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+%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
+
+<h4 align="center">
+
+![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)
+
+</h4>
+
+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
+
+<h4 align="center">
+
+![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)
+
+</h4>
+
+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
+
+<h4 align="center">
+
+![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)
+
+</h4>
+
+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 <Python_Bot@openeuler.org> - 0.4.1-1
+- Package Spec generated