automatic import of python-nbeats-pytorchopeneuler20.03

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+/nbeats-pytorch-1.8.0.tar.gz
diff --git a/python-nbeats-pytorch.spec b/python-nbeats-pytorch.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-nbeats-pytorch
+Version:	1.8.0
+Release:	1
+Summary:	N-Beats
+License:	MIT
+URL:		https://pypi.org/project/nbeats-pytorch/
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/9c/78/f6464cfd436a07bc83fbdf15dd79f3122f17a459bb45aba2cfda0fb20334/nbeats-pytorch-1.8.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-keract
+Requires:	python3-pandas
+Requires:	python3-matplotlib
+Requires:	python3-protobuf
+Requires:	python3-torch
+
+%description
+## NBEATS<br/>Neural basis expansion analysis for interpretable time series forecasting
+
+Tensorflow/Pytorch implementation | [Paper](https://arxiv.org/abs/1905.10437)
+| [Results](https://github.com/fecet/NBeats-M4)
+
+![NBeats CI](https://github.com/philipperemy/n-beats/workflows/N%20Beats%20CI/badge.svg?branch=master)
+
+<p align="center">
+  <img src="assets/interpretable.png"><br/>
+  <i>Outputs of the generic and interpretable layers</i>
+</p>
+
+### Installation
+
+It is possible to install the two backends at the same time.
+
+#### From PyPI
+
+Install the Tensorflow/Keras backend: `pip install nbeats-keras`
+
+[![NBEATS - Keras - Downloads](https://pepy.tech/badge/nbeats-keras)](https://pepy.tech/project/nbeats-keras)
+
+Install the Pytorch backend: `pip install nbeats-pytorch`
+
+[![NBEATS - PyTorch - Downloads](https://pepy.tech/badge/nbeats-pytorch)](https://pepy.tech/project/nbeats-pytorch)
+
+#### From the sources
+
+Installation is based on a MakeFile.
+
+Command to install N-Beats with Keras: `make install-keras`
+
+Command to install N-Beats with Pytorch: `make install-pytorch`
+
+#### Run on the GPU
+
+It is possible that this is no longer necessary on the recent versions of Tensorflow.  To force the utilization of the GPU (with the Keras backend),
+run: `pip uninstall -y tensorflow && pip install tensorflow-gpu`. 
+
+### Example
+
+Here is an example to get familiar with both backends. Note that only the Keras backend supports `input_dim>1` at the moment.
+
+```python
+import warnings
+
+import numpy as np
+
+from nbeats_keras.model import NBeatsNet as NBeatsKeras
+from nbeats_pytorch.model import NBeatsNet as NBeatsPytorch
+
+warnings.filterwarnings(action='ignore', message='Setting attributes')
+
+
+def main():
+    # https://keras.io/layers/recurrent/
+    # At the moment only Keras supports input_dim > 1. In the original paper, input_dim=1.
+    num_samples, time_steps, input_dim, output_dim = 50_000, 10, 1, 1
+
+    # This example is for both Keras and Pytorch. In practice, choose the one you prefer.
+    for BackendType in [NBeatsKeras, NBeatsPytorch]:
+        # NOTE: If you choose the Keras backend with input_dim>1, you have 
+        # to set the value here too (in the constructor).
+        backend = BackendType(
+            backcast_length=time_steps, forecast_length=output_dim,
+            stack_types=(NBeatsKeras.GENERIC_BLOCK, NBeatsKeras.GENERIC_BLOCK),
+            nb_blocks_per_stack=2, thetas_dim=(4, 4), share_weights_in_stack=True,
+            hidden_layer_units=64
+        )
+
+        # Definition of the objective function and the optimizer.
+        backend.compile(loss='mae', optimizer='adam')
+
+        # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
+        # where f = np.mean.
+        x = np.random.uniform(size=(num_samples, time_steps, input_dim))
+        y = np.mean(x, axis=1, keepdims=True)
+
+        # Split data into training and testing datasets.
