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|
%global _empty_manifest_terminate_build 0
Name: python-sbi
Version: 0.21.0
Release: 1
Summary: Simulation-based inference.
License: AGPLv3
URL: https://github.com/mackelab/sbi
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8c/02/c6189720842fe1ab00739201bd8d0e52ee0c5c7841574440edd334964b6c/sbi-0.21.0.tar.gz
BuildArch: noarch
Requires: python3-arviz
Requires: python3-joblib
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-pillow
Requires: python3-pyknos
Requires: python3-pyro-ppl
Requires: python3-scikit-learn
Requires: python3-scipy
Requires: python3-tensorboard
Requires: python3-torch
Requires: python3-tqdm
Requires: python3-autoflake
Requires: python3-black
Requires: python3-deepdiff
Requires: python3-flake8
Requires: python3-isort
Requires: python3-jupyter
Requires: python3-mkdocs
Requires: python3-mkdocs-material
Requires: python3-markdown-include
Requires: python3-mkdocs-redirects
Requires: python3-mkdocstrings
Requires: python3-nbconvert
Requires: python3-pep517
Requires: python3-pytest
Requires: python3-pyyaml
Requires: python3-pyright
Requires: python3-torchtestcase
Requires: python3-twine
%description
[](https://badge.fury.io/py/sbi)
[](https://github.com/mackelab/sbi/blob/master/CONTRIBUTING.md)
[](https://github.com/mackelab/sbi/actions)
[](https://codecov.io/gh/mackelab/sbi)
[](https://github.com/mackelab/sbi/blob/master/LICENSE.txt)
[](https://doi.org/10.21105/joss.02505)
## sbi: simulation-based inference
[Getting Started](https://www.mackelab.org/sbi/tutorial/00_getting_started/) | [Documentation](https://www.mackelab.org/sbi/)
`sbi` is a PyTorch package for simulation-based inference. Simulation-based inference is
the process of finding parameters of a simulator from observations.
`sbi` takes a Bayesian approach and returns a full posterior distribution
over the parameters, conditional on the observations. This posterior can be amortized (i.e.
useful for any observation) or focused (i.e. tailored to a particular observation), with different
computational trade-offs.
`sbi` offers a simple interface for one-line posterior inference.
```python
from sbi.inference import infer
# import your simulator, define your prior over the parameters
parameter_posterior = infer(simulator, prior, method='SNPE', num_simulations=100)
```
See below for the available methods of inference, `SNPE`, `SNRE` and `SNLE`.
## Installation
`sbi` requires Python 3.6 or higher. We recommend to use a [`conda`](https://docs.conda.io/en/latest/miniconda.html) virtual
environment ([Miniconda installation instructions](https://docs.conda.io/en/latest/miniconda.html])). If `conda` is installed on the system, an environment for
installing `sbi` can be created as follows:
```commandline
# Create an environment for sbi (indicate Python 3.6 or higher); activate it
$ conda create -n sbi_env python=3.7 && conda activate sbi_env
```
Independent of whether you are using `conda` or not, `sbi` can be installed using `pip`:
```commandline
pip install sbi
```
To test the installation, drop into a python prompt and run
```python
from sbi.examples.minimal import simple
posterior = simple()
print(posterior)
```
## Inference Algorithms
The following algorithms are currently available:
#### Sequential Neural Posterior Estimation (SNPE)
* [`SNPE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_a.SNPE_A) from Papamakarios G and Murray I [_Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation_](https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html) (NeurIPS 2016).
* [`SNPE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_c.SNPE_C) or `APT` from Greenberg D, Nonnenmacher M, and Macke J [_Automatic
Posterior Transformation for likelihood-free
inference_](https://arxiv.org/abs/1905.07488) (ICML 2019).
#### Sequential Neural Likelihood Estimation (SNLE)
* [`SNLE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.snle_a.SNLE_A) or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential
Neural Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).
#### Sequential Neural Ratio Estimation (SNRE)
* [`SNRE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_a.SNRE_A) or `AALR` from Hermans J, Begy V, and Louppe G. [_Likelihood-free Inference with Amortized Approximate Likelihood Ratios_](https://arxiv.org/abs/1903.04057) (ICML 2020).
* [`SNRE_B`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_b.SNRE_B) or `SRE` from Durkan C, Murray I, and Papamakarios G. [_On Contrastive Learning for Likelihood-free Inference_](https://arxiv.org/abs/2002.03712) (ICML 2020).
* [`BNRE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.bnre.BNRE) from Delaunoy A, Hermans J, Rozet F, Wehenkel A, and Louppe G. [_Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation_](https://arxiv.org/abs/2208.13624) (NeurIPS 2022).
* [`SNRE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_c.SNRE_C) or `NRE-C` from Miller BK, Weniger C, Forré P. [_Contrastive Neural Ratio Estimation_](https://arxiv.org/abs/2210.06170) (NeurIPS 2022).
