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| author | CoprDistGit <infra@openeuler.org> | 2023-05-18 05:25:00 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-18 05:25:00 +0000 |
| commit | 13bc990a9bc1b13144f8da9163fb3933dcc98b4c (patch) | |
| tree | 2e405fb1f803787d6eb1c18f6e02c12d17a74e69 | |
| parent | ad74597555b19ac6927ce1eacf6ab9d6a779d2c0 (diff) | |
automatic import of python-regain
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-regain.spec | 387 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 389 insertions, 0 deletions
@@ -0,0 +1 @@ +/regain-0.3.8.tar.gz diff --git a/python-regain.spec b/python-regain.spec new file mode 100644 index 0000000..5cbdee3 --- /dev/null +++ b/python-regain.spec @@ -0,0 +1,387 @@ +%global _empty_manifest_terminate_build 0 +Name: python-regain +Version: 0.3.8 +Release: 1 +Summary: REGAIN (Regularised Graph Inference) +License: FreeBSD +URL: https://github.com/fdtomasi/regain +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/56/ef/08e2b6c90ac36cabca2b6f1d5a13e070f1af8565ea98054edce33c241fc3/regain-0.3.8.tar.gz +BuildArch: noarch + + +%description +[](https://travis-ci.org/fdtomasi/regain) [](http://codecov.io/github/fdtomasi/regain?branch=master) [](http://opensource.org/licenses/BSD-3-Clause) [](https://pypi.python.org/pypi/regain) [](https://anaconda.org/fdtomasi/regain) + +# regain +Regularised graph inference across multiple time stamps, considering the influence of latent variables. +It inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package. + +## Getting started +### Dependencies +`REGAIN` requires: +- Python (>= 3.6) +- NumPy (>= 1.8.2) +- scikit-learn (>= 0.17) + +You can install (required) dependencies by running: +```bash +pip install -r requirements.txt +``` + +To use the parameter selection via gaussian process optimisation, [skopt](https://scikit-optimize.github.io/) is required. + +### Installation +The simplest way to install regain is using pip +```bash +pip install regain +``` +or `conda` + +```bash +conda install -c fdtomasi regain +``` + +If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH. +```bash +git clone https://github.com/fdtomasi/regain.git +cd regain +python setup.py develop +``` + +## Quickstart +A simple example for how to use LTGL. +```python +import numpy as np +from regain.covariance import LatentTimeGraphicalLasso +from regain.datasets import make_dataset +from regain.utils import error_norm_time + +np.random.seed(42) +data = make_dataset(n_dim_lat=1, n_dim_obs=3) +X = data.X +y = data.y +theta = data.thetas + +mdl = LatentTimeGraphicalLasso(max_iter=50).fit(X, y) +print("Error: %.2f" % error_norm_time(theta, mdl.precision_)) +``` +**IMPORTANT** +We moved the API to be more consistent with `scikit-learn`. +Now the input of `LatentTimeGraphicalLasso` is a two-dimensional matrix `X` with shape `(n_samples, n_dimensions)`, where the belonging of samples to a different index (for example, a different time point) is indicated in `y`. + + +## Citation + +`REGAIN` appeared in the following two publications. +For the `LatentTimeGraphicalLasso` please use + +```latex +@inproceedings{Tomasi:2018:LVT:3219819.3220121, + author = {Tomasi, Federico and Tozzo, Veronica and Salzo, Saverio and Verri, Alessandro}, + title = {Latent Variable Time-varying Network Inference}, + booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \&\#38; Data Mining}, + series = {KDD '18}, + year = {2018}, + isbn = {978-1-4503-5552-0}, + location = {London, United Kingdom}, + pages = {2338--2346}, + numpages = {9}, + url = {http://doi.acm.org/10.1145/3219819.3220121}, + doi = {10.1145/3219819.3220121}, + acmid = {3220121}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {convex optimization, graphical models, latent variables, network inference, time-series}, +} +``` + +and for the `TimeGraphicalLassoForwardBackward` plase use + +```latex +@InProceedings{pmlr-v72-tomasi18a, + title = {Forward-Backward Splitting for Time-Varying Graphical Models}, + author = {Tomasi, Federico and Tozzo, Veronica and Verri, Alessandro and Salzo, Saverio}, + booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, + pages = {475--486}, + year = {2018}, + editor = {Kratochv\'{i}l, V\'{a}clav and Studen\'{y}, Milan}, + volume = {72}, + series = {Proceedings of Machine Learning Research}, + address = {Prague, Czech Republic}, + month = {11--14 Sep}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v72/tomasi18a/tomasi18a.pdf}, + url = {http://proceedings.mlr.press/v72/tomasi18a.html}, + abstract = {Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. However, the optimisation of such complex models suffer from computational issues both in terms of convergence rates and memory requirements. Here, we present a forward-backward splitting (FBS) procedure for Gaussian graphical modelling of multivariate time-series which relies on recent theoretical studies ensuring convergence under mild assumptions. Our experiments show that a FBS-based implementation achieves, with very fast convergence rates, optimal results with respect to ground truth and standard methods for dynamical network inference. Optimisation algorithms which are usually exploited for network inference suffer from drawbacks when considering large sets of unknowns. Particularly for increasing data sets and model complexity, we argue for the use of fast and theoretically sound optimisation algorithms to be significant to the graphical modelling community.