path: root/python-bambi.spec

%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>

[![PyPi version](https://badge.fury.io/py/bambi.svg)](https://badge.fury.io/py/bambi)
[![Build Status](https://github.com/bambinos/bambi/actions/workflows/test.yml/badge.svg)](https://github.com/bambinos/bambi/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/bambinos/bambi/branch/master/graph/badge.svg?token=ZqH0KCLKAE)](https://codecov.io/gh/bambinos/bambi)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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>

[![PyPi version](https://badge.fury.io/py/bambi.svg)](https://badge.fury.io/py/bambi)
[![Build Status](https://github.com/bambinos/bambi/actions/workflows/test.yml/badge.svg)](https://github.com/bambinos/bambi/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/bambinos/bambi/branch/master/graph/badge.svg?token=ZqH0KCLKAE)](https://codecov.io/gh/bambinos/bambi)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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>

[![PyPi version](https://badge.fury.io/py/bambi.svg)](https://badge.fury.io/py/bambi)
[![Build Status](https://github.com/bambinos/bambi/actions/workflows/test.yml/badge.svg)](https://github.com/bambinos/bambi/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/bambinos/bambi/branch/master/graph/badge.svg?token=ZqH0KCLKAE)](https://codecov.io/gh/bambinos/bambi)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](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