#### Sequential Neural Variational Inference (SNVI)
* [`SNVI`](https://www.mackelab.org/sbi/reference/#sbi.inference.posteriors.vi_posterior) from Glöckler M, Deistler M, Macke J, [_Variational methods for simulation-based inference_](https://openreview.net/forum?id=kZ0UYdhqkNY) (ICLR 2022).
#### Mixed Neural Likelihood Estimation (MNLE)
* [`MNLE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.mnle.MNLE) from Boelts J, Lueckmann JM, Gao R, Macke J, [_Flexible and efficient simulation-based inference for models of decision-making](https://elifesciences.org/articles/77220) (eLife 2022).
## Feedback and Contributions
We would like to hear how `sbi` is working for your inference problems as well as receive bug reports, pull requests and other feedback (see
[contribute](http://www.mackelab.org/sbi/contribute/)).
## Acknowledgements
`sbi` is the successor (using PyTorch) of the
[`delfi`](https://github.com/mackelab/delfi) package. It was started as a fork of Conor
M. Durkan's `lfi`. `sbi` runs as a community project; development is coordinated at the
[mackelab](https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning/research/research/cluster-research-groups/professorships/machine-learning-in-science/). See also [credits](https://github.com/mackelab/sbi/blob/master/docs/docs/credits.md).
## Support
`sbi` has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). [ADIMEM](https://fit.uni-tuebingen.de/Project/Details?id=9199) is a collaborative project between the groups of Jakob Macke (Uni Tübingen), Philipp Berens (Uni Tübingen), Philipp Hennig (Uni Tübingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.
## License
[Affero General Public License v3 (AGPLv3)](https://www.gnu.org/licenses/)
## Citation
If you use `sbi` consider citing the [sbi software paper](https://doi.org/10.21105/joss.02505), in addition to the original research articles describing the specifc sbi-algorithm(s) you are using:
```
@article{tejero-cantero2020sbi,
doi = {10.21105/joss.02505},
url = {https://doi.org/10.21105/joss.02505},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {52},
pages = {2505},
author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
title = {sbi: A toolkit for simulation-based inference},
journal = {Journal of Open Source Software}
}
```
%package -n python3-sbi
Summary: Simulation-based inference.
Provides: python-sbi
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-sbi
[](https://badge.fury.io/py/sbi)
[](https://github.com/mackelab/sbi/blob/master/CONTRIBUTING.md)
[](https://github.com/mackelab/sbi/actions)
[](https://codecov.io/gh/mackelab/sbi)
[](https://github.com/mackelab/sbi/blob/master/LICENSE.txt)
[](https://doi.org/10.21105/joss.02505)
## sbi: simulation-based inference
[Getting Started](https://www.mackelab.org/sbi/tutorial/00_getting_started/) | [Documentation](https://www.mackelab.org/sbi/)
`sbi` is a PyTorch package for simulation-based inference. Simulation-based inference is
the process of finding parameters of a simulator from observations.
`sbi` takes a Bayesian approach and returns a full posterior distribution
over the parameters, conditional on the observations. This posterior can be amortized (i.e.
useful for any observation) or focused (i.e. tailored to a particular observation), with different
computational trade-offs.
`sbi` offers a simple interface for one-line posterior inference.
```python
from sbi.inference import infer
# import your simulator, define your prior over the parameters
parameter_posterior = infer(simulator, prior, method='SNPE', num_simulations=100)
```
See below for the available methods of inference, `SNPE`, `SNRE` and `SNLE`.
## Installation
`sbi` requires Python 3.6 or higher. We recommend to use a [`conda`](https://docs.conda.io/en/latest/miniconda.html) virtual
environment ([Miniconda installation instructions](https://docs.conda.io/en/latest/miniconda.html])). If `conda` is installed on the system, an environment for
installing `sbi` can be created as follows:
```commandline
# Create an environment for sbi (indicate Python 3.6 or higher); activate it
$ conda create -n sbi_env python=3.7 && conda activate sbi_env
```
Independent of whether you are using `conda` or not, `sbi` can be installed using `pip`:
```commandline
pip install sbi
```
To test the installation, drop into a python prompt and run
```python
from sbi.examples.minimal import simple
posterior = simple()
print(posterior)
```
## Inference Algorithms
The following algorithms are currently available:
#### Sequential Neural Posterior Estimation (SNPE)
* [`SNPE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_a.SNPE_A) from Papamakarios G and Murray I [_Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation_](https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html) (NeurIPS 2016).
* [`SNPE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_c.SNPE_C) or `APT` from Greenberg D, Nonnenmacher M, and Macke J [_Automatic
Posterior Transformation for likelihood-free
inference_](https://arxiv.org/abs/1905.07488) (ICML 2019).