} +} +``` + + +%package -n python3-regain +Summary: REGAIN (Regularised Graph Inference) +Provides: python-regain +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-regain +[](https://travis-ci.org/fdtomasi/regain) [](http://codecov.io/github/fdtomasi/regain?branch=master) [](http://opensource.org/licenses/BSD-3-Clause) [](https://pypi.python.org/pypi/regain) [](https://anaconda.org/fdtomasi/regain) + +# regain +Regularised graph inference across multiple time stamps, considering the influence of latent variables. +It inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package. + +## Getting started +### Dependencies +`REGAIN` requires: +- Python (>= 3.6) +- NumPy (>= 1.8.2) +- scikit-learn (>= 0.17) + +You can install (required) dependencies by running: +```bash +pip install -r requirements.txt +``` + +To use the parameter selection via gaussian process optimisation, [skopt](https://scikit-optimize.github.io/) is required. + +### Installation +The simplest way to install regain is using pip +```bash +pip install regain +``` +or `conda` + +```bash +conda install -c fdtomasi regain +``` + +If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH. +```bash +git clone https://github.com/fdtomasi/regain.git +cd regain +python setup.py develop +``` + +## Quickstart +A simple example for how to use LTGL. +```python +import numpy as np +from regain.covariance import LatentTimeGraphicalLasso +from regain.datasets import make_dataset +from regain.utils import error_norm_time + +np.random.seed(42) +data = make_dataset(n_dim_lat=1, n_dim_obs=3) +X = data.X +y = data.y +theta = data.thetas + +mdl = LatentTimeGraphicalLasso(max_iter=50).fit(X, y) +print("Error: %.2f" % error_norm_time(theta, mdl.precision_)) +``` +**IMPORTANT** +We moved the API to be more consistent with `scikit-learn`. +Now the input of `LatentTimeGraphicalLasso` is a two-dimensional matrix `X` with shape `(n_samples, n_dimensions)`, where the belonging of samples to a different index (for example, a different time point) is indicated in `y`. + + +## Citation + +`REGAIN` appeared in the following two publications. +For the `LatentTimeGraphicalLasso` please use + +```latex +@inproceedings{Tomasi:2018:LVT:3219819.3220121, + author = {Tomasi, Federico and Tozzo, Veronica and Salzo, Saverio and Verri, Alessandro}, + title = {Latent Variable Time-varying Network Inference}, + booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \&\#38; Data Mining}, + series = {KDD '18}, + year = {2018}, + isbn = {978-1-4503-5552-0}, + location = {London, United Kingdom}, + pages = {2338--2346}, + numpages = {9}, + url = {http://doi.acm.org/10.1145/3219819.3220121}, + doi = {10.1145/3219819.3220121}, + acmid = {3220121}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {convex optimization, graphical models, latent variables, network inference, time-series}, +} +``` + +and for the `TimeGraphicalLassoForwardBackward` plase use + +```latex +@InProceedings{pmlr-v72-tomasi18a, + title = {Forward-Backward Splitting for Time-Varying Graphical Models}, + author = {Tomasi, Federico and Tozzo, Veronica and Verri, Alessandro and Salzo, Saverio}, + booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, + pages = {475--486}, + year = {2018}, + editor = {Kratochv\'{i}l, V\'{a}clav and Studen\'{y}, Milan}, + volume = {72}, + series = {Proceedings of Machine Learning Research}, + address = {Prague, Czech Republic}, + month = {11--14 Sep}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v72/tomasi18a/tomasi18a.pdf}, + url = {http://proceedings.mlr.press/v72/tomasi18a.html}, + abstract = {Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. However, the optimisation of such complex models suffer from computational issues both in terms of convergence rates and memory requirements. Here, we present a forward-backward splitting (FBS) procedure for Gaussian graphical modelling of multivariate time-series which relies on recent theoretical studies ensuring convergence under mild assumptions. Our experiments show that a FBS-based implementation achieves, with very fast convergence rates, optimal results with respect to ground truth and standard methods for dynamical network inference. Optimisation algorithms which are usually exploited for network inference suffer from drawbacks when considering large sets of unknowns. Particularly for increasing data sets and model complexity, we argue for the use of fast and theoretically sound optimisation algorithms to be significant to the graphical modelling community.