<p align="center">
<img height="120" src="https://github.com/perpetual-ml/perpetual/raw/main/resources/perp_logo.png">
</p>
<div align="center">
[](https://pypi.org/project/perpetual)
[](https://pypi.org/project/perpetual)
[](https://crates.io/crates/perpetual)
[](https://discord.gg/AyUK7rr6wy)
</div>
# 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](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html) 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](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_covtype.html) 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](./python-package/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](https://automlbenchmark.streamlit.app/cd_diagram). Top 10 datasets with the most number of rows are selected from [OpenML datasets](https://www.openml.org/). 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](openml.org/t/359929) | 518 | 11.3 | 29.0 | 520 | 30.9 <td style="background-color:green;color:white;"> 28.8 </td> |
| [bates_regr_100](openml.org/t/361940) | 3421 | 15.1 <td style="background-color:green;color:white;"> 1.084 </td> | OOM | OOM | OOM |
| [BNG(libras_move)](openml.org/t/7327) | 1956 | 4.2 <td style="background-color:green;color:white;"> 2.51 </td> | 1922 | 97.6 | 2.53 |
| [BNG(satellite_image)](openml.org/t/7326) | 334 | 1.6 | 0.731 | 337 | 10.0 <td style="background-color:green;color:white;"> 0.721 </td> |
| [COMET_MC](openml.org/t/14949) | 44 | 1.0 <td style="background-color:green;color:white;"> 0.0615 </td> | 47 | 5.0 | 0.0662 |
| [friedman1](openml.org/t/361939) | 275 | 4.2 <td style="background-color:green;color:white;"> 1.047 </td> | 278 | 5.1 | 1.487 |
| [poker](openml.org/t/10102) | 38 | 0.6 <td style="background-color:green;color:white;"> 0.256 </td> | 41 | 1.2 | 0.722 |
| [subset_higgs](openml.org/t/361955) | 868 | 10.6 <td style="background-color:green;color:white;"> 0.420 </td> | 870 | 24.5 | 0.421 |
| [BNG(autoHorse)](openml.org/t/7319) | 107 | 1.1 <td style="background-color:green;color:white;"> 19.0 </td> | 107 | 3.2 | 20.5 |
| [BNG(pbc)](openml.org/t/7318) | 48 | 0.6 <td style="background-color:green;color:white;"> 836.5 </td> | 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](https://github.com/deadsoul44/automlbenchmark).
## Usage
You can use the algorithm like in the example below. Check examples folders for both Rust and Python.
```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](https://perpetual-ml.github.io/perpetual) and for the Rust API [here](https://docs.rs/perpetual/latest/perpetual/).
## Installation
The package can be installed directly from [pypi](https://pypi.org/project/perpetual):
```shell
pip install perpetual
```
Using [conda-forge](https://anaconda.org/conda-forge/perpetual):
```shell
conda install conda-forge::perpetual
```
To use in a Rust project and to get the package from [crates.io](https://crates.io/crates/perpetual):
```shell
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](https://perpetual-ml.com/blog/how-perpetual-works) for a high level introduction to the algorithm.
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"description": "<p align=\"center\">\n <img height=\"120\" src=\"https://github.com/perpetual-ml/perpetual/raw/main/resources/perp_logo.png\">\n</p>\n\n<div align=\"center\">\n\n[](https://pypi.org/project/perpetual)\n[](https://pypi.org/project/perpetual)\n[](https://crates.io/crates/perpetual)\n[](https://discord.gg/AyUK7rr6wy)\n\n\n</div>\n\n# Perpetual\n\nPerpetualBooster 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. 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Thus, it achieves up to 100x speed-up at the same accuracy with different `budget` levels and with different datasets.\n\nThe following table summarizes the results for the [California Housing](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html) dataset (regression):\n\n| Perpetual budget | LightGBM n_estimators | Perpetual mse | LightGBM mse | Speed-up wall time | Speed-up cpu time |\n| ---------------- | --------------------- | ------------- | ------------ | ------------------ | ----------------- |\n| 1.0 | 100 | 0.192 | 0.192 | 54x | 56x |\n| 1.5 | 300 | 0.188 | 0.188 | 59x | 58x |\n| 2.1 | 1000 | 0.185 | 0.186 | 42x | 41x |\n\nThe following table summarizes the results for the [Cover Types](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_covtype.html) dataset (classification):\n\n| Perpetual budget | LightGBM n_estimators | Perpetual log loss | LightGBM log loss | Speed-up wall time | Speed-up cpu time |\n| ---------------- | --------------------- | ------------------ | ----------------- | ------------------ | ----------------- |\n| 0.9 | 100 | 0.091 | 0.084 | 72x | 78x |\n\nThe results can be reproduced using the scripts in the [examples](./python-package/examples) folder.\n\nPerpetualBooster 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](https://automlbenchmark.streamlit.app/cd_diagram). Top 10 datasets with the most number of rows are selected from [OpenML datasets](https://www.openml.org/). The results are summarized in the following table for regression tasks:\n\n| OpenML Task | Perpetual Training Duration | Perpetual Inference Duration | Perpetual RMSE | AutoGluon Training Duration | AutoGluon Inference Duration | AutoGluon RMSE |\n| -------------------------------------------- | --------------------------- | ----------------------------------------------------------------- | -------------- | --------------------------- | ----------------------------------------------------------------- | -------------- |\n| [Airlines_DepDelay_10M](openml.org/t/359929) | 518 | 11.3 | 29.0 | 520 | 30.9 <td style=\"background-color:green;color:white;\"> 28.8 </td> |\n| [bates_regr_100](openml.org/t/361940) | 3421 | 15.1 <td style=\"background-color:green;color:white;\"> 1.084 </td> | OOM | OOM | OOM |\n| [BNG(libras_move)](openml.org/t/7327) | 1956 | 4.2 <td style=\"background-color:green;color:white;\"> 2.51 </td> | 1922 | 97.6 | 2.53 |\n| [BNG(satellite_image)](openml.org/t/7326) | 334 | 1.6 | 0.731 | 337 | 10.0 <td style=\"background-color:green;color:white;\"> 0.721 </td> |\n| [COMET_MC](openml.org/t/14949) | 44 | 1.0 <td style=\"background-color:green;color:white;\"> 0.0615 </td> | 47 | 5.0 | 0.0662 |\n| [friedman1](openml.org/t/361939) | 275 | 4.2 <td style=\"background-color:green;color:white;\"> 1.047 </td> | 278 | 5.1 | 1.487 |\n| [poker](openml.org/t/10102) | 38 | 0.6 <td style=\"background-color:green;color:white;\"> 0.256 </td> | 41 | 1.2 | 0.722 |\n| [subset_higgs](openml.org/t/361955) | 868 | 10.6 <td style=\"background-color:green;color:white;\"> 0.420 </td> | 870 | 24.5 | 0.421 |\n| [BNG(autoHorse)](openml.org/t/7319) | 107 | 1.1 <td style=\"background-color:green;color:white;\"> 19.0 </td> | 107 | 3.2 | 20.5 |\n| [BNG(pbc)](openml.org/t/7318) | 48 | 0.6 <td style=\"background-color:green;color:white;\"> 836.5 </td> | 51 | 0.2 | 957.1 |\n| average | 465 | 3.9 | - | 464 | 19.7 | - |\n\nPerpetualBooster outperformed AutoGluon on 8 out of 10 datasets, training equally fast and inferring 5x faster. 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