automatic import of python-tempehopeneuler20.03

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diff --git a/.gitignore b/.gitignore
index e69de29..435d1ca 100644
--- a/.gitignore
+++ b/.gitignore
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+/tempeh-0.1.12.tar.gz
diff --git a/python-tempeh.spec b/python-tempeh.spec
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--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name:		python-tempeh
+Version:	0.1.12
+Release:	1
+Summary:	Machine Learning Performance Testing Framework
+License:	MIT License
+URL:		https://github.com/microsoft/tempeh
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/76/1b/6cec6ab29489a619141fa0312da7157acebdf2cf9ffe37ecfd4a61365ea1/tempeh-0.1.12.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-memory-profiler
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-pytest
+Requires:	python3-requests
+Requires:	python3-scipy
+Requires:	python3-shap
+Requires:	python3-scikit-learn
+
+%description
+[![Build Status](https://img.shields.io/azure-devops/build/responsibleai/tempeh/19/master?failed_label=bad&passed_label=good&label=GatedCheckin%3ADev)](https://dev.azure.com/responsibleai/tempeh/_build/latest?definitionId=19&branchName=master) ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg) ![pypi badge](https://img.shields.io/pypi/v/tempeh?color=blue)
+
+
+# tempeh
+
+tempeh is a framework to
+
+**TE**st
+
+**M**achine learning
+
+**PE**rformance
+
+ex**H**austively
+
+which includes tracking memory usage and run time. This is particularly useful as a pluggable tool for your repository's performance tests. Typically, people want to run them periodically over various datasets and/or with a number of models to catch regressions with respect to run time or memory consumption. This should be as easy as
+
+```python
+import pytest
+from time import time
+from tempeh.configurations import datasets, models
+
+@pytest.mark.parametrize('Dataset', datasets.values())
+@pytest.mark.parametrize('Model', models.values())
+def test_fit_predict_regression(Dataset, Model):
+    dataset = Dataset()
+    X_train, X_test = dataset.get_X()
+    y_train, y_test = dataset.get_y()
+    model = Model()
+    max_execution_time = get_max_execution_time(dataset, model)
+    if model.compatible_with_dataset(dataset):
+        start_time = time()
+        model.fit(X_train, y_train)
+        model.predict(X_test)
+        duration = time() - start_time
+
+        assert duration < max_execution_time
+```
+
+## Installation
+
+tempeh depends on various packages to provide models, including `tensorflow`, `torch`, `xgboost`, `lightgbm`. To install a release version of `tempeh` just run
+
+```python
+pip install tempeh
+```
+
+<details>
+<summary>
+<strong>
+<em>
+Common issues
+</em>
+</strong>
+</summary>
+
+- If you're using a 32-bit Python version you might need to switch to a 64-bit Python version first to successfully install tensorflow.
+- If the installation of `torch` fails try using the recommendation from the [pytorch website](https://pytorch.org/get-started/locally/) for stable versions without CUDA for your python version on your operating system.
+- If the installation of `lightgbm` or `xgboost` fails try to use a pip version less than 20.0 until their bug is resolved.
+</details>
+
+## Structure
+
+### Datasets
+
+Datasets (located in the `datasets/` directory) encapsulate different datasets used for testing.
+
+#### To add a new one
+
++ Create a python file in the `datasets/` directory with naming convention `[name]_datasets.py`
++ Subclass `BasePerformanceDatasetWrapper`. The naming convention is `[dataset_name]PerformanceDatasetWrapper`
++ In `__init__` load the dataset and call `super().__init__(data, targets, size)`
++ Add the class to `__init__.py`
++ Make sure the class contains class variables `task`, `data_type`, `size`
++ Add an entry to the `datasets` dictionary in `configurations.py`.
+
+### Models
+
+Models (`models/` directory) wrap different machine learning models.
+
+#### To add a new one
+
++ Create a python file in the `models/` directory with naming convention `[name]_model.py`
++ Subclass `BaseModelWrapper` and name the class `[name]ModelWrapper`
++ In `__init__` train the model; we expect format `__init__(self, ...)`
++ Models must contain `tasks` and `algorithm`
++ Add an entry to the `models` dictionary in `configurations.py`.
+
+
+## Maintainers
+
+In alphabetical order:
+
+- [Eduardo de Leon](https://github.com/eedeleon)
+- [Ilya Matiach](https://github.com/imatiach-msft)
+- [Roman Lutz](https://github.com/romanlutz)
+
+
+# Contributing
+
+To contribute please check our [Contributing Guide](CONTRIBUTING.md).