} +} +``` + + +%package help +Summary: Development documents and examples for regain +Provides: python3-regain-doc +%description help +[](https://travis-ci.org/fdtomasi/regain) [](http://codecov.io/github/fdtomasi/regain?branch=master) [](http://opensource.org/licenses/BSD-3-Clause) [](https://pypi.python.org/pypi/regain) [](https://anaconda.org/fdtomasi/regain) + +# regain +Regularised graph inference across multiple time stamps, considering the influence of latent variables. +It inherits functionalities from the [scikit-learn](https://github.com/scikit-learn/scikit-learn) package. + +## Getting started +### Dependencies +`REGAIN` requires: +- Python (>= 3.6) +- NumPy (>= 1.8.2) +- scikit-learn (>= 0.17) + +You can install (required) dependencies by running: +```bash +pip install -r requirements.txt +``` + +To use the parameter selection via gaussian process optimisation, [skopt](https://scikit-optimize.github.io/) is required. + +### Installation +The simplest way to install regain is using pip +```bash +pip install regain +``` +or `conda` + +```bash +conda install -c fdtomasi regain +``` + +If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on. Clone the repository in GitHub and add it to your $PYTHONPATH. +```bash +git clone https://github.com/fdtomasi/regain.git +cd regain +python setup.py develop +``` + +## Quickstart +A simple example for how to use LTGL. +```python +import numpy as np +from regain.covariance import LatentTimeGraphicalLasso +from regain.datasets import make_dataset +from regain.utils import error_norm_time + +np.random.seed(42) +data = make_dataset(n_dim_lat=1, n_dim_obs=3) +X = data.X +y = data.y +theta = data.thetas + +mdl = LatentTimeGraphicalLasso(max_iter=50).fit(X, y) +print("Error: %.2f" % error_norm_time(theta, mdl.precision_)) +``` +**IMPORTANT** +We moved the API to be more consistent with `scikit-learn`. +Now the input of `LatentTimeGraphicalLasso` is a two-dimensional matrix `X` with shape `(n_samples, n_dimensions)`, where the belonging of samples to a different index (for example, a different time point) is indicated in `y`. + + +## Citation + +`REGAIN` appeared in the following two publications. +For the `LatentTimeGraphicalLasso` please use + +```latex +@inproceedings{Tomasi:2018:LVT:3219819.3220121, + author = {Tomasi, Federico and Tozzo, Veronica and Salzo, Saverio and Verri, Alessandro}, + title = {Latent Variable Time-varying Network Inference}, + booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \&\#38; Data Mining}, + series = {KDD '18}, + year = {2018}, + isbn = {978-1-4503-5552-0}, + location = {London, United Kingdom}, + pages = {2338--2346}, + numpages = {9}, + url = {http://doi.acm.org/10.1145/3219819.3220121}, + doi = {10.1145/3219819.3220121}, + acmid = {3220121}, + publisher = {ACM}, + address = {New York, NY, USA}, + keywords = {convex optimization, graphical models, latent variables, network inference, time-series}, +} +``` + +and for the `TimeGraphicalLassoForwardBackward` plase use + +```latex +@InProceedings{pmlr-v72-tomasi18a, + title = {Forward-Backward Splitting for Time-Varying Graphical Models}, + author = {Tomasi, Federico and Tozzo, Veronica and Verri, Alessandro and Salzo, Saverio}, + booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, + pages = {475--486}, + year = {2018}, + editor = {Kratochv\'{i}l, V\'{a}clav and Studen\'{y}, Milan}, + volume = {72}, + series = {Proceedings of Machine Learning Research}, + address = {Prague, Czech Republic}, + month = {11--14 Sep}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v72/tomasi18a/tomasi18a.pdf}, + url = {http://proceedings.mlr.press/v72/tomasi18a.html}, + abstract = {Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. However, the optimisation of such complex models suffer from computational issues both in terms of convergence rates and memory requirements. Here, we present a forward-backward splitting (FBS) procedure for Gaussian graphical modelling of multivariate time-series which relies on recent theoretical studies ensuring convergence under mild assumptions. Our experiments show that a FBS-based implementation achieves, with very fast convergence rates, optimal results with respect to ground truth and standard methods for dynamical network inference. Optimisation algorithms which are usually exploited for network inference suffer from drawbacks when considering large sets of unknowns. Particularly for increasing data sets and model complexity, we argue for the use of fast and theoretically sound optimisation algorithms to be significant to the graphical modelling community.} +} +``` + + +%prep +%autosetup -n regain-0.3.8 + +%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-regain -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.8-1 +- Package Spec generated @@ -0,0 +1 @@ +3ba86f5f2a8ad82b0b0bf562647f6c2b regain-0.3.8.tar.gz |