automatic import of python-chia

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+/chia-2.5.0.tar.gz
diff --git a/python-chia.spec b/python-chia.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-chia
+Version:	2.5.0
+Release:	1
+Summary:	Concept Hierarchies for Incremental and Active Learning
+License:	BSD License
+URL:		https://github.com/cabrust/chia
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6b/3c/5d34b6146b2a6e437795093e1c5b16b3fa86cf661ae4fba5d9da5ad6c1f3/chia-2.5.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-configuration
+Requires:	python3-nltk
+Requires:	python3-imageio
+Requires:	python3-pillow
+Requires:	python3-gputil
+Requires:	python3-networkx
+Requires:	python3-numpy
+Requires:	python3-tensorflow-addons
+Requires:	python3-tensorflow
+
+%description
+# CHIA: Concept Hierarchies for Incremental and Active Learning
+![PyPI](https://img.shields.io/pypi/v/chia)
+![PyPI - License](https://img.shields.io/pypi/l/chia)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia)
+![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia)
+![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg)
+
+CHIA implements methods centered around hierarchical classification in a lifelong learning environment.
+It forms the basis for some of the experiments and tools developed at [Computer Vision Group Jena](http://www.inf-cv.uni-jena.de/).
+Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/)
+
+**Methods**\
+CHIA implements:
+ * **One-Hot Softmax Classifier** as a baseline.
+ * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR)
+ * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR).
+ * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP)
+ * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV).
+
+**Datasets**\
+CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list.
+
+
+## Installation and Getting Started
+CHIA is available on PyPI. To install, simply run:
+```bash
+pip install chia
+```
+or clone this repository, and run:
+```bash
+pip install -e .
+```
+
+To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command:
+```bash
+python examples/experiment.py examples/configuration.json
+```
+After a few minutes, the last lines of output should look like this:
+```text
+[SHUTDOWN] [Experiment] Successful: True
+```
+
+## Documentation
+The following articles explain more about CHIA:
+ * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes.
+ * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured.
+ * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data.
+
+## Citation
+If you use CHIA for your research, kindly cite:
+> Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham.
+
+You can refer to the following BibTeX:
+```bibtex
+@inproceedings{Brust2019IDK,
+author = {Clemens-Alexander Brust and Joachim Denzler},
+booktitle = {Asian Conference on Pattern Recognition (ACPR)},
+title = {Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers},
+year = {2019},
+doi = {10.1007/978-3-030-41404-7_1}
+}
+```
+
+
+
+
+%package -n python3-chia
+Summary:	Concept Hierarchies for Incremental and Active Learning
+Provides:	python-chia
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-chia
+# CHIA: Concept Hierarchies for Incremental and Active Learning
+![PyPI](https://img.shields.io/pypi/v/chia)
+![PyPI - License](https://img.shields.io/pypi/l/chia)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia)
+![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia)
+![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg)
+
+CHIA implements methods centered around hierarchical classification in a lifelong learning environment.
+It forms the basis for some of the experiments and tools developed at [Computer Vision Group Jena](http://www.inf-cv.uni-jena.de/).
+Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/)
+
+**Methods**\
+CHIA implements:
+ * **One-Hot Softmax Classifier** as a baseline.
+ * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR)
+ * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR).
+ * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP)
+ * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV).
+
+**Datasets**\
+CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list.
+
+
+## Installation and Getting Started
+CHIA is available on PyPI. To install, simply run:
+```bash
+pip install chia
+```
+or clone this repository, and run:
+```bash
+pip install -e .
+```
+
+To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command:
+```bash
+python examples/experiment.py examples/configuration.json
+```
+After a few minutes, the last lines of output should look like this:
+```text
+[SHUTDOWN] [Experiment] Successful: True
+```
+
+## Documentation
+The following articles explain more about CHIA:
+ * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes.
+ * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured.
+ * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data.
+
+## Citation
+If you use CHIA for your research, kindly cite:
+> Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham.
+
+You can refer to the following BibTeX:
+```bibtex
+@inproceedings{Brust2019IDK,
+author = {Clemens-Alexander Brust and Joachim Denzler},
+booktitle = {Asian Conference on Pattern Recognition (ACPR)},
+title = {Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers},
+year = {2019},
+doi = {10.1007/978-3-030-41404-7_1}
+}
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for chia
+Provides:	python3-chia-doc
+%description help
+# CHIA: Concept Hierarchies for Incremental and Active Learning
+![PyPI](https://img.shields.io/pypi/v/chia)
+![PyPI - License](https://img.shields.io/pypi/l/chia)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chia)
+![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability/cabrust/chia)
+![codecov](https://codecov.io/gh/cabrust/chia/branch/main/graph/badge.svg)
+
+CHIA implements methods centered around hierarchical classification in a lifelong learning environment.
+It forms the basis for some of the experiments and tools developed at [Computer Vision Group Jena](http://www.inf-cv.uni-jena.de/).
+Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/)
+
+**Methods**\
+CHIA implements:
+ * **One-Hot Softmax Classifier** as a baseline.
+ * **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR)
+ * **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR).
+ * **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP)
+ * **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV).
+
+**Datasets**\
+CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list.
+
+
+## Installation and Getting Started
+CHIA is available on PyPI. To install, simply run:
+```bash
+pip install chia
+```
+or clone this repository, and run:
+```bash
+pip install -e .
+```
+
+To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command:
+```bash
+python examples/experiment.py examples/configuration.json
+```
+After a few minutes, the last lines of output should look like this:
+```text
+[SHUTDOWN] [Experiment] Successful: True
+```
+
+## Documentation
+The following articles explain more about CHIA:
+ * [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes.
+ * [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured.
+ * [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data.
+
+## Citation
+If you use CHIA for your research, kindly cite:
+> Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham.
+
+You can refer to the following BibTeX:
+```bibtex
+@inproceedings{Brust2019IDK,
+author = {Clemens-Alexander Brust and Joachim Denzler},
+booktitle = {Asian Conference on Pattern Recognition (ACPR)},
+title = {Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers},
+year = {2019},
+doi = {10.1007/978-3-030-41404-7_1}
+}
+```
+
+
+
+
+%prep
+%autosetup -n chia-2.5.0
+
+%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-chia -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..ab552bd
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ea01715a6177d51bc666f3e18739fb63  chia-2.5.0.tar.gz