A self-generalizing gradient boosting machine that doesn't need hyperparameter optimization

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  • Tags rust , perpetual , machine learning , tree model , decision tree , gradient boosted decision tree , gradient boosting machine
  • Requires: Python >=3.10
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Project description

Perpetual

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Python Versions PyPI Version Conda Version Crates.io Version R-Universe status Static Badge PyPI - Downloads

PerpetualBooster is a gradient boosting machine (GBM) that doesn't need hyperparameter optimization unlike other GBMs. 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. 0.5) and increase it (e.g. 1.0) 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.

Supported Languages

Perpetual is built in Rust and provides high-performance bindings for Python and R.

Language Installation Documentation Source Package
Python pip install perpetual

conda install -c conda-forge perpetual		 Python API package-python PyPI

Conda Forge
Rust cargo add perpetual docs.rs src crates.io
R install.packages("perpetual") pkgdown Site package-r R-universe

Optional Dependencies

  • pandas: Enables support for training directly on Pandas DataFrames.
  • polars: Enables zero-copy training support for Polars DataFrames.
  • scikit-learn: Provides a scikit-learn compatible wrapper interface.
  • xgboost: Enables saving and loading models in XGBoost format for interoperability.
  • onnxruntime: Enables exporting and loading models in ONNX standard format.

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", budget=0.5)
model.fit(X, y)

Benchmark

PerpetualBooster vs. Optuna + LightGBM

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
0.76 50 0.201 0.201 72x 326x
0.85 100 0.196 0.196 113x 613x
1.15 200 0.190 0.190 405x 1985x

The following table summarizes the results for the Pumpkin Seeds dataset (classification):

Perpetual budget LightGBM n_estimators Perpetual auc LightGBM auc Speed-up wall time Speed-up cpu time
1.0 100 0.944 0.945 91x 184x

The results can be reproduced using the scripts in the examples folder.

PerpetualBooster vs. AutoGluon

PerpetualBooster is a GBM but behaves like AutoML so it is benchmarked also against AutoGluon (v1.2, best quality preset), the current leader in AutoML benchmark. Top 10 datasets with the most number of rows are selected from OpenML datasets for both regression and classification tasks.

The results are summarized in the following table for regression tasks:

OpenML Task Perpetual Training Duration Perpetual Inference Duration Perpetual RMSE AutoGluon Training Duration AutoGluon Inference Duration AutoGluon RMSE
Airlines_DepDelay_10M 518 11.3 29.0 520 30.9 28.8
bates_regr_100 3421 15.1 1.084 OOM OOM OOM
BNG(libras_move) 1956 4.2 2.51 1922 97.6 2.53
BNG(satellite_image) 334 1.6 0.731 337 10.0 0.721
COMET_MC 44 1.0 0.0615 47 5.0 0.0662
friedman1 275 4.2 1.047 278 5.1 1.487
poker 38 0.6 0.256 41 1.2 0.722
subset_higgs 868 10.6 0.420 870 24.5 0.421
BNG(autoHorse) 107 1.1 19.0 107 3.2 20.5
BNG(pbc) 48 0.6 836.5 51 0.2 957.1
average 465 3.9 - 464 19.7 -

PerpetualBooster outperformed AutoGluon on 8 out of 10 regression tasks, training equally fast and inferring 5.1x faster.

The results are summarized in the following table for classification tasks:

OpenML Task Perpetual Training Duration Perpetual Inference Duration Perpetual AUC AutoGluon Training Duration AutoGluon Inference Duration AutoGluon AUC
BNG(spambase) 70.1 2.1 0.671 73.1 3.7 0.669
BNG(trains) 89.5 1.7 0.996 106.4 2.4 0.994
breast 13699.3 97.7 0.991 13330.7 79.7 0.949
Click_prediction_small 89.1 1.0 0.749 101.0 2.8 0.703
colon 12435.2 126.7 0.997 12356.2 152.3 0.997
Higgs 3485.3 40.9 0.843 3501.4 67.9 0.816
SEA(50000) 21.9 0.2 0.936 25.6 0.5 0.935
sf-police-incidents 85.8 1.5 0.687 99.4 2.8 0.659
bates_classif_100 11152.8 50.0 0.864 OOM OOM OOM
prostate 13699.9 79.8 0.987 OOM OOM OOM
average 3747.0 34.0 - 3699.2 39.0 -

PerpetualBooster outperformed AutoGluon on 10 out of 10 classification tasks, training equally fast and inferring 1.1x faster.

PerpetualBooster demonstrates greater robustness compared to AutoGluon, successfully training on all 20 tasks, whereas AutoGluon encountered out-of-memory errors on 3 of those tasks.

The results can be reproduced using the automlbenchmark fork.

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.

Perpetual ML Suite

The Perpetual ML Suite is a comprehensive, batteries-included ML platform designed to deliver maximum predictive power with minimal effort. It allows you to track experiments, monitor metrics, and manage model drift through an intuitive interface.

For a fully managed, serverless ML experience, visit app.perpetual-ml.com.

  • Serverless Marimo Notebooks: Run interactive, reactive notebooks without managing any infrastructure.
  • Serverless ML Endpoints: One-click deployment of models as production-ready endpoints for real-time inference.

Perpetual is also designed to live where your data lives. It is available as a native application on the Snowflake Marketplace, with support for Databricks and other major data warehouses coming soon.

Project details

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  • Tags rust , perpetual , machine learning , tree model , decision tree , gradient boosted decision tree , gradient boosting machine
  • Requires: Python >=3.10
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