automatic import of python-spektralopeneuler20.03

3 files changed, 363 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/spektral-1.2.0.tar.gz
diff --git a/python-spektral.spec b/python-spektral.spec
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+++ b/python-spektral.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-spektral
+Version:	1.2.0
+Release:	1
+Summary:	Graph Neural Networks with Keras and Tensorflow 2.
+License:	MIT
+URL:		https://github.com/danielegrattarola/spektral
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/97/a8/17e75a48deba2a30e5172d8c888238102e8336bfd200f4a03c408f520f97/spektral-1.2.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-joblib
+Requires:	python3-lxml
+Requires:	python3-networkx
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-requests
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-tensorflow
+Requires:	python3-tqdm
+
+%description
+<img src="https://danielegrattarola.github.io/spektral/img/logo_dark.svg" style="max-width: 400px; width: 100%;"/>
+
+# Welcome to Spektral
+Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.
+The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs).
+
+You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. 
+
+Spektral implements some of the most popular layers for graph deep learning, including: 
+
+- [Graph Convolutional Networks (GCN)](https://arxiv.org/abs/1609.02907)
+- [Chebyshev convolutions](https://arxiv.org/abs/1606.09375)
+- [GraphSAGE](https://arxiv.org/abs/1706.02216)
+- [ARMA convolutions](https://arxiv.org/abs/1901.01343)
+- [Edge-Conditioned Convolutions (ECC)](https://arxiv.org/abs/1704.02901)
+- [Graph attention networks (GAT)](https://arxiv.org/abs/1710.10903)
+- [Approximated Personalized Propagation of Neural Predictions (APPNP)](https://arxiv.org/abs/1810.05997)
+- [Graph Isomorphism Networks (GIN)](https://arxiv.org/abs/1810.00826)
+- [Diffusional Convolutions](https://arxiv.org/abs/1707.01926)
+
+and many others (see [convolutional layers](https://graphneural.network/layers/convolution/)).
+
+You can also find [pooling layers](https://graphneural.network/layers/pooling/), including:
+
+- [MinCut pooling](https://arxiv.org/abs/1907.00481)
+- [DiffPool](https://arxiv.org/abs/1806.08804)
+- [Top-K pooling](http://proceedings.mlr.press/v97/gao19a/gao19a.pdf)
+- [Self-Attention Graph (SAG) pooling](https://arxiv.org/abs/1904.08082)
+- Global pooling
+- [Global gated attention pooling](https://arxiv.org/abs/1511.05493)
+- [SortPool](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)
+
+Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects.
+
+See how to [get started with Spektral](https://graphneural.network/getting-started/) and have a look at the [examples](https://danielegrattarola.github.io/spektral/examples/) for some templates.
+
+The source code of the project is available on [Github](https://github.com/danielegrattarola/spektral).  
+Read the documentation [here](https://graphneural.network).  
+
+If you want to cite Spektral in your work, refer to our paper: 
+
+> [Graph Neural Networks in TensorFlow and Keras with Spektral](https://arxiv.org/abs/2006.12138)<br>
+> Daniele Grattarola and Cesare Alippi
+
+## Installation
+Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. 
+Other Linux distros should work as well. 
+
+The simplest way to install Spektral is from PyPi: 
+
+```bash
+pip install spektral
+```
+
+To install Spektral from source, run this in a terminal:
+
+```bash
+git clone https://github.com/danielegrattarola/spektral.git
+cd spektral
+python setup.py install  # Or 'pip install .'
+```
+
+To install Spektral on [Google Colab](https://colab.research.google.com/):
+
+```
+! pip install spektral
+```
+
+## New in Spektral 1.0
+
+The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. 
+
+If you have already used Spektral in your projects, the only major change that you need to be aware of is the new `datasets` API.
+
+This is a summary of the new features and changes: 
+
+- The new `Graph` and `Dataset` containers standardize how Spektral handles data. 
+**This does not impact your models**, but makes it easier to use your data in Spektral.
+- The new `Loader` class hides away all the complexity of creating graph batches. 
+Whether you want to write a custom training loop or use Keras' famous model-dot-fit approach, you only need to worry about the training logic and not the data. 
+- The new `transforms` module implements a wide variety of common operations on graphs, that you can now `apply()` to your datasets. 
+- The new `GeneralConv` and `GeneralGNN` classes let you build models that are, well... general. Using state-of-the-art results from recent literature means that you don't need to worry about which layers or architecture to choose. The defaults will work well everywhere. 
+- New datasets: QM7 and ModelNet10/40, and a new wrapper for OGB datasets. 
+- Major clean-up of the library's structure and dependencies.
+- New examples and tutorials.
+
+
+
+## Contributing
+Spektral is an open-source project available [on Github](https://github.com/danielegrattarola/spektral), and contributions of all types are welcome. 
