latest)
[![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.

<img src="https://raw.githubusercontent.com/ilias-ant/adversarial-validation/main/static/logo.png" width="95%" text="figjam">

**What is adversarial validation?**

A common workflow in machine learning projects (*especially in Kaggle competitions*) is to:

1. train your ML model in a training dataset.
2. tune and validate your ML model in a validation dataset (*which typically originates as a fraction of the training 
dataset*). 
3. finally, assess the actual generalization ability of your ML model in a held-out test dataset.

This strategy is widely accepted, but it heavily relies on the assumption that the training and test datasets are drawn 
from the same underlying distribution. This is often referred to as the “identically distributed” property in the 
literature.

This package helps you easily assert whether the "identically distributed" property holds true for your training and 
test datasets or equivalently whether your validation dataset is a good proxy for your model's performance on the unseen 
test instances.

## 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("...")

validate(
    trainset=train,
    testset=test,
    target="label",
)

# // {
# //     "datasets_follow_same_distribution": True,
# //     'mean_roc_auc': 0.5021320833333334,
# //     "adversarial_features': ['id'],
# // }
```