#### Sequential Neural Likelihood Estimation (SNLE)
* [`SNLE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.snle_a.SNLE_A) or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential
Neural Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).
#### Sequential Neural Ratio Estimation (SNRE)
* [`SNRE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_a.SNRE_A) or `AALR` from Hermans J, Begy V, and Louppe G. [_Likelihood-free Inference with Amortized Approximate Likelihood Ratios_](https://arxiv.org/abs/1903.04057) (ICML 2020).
* [`SNRE_B`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_b.SNRE_B) or `SRE` from Durkan C, Murray I, and Papamakarios G. [_On Contrastive Learning for Likelihood-free Inference_](https://arxiv.org/abs/2002.03712) (ICML 2020).
* [`BNRE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.bnre.BNRE) from Delaunoy A, Hermans J, Rozet F, Wehenkel A, and Louppe G. [_Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation_](https://arxiv.org/abs/2208.13624) (NeurIPS 2022).
* [`SNRE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_c.SNRE_C) or `NRE-C` from Miller BK, Weniger C, Forré P. [_Contrastive Neural Ratio Estimation_](https://arxiv.org/abs/2210.06170) (NeurIPS 2022).
#### Sequential Neural Variational Inference (SNVI)
* [`SNVI`](https://www.mackelab.org/sbi/reference/#sbi.inference.posteriors.vi_posterior) from Glöckler M, Deistler M, Macke J, [_Variational methods for simulation-based inference_](https://openreview.net/forum?id=kZ0UYdhqkNY) (ICLR 2022).
#### Mixed Neural Likelihood Estimation (MNLE)
* [`MNLE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.mnle.MNLE) from Boelts J, Lueckmann JM, Gao R, Macke J, [_Flexible and efficient simulation-based inference for models of decision-making](https://elifesciences.org/articles/77220) (eLife 2022).
## Feedback and Contributions
We would like to hear how `sbi` is working for your inference problems as well as receive bug reports, pull requests and other feedback (see
[contribute](http://www.mackelab.org/sbi/contribute/)).
## Acknowledgements
`sbi` is the successor (using PyTorch) of the
[`delfi`](https://github.com/mackelab/delfi) package. It was started as a fork of Conor
M. Durkan's `lfi`. `sbi` runs as a community project; development is coordinated at the
[mackelab](https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning/research/research/cluster-research-groups/professorships/machine-learning-in-science/). See also [credits](https://github.com/mackelab/sbi/blob/master/docs/docs/credits.md).
## Support
`sbi` has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). [ADIMEM](https://fit.uni-tuebingen.de/Project/Details?id=9199) is a collaborative project between the groups of Jakob Macke (Uni Tübingen), Philipp Berens (Uni Tübingen), Philipp Hennig (Uni Tübingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.
## License
[Affero General Public License v3 (AGPLv3)](https://www.gnu.org/licenses/)
## Citation
If you use `sbi` consider citing the [sbi software paper](https://doi.org/10.21105/joss.02505), in addition to the original research articles describing the specifc sbi-algorithm(s) you are using:
```
@article{tejero-cantero2020sbi,
doi = {10.21105/joss.02505},
url = {https://doi.org/10.21105/joss.02505},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {52},
pages = {2505},
author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
title = {sbi: A toolkit for simulation-based inference},
journal = {Journal of Open Source Software}
}
```
%package help
Summary: Development documents and examples for sbi
Provides: python3-sbi-doc
%description help
[](https://badge.fury.io/py/sbi)
[](https://github.com/mackelab/sbi/blob/master/CONTRIBUTING.md)
[](https://github.com/mackelab/sbi/actions)
[](https://codecov.io/gh/mackelab/sbi)
[](https://github.com/mackelab/sbi/blob/master/LICENSE.txt)
[](https://doi.org/10.21105/joss.02505)
## sbi: simulation-based inference
[Getting Started](https://www.mackelab.org/sbi/tutorial/00_getting_started/) | [Documentation](https://www.mackelab.org/sbi/)
`sbi` is a PyTorch package for simulation-based inference. Simulation-based inference is
the process of finding parameters of a simulator from observations.
`sbi` takes a Bayesian approach and returns a full posterior distribution
over the parameters, conditional on the observations. This posterior can be amortized (i.e.
useful for any observation) or focused (i.e. tailored to a particular observation), with different
computational trade-offs.
`sbi` offers a simple interface for one-line posterior inference.
```python
from sbi.inference import infer
# import your simulator, define your prior over the parameters
parameter_posterior = infer(simulator, prior, method='SNPE', num_simulations=100)
```
See below for the available methods of inference, `SNPE`, `SNRE` and `SNLE`.
