%global _empty_manifest_terminate_build 0
Name: python-asreview
Version: 1.2
Release: 1
Summary: ASReview LAB - A tool for AI-assisted systematic reviews
License: Apache Software License
URL: https://github.com/asreview/asreview
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c5/25/1f06e4790eb60abd42e32817fcbbfe896ff66585925068b10260f12e399c/asreview-1.2.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scikit-learn
Requires: python3-pandas
Requires: python3-rispy
Requires: python3-dill
Requires: python3-xlrd
Requires: python3-setuptools
Requires: python3-flask
Requires: python3-flask-cors
Requires: python3-openpyxl
Requires: python3-gevent
Requires: python3-jsonschema
Requires: python3-filelock
Requires: python3-tqdm
Requires: python3-datahugger
Requires: python3-sentence-transformers
Requires: python3-gensim
Requires: python3-check-manifest
Requires: python3-tensorflow
Requires: python3-check-manifest
Requires: python3-gensim
Requires: python3-sentence-transformers
Requires: python3-tensorflow
Requires: python3-coverage
Requires: python3-pytest
%description
## ASReview: Active learning for Systematic Reviews
[](https://badge.fury.io/py/asreview) [](https://actions-badge.atrox.dev/asreview/asreview/goto?ref=master) [](https://asreview.readthedocs.io/en/latest/?badge=latest) [](https://zenodo.org/badge/latestdoi/164874894)
[](https://github.com/asreview/asreview#installation) [](https://bestpractices.coreinfrastructure.org/projects/4755)
Systematically screening large amounts of textual data is time-consuming and
often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has
allowed the development of AI-aided pipelines that assist in finding relevant
texts for search tasks. A well-established approach to increasing efficiency
is screening prioritization via [Active
Learning](https://asreview.readthedocs.io/en/latest/guides/activelearning.html).
The Active learning for Systematic Reviews (ASReview) project, published in
[*Nature Machine Intelligence*](https://doi.org/10.1038/s42256-020-00287-7)
implements different machine learning algorithms that interactively query the
researcher. ASReview LAB is designed to accelerate the step of screening
textual data with a minimum of records to be read by a human with no or very
few false negatives. ASReview LAB will save time, increase the quality of
output and strengthen the transparency of work when screening large amounts of
textual data to retrieve relevant information. Active Learning will support
decision-making in any discipline or industry.
ASReview software implements three different modes:
- **Oracle** Screen textual data in
interaction with the active learning model. The reviewer is the 'oracle',
making the labeling decisions.
- **Exploration** Explore or
demonstrate ASReview LAB with a completely labeled dataset. This mode is
suitable for teaching purposes.
- **Simulation** Evaluate
the performance of active learning models on fully labeled data. Simulations
can be run in ASReview LAB or via the command line interface with more
advanced options.
## Installation
The ASReview software requires Python 3.7+. Detailed step-by-step instructions
to install Python and ASReview are available for
[Windows](https://asreview.ai/installation-guide-windows/) and
[macOS](https://asreview.ai/installation-guide-macos/) users.
```bash
pip install asreview
```
Upgrade ASReview with the following command:
```bash
pip install --upgrade asreview
```
To install ASReview LAB with Docker, see [Install with Docker](https://asreview.readthedocs.io/en/latest/installation.html).
## How it works
[](https://www.youtube.com/watch?v=k-a2SCq-LtA)
## Getting started
[Getting Started with ASReview
LAB](https://asreview.readthedocs.io/en/latest/about.html).
[](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html "ASReview LAB")
## Citation
The following publication in [Nature Machine
Intelligence](https://doi.org/10.1038/s42256-020-00287-7) can be used to cite
the project.
> van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine
learning framework for efficient and transparent systematic reviews.
Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
For citing the software, please refer to the specific release of
the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the
right can be used to find the citation format of prevalence.
For more scientific publications on the ASReview software, go to
[asreview.ai/papers](https://asreview.ai/papers/).
## Contact
For an overview of the team working on ASReview, see [ASReview Research Team](https://asreview.ai/about).
ASReview LAB is maintained by
[Jonathan de Bruin](https://github.com/J535D165) and [Yongchao Terry Ma](https://github.com/terrymyc).
