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):

Perpetual budget	LightGBM n_estimators	Perpetual mse	LightGBM mse	Speed-up wall time	Speed-up cpu time
1.0	100	0.192	0.192	54x	56x
1.5	300	0.188	0.188	59x	58x
2.1	1000	0.185	0.186	42x	41x

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

Perpetual budget	LightGBM n_estimators	Perpetual log loss	LightGBM log loss	Speed-up wall time	Speed-up cpu time
0.9	100	0.091	0.084	72x	78x

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

To use in a Rust project, add the following to your Cargo.toml file to get the package from crates.io.

perpetual = "0.4.7"

Paper