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#57 in Machine learning
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355KB
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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 |
The results can be reproduced using the scripts in the examples folder.
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. 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 datasets, training equally fast and inferring 5x faster. The results can be reproduced using the automlbenchmark fork here.
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.
Dependencies
~3–4.5MB
~87K SLoC