The best resources to find an answer to your question or ways to get in
contact with the team are:
- Documentation - [asreview.readthedocs.io](https://asreview.readthedocs.io/)
- Newsletter - [asreview.ai/newsletter/subscribe](https://asreview.ai/newsletter/subscribe)
- Quick tour - [ASReview LAB quick tour](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html)
- Issues or feature requests - [ASReview issue tracker](https://github.com/asreview/asreview/issues)
- FAQ - [ASReview Discussions](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
- Donation - [asreview.ai/donate](https://asreview.ai/donate)
- Contact - [asreview@uu.nl](mailto:asreview@uu.nl)
## License
The ASReview software has an Apache 2.0 [LICENSE](LICENSE). The ASReview team
accepts no responsibility or liability for the use of the ASReview tool or any
direct or indirect damages arising out of the application of the tool.
%package -n python3-asreview
Summary: ASReview LAB - A tool for AI-assisted systematic reviews
Provides: python-asreview
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-asreview
## ASReview: Active learning for Systematic Reviews
[](https://badge.fury.io/py/asreview) [](https://actions-badge.atrox.dev/asreview/asreview/goto?ref=master) [](https://asreview.readthedocs.io/en/latest/?badge=latest) [](https://zenodo.org/badge/latestdoi/164874894)
[](https://github.com/asreview/asreview#installation) [](https://bestpractices.coreinfrastructure.org/projects/4755)
Systematically screening large amounts of textual data is time-consuming and
often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has
allowed the development of AI-aided pipelines that assist in finding relevant
texts for search tasks. A well-established approach to increasing efficiency
is screening prioritization via [Active
Learning](https://asreview.readthedocs.io/en/latest/guides/activelearning.html).
The Active learning for Systematic Reviews (ASReview) project, published in
[*Nature Machine Intelligence*](https://doi.org/10.1038/s42256-020-00287-7)
implements different machine learning algorithms that interactively query the
researcher. ASReview LAB is designed to accelerate the step of screening
textual data with a minimum of records to be read by a human with no or very
few false negatives. ASReview LAB will save time, increase the quality of
output and strengthen the transparency of work when screening large amounts of
textual data to retrieve relevant information. Active Learning will support
decision-making in any discipline or industry.
ASReview software implements three different modes:
- **Oracle** Screen textual data in
interaction with the active learning model. The reviewer is the 'oracle',
making the labeling decisions.
- **Exploration** Explore or
demonstrate ASReview LAB with a completely labeled dataset. This mode is
suitable for teaching purposes.
- **Simulation** Evaluate
the performance of active learning models on fully labeled data. Simulations
can be run in ASReview LAB or via the command line interface with more
advanced options.
## Installation
The ASReview software requires Python 3.7+. Detailed step-by-step instructions
to install Python and ASReview are available for
[Windows](https://asreview.ai/installation-guide-windows/) and
[macOS](https://asreview.ai/installation-guide-macos/) users.
```bash
pip install asreview
```
Upgrade ASReview with the following command:
```bash
pip install --upgrade asreview
```
To install ASReview LAB with Docker, see [Install with Docker](https://asreview.readthedocs.io/en/latest/installation.html).
## How it works
[](https://www.youtube.com/watch?v=k-a2SCq-LtA)
## Getting started
[Getting Started with ASReview
LAB](https://asreview.readthedocs.io/en/latest/about.html).
[](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html "ASReview LAB")
## Citation
The following publication in [Nature Machine
Intelligence](https://doi.org/10.1038/s42256-020-00287-7) can be used to cite
the project.
> van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine
learning framework for efficient and transparent systematic reviews.
Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
For citing the software, please refer to the specific release of
the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the
right can be used to find the citation format of prevalence.
For more scientific publications on the ASReview software, go to
[asreview.ai/papers](https://asreview.ai/papers/).
## Contact
For an overview of the team working on ASReview, see [ASReview Research Team](https://asreview.ai/about).
ASReview LAB is maintained by
[Jonathan de Bruin](https://github.com/J535D165) and [Yongchao Terry Ma](https://github.com/terrymyc).
The best resources to find an answer to your question or ways to get in
contact with the team are:
- Documentation - [asreview.readthedocs.io](https://asreview.readthedocs.io/)
- Newsletter - [asreview.ai/newsletter/subscribe](https://asreview.ai/newsletter/subscribe)
- Quick tour - [ASReview LAB quick tour](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html)
- Issues or feature requests - [ASReview issue tracker](https://github.com/asreview/asreview/issues)
- FAQ - [ASReview Discussions](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
- Donation - [asreview.ai/donate](https://asreview.ai/donate)
- Contact - [asreview@uu.nl](mailto:asreview@uu.nl)
## License
The ASReview software has an Apache 2.0 [LICENSE](LICENSE). The ASReview team
accepts no responsibility or liability for the use of the ASReview tool or any
direct or indirect damages arising out of the application of the tool.