## Installation
`sbi` requires Python 3.6 or higher. We recommend to use a [`conda`](https://docs.conda.io/en/latest/miniconda.html) virtual
environment ([Miniconda installation instructions](https://docs.conda.io/en/latest/miniconda.html])). If `conda` is installed on the system, an environment for
installing `sbi` can be created as follows:
```commandline
# Create an environment for sbi (indicate Python 3.6 or higher); activate it
$ conda create -n sbi_env python=3.7 && conda activate sbi_env
```
Independent of whether you are using `conda` or not, `sbi` can be installed using `pip`:
```commandline
pip install sbi
```
To test the installation, drop into a python prompt and run
```python
from sbi.examples.minimal import simple
posterior = simple()
print(posterior)
```
## Inference Algorithms
The following algorithms are currently available:
#### Sequential Neural Posterior Estimation (SNPE)
* [`SNPE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_a.SNPE_A) from Papamakarios G and Murray I [_Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation_](https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html) (NeurIPS 2016).
* [`SNPE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_c.SNPE_C) or `APT` from Greenberg D, Nonnenmacher M, and Macke J [_Automatic
Posterior Transformation for likelihood-free
inference_](https://arxiv.org/abs/1905.07488) (ICML 2019).
#### Sequential Neural Likelihood Estimation (SNLE)
* [`SNLE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.snle_a.SNLE_A) or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential
Neural Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).
#### Sequential Neural Ratio Estimation (SNRE)
* [`SNRE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_a.SNRE_A) or `AALR` from Hermans J, Begy V, and Louppe G. [_Likelihood-free Inference with Amortized Approximate Likelihood Ratios_](https://arxiv.org/abs/1903.04057) (ICML 2020).
* [`SNRE_B`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_b.SNRE_B) or `SRE` from Durkan C, Murray I, and Papamakarios G. [_On Contrastive Learning for Likelihood-free Inference_](https://arxiv.org/abs/2002.03712) (ICML 2020).
* [`BNRE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.bnre.BNRE) from Delaunoy A, Hermans J, Rozet F, Wehenkel A, and Louppe G. [_Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation_](https://arxiv.org/abs/2208.13624) (NeurIPS 2022).
* [`SNRE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_c.SNRE_C) or `NRE-C` from Miller BK, Weniger C, Forré P. [_Contrastive Neural Ratio Estimation_](https://arxiv.org/abs/2210.06170) (NeurIPS 2022).
#### Sequential Neural Variational Inference (SNVI)
* [`SNVI`](https://www.mackelab.org/sbi/reference/#sbi.inference.posteriors.vi_posterior) from Glöckler M, Deistler M, Macke J, [_Variational methods for simulation-based inference_](https://openreview.net/forum?id=kZ0UYdhqkNY) (ICLR 2022).
#### Mixed Neural Likelihood Estimation (MNLE)
* [`MNLE`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.mnle.MNLE) from Boelts J, Lueckmann JM, Gao R, Macke J, [_Flexible and efficient simulation-based inference for models of decision-making](https://elifesciences.org/articles/77220) (eLife 2022).
## Feedback and Contributions
We would like to hear how `sbi` is working for your inference problems as well as receive bug reports, pull requests and other feedback (see
[contribute](http://www.mackelab.org/sbi/contribute/)).
## Acknowledgements
`sbi` is the successor (using PyTorch) of the
[`delfi`](https://github.com/mackelab/delfi) package. It was started as a fork of Conor
M. Durkan's `lfi`. `sbi` runs as a community project; development is coordinated at the
[mackelab](https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning/research/research/cluster-research-groups/professorships/machine-learning-in-science/). See also [credits](https://github.com/mackelab/sbi/blob/master/docs/docs/credits.md).
## Support
`sbi` has been supported by the German Federal Ministry of Education and Research (BMBF) through the project ADIMEM, FKZ 01IS18052 A-D). [ADIMEM](https://fit.uni-tuebingen.de/Project/Details?id=9199) is a collaborative project between the groups of Jakob Macke (Uni Tübingen), Philipp Berens (Uni Tübingen), Philipp Hennig (Uni Tübingen) and Marcel Oberlaender (caesar Bonn) which aims to develop inference methods for mechanistic models.
## License
[Affero General Public License v3 (AGPLv3)](https://www.gnu.org/licenses/)
## Citation
If you use `sbi` consider citing the [sbi software paper](https://doi.org/10.21105/joss.02505), in addition to the original research articles describing the specifc sbi-algorithm(s) you are using:
```
@article{tejero-cantero2020sbi,
doi = {10.21105/joss.02505},
url = {https://doi.org/10.21105/joss.02505},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {52},
pages = {2505},
author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
title = {sbi: A toolkit for simulation-based inference},
journal = {Journal of Open Source Software}
}
```
%prep
%autosetup -n sbi-0.21.0
%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-sbi -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.21.0-1
- Package Spec generated
|