+        c = num_samples // 10
+        x_train, y_train, x_test, y_test = x[c:], y[c:], x[:c], y[:c]
+        test_size = len(x_test)
+
+        # Train the model.
+        print('Training...')
+        backend.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)
+
+        # Save the model for later.
+        backend.save('n_beats_model.h5')
+
+        # Predict on the testing set (forecast).
+        predictions_forecast = backend.predict(x_test)
+        np.testing.assert_equal(predictions_forecast.shape, (test_size, backend.forecast_length, output_dim))
+
+        # Predict on the testing set (backcast).
+        predictions_backcast = backend.predict(x_test, return_backcast=True)
+        np.testing.assert_equal(predictions_backcast.shape, (test_size, backend.backcast_length, output_dim))
+
+        # Load the model.
+        model_2 = BackendType.load('n_beats_model.h5')
+
+        np.testing.assert_almost_equal(predictions_forecast, model_2.predict(x_test))
+
+
+if __name__ == '__main__':
+    main()
+```
+
+Browse the [examples](examples) for more. It includes Jupyter notebooks.
+
+Jupyter notebook: [NBeats.ipynb](examples/NBeats.ipynb): `make run-jupyter`.
+
+<p align="center">
+  <img src="assets/nbeats.png" width="500"><br/>
+</p>
+
+### Citation
+
+```
+@misc{NBeatsPRemy,
+  author = {Philippe Remy},
+  title = {N-BEATS: Neural basis expansion analysis for interpretable time series forecasting},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/philipperemy/n-beats}},
+}
+```
+
+### Contributors
+
+Thank you!
+
+<a href="https://github.com/philipperemy/n-beats/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=philipperemy/n-beats" />
+</a>
+
+
+%package -n python3-nbeats-pytorch
+Summary:	N-Beats
+Provides:	python-nbeats-pytorch
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-nbeats-pytorch
+## NBEATS<br/>Neural basis expansion analysis for interpretable time series forecasting
+
+Tensorflow/Pytorch implementation | [Paper](https://arxiv.org/abs/1905.10437)
+| [Results](https://github.com/fecet/NBeats-M4)
+
+![NBeats CI](https://github.com/philipperemy/n-beats/workflows/N%20Beats%20CI/badge.svg?branch=master)
+
+<p align="center">
+  <img src="assets/interpretable.png"><br/>
+  <i>Outputs of the generic and interpretable layers</i>
+</p>
+
+### Installation
+
+It is possible to install the two backends at the same time.
+
+#### From PyPI
+
+Install the Tensorflow/Keras backend: `pip install nbeats-keras`
+
+[![NBEATS - Keras - Downloads](https://pepy.tech/badge/nbeats-keras)](https://pepy.tech/project/nbeats-keras)
+
+Install the Pytorch backend: `pip install nbeats-pytorch`
+
+[![NBEATS - PyTorch - Downloads](https://pepy.tech/badge/nbeats-pytorch)](https://pepy.tech/project/nbeats-pytorch)
+
+#### From the sources
+
+Installation is based on a MakeFile.
+
+Command to install N-Beats with Keras: `make install-keras`
+
+Command to install N-Beats with Pytorch: `make install-pytorch`
+
+#### Run on the GPU
+
+It is possible that this is no longer necessary on the recent versions of Tensorflow.  To force the utilization of the GPU (with the Keras backend),
+run: `pip uninstall -y tensorflow && pip install tensorflow-gpu`. 
+
+### Example
+
+Here is an example to get familiar with both backends. Note that only the Keras backend supports `input_dim>1` at the moment.
+
+```python
+import warnings
+
+import numpy as np
+
+from nbeats_keras.model import NBeatsNet as NBeatsKeras
+from nbeats_pytorch.model import NBeatsNet as NBeatsPytorch
+
+warnings.filterwarnings(action='ignore', message='Setting attributes')
+
+
+def main():
+    # https://keras.io/layers/recurrent/
+    # At the moment only Keras supports input_dim > 1. In the original paper, input_dim=1.