+Feel free to open a pull request if you have something interesting that you want to add to the framework.
+
+The contribution guidelines are available [here](https://github.com/danielegrattarola/spektral/blob/master/CONTRIBUTING.md) and a list of feature requests is available [here](https://github.com/danielegrattarola/spektral/projects/1).
+
+
+%package -n python3-spektral
+Summary:	Graph Neural Networks with Keras and Tensorflow 2.
+Provides:	python-spektral
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-spektral
+<img src="https://danielegrattarola.github.io/spektral/img/logo_dark.svg" style="max-width: 400px; width: 100%;"/>
+
+# Welcome to Spektral
+Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.
+The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs).
+
+You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. 
+
+Spektral implements some of the most popular layers for graph deep learning, including: 
+
+- [Graph Convolutional Networks (GCN)](https://arxiv.org/abs/1609.02907)
+- [Chebyshev convolutions](https://arxiv.org/abs/1606.09375)
+- [GraphSAGE](https://arxiv.org/abs/1706.02216)
+- [ARMA convolutions](https://arxiv.org/abs/1901.01343)
+- [Edge-Conditioned Convolutions (ECC)](https://arxiv.org/abs/1704.02901)
+- [Graph attention networks (GAT)](https://arxiv.org/abs/1710.10903)
+- [Approximated Personalized Propagation of Neural Predictions (APPNP)](https://arxiv.org/abs/1810.05997)
+- [Graph Isomorphism Networks (GIN)](https://arxiv.org/abs/1810.00826)
+- [Diffusional Convolutions](https://arxiv.org/abs/1707.01926)
+
+and many others (see [convolutional layers](https://graphneural.network/layers/convolution/)).
+
+You can also find [pooling layers](https://graphneural.network/layers/pooling/), including:
+
+- [MinCut pooling](https://arxiv.org/abs/1907.00481)
+- [DiffPool](https://arxiv.org/abs/1806.08804)
+- [Top-K pooling](http://proceedings.mlr.press/v97/gao19a/gao19a.pdf)
+- [Self-Attention Graph (SAG) pooling](https://arxiv.org/abs/1904.08082)
+- Global pooling
+- [Global gated attention pooling](https://arxiv.org/abs/1511.05493)
+- [SortPool](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)
+
+Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects.
+
+See how to [get started with Spektral](https://graphneural.network/getting-started/) and have a look at the [examples](https://danielegrattarola.github.io/spektral/examples/) for some templates.
+
+The source code of the project is available on [Github](https://github.com/danielegrattarola/spektral).  
+Read the documentation [here](https://graphneural.network).  
+
+If you want to cite Spektral in your work, refer to our paper: 
+
+> [Graph Neural Networks in TensorFlow and Keras with Spektral](https://arxiv.org/abs/2006.12138)<br>
+> Daniele Grattarola and Cesare Alippi
+
+## Installation
+Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. 
+Other Linux distros should work as well. 
+
+The simplest way to install Spektral is from PyPi: 
+
+```bash
+pip install spektral
+```
+
+To install Spektral from source, run this in a terminal:
+
+```bash
+git clone https://github.com/danielegrattarola/spektral.git
+cd spektral
+python setup.py install  # Or 'pip install .'
+```
+
+To install Spektral on [Google Colab](https://colab.research.google.com/):
+
+```
+! pip install spektral
+```
+
+## New in Spektral 1.0
+
+The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. 
+
+If you have already used Spektral in your projects, the only major change that you need to be aware of is the new `datasets` API.
+
+This is a summary of the new features and changes: 
+
+- The new `Graph` and `Dataset` containers standardize how Spektral handles data. 
+**This does not impact your models**, but makes it easier to use your data in Spektral.
+- The new `Loader` class hides away all the complexity of creating graph batches. 
+Whether you want to write a custom training loop or use Keras' famous model-dot-fit approach, you only need to worry about the training logic and not the data. 
+- The new `transforms` module implements a wide variety of common operations on graphs, that you can now `apply()` to your datasets. 
+- The new `GeneralConv` and `GeneralGNN` classes let you build models that are, well... general. Using state-of-the-art results from recent literature means that you don't need to worry about which layers or architecture to choose. The defaults will work well everywhere. 
+- New datasets: QM7 and ModelNet10/40, and a new wrapper for OGB datasets. 
+- Major clean-up of the library's structure and dependencies.
+- New examples and tutorials.
+
+
+
+## Contributing
+Spektral is an open-source project available [on Github](https://github.com/danielegrattarola/spektral), and contributions of all types are welcome. 
+Feel free to open a pull request if you have something interesting that you want to add to the framework.
+
+The contribution guidelines are available [here](https://github.com/danielegrattarola/spektral/blob/master/CONTRIBUTING.md) and a list of feature requests is available [here](https://github.com/danielegrattarola/spektral/projects/1).
