%global _empty_manifest_terminate_build 0 Name: python-pgbm Version: 2.1.1 Release: 1 Summary: Probabilistic Gradient Boosting Machines License: Apache Software License URL: https://github.com/elephaint/pgbm Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0f/8e/007bbcb8e0bc4bab75bd6e4bc47f51b35de321e19ab76dbd8b638ac62b8f/pgbm-2.1.1.tar.gz Requires: python3-scikit-learn Requires: python3-ninja Requires: python3-numba %description # PGBM Airlab Amsterdam # [![PyPi version](https://img.shields.io/pypi/v/pgbm)](https://pypi.org/project/pgbm/) [![Python version](https://img.shields.io/pypi/pyversions/pgbm)](https://docs.conda.io/en/latest/miniconda.html) [![GitHub license](https://img.shields.io/pypi/l/pgbm)](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: ``` 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/). %package -n python3-pgbm Summary: Probabilistic Gradient Boosting Machines Provides: python-pgbm BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-pgbm # PGBM Airlab Amsterdam # [![PyPi version](https://img.shields.io/pypi/v/pgbm)](https://pypi.org/project/pgbm/) [![Python version](https://img.shields.io/pypi/pyversions/pgbm)](https://docs.conda.io/en/latest/miniconda.html) [![GitHub license](https://img.shields.io/pypi/l/pgbm)](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: ``` 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/). %package help Summary: Development documents and examples for pgbm Provides: python3-pgbm-doc %description help # PGBM Airlab Amsterdam # [![PyPi version](https://img.shields.io/pypi/v/pgbm)](https://pypi.org/project/pgbm/) [![Python version](https://img.shields.io/pypi/pyversions/pgbm)](https://docs.conda.io/en/latest/miniconda.html) [![GitHub license](https://img.shields.io/pypi/l/pgbm)](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: ``` 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/). %prep %autosetup -n pgbm-2.1.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pgbm -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 2.1.1-1 - Package Spec generated