automatic import of python-mtse

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+/mtse-0.1.6.tar.gz
diff --git a/python-mtse.spec b/python-mtse.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mtse
+Version:	0.1.6
+Release:	1
+Summary:	Multi Time Series Encoders
+License:	Apache 2.0
+URL:		https://github.com/FractalySyn/mtse
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7f/e2/83c5b6608acfaa38c6853d19cc5871f5409d3a8e89425b28f36443dcb077/mtse-0.1.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-matplotlib
+Requires:	python3-setuptools
+
+%description
+# Multi Time Series Encoders
+
+The objective of this python package is to make easy the encoding and the classification/regression of multivariate time series (**mts**) data even when these are asynchronous. We say that data are of type **mts** when each observation is associated with multiple time series (e.g. the vital signs of a patient at a specific period).
+
+## Installation
+
+The current version has been developed in Python 3.7. It also works in Python 3.8. If you encounter an issue, please try to run it again in a virtual machine containing Python 3.7 or 3.8.
+
+```bash
+pip install mtse
+```
+
+## Sample code
+
+```python
+import mtse
+
+### Load sample data ###
+train, val, test, norm = mtse.get_sample(return_norm=True)
+
+### Using the class `mtse` ###
+mtan = mtse.mtse(device='cuda', seed=1, experiment_id='mtan')
+mtan.load_data(train, val, test, norm=norm)
+mtan.build_model('mtan', 'regression', learn_emb=True, early_stop=10, cuda_empty_cache=True)
+mtan.train(lossf='mape', n_iters=200, save_startegy='best')
+mtan.predict(checkpoint='best')
+mtan.encode_ts(data_to_embed='test', embed_pandas=True)
+```
+
+**More details and examples in the documentation**
+
+## What can be implemented / improved
+
+#### Encoders
+  - [x] mTAN - Multi Time Attention Network - encoder
+  - [ ] mTAN - Multi Time Attention Network - encoder-decoder
+  - [ ] SeFT - Set Function for Time series
+  - [ ] STraTS - Self-supervised Transformer for Time-Series
+  - [ ] ODE-based encoders
+
+Note that we only implemented the mTAN encoder as a baseline for now. At this stage, this model works only for supervised learning, meaning that it uses the target variable to compute the loss and update the encoder weights. Thus, the priority would be to implement an unsupervised encoder next (encoder-decoder models or self-supervised encoders).
+
+#### Other features
+  - Cross-validation evaluation, prediction and encoding
+  - Support for other data inputs in the dataset classes (currently the `mtan_Dataset` class)
+  - Support for time-series forecasting and inference tasks
+
+## References
+
+Satya Narayan Shukla and Benjamin Marlin, ["Multi-Time Attention Networks for Irregularly Sampled Time Series"](https://openreview.net/forum?id=4c0J6lwQ4_), *International Conference on Learning Representations*, 2021.
+
+
+
+
+%package -n python3-mtse
+Summary:	Multi Time Series Encoders
+Provides:	python-mtse
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mtse
+# Multi Time Series Encoders
+
+The objective of this python package is to make easy the encoding and the classification/regression of multivariate time series (**mts**) data even when these are asynchronous. We say that data are of type **mts** when each observation is associated with multiple time series (e.g. the vital signs of a patient at a specific period).
+
+## Installation
+
+The current version has been developed in Python 3.7. It also works in Python 3.8. If you encounter an issue, please try to run it again in a virtual machine containing Python 3.7 or 3.8.
