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add tests for PyPI METADATA versions
Ref: #4175 Signed-off-by: Alok Kumar <[email protected]>
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‎tests/packagedcode/data/pypi/metadata/v22/PKG-INFO‎

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Metadata-Version: 2.2
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Name: ForeTiS
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Version: 0.0.7
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Summary: state-of-the-art and easy-to-use time series forecasting
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Home-page: https://github.com/grimmlab/ForeTiS
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Author: Josef Eiglsperger, Florian Haselbeck; Dominik G. Grimm
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Author-email: [email protected]
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License: MIT
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Project-URL: Documentation, https://ForeTiS.readthedocs.io/
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Project-URL: Source, https://github.com/grimmlab/ForeTiS
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Classifier: Development Status :: 2 - Pre-Alpha
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Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: POSIX :: Linux
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Classifier: Programming Language :: Python :: 3
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Python: >=3.8
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: torch>=1.11.0
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Requires-Dist: xgboost>=1.5.2
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Requires-Dist: optuna>=2.10.0
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Requires-Dist: sqlalchemy==1.4.46
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Requires-Dist: joblib>=1.1.0
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Requires-Dist: numpy>=1.22.2
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Requires-Dist: pandas>=1.4.1
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Requires-Dist: scikit-learn>=1.0.2
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Requires-Dist: tensorflow>=2.8.0
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Requires-Dist: tensorflow-probability>=0.18
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Requires-Dist: statsmodels>=0.13.2
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Requires-Dist: scipy>=1.8.1
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Requires-Dist: pmdarima>=2.0.1
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Requires-Dist: gpflow>=2.5.2
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Requires-Dist: matplotlib>=3.3.0
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Requires-Dist: changefinder>=0.3
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Requires-Dist: bayesian_torch
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Requires-Dist: blitz-bayesian-pytorch
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Requires-Dist: tables>=3.7.0
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<div align="left"><img src="https://raw.githubusercontent.com/grimmlab/ForeTiS/master/docs/image/Logo_ForeTiS_Text.png" height="80"/></div>
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# ForeTiS: A Forecasting Time Series framework
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[![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-388/)
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ForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models.
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ForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively.
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These range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP).
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To enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques.
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In addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.
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## Documentation
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For more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/
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## Contributors
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This pipeline is developed and maintained by members of the [Bioinformatics lab](https://bit.cs.tum.de) lead by [Prof. Dr. Dominik Grimm](https://bit.cs.tum.de/team/dominik-grimm/):
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- [Josef Eiglsperger, M.Sc.](https://bit.cs.tum.de/team/josef-eiglsperger/)
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- [Florian Haselbeck, M.Sc.](https://bit.cs.tum.de/team/florian-haselbeck/)
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## Citation
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When using ForeTiS, please cite our publication:
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**ForeTiS: A comprehensive time series forecasting framework in Python.** <br />
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Josef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm. <br />
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*Machine Learning with Applications, 2023.* [doi: 10.1016/j.mlwa.2023.100467](https://doi.org/10.1016/j.mlwa.2023.100467) <br />
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**These authors have contributed equally to this work and share first authorship.* <br />

‎tests/packagedcode/data/pypi/metadata/v22/PKG-INFO-expected.json‎

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{
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"type": "pypi",
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"namespace": null,
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"name": "ForeTiS",
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"version": "0.0.7",
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"qualifiers": {},
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"subpath": null,
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"primary_language": "Python",
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"description": "state-of-the-art and easy-to-use time series forecasting\n<div align=\"left\"><img src=\"https://raw.githubusercontent.com/grimmlab/ForeTiS/master/docs/image/Logo_ForeTiS_Text.png\" height=\"80\"/></div>\n\n# ForeTiS: A Forecasting Time Series framework\n\n[![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-388/)\n\nForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models. \nForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively. \nThese range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP). \nTo enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques. \nIn addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.\n\n## Documentation\nFor more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/\n\n## Contributors\nThis pipeline is developed and maintained by members of the [Bioinformatics lab](https://bit.cs.tum.de) lead by [Prof. Dr. Dominik Grimm](https://bit.cs.tum.de/team/dominik-grimm/):\n- [Josef Eiglsperger, M.Sc.](https://bit.cs.tum.de/team/josef-eiglsperger/)\n- [Florian Haselbeck, M.Sc.](https://bit.cs.tum.de/team/florian-haselbeck/)\n\n## Citation\nWhen using ForeTiS, please cite our publication:\n\n**ForeTiS: A comprehensive time series forecasting framework in Python.** <br />\nJosef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm. <br />\n*Machine Learning with Applications, 2023.* [doi: 10.1016/j.mlwa.2023.100467](https://doi.org/10.1016/j.mlwa.2023.100467) <br />\n**These authors have contributed equally to this work and share first authorship.* <br />",
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