path: root/python-tempeh.spec

%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