arfpy: Adversarial random forests

Introduction

This is a python implementation of adversarial random forests (ARFs) for density estimation and generative modelling. Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data become increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of RFs, including speed, flexibility, and solid performance with default parameters.

Installation

The arfpy package is available on PyPI:

$ pip install arfpy

To install the development version from GitHub, run:

git clone https://bips-hb.github.io/arfpy/
python setup.py install

We recommend to use python>=3.8 with pandas==1.4.0, numpy==1.21.0, scikit-learn==0.24.0 and scipy==1.7.1, see requirements.txt for more details.

Usage

Using Fisher's iris dataset, we train an ARF, estimate distribution parameters and generate new data:

from sklearn.datasets import load_iris
from arfpy import arf
import pandas as pd

# Load data
iris = load_iris() 
df = pd.DataFrame(iris['data'], columns=iris['feature_names'])

# Train the ARF
my_arf = arf.arf(x = df)

# Get density estimates
my_arf.forde()

# Generate data
my_arf.forge(n = 10)

For a detailed documentation on arfpy's functionalities, please have a look at this website.

Other distributions

An R implementation of ARF is available on CRAN. For the development version, see here.

Contributing

If you'd like to contribute, please open an issue or submit a pull request. For further questions, please don't hesitate to write an email to Kristin Blesch (blesch@leibniz-bips.de).

References

Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N. (2023). Adversarial random forests for density estimation and generative modeling. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics. Link here.