KO
|
EN
gitlite — search
Search
#typescript
#python
#nextjs
#tailwindcss
#css
#nodejs
#postgresql
#javascript
#react
#docker
#svelte
#transformers
pgbm
★ 163
Open GitHub ↗
Probabilistic Gradient Boosting Machines
Download README (.md)
Explore Similar Repositories
Reinforcement-Learning
:
Implementations of Deep Reinforcement Learning Algorithms and Bench-marking with PyTorch
num.cr
:
Scientific computing in pure Crystal
Practical-Deep-Learning-at-Scale-with-MLFlow
:
Practical Deep Learning at Scale with MLFlow, published by Packt
Universal-Kubernetes-Helm-Charts
:
Some universal helm charts used for deploying services onto Kubernetes. All-in-one best-practices
Stl.Fusion.Samples
:
A collection of samples for Fusion library: https://github.com/servicetitan/Stl.Fusion
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
pgbm
?
Download (.md)
# PGBM <img src="https://icai.ai/wp-content/uploads/2020/01/AIRLabAmsterdam-10-6-gecomprimeerd-transparant.png" width="300" alt="Airlab Amsterdam" align="right"> # [](https://pypi.org/project/pgbm/) [](https://docs.conda.io/en/latest/miniconda.html) [](https://github.com/elephaint/pgbm/blob/main/LICENSE) _Probabilistic Gradient Boosting Machines_ (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Airlab in Amsterdam. It provides the following advantages over existing frameworks: * Probabilistic regression estimates instead of only point estimates. ([example](https://github.com/elephaint/pgbm/blob/main/examples/torch/example01_housing_cpu.py)) * Auto-differentiation of custom loss functions. ([example](https://github.com/elephaint/pgbm/blob/main/examples/torch/example08_housing_autodiff.py), [example](https://github.com/elephaint/pgbm/blob/main/examples/torch/example10_covidhospitaladmissions.py)) * Native GPU-acceleration. ([example](https://github.com/elephaint/pgbm/blob/main/examples/torch/example02_housing_gpu.py)) * Distributed training for CPU and GPU, across multiple nodes. ([examples](https://github.com/elephaint/pgbm/blob/main/examples/torch_dist/)) * Ability to optimize probabilistic estimates after training for a set of common distributions, without retraining the model. ([example](https://github.com/elephaint/pgbm/blob/main/examples/torch/example07_optimizeddistribution.py)) * Full integration with scikit-learn through a fork of HistGradientBoostingRegressor ([examples](https://github.com/elephaint/pgbm/tree/main/examples/sklearn)) It is aimed at users interested in solving large-scale tabular probabilistic regression problems, such as probabilistic time series forecasting. For more details, [read the docs](https://pgbm.readthedocs.io/en/latest/index.html) or [our paper](https://arxiv.org/abs/2106.01682) or check out the [examples](https://github.com/elephaint/pgbm/tree/main/examples). Below a simple example to generate 1000 estimates for each of our test points: ```py from pgbm.sklearn import HistGradientBoostingRegressor from sklearn.model_selection import train_test_split from sklearn.datasets import fetch_california_housing X, y = fetch_california_housing(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) model = HistGradientBoostingRegressor().fit(X_train, y_train) yhat_test, yhat_test_std = model.predict(X_test, return_std=True) yhat_dist = model.sample(yhat_test, yhat_test_std, n_estimates=1000) ``` See also [this example](https://github.com/elephaint/pgbm/blob/main/examples/sklearn/example14_probregression.py) where we compare PGBM to standard gradient boosting quantile regression methods, demonstrating that we can achieve comparable or better probabilistic performance whilst only training a single model. ### Installation ### See [Installation](https://pgbm.readthedocs.io/en/latest/installation.html) section in our [docs](https://pgbm.readthedocs.io/en/latest/index.html). ### Support ### In general, PGBM works similar to existing gradient boosting packages such as LightGBM or xgboost (and it should be possible to more or less use it as a drop-in replacement). * Read the docs for an overview of [hyperparameters](https://pgbm.readthedocs.io/en/latest/parameters.html) and a [function reference](https://pgbm.readthedocs.io/en/latest/function_reference.html). * See the [examples](https://github.com/elephaint/pgbm/tree/main/examples) folder for examples. In case further support is required, [open an issue](https://github.com/elephaint/pgbm/issues). ### Reference ### [Olivier Sprangers](mailto:o.r.sprangers@uva.nl), Sebastian Schelter, Maarten de Rijke. [Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression](https://arxiv.org/abs/2106.01682). Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ([KDD 21](https://www.kdd.org/kdd2021/)), August 14–18, 2021, Virtual Event, Singapore. The experiments from our paper can be replicated by running the scripts in the [experiments](https://github.com/elephaint/pgbm/tree/main/paper/experiments) folder. Datasets are downloaded when needed in the experiments except for higgs and m5, which should be pre-downloaded and saved to the [datasets](https://github.com/elephaint/pgbm/tree/main/paper/datasets) folder (Higgs) and to datasets/m5 (m5). ### License ### This project is licensed under the terms of the [Apache 2.0 license](https://github.com/elephaint/pgbm/blob/main/LICENSE). ### Acknowledgements ### This project was developed by [Airlab Amsterdam](https://icai.ai/airlab/).
