| Name | patsemb JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | PaTSEmb: Pattern-based Time Series Embedding |
| upload_time | 2024-09-18 12:18:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <=3.12,>=3.8 |
| license | MIT License Copyright (c) 2024 KU Leuven, DTAI Research Group Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
machine-learning
time-series
semantic-segmentation
data-mining
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PaTSEmb
[](https://gitlab.kuleuven.be/u0143709/patsemb/-/commits/main)
[](https://gitlab.kuleuven.be/u0143709/patsemb/-/commits/main)
Welcome to ``PaTSEmb``, a fast and extendable Python package for creating a pattern-based embedding
of the time series. This is an embedding of the time series which contains information
about the typical shapes are occurring at which locations in the time series.
Below, we give a small example of how to do this, but be sure to check out the
[documentation](https://patsemb-u0143709-3a07c9d27a51b62b1b2bad2f623ad154a9a19db833f1f7.pages.gitlab.kuleuven.be/index.html)!
## Installation
You can install ``PaTSEmb`` using the following command:
```
pip install patsemb
```
If you want to mine frequent, sequential patterns, Java 1.7 or higher should also be
available on your machine. More information about installing ``PaTSEmb`` can be found
in the [documentation](https://patsemb-u0143709-3a07c9d27a51b62b1b2bad2f623ad154a9a19db833f1f7.pages.gitlab.kuleuven.be/getting_started/installation.html).
## Example
The code snippet below shows how to create the pattern-based embedding of a
time series. Be sure to check out the [example notebook](https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/patsemb/-/blob/main/notebooks/examples.ipynb?ref_type=heads)
for more examples!
```python
from patsemb.discretization import SAXDiscretizer
from patsemb.pattern_mining import QCSP
from patsemb.pattern_based_embedding import PatternBasedEmbedder
# Specify a discretizer and pattern miner, or use the default values
pattern_based_embedder = PatternBasedEmbedder(
discretizer=SAXDiscretizer(alphabet_size=8, word_size=5),
pattern_miner=QCSP(minimum_support=3, top_k_patterns=20)
)
# Create the pattern-based embedding
time_series = ... # Load here your time series as a numpy array
embedding = pattern_based_embedder.fit_transform(time_series)
```
## Contact
Feel free to email to [louis.carpentier@kuleuven.be](mailto:louis.carpentier@kuleuven.be) if
there are any questions, remarks, ideas, ...
## Acknowledgments
If you use ``PaTSEmb`` in your research or project, please add the following citation:
```bibtex
@inproceedings{carpentier2024pattern,
title={Pattern-based Time Series Semantic Segmentation with Gradual State Transitions},
author={Carpentier, Louis and Feremans, Len and Meert, Wannes and Verbeke, Mathias},
booktitle={Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},
pages={316--324},
year={2024},
month={April},
organization={SIAM},
doi={10.1137/1.9781611978032.36}
}
```
> L. Carpentier, L. Feremans, W. Meert, and M. Verbeke.
> "Pattern-based time series semantic segmentation with gradual state transitions".
> In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM),
> pages 316–324. SIAM, april 2024. doi: [10.1137/1.9781611978032.36](https://doi.org/10.1137/1.9781611978032.36).
## License
Copyright (c) 2024 KU Leuven, DTAI Research Group
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Raw data
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"description": "# PaTSEmb\r\n\r\n[](https://gitlab.kuleuven.be/u0143709/patsemb/-/commits/main)\r\n[](https://gitlab.kuleuven.be/u0143709/patsemb/-/commits/main)\r\n\r\nWelcome to ``PaTSEmb``, a fast and extendable Python package for creating a pattern-based embedding\r\nof the time series. This is an embedding of the time series which contains information\r\nabout the typical shapes are occurring at which locations in the time series. \r\nBelow, we give a small example of how to do this, but be sure to check out the \r\n[documentation](https://patsemb-u0143709-3a07c9d27a51b62b1b2bad2f623ad154a9a19db833f1f7.pages.gitlab.kuleuven.be/index.html)!\r\n\r\n## Installation\r\n\r\nYou can install ``PaTSEmb`` using the following command:\r\n```\r\npip install patsemb\r\n```\r\nIf you want to mine frequent, sequential patterns, Java 1.7 or higher should also be \r\navailable on your machine. More information about installing ``PaTSEmb`` can be found \r\nin the [documentation](https://patsemb-u0143709-3a07c9d27a51b62b1b2bad2f623ad154a9a19db833f1f7.pages.gitlab.kuleuven.be/getting_started/installation.html).\r\n\r\n## Example \r\n\r\nThe code snippet below shows how to create the pattern-based embedding of a \r\ntime series. Be sure to check out the [example notebook](https://gitlab.kuleuven.be/m-group-campus-brugge/dtai_public/patsemb/-/blob/main/notebooks/examples.ipynb?ref_type=heads) \r\nfor more examples!\r\n\r\n```python\r\nfrom patsemb.discretization import SAXDiscretizer\r\nfrom patsemb.pattern_mining import QCSP\r\nfrom patsemb.pattern_based_embedding import PatternBasedEmbedder\r\n\r\n# Specify a discretizer and pattern miner, or use the default values\r\npattern_based_embedder = PatternBasedEmbedder(\r\n discretizer=SAXDiscretizer(alphabet_size=8, word_size=5),\r\n pattern_miner=QCSP(minimum_support=3, top_k_patterns=20)\r\n)\r\n\r\n# Create the pattern-based embedding\r\ntime_series = ... # Load here your time series as a numpy array\r\nembedding = pattern_based_embedder.fit_transform(time_series)\r\n```\r\n\r\n## Contact\r\n\r\nFeel free to email to [louis.carpentier@kuleuven.be](mailto:louis.carpentier@kuleuven.be) if \r\nthere are any questions, remarks, ideas, ...\r\n\r\n## Acknowledgments \r\n\r\nIf you use ``PaTSEmb`` in your research or project, please add the following citation:\r\n\r\n```bibtex\r\n@inproceedings{carpentier2024pattern,\r\n title={Pattern-based Time Series Semantic Segmentation with Gradual State Transitions},\r\n author={Carpentier, Louis and Feremans, Len and Meert, Wannes and Verbeke, Mathias},\r\n booktitle={Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},\r\n pages={316--324},\r\n year={2024},\r\n month={April},\r\n organization={SIAM},\r\n doi={10.1137/1.9781611978032.36}\r\n}\r\n```\r\n> L. Carpentier, L. Feremans, W. Meert, and M. Verbeke. \r\n> \"Pattern-based time series semantic segmentation with gradual state transitions\". \r\n> In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), \r\n> pages 316\u2013324. SIAM, april 2024. doi: [10.1137/1.9781611978032.36](https://doi.org/10.1137/1.9781611978032.36).\r\n\r\n## License\r\n\r\n Copyright (c) 2024 KU Leuven, DTAI Research Group\r\n\r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in all\r\n copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n SOFTWARE.\r\n",
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