%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.aliyun.com/pypi/web/packages/0f/8e/007bbcb8e0bc4bab75bd6e4bc47f51b35de321e19ab76dbd8b638ac62b8f/pgbm-2.1.1.tar.gz
Requires: python3-scikit-learn
Requires: python3-ninja
Requires: python3-numba
%description
# 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:
```
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 <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:
```
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 <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:
```
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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.1-1
- Package Spec generated