+
+# Issues
+
+## Regular (non-Security) Issues
+Please submit a report through [Github issues](https://github.com/microsoft/tempeh/issues). A maintainer will respond within a reasonable period of time to handle the issue as follows:
+- bug: triage as `bug` and provide estimated timeline based on severity
+- feature request: triage as `feature request` and provide estimated timeline
+- question or discussion: triage as `question` and respond or notify/identify a suitable expert to respond
+
+Maintainers are supposed to link duplicate issues when possible.
+
+
+## Reporting Security Issues
+
+Please take a look at our guidelines for reporting [security issues](SECURITY.md).
+
+
+
+
+%package -n python3-tempeh
+Summary:	Machine Learning Performance Testing Framework
+Provides:	python-tempeh
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tempeh
+[![Build Status](https://img.shields.io/azure-devops/build/responsibleai/tempeh/19/master?failed_label=bad&passed_label=good&label=GatedCheckin%3ADev)](https://dev.azure.com/responsibleai/tempeh/_build/latest?definitionId=19&branchName=master) ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg) ![pypi badge](https://img.shields.io/pypi/v/tempeh?color=blue)
+
+
+# tempeh
+
+tempeh is a framework to
+
+**TE**st
+
+**M**achine learning
+
+**PE**rformance
+
+ex**H**austively
+
+which includes tracking memory usage and run time. This is particularly useful as a pluggable tool for your repository's performance tests. Typically, people want to run them periodically over various datasets and/or with a number of models to catch regressions with respect to run time or memory consumption. This should be as easy as
+
+```python
+import pytest
+from time import time
+from tempeh.configurations import datasets, models
+
+@pytest.mark.parametrize('Dataset', datasets.values())
+@pytest.mark.parametrize('Model', models.values())
+def test_fit_predict_regression(Dataset, Model):
+    dataset = Dataset()
+    X_train, X_test = dataset.get_X()
+    y_train, y_test = dataset.get_y()
+    model = Model()
+    max_execution_time = get_max_execution_time(dataset, model)
+    if model.compatible_with_dataset(dataset):
+        start_time = time()
+        model.fit(X_train, y_train)
+        model.predict(X_test)
+        duration = time() - start_time
+
+        assert duration < max_execution_time
+```
+
+## Installation
+
+tempeh depends on various packages to provide models, including `tensorflow`, `torch`, `xgboost`, `lightgbm`. To install a release version of `tempeh` just run
+
+```python
+pip install tempeh
+```
+
+<details>
+<summary>
+<strong>
+<em>
+Common issues
+</em>
+</strong>
+</summary>
+
+- If you're using a 32-bit Python version you might need to switch to a 64-bit Python version first to successfully install tensorflow.
+- If the installation of `torch` fails try using the recommendation from the [pytorch website](https://pytorch.org/get-started/locally/) for stable versions without CUDA for your python version on your operating system.
+- If the installation of `lightgbm` or `xgboost` fails try to use a pip version less than 20.0 until their bug is resolved.
+</details>
+
+## Structure
+
+### Datasets
+
+Datasets (located in the `datasets/` directory) encapsulate different datasets used for testing.
+
+#### To add a new one
+
++ Create a python file in the `datasets/` directory with naming convention `[name]_datasets.py`
++ Subclass `BasePerformanceDatasetWrapper`. The naming convention is `[dataset_name]PerformanceDatasetWrapper`
++ In `__init__` load the dataset and call `super().__init__(data, targets, size)`
++ Add the class to `__init__.py`
++ Make sure the class contains class variables `task`, `data_type`, `size`
++ Add an entry to the `datasets` dictionary in `configurations.py`.
+
+### Models
+
+Models (`models/` directory) wrap different machine learning models.
+
+#### To add a new one
+
++ Create a python file in the `models/` directory with naming convention `[name]_model.py`
++ Subclass `BaseModelWrapper` and name the class `[name]ModelWrapper`
++ In `__init__` train the model; we expect format `__init__(self, ...)`
++ Models must contain `tasks` and `algorithm`
++ Add an entry to the `models` dictionary in `configurations.py`.
+
+
+## Maintainers
+
+In alphabetical order:
+
+- [Eduardo de Leon](https://github.com/eedeleon)
+- [Ilya Matiach](https://github.com/imatiach-msft)
+- [Roman Lutz](https://github.com/romanlutz)
+
+
+# Contributing
+
+To contribute please check our [Contributing Guide](CONTRIBUTING.md).
+
+# Issues
+
+## Regular (non-Security) Issues
+Please submit a report through [Github issues](https://github.com/microsoft/tempeh/issues). A maintainer will respond within a reasonable period of time to handle the issue as follows:
+- bug: triage as `bug` and provide estimated timeline based on severity
+- feature request: triage as `feature request` and provide estimated timeline
+- question or discussion: triage as `question` and respond or notify/identify a suitable expert to respond
+
+Maintainers are supposed to link duplicate issues when possible.
