%global _empty_manifest_terminate_build 0 Name: python-ngboost Version: 0.4.1 Release: 1 Summary: Library for probabilistic predictions via gradient boosting. License: Apache-2.0 URL: https://github.com/stanfordmlgroup/ngboost Source0: https://mirrors.nju.edu.cn/pypi/web/packages/90/a5/ee3cf88698107fcbe59ca8231e43cba338c0be6c229ba1e2ec26cd6fff1f/ngboost-0.4.1.tar.gz BuildArch: noarch Requires: python3-lifelines Requires: python3-numpy Requires: python3-pandas Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-tqdm %description # NGBoost: Natural Gradient Boosting for Probabilistic Prediction

![Python package](https://github.com/stanfordmlgroup/ngboost/workflows/Python%20package/badge.svg) [![GitHub Repo Size](https://img.shields.io/github/repo-size/stanfordmlgroup/ngboost?label=Repo+Size)](https://github.com/stanfordmlgroup/ngboost/graphs/contributors) [![Github License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![PyPI](https://img.shields.io/pypi/v/ngboost?logo=pypi&logoColor=white)](https://pypi.org/project/ngboost) [![PyPI Downloads](https://img.shields.io/pypi/dm/ngboost?logo=icloud&logoColor=white)](https://pypistats.org/packages/ngboost)

ngboost is a Python library that implements Natural Gradient Boosting, as described in ["NGBoost: Natural Gradient Boosting for Probabilistic Prediction"](https://stanfordmlgroup.github.io/projects/ngboost/). It is built on top of [Scikit-Learn](https://scikit-learn.org/stable/), and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this [slide deck](https://drive.google.com/file/d/183BWFAdFms81MKy6hSku8qI97OwS_JH_/view?usp=sharing). ## Installation ```sh via pip pip install --upgrade ngboost via conda-forge conda install -c conda-forge ngboost ``` ## Usage Probabilistic regression example on the Boston housing dataset: ```python from ngboost import NGBRegressor from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error X, Y = load_boston(True) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2) ngb = NGBRegressor().fit(X_train, Y_train) Y_preds = ngb.predict(X_test) Y_dists = ngb.pred_dist(X_test) # test Mean Squared Error test_MSE = mean_squared_error(Y_preds, Y_test) print('Test MSE', test_MSE) # test Negative Log Likelihood test_NLL = -Y_dists.logpdf(Y_test).mean() print('Test NLL', test_NLL) ``` Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our [user guide](https://stanfordmlgroup.github.io/ngboost/intro.html), which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost. ## License [Apache License 2.0](https://github.com/stanfordmlgroup/ngboost/blob/master/LICENSE). ## Reference Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. [arXiv](https://arxiv.org/abs/1910.03225) %package -n python3-ngboost Summary: Library for probabilistic predictions via gradient boosting. Provides: python-ngboost BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-ngboost # NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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ngboost is a Python library that implements Natural Gradient Boosting, as described in ["NGBoost: Natural Gradient Boosting for Probabilistic Prediction"](https://stanfordmlgroup.github.io/projects/ngboost/). It is built on top of [Scikit-Learn](https://scikit-learn.org/stable/), and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this [slide deck](https://drive.google.com/file/d/183BWFAdFms81MKy6hSku8qI97OwS_JH_/view?usp=sharing). ## Installation ```sh via pip pip install --upgrade ngboost via conda-forge conda install -c conda-forge ngboost ``` ## Usage Probabilistic regression example on the Boston housing dataset: ```python from ngboost import NGBRegressor from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error X, Y = load_boston(True) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2) ngb = NGBRegressor().fit(X_train, Y_train) Y_preds = ngb.predict(X_test) Y_dists = ngb.pred_dist(X_test) # test Mean Squared Error test_MSE = mean_squared_error(Y_preds, Y_test) print('Test MSE', test_MSE) # test Negative Log Likelihood test_NLL = -Y_dists.logpdf(Y_test).mean() print('Test NLL', test_NLL) ``` Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our [user guide](https://stanfordmlgroup.github.io/ngboost/intro.html), which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost. ## License [Apache License 2.0](https://github.com/stanfordmlgroup/ngboost/blob/master/LICENSE). ## Reference Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. [arXiv](https://arxiv.org/abs/1910.03225) %package help Summary: Development documents and examples for ngboost Provides: python3-ngboost-doc %description help # NGBoost: Natural Gradient Boosting for Probabilistic Prediction

![Python package](https://github.com/stanfordmlgroup/ngboost/workflows/Python%20package/badge.svg) [![GitHub Repo Size](https://img.shields.io/github/repo-size/stanfordmlgroup/ngboost?label=Repo+Size)](https://github.com/stanfordmlgroup/ngboost/graphs/contributors) [![Github License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![PyPI](https://img.shields.io/pypi/v/ngboost?logo=pypi&logoColor=white)](https://pypi.org/project/ngboost) [![PyPI Downloads](https://img.shields.io/pypi/dm/ngboost?logo=icloud&logoColor=white)](https://pypistats.org/packages/ngboost)

ngboost is a Python library that implements Natural Gradient Boosting, as described in ["NGBoost: Natural Gradient Boosting for Probabilistic Prediction"](https://stanfordmlgroup.github.io/projects/ngboost/). It is built on top of [Scikit-Learn](https://scikit-learn.org/stable/), and is designed to be scalable and modular with respect to choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this [slide deck](https://drive.google.com/file/d/183BWFAdFms81MKy6hSku8qI97OwS_JH_/view?usp=sharing). ## Installation ```sh via pip pip install --upgrade ngboost via conda-forge conda install -c conda-forge ngboost ``` ## Usage Probabilistic regression example on the Boston housing dataset: ```python from ngboost import NGBRegressor from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error X, Y = load_boston(True) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2) ngb = NGBRegressor().fit(X_train, Y_train) Y_preds = ngb.predict(X_test) Y_dists = ngb.pred_dist(X_test) # test Mean Squared Error test_MSE = mean_squared_error(Y_preds, Y_test) print('Test MSE', test_MSE) # test Negative Log Likelihood test_NLL = -Y_dists.logpdf(Y_test).mean() print('Test NLL', test_NLL) ``` Details on available distributions, scoring rules, learners, tuning, and model interpretation are available in our [user guide](https://stanfordmlgroup.github.io/ngboost/intro.html), which also includes numerous usage examples and information on how to add new distributions or scores to NGBoost. ## License [Apache License 2.0](https://github.com/stanfordmlgroup/ngboost/blob/master/LICENSE). ## Reference Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler. 2019. NGBoost: Natural Gradient Boosting for Probabilistic Prediction. [arXiv](https://arxiv.org/abs/1910.03225) %prep %autosetup -n ngboost-0.4.1 %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-ngboost -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.4.1-1 - Package Spec generated