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diff --git a/python-bambi.spec b/python-bambi.spec new file mode 100644 index 0000000..d78f478 --- /dev/null +++ b/python-bambi.spec @@ -0,0 +1,430 @@ +%global _empty_manifest_terminate_build 0 +Name: python-bambi +Version: 0.11.0 +Release: 1 +Summary: BAyesian Model Building Interface in Python +License: MIT License Copyright (c) 2016 the developers of Bambi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +URL: https://pypi.org/project/bambi/ +Source0: https://mirrors.aliyun.com/pypi/web/packages/59/37/f53bdf1815aad8e6e9bd149116044aa7eed814554eca916e0da260f97a9c/bambi-0.11.0.tar.gz +BuildArch: noarch + +Requires: python3-arviz +Requires: python3-formulae +Requires: python3-graphviz +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-pymc +Requires: python3-pytensor +Requires: python3-scipy +Requires: python3-black +Requires: python3-ipython +Requires: python3-nbsphinx +Requires: python3-pre-commit +Requires: python3-pydata-sphinx-theme +Requires: python3-pylint +Requires: python3-pytest-cov +Requires: python3-pytest +Requires: python3-seaborn +Requires: python3-sphinx +Requires: python3-blackjax +Requires: python3-jax +Requires: python3-jaxlib +Requires: python3-numpyro + +%description +<img src="https://raw.githubusercontent.com/bambinos/bambi/main/docs/logos/RGB/Bambi_logo.png" width=200></img> + +[](https://badge.fury.io/py/bambi) +[](https://github.com/bambinos/bambi/actions/workflows/test.yml) +[](https://codecov.io/gh/bambinos/bambi) +[](https://github.com/ambv/black) + +BAyesian Model-Building Interface in Python + +## Overview + +Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the [PyMC](https://github.com/pymc-devs/pymc) probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach. + +## Installation + +Bambi requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms. + +Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip: + + pip install bambi + +Alternatively, if you want the bleeding edge version of the package you can install from GitHub: + + pip install git+https://github.com/bambinos/bambi.git + +### Dependencies + +Bambi requires working versions of ArviZ, formulae, NumPy, pandas and PyMC. Dependencies are listed in `pyproject.toml` and should all be installed by the Bambi installer; no further action should be required. + +## Example + +In the following two examples we assume the following basic setup + +```python +import bambi as bmb +import numpy as np +import pandas as pd + +data = pd.DataFrame({ + "y": np.random.normal(size=50), + "g": np.random.choice(["Yes", "No"], size=50), + "x1": np.random.normal(size=50), + "x2": np.random.normal(size=50) +}) +``` + +### Linear regression + +```python +model = bmb.Model("y ~ x1 + x2", data) +fitted = model.fit() +``` + +In the first line we create and build a Bambi `Model`. The second line tells the sampler to start +running and it returns an `InferenceData` object, which can be passed to several ArviZ functions +such as `az.summary()` to get a summary of the parameters distribution and sample diagnostics or + `az.plot_trace()` to visualize them. + + +### Logistic regression + +Here we just add the `family` argument set to `"bernoulli"` to tell Bambi we are modelling a binary +response. By default, it uses a logit link. We can also use some syntax sugar to specify which event +we want to model. We just say `g['Yes']` and Bambi will understand we want to model the probability +of a `"Yes"` response. But this notation is not mandatory. If we use `"g ~ x1 + x2"`, Bambi will +pick one of the events to model and will inform us which one it picked. + + +```python +model = bmb.Model("g['Yes'] ~ x1 + x2", data, family="bernoulli") +fitted = model.fit() +``` + +## Documentation + +The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html) + +## Citation + +If you use Bambi and want to cite it please use + +``` +@article{Capretto2022, + title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python}, + volume={103}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15}, + doi={10.18637/jss.v103.i15}, + number={15}, + journal={Journal of Statistical Software}, + author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A}, + year={2022}, + pages={1–29} +} +``` + +## Contributions + +Bambi is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/bambi/blob/main/docs/CONTRIBUTING.md) Readme. + +For a list of contributors see the [GitHub contributor](https://github.com/bambinos/bambi/graphs/contributors) page + +## Donations + +If you want to support Bambi financially, you can [make a donation](https://numfocus.org/donate-to-pymc) to our sister project PyMC. + +## Code of Conduct + +Bambi wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/bambinos/bambi/blob/main/docs/CODE_OF_CONDUCT.md) + +## License + +[MIT License](https://github.com/bambinos/bambi/blob/main/LICENSE) + + +%package -n python3-bambi +Summary: BAyesian Model Building Interface in Python +Provides: python-bambi +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-bambi +<img src="https://raw.githubusercontent.com/bambinos/bambi/main/docs/logos/RGB/Bambi_logo.png" width=200></img> + +[](https://badge.fury.io/py/bambi) +[](https://github.com/bambinos/bambi/actions/workflows/test.yml) +[](https://codecov.io/gh/bambinos/bambi) +[](https://github.com/ambv/black) + +BAyesian Model-Building Interface in Python + +## Overview + +Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the [PyMC](https://github.com/pymc-devs/pymc) probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach. + +## Installation + +Bambi requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms. + +Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip: + + pip install bambi + +Alternatively, if you want the bleeding edge version of the package you can install from GitHub: + + pip install git+https://github.com/bambinos/bambi.git + +### Dependencies + +Bambi requires working versions of ArviZ, formulae, NumPy, pandas and PyMC. Dependencies are listed in `pyproject.toml` and should all be installed by the Bambi installer; no further action should be required. + +## Example + +In the following two examples we assume the following basic setup + +```python +import bambi as bmb +import numpy as np +import pandas as pd + +data = pd.DataFrame({ + "y": np.random.normal(size=50), + "g": np.random.choice(["Yes", "No"], size=50), + "x1": np.random.normal(size=50), + "x2": np.random.normal(size=50) +}) +``` + +### Linear regression + +```python +model = bmb.Model("y ~ x1 + x2", data) +fitted = model.fit() +``` + +In the first line we create and build a Bambi `Model`. The second line tells the sampler to start +running and it returns an `InferenceData` object, which can be passed to several ArviZ functions +such as `az.summary()` to get a summary of the parameters distribution and sample diagnostics or + `az.plot_trace()` to visualize them. + + +### Logistic regression + +Here we just add the `family` argument set to `"bernoulli"` to tell Bambi we are modelling a binary +response. By default, it uses a logit link. We can also use some syntax sugar to specify which event +we want to model. We just say `g['Yes']` and Bambi will understand we want to model the probability +of a `"Yes"` response. But this notation is not mandatory. If we use `"g ~ x1 + x2"`, Bambi will +pick one of the events to model and will inform us which one it picked. + + +```python +model = bmb.Model("g['Yes'] ~ x1 + x2", data, family="bernoulli") +fitted = model.fit() +``` + +## Documentation + +The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html) + +## Citation + +If you use Bambi and want to cite it please use + +``` +@article{Capretto2022, + title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python}, + volume={103}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15}, + doi={10.18637/jss.v103.i15}, + number={15}, + journal={Journal of Statistical Software}, + author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A}, + year={2022}, + pages={1–29} +} +``` + +## Contributions + +Bambi is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/bambi/blob/main/docs/CONTRIBUTING.md) Readme. + +For a list of contributors see the [GitHub contributor](https://github.com/bambinos/bambi/graphs/contributors) page + +## Donations + +If you want to support Bambi financially, you can [make a donation](https://numfocus.org/donate-to-pymc) to our sister project PyMC. + +## Code of Conduct + +Bambi wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/bambinos/bambi/blob/main/docs/CODE_OF_CONDUCT.md) + +## License + +[MIT License](https://github.com/bambinos/bambi/blob/main/LICENSE) + + +%package help +Summary: Development documents and examples for bambi +Provides: python3-bambi-doc +%description help +<img src="https://raw.githubusercontent.com/bambinos/bambi/main/docs/logos/RGB/Bambi_logo.png" width=200></img> + +[](https://badge.fury.io/py/bambi) +[](https://github.com/bambinos/bambi/actions/workflows/test.yml) +[](https://codecov.io/gh/bambinos/bambi) +[](https://github.com/ambv/black) + +BAyesian Model-Building Interface in Python + +## Overview + +Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the [PyMC](https://github.com/pymc-devs/pymc) probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach. + +## Installation + +Bambi requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms. + +Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip: + + pip install bambi + +Alternatively, if you want the bleeding edge version of the package you can install from GitHub: + + pip install git+https://github.com/bambinos/bambi.git + +### Dependencies + +Bambi requires working versions of ArviZ, formulae, NumPy, pandas and PyMC. Dependencies are listed in `pyproject.toml` and should all be installed by the Bambi installer; no further action should be required. + +## Example + +In the following two examples we assume the following basic setup + +```python +import bambi as bmb +import numpy as np +import pandas as pd + +data = pd.DataFrame({ + "y": np.random.normal(size=50), + "g": np.random.choice(["Yes", "No"], size=50), + "x1": np.random.normal(size=50), + "x2": np.random.normal(size=50) +}) +``` + +### Linear regression + +```python +model = bmb.Model("y ~ x1 + x2", data) +fitted = model.fit() +``` + +In the first line we create and build a Bambi `Model`. The second line tells the sampler to start +running and it returns an `InferenceData` object, which can be passed to several ArviZ functions +such as `az.summary()` to get a summary of the parameters distribution and sample diagnostics or + `az.plot_trace()` to visualize them. + + +### Logistic regression + +Here we just add the `family` argument set to `"bernoulli"` to tell Bambi we are modelling a binary +response. By default, it uses a logit link. We can also use some syntax sugar to specify which event +we want to model. We just say `g['Yes']` and Bambi will understand we want to model the probability +of a `"Yes"` response. But this notation is not mandatory. If we use `"g ~ x1 + x2"`, Bambi will +pick one of the events to model and will inform us which one it picked. + + +```python +model = bmb.Model("g['Yes'] ~ x1 + x2", data, family="bernoulli") +fitted = model.fit() +``` + +## Documentation + +The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html) + +## Citation + +If you use Bambi and want to cite it please use + +``` +@article{Capretto2022, + title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python}, + volume={103}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15}, + doi={10.18637/jss.v103.i15}, + number={15}, + journal={Journal of Statistical Software}, + author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A}, + year={2022}, + pages={1–29} +} +``` + +## Contributions + +Bambi is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/bambi/blob/main/docs/CONTRIBUTING.md) Readme. + +For a list of contributors see the [GitHub contributor](https://github.com/bambinos/bambi/graphs/contributors) page + +## Donations + +If you want to support Bambi financially, you can [make a donation](https://numfocus.org/donate-to-pymc) to our sister project PyMC. + +## Code of Conduct + +Bambi wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/bambinos/bambi/blob/main/docs/CODE_OF_CONDUCT.md) + +## License + +[MIT License](https://github.com/bambinos/bambi/blob/main/LICENSE) + + +%prep +%autosetup -n bambi-0.11.0 + +%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-bambi -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.11.0-1 +- Package Spec generated |