+
+
+## Reporting Security Issues
+
+Please take a look at our guidelines for reporting [security issues](SECURITY.md).
+
+
+
+
+%package help
+Summary:	Development documents and examples for tempeh
+Provides:	python3-tempeh-doc
+%description help
+[![Build Status](https://img.shields.io/azure-devops/build/responsibleai/tempeh/19/master?failed_label=bad&passed_label=good&label=GatedCheckin%3ADev)](https://dev.azure.com/responsibleai/tempeh/_build/latest?definitionId=19&branchName=master) ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg) ![pypi badge](https://img.shields.io/pypi/v/tempeh?color=blue)
+
+
+# tempeh
+
+tempeh is a framework to
+
+**TE**st
+
+**M**achine learning
+
+**PE**rformance
+
+ex**H**austively
+
+which includes tracking memory usage and run time. This is particularly useful as a pluggable tool for your repository's performance tests. Typically, people want to run them periodically over various datasets and/or with a number of models to catch regressions with respect to run time or memory consumption. This should be as easy as
+
+```python
+import pytest
+from time import time
+from tempeh.configurations import datasets, models
+
+@pytest.mark.parametrize('Dataset', datasets.values())
+@pytest.mark.parametrize('Model', models.values())
+def test_fit_predict_regression(Dataset, Model):
+    dataset = Dataset()
+    X_train, X_test = dataset.get_X()
+    y_train, y_test = dataset.get_y()
+    model = Model()
+    max_execution_time = get_max_execution_time(dataset, model)
+    if model.compatible_with_dataset(dataset):
+        start_time = time()
+        model.fit(X_train, y_train)
+        model.predict(X_test)
+        duration = time() - start_time
+
+        assert duration < max_execution_time
+```
+
+## Installation
+
+tempeh depends on various packages to provide models, including `tensorflow`, `torch`, `xgboost`, `lightgbm`. To install a release version of `tempeh` just run
+
+```python
+pip install tempeh
+```
+
+<details>
+<summary>
+<strong>
+<em>
+Common issues
+</em>
+</strong>
+</summary>
+
+- If you're using a 32-bit Python version you might need to switch to a 64-bit Python version first to successfully install tensorflow.
+- If the installation of `torch` fails try using the recommendation from the [pytorch website](https://pytorch.org/get-started/locally/) for stable versions without CUDA for your python version on your operating system.
+- If the installation of `lightgbm` or `xgboost` fails try to use a pip version less than 20.0 until their bug is resolved.
+</details>
+
+## Structure
+
+### Datasets
+
+Datasets (located in the `datasets/` directory) encapsulate different datasets used for testing.
+
+#### To add a new one
+
++ Create a python file in the `datasets/` directory with naming convention `[name]_datasets.py`
++ Subclass `BasePerformanceDatasetWrapper`. The naming convention is `[dataset_name]PerformanceDatasetWrapper`
++ In `__init__` load the dataset and call `super().__init__(data, targets, size)`
++ Add the class to `__init__.py`
++ Make sure the class contains class variables `task`, `data_type`, `size`
++ Add an entry to the `datasets` dictionary in `configurations.py`.
+
+### Models
+
+Models (`models/` directory) wrap different machine learning models.
+
+#### To add a new one
+
++ Create a python file in the `models/` directory with naming convention `[name]_model.py`
++ Subclass `BaseModelWrapper` and name the class `[name]ModelWrapper`
++ In `__init__` train the model; we expect format `__init__(self, ...)`
++ Models must contain `tasks` and `algorithm`
++ Add an entry to the `models` dictionary in `configurations.py`.
+
+
+## Maintainers
+
+In alphabetical order:
+
+- [Eduardo de Leon](https://github.com/eedeleon)
+- [Ilya Matiach](https://github.com/imatiach-msft)
+- [Roman Lutz](https://github.com/romanlutz)
+
+
+# Contributing
+
+To contribute please check our [Contributing Guide](CONTRIBUTING.md).
+
+# Issues
+
+## Regular (non-Security) Issues
+Please submit a report through [Github issues](https://github.com/microsoft/tempeh/issues). A maintainer will respond within a reasonable period of time to handle the issue as follows:
+- bug: triage as `bug` and provide estimated timeline based on severity
+- feature request: triage as `feature request` and provide estimated timeline
+- question or discussion: triage as `question` and respond or notify/identify a suitable expert to respond
+
+Maintainers are supposed to link duplicate issues when possible.
+
+
+## Reporting Security Issues
+
+Please take a look at our guidelines for reporting [security issues](SECURITY.md).
+
+
+
+
+%prep
+%autosetup -n tempeh-0.1.12
+
+%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-tempeh -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.12-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0ed757d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+85629780899e8039343572640a29bd60  tempeh-0.1.12.tar.gz