+
+```bash
+pip install mtse
+```
+
+## Sample code
+
+```python
+import mtse
+
+### Load sample data ###
+train, val, test, norm = mtse.get_sample(return_norm=True)
+
+### Using the class `mtse` ###
+mtan = mtse.mtse(device='cuda', seed=1, experiment_id='mtan')
+mtan.load_data(train, val, test, norm=norm)
+mtan.build_model('mtan', 'regression', learn_emb=True, early_stop=10, cuda_empty_cache=True)
+mtan.train(lossf='mape', n_iters=200, save_startegy='best')
+mtan.predict(checkpoint='best')
+mtan.encode_ts(data_to_embed='test', embed_pandas=True)
+```
+
+**More details and examples in the documentation**
+
+## What can be implemented / improved
+
+#### Encoders
+  - [x] mTAN - Multi Time Attention Network - encoder
+  - [ ] mTAN - Multi Time Attention Network - encoder-decoder
+  - [ ] SeFT - Set Function for Time series
+  - [ ] STraTS - Self-supervised Transformer for Time-Series
+  - [ ] ODE-based encoders
+
+Note that we only implemented the mTAN encoder as a baseline for now. At this stage, this model works only for supervised learning, meaning that it uses the target variable to compute the loss and update the encoder weights. Thus, the priority would be to implement an unsupervised encoder next (encoder-decoder models or self-supervised encoders).
+
+#### Other features
+  - Cross-validation evaluation, prediction and encoding
+  - Support for other data inputs in the dataset classes (currently the `mtan_Dataset` class)
+  - Support for time-series forecasting and inference tasks
+
+## References
+
+Satya Narayan Shukla and Benjamin Marlin, ["Multi-Time Attention Networks for Irregularly Sampled Time Series"](https://openreview.net/forum?id=4c0J6lwQ4_), *International Conference on Learning Representations*, 2021.
+
+
+
+
+%package help
+Summary:	Development documents and examples for mtse
+Provides:	python3-mtse-doc
+%description help
+# Multi Time Series Encoders
+
+The objective of this python package is to make easy the encoding and the classification/regression of multivariate time series (**mts**) data even when these are asynchronous. We say that data are of type **mts** when each observation is associated with multiple time series (e.g. the vital signs of a patient at a specific period).
+
+## Installation
+
+The current version has been developed in Python 3.7. It also works in Python 3.8. If you encounter an issue, please try to run it again in a virtual machine containing Python 3.7 or 3.8.
+
+```bash
+pip install mtse
+```
+
+## Sample code
+
+```python
+import mtse
+
+### Load sample data ###
+train, val, test, norm = mtse.get_sample(return_norm=True)
+
+### Using the class `mtse` ###
+mtan = mtse.mtse(device='cuda', seed=1, experiment_id='mtan')
+mtan.load_data(train, val, test, norm=norm)
+mtan.build_model('mtan', 'regression', learn_emb=True, early_stop=10, cuda_empty_cache=True)
+mtan.train(lossf='mape', n_iters=200, save_startegy='best')
+mtan.predict(checkpoint='best')
+mtan.encode_ts(data_to_embed='test', embed_pandas=True)
+```
+
+**More details and examples in the documentation**
+
+## What can be implemented / improved
+
+#### Encoders
+  - [x] mTAN - Multi Time Attention Network - encoder
+  - [ ] mTAN - Multi Time Attention Network - encoder-decoder
+  - [ ] SeFT - Set Function for Time series
+  - [ ] STraTS - Self-supervised Transformer for Time-Series
+  - [ ] ODE-based encoders
+
+Note that we only implemented the mTAN encoder as a baseline for now. At this stage, this model works only for supervised learning, meaning that it uses the target variable to compute the loss and update the encoder weights. Thus, the priority would be to implement an unsupervised encoder next (encoder-decoder models or self-supervised encoders).
+
+#### Other features
+  - Cross-validation evaluation, prediction and encoding
+  - Support for other data inputs in the dataset classes (currently the `mtan_Dataset` class)
+  - Support for time-series forecasting and inference tasks
+
+## References
+
+Satya Narayan Shukla and Benjamin Marlin, ["Multi-Time Attention Networks for Irregularly Sampled Time Series"](https://openreview.net/forum?id=4c0J6lwQ4_), *International Conference on Learning Representations*, 2021.
+
+
+
+
+%prep
+%autosetup -n mtse-0.1.6
+
+%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-mtse -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.6-1
+- Package Spec generated
diff --git a/sources b/sources
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+55fd152092ba8c4d51cba85119d17a4e  mtse-0.1.6.tar.gz