+    num_samples, time_steps, input_dim, output_dim = 50_000, 10, 1, 1
+
+    # This example is for both Keras and Pytorch. In practice, choose the one you prefer.
+    for BackendType in [NBeatsKeras, NBeatsPytorch]:
+        # NOTE: If you choose the Keras backend with input_dim>1, you have 
+        # to set the value here too (in the constructor).
+        backend = BackendType(
+            backcast_length=time_steps, forecast_length=output_dim,
+            stack_types=(NBeatsKeras.GENERIC_BLOCK, NBeatsKeras.GENERIC_BLOCK),
+            nb_blocks_per_stack=2, thetas_dim=(4, 4), share_weights_in_stack=True,
+            hidden_layer_units=64
+        )
+
+        # Definition of the objective function and the optimizer.
+        backend.compile(loss='mae', optimizer='adam')
+
+        # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
+        # where f = np.mean.
+        x = np.random.uniform(size=(num_samples, time_steps, input_dim))
+        y = np.mean(x, axis=1, keepdims=True)
+
+        # Split data into training and testing datasets.
+        c = num_samples // 10
+        x_train, y_train, x_test, y_test = x[c:], y[c:], x[:c], y[:c]
+        test_size = len(x_test)
+
+        # Train the model.
+        print('Training...')
+        backend.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)
+
+        # Save the model for later.
+        backend.save('n_beats_model.h5')
+
+        # Predict on the testing set (forecast).
+        predictions_forecast = backend.predict(x_test)
+        np.testing.assert_equal(predictions_forecast.shape, (test_size, backend.forecast_length, output_dim))
+
+        # Predict on the testing set (backcast).
+        predictions_backcast = backend.predict(x_test, return_backcast=True)
+        np.testing.assert_equal(predictions_backcast.shape, (test_size, backend.backcast_length, output_dim))
+
+        # Load the model.
+        model_2 = BackendType.load('n_beats_model.h5')
+
+        np.testing.assert_almost_equal(predictions_forecast, model_2.predict(x_test))
+
+
+if __name__ == '__main__':
+    main()
+```
+
+Browse the [examples](examples) for more. It includes Jupyter notebooks.
+
+Jupyter notebook: [NBeats.ipynb](examples/NBeats.ipynb): `make run-jupyter`.
+
+<p align="center">
+  <img src="assets/nbeats.png" width="500"><br/>
+</p>
+
+### Citation
+
+```
+@misc{NBeatsPRemy,
+  author = {Philippe Remy},
+  title = {N-BEATS: Neural basis expansion analysis for interpretable time series forecasting},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/philipperemy/n-beats}},
+}
+```
+
+### Contributors
+
+Thank you!
+
+<a href="https://github.com/philipperemy/n-beats/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=philipperemy/n-beats" />
+</a>
+
+
+%package help
+Summary:	Development documents and examples for nbeats-pytorch
+Provides:	python3-nbeats-pytorch-doc
+%description help
+## NBEATS<br/>Neural basis expansion analysis for interpretable time series forecasting
+
+Tensorflow/Pytorch implementation | [Paper](https://arxiv.org/abs/1905.10437)
+| [Results](https://github.com/fecet/NBeats-M4)
+
+![NBeats CI](https://github.com/philipperemy/n-beats/workflows/N%20Beats%20CI/badge.svg?branch=master)
+
+<p align="center">
+  <img src="assets/interpretable.png"><br/>
+  <i>Outputs of the generic and interpretable layers</i>
+</p>
+
+### Installation
+
+It is possible to install the two backends at the same time.
+
+#### From PyPI
+
+Install the Tensorflow/Keras backend: `pip install nbeats-keras`
+
+[![NBEATS - Keras - Downloads](https://pepy.tech/badge/nbeats-keras)](https://pepy.tech/project/nbeats-keras)
+
+Install the Pytorch backend: `pip install nbeats-pytorch`
+
+[![NBEATS - PyTorch - Downloads](https://pepy.tech/badge/nbeats-pytorch)](https://pepy.tech/project/nbeats-pytorch)
+
+#### From the sources
+
+Installation is based on a MakeFile.