+
+
+%package help
+Summary:	Development documents and examples for spektral
+Provides:	python3-spektral-doc
+%description help
+<img src="https://danielegrattarola.github.io/spektral/img/logo_dark.svg" style="max-width: 400px; width: 100%;"/>
+
+# Welcome to Spektral
+Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.
+The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs).
+
+You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. 
+
+Spektral implements some of the most popular layers for graph deep learning, including: 
+
+- [Graph Convolutional Networks (GCN)](https://arxiv.org/abs/1609.02907)
+- [Chebyshev convolutions](https://arxiv.org/abs/1606.09375)
+- [GraphSAGE](https://arxiv.org/abs/1706.02216)
+- [ARMA convolutions](https://arxiv.org/abs/1901.01343)
+- [Edge-Conditioned Convolutions (ECC)](https://arxiv.org/abs/1704.02901)
+- [Graph attention networks (GAT)](https://arxiv.org/abs/1710.10903)
+- [Approximated Personalized Propagation of Neural Predictions (APPNP)](https://arxiv.org/abs/1810.05997)
+- [Graph Isomorphism Networks (GIN)](https://arxiv.org/abs/1810.00826)
+- [Diffusional Convolutions](https://arxiv.org/abs/1707.01926)
+
+and many others (see [convolutional layers](https://graphneural.network/layers/convolution/)).
+
+You can also find [pooling layers](https://graphneural.network/layers/pooling/), including:
+
+- [MinCut pooling](https://arxiv.org/abs/1907.00481)
+- [DiffPool](https://arxiv.org/abs/1806.08804)
+- [Top-K pooling](http://proceedings.mlr.press/v97/gao19a/gao19a.pdf)
+- [Self-Attention Graph (SAG) pooling](https://arxiv.org/abs/1904.08082)
+- Global pooling
+- [Global gated attention pooling](https://arxiv.org/abs/1511.05493)
+- [SortPool](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)
+
+Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects.
+
+See how to [get started with Spektral](https://graphneural.network/getting-started/) and have a look at the [examples](https://danielegrattarola.github.io/spektral/examples/) for some templates.
+
+The source code of the project is available on [Github](https://github.com/danielegrattarola/spektral).  
+Read the documentation [here](https://graphneural.network).  
+
+If you want to cite Spektral in your work, refer to our paper: 
+
+> [Graph Neural Networks in TensorFlow and Keras with Spektral](https://arxiv.org/abs/2006.12138)<br>
+> Daniele Grattarola and Cesare Alippi
+
+## Installation
+Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. 
+Other Linux distros should work as well. 
+
+The simplest way to install Spektral is from PyPi: 
+
+```bash
+pip install spektral
+```
+
+To install Spektral from source, run this in a terminal:
+
+```bash
+git clone https://github.com/danielegrattarola/spektral.git
+cd spektral
+python setup.py install  # Or 'pip install .'
+```
+
+To install Spektral on [Google Colab](https://colab.research.google.com/):
+
+```
+! pip install spektral
+```
+
+## New in Spektral 1.0
+
+The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. 
+
+If you have already used Spektral in your projects, the only major change that you need to be aware of is the new `datasets` API.
+
+This is a summary of the new features and changes: 
+
+- The new `Graph` and `Dataset` containers standardize how Spektral handles data. 
+**This does not impact your models**, but makes it easier to use your data in Spektral.
+- The new `Loader` class hides away all the complexity of creating graph batches. 
+Whether you want to write a custom training loop or use Keras' famous model-dot-fit approach, you only need to worry about the training logic and not the data. 
+- The new `transforms` module implements a wide variety of common operations on graphs, that you can now `apply()` to your datasets. 
+- The new `GeneralConv` and `GeneralGNN` classes let you build models that are, well... general. Using state-of-the-art results from recent literature means that you don't need to worry about which layers or architecture to choose. The defaults will work well everywhere. 
+- New datasets: QM7 and ModelNet10/40, and a new wrapper for OGB datasets. 
+- Major clean-up of the library's structure and dependencies.
+- New examples and tutorials.
+
+
+
+## Contributing
+Spektral is an open-source project available [on Github](https://github.com/danielegrattarola/spektral), and contributions of all types are welcome. 
+Feel free to open a pull request if you have something interesting that you want to add to the framework.
+
+The contribution guidelines are available [here](https://github.com/danielegrattarola/spektral/blob/master/CONTRIBUTING.md) and a list of feature requests is available [here](https://github.com/danielegrattarola/spektral/projects/1).
+
+
+%prep
+%autosetup -n spektral-1.2.0
+
+%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-spektral -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..afcdd5c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d28e3fce2a90e4faceb23739837c24a2  spektral-1.2.0.tar.gz