%package help
Summary: Development documents and examples for asreview
Provides: python3-asreview-doc
%description help
## ASReview: Active learning for Systematic Reviews
[](https://badge.fury.io/py/asreview) [](https://actions-badge.atrox.dev/asreview/asreview/goto?ref=master) [](https://asreview.readthedocs.io/en/latest/?badge=latest) [](https://zenodo.org/badge/latestdoi/164874894)
[](https://github.com/asreview/asreview#installation) [](https://bestpractices.coreinfrastructure.org/projects/4755)
Systematically screening large amounts of textual data is time-consuming and
often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has
allowed the development of AI-aided pipelines that assist in finding relevant
texts for search tasks. A well-established approach to increasing efficiency
is screening prioritization via [Active
Learning](https://asreview.readthedocs.io/en/latest/guides/activelearning.html).
The Active learning for Systematic Reviews (ASReview) project, published in
[*Nature Machine Intelligence*](https://doi.org/10.1038/s42256-020-00287-7)
implements different machine learning algorithms that interactively query the
researcher. ASReview LAB is designed to accelerate the step of screening
textual data with a minimum of records to be read by a human with no or very
few false negatives. ASReview LAB will save time, increase the quality of
output and strengthen the transparency of work when screening large amounts of
textual data to retrieve relevant information. Active Learning will support
decision-making in any discipline or industry.
ASReview software implements three different modes:
- **Oracle** Screen textual data in
interaction with the active learning model. The reviewer is the 'oracle',
making the labeling decisions.
- **Exploration** Explore or
demonstrate ASReview LAB with a completely labeled dataset. This mode is
suitable for teaching purposes.
- **Simulation** Evaluate
the performance of active learning models on fully labeled data. Simulations
can be run in ASReview LAB or via the command line interface with more
advanced options.
## Installation
The ASReview software requires Python 3.7+. Detailed step-by-step instructions
to install Python and ASReview are available for
[Windows](https://asreview.ai/installation-guide-windows/) and
[macOS](https://asreview.ai/installation-guide-macos/) users.
```bash
pip install asreview
```
Upgrade ASReview with the following command:
```bash
pip install --upgrade asreview
```
To install ASReview LAB with Docker, see [Install with Docker](https://asreview.readthedocs.io/en/latest/installation.html).
## How it works
[](https://www.youtube.com/watch?v=k-a2SCq-LtA)
## Getting started
[Getting Started with ASReview
LAB](https://asreview.readthedocs.io/en/latest/about.html).
[](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html "ASReview LAB")
## Citation
The following publication in [Nature Machine
Intelligence](https://doi.org/10.1038/s42256-020-00287-7) can be used to cite
the project.
> van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine
learning framework for efficient and transparent systematic reviews.
Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
For citing the software, please refer to the specific release of
the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the
right can be used to find the citation format of prevalence.
For more scientific publications on the ASReview software, go to
[asreview.ai/papers](https://asreview.ai/papers/).
## Contact
For an overview of the team working on ASReview, see [ASReview Research Team](https://asreview.ai/about).
ASReview LAB is maintained by
[Jonathan de Bruin](https://github.com/J535D165) and [Yongchao Terry Ma](https://github.com/terrymyc).
The best resources to find an answer to your question or ways to get in
contact with the team are:
- Documentation - [asreview.readthedocs.io](https://asreview.readthedocs.io/)
- Newsletter - [asreview.ai/newsletter/subscribe](https://asreview.ai/newsletter/subscribe)
- Quick tour - [ASReview LAB quick tour](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html)
- Issues or feature requests - [ASReview issue tracker](https://github.com/asreview/asreview/issues)
- FAQ - [ASReview Discussions](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
- Donation - [asreview.ai/donate](https://asreview.ai/donate)
- Contact - [asreview@uu.nl](mailto:asreview@uu.nl)
## License
The ASReview software has an Apache 2.0 [LICENSE](LICENSE). The ASReview team
accepts no responsibility or liability for the use of the ASReview tool or any
direct or indirect damages arising out of the application of the tool.
%prep
%autosetup -n asreview-1.2
%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-asreview -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.2-1
- Package Spec generated