+
+Command to install N-Beats with Keras: `make install-keras`
+
+Command to install N-Beats with Pytorch: `make install-pytorch`
+
+#### Run on the GPU
+
+It is possible that this is no longer necessary on the recent versions of Tensorflow.  To force the utilization of the GPU (with the Keras backend),
+run: `pip uninstall -y tensorflow && pip install tensorflow-gpu`. 
+
+### Example
+
+Here is an example to get familiar with both backends. Note that only the Keras backend supports `input_dim>1` at the moment.
+
+```python
+import warnings
+
+import numpy as np
+
+from nbeats_keras.model import NBeatsNet as NBeatsKeras
+from nbeats_pytorch.model import NBeatsNet as NBeatsPytorch
+
+warnings.filterwarnings(action='ignore', message='Setting attributes')
+
+
+def main():
+    # https://keras.io/layers/recurrent/
+    # At the moment only Keras supports input_dim > 1. In the original paper, input_dim=1.
+    num_samples, time_steps, input_dim, output_dim = 50_000, 10, 1, 1
+
+    # This example is for both Keras and Pytorch. In practice, choose the one you prefer.
+    for BackendType in [NBeatsKeras, NBeatsPytorch]:
+        # NOTE: If you choose the Keras backend with input_dim>1, you have 
+        # to set the value here too (in the constructor).
+        backend = BackendType(
+            backcast_length=time_steps, forecast_length=output_dim,
+            stack_types=(NBeatsKeras.GENERIC_BLOCK, NBeatsKeras.GENERIC_BLOCK),
+            nb_blocks_per_stack=2, thetas_dim=(4, 4), share_weights_in_stack=True,
+            hidden_layer_units=64
+        )
+
+        # Definition of the objective function and the optimizer.
+        backend.compile(loss='mae', optimizer='adam')
+
+        # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
+        # where f = np.mean.
+        x = np.random.uniform(size=(num_samples, time_steps, input_dim))
+        y = np.mean(x, axis=1, keepdims=True)
+
+        # Split data into training and testing datasets.
+        c = num_samples // 10
+        x_train, y_train, x_test, y_test = x[c:], y[c:], x[:c], y[:c]
+        test_size = len(x_test)
+
+        # Train the model.
+        print('Training...')
+        backend.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)
+
+        # Save the model for later.
+        backend.save('n_beats_model.h5')
+
+        # Predict on the testing set (forecast).
+        predictions_forecast = backend.predict(x_test)
+        np.testing.assert_equal(predictions_forecast.shape, (test_size, backend.forecast_length, output_dim))
+
+        # Predict on the testing set (backcast).
+        predictions_backcast = backend.predict(x_test, return_backcast=True)
+        np.testing.assert_equal(predictions_backcast.shape, (test_size, backend.backcast_length, output_dim))
+
+        # Load the model.
+        model_2 = BackendType.load('n_beats_model.h5')
+
+        np.testing.assert_almost_equal(predictions_forecast, model_2.predict(x_test))
+
+
+if __name__ == '__main__':
+    main()
+```
+
+Browse the [examples](examples) for more. It includes Jupyter notebooks.
+
+Jupyter notebook: [NBeats.ipynb](examples/NBeats.ipynb): `make run-jupyter`.
+
+<p align="center">
+  <img src="assets/nbeats.png" width="500"><br/>
+</p>
+
+### Citation
+
+```
+@misc{NBeatsPRemy,
+  author = {Philippe Remy},
+  title = {N-BEATS: Neural basis expansion analysis for interpretable time series forecasting},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/philipperemy/n-beats}},
+}
+```
+
+### Contributors
+
+Thank you!
+
+<a href="https://github.com/philipperemy/n-beats/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=philipperemy/n-beats" />
+</a>
+
+
+%prep
+%autosetup -n nbeats-pytorch-1.8.0
+
+%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-nbeats-pytorch -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..92ee856
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+260712da6f402274f4e75df3c893595e  nbeats-pytorch-1.8.0.tar.gz