Perpetual

PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't need hyperparameter optimization unlike other GBM algorithms. Similar to AutoML libraries, it has a budget parameter. Increasing the budget parameter increases the predictive power of the algorithm and gives better results on unseen data. Start with a small budget (e.g. 1.0) and increase it (e.g. 2.0) once you are confident with your features. If you don't see any improvement with further increasing the budget, it means that you are already extracting the most predictive power out of your data.

Benchmark

Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in a single run. Thus, it achieves up to 100x speed-up at the same accuracy with different budget levels and with different datasets.

The following table summarizes the results for the California Housing dataset (regression):

The following table summarizes the results for the Cover Types dataset (classification):

You can reproduce the results using the scripts in the examples folder.

Usage

You can use the algorithm like in the example below. Check examples folders for both Rust and Python.

from perpetual import PerpetualBooster

model = PerpetualBooster(objective="SquaredLoss")
model.fit(X, y, budget=1.0)

Documentation

Documentation for the Python API can be found here and for the Rust API here.

Installation

The package can be installed directly from pypi:

pip install perpetual

Using conda-forge:

conda install conda-forge::perpetual

To use in a Rust project and to get the package from crates.io:

cargo add perpetual

Contribution

Contributions are welcome. Check CONTRIBUTING.md for the guideline.

Paper

PerpetualBooster prevents overfitting with a generalization algorithm. The paper is work-in-progress to explain how the algorithm works. Check our blog post for a high level introduction to the algorithm.