Science Score: 59.0%
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Low similarity (18.2%) to scientific vocabulary
Keywords
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Repository
A Python package for time series classification
Basic Info
- Host: GitHub
- Owner: johannfaouzi
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Homepage: https://pyts.readthedocs.io
- Size: 7.53 MB
Statistics
- Stars: 1,841
- Watchers: 24
- Forks: 174
- Open Issues: 52
- Releases: 8
Topics
Metadata Files
README.md
pyts: a Python package for time series classification
pyts is a Python package for time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.
Installation
Dependencies
pyts requires:
- Python (>= 3.8)
- NumPy (>= 1.22.4)
- SciPy (>= 1.8.1)
- Scikit-Learn (>= 1.2.0)
- Joblib (>= 1.1.1)
- Numba (>= 0.55.2)
To run the examples Matplotlib (>=2.0.0) is required.
User installation
If you already have a working installation of numpy, scipy, scikit-learn,
joblib and numba, you can easily install pyts using pip
pip install pyts
or conda via the conda-forge channel
conda install -c conda-forge pyts
You can also get the latest version of pyts by cloning the repository
git clone https://github.com/johannfaouzi/pyts.git
cd pyts
pip install .
Testing
After installation, you can launch the test suite from outside the source directory using pytest:
pytest pyts
Changelog
See the changelog for a history of notable changes to pyts.
Development
The development of this package is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide. A slight difference is the use of Numba instead of Cython for optimization.
Documentation
The section below gives some information about the implemented algorithms in pyts. For more information, please have a look at the HTML documentation available via ReadTheDocs.
Citation
If you use pyts in a scientific publication, we would appreciate
citations to the following paper:
Johann Faouzi and Hicham Janati. pyts: A python package for time series classification.
Journal of Machine Learning Research, 21(46):1−6, 2020.
Bibtex entry:
@article{JMLR:v21:19-763,
author = {Johann Faouzi and Hicham Janati},
title = {pyts: A Python Package for Time Series Classification},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {46},
pages = {1-6},
url = {http://jmlr.org/papers/v21/19-763.html}
}
Implemented features
Note: the content described in this section corresponds to the main branch (i.e., the latest version), and not the latest released version. You may have to install the latest version to use some of these features.
pyts consists of the following modules:
approximation: This module provides implementations of algorithms that approximate time series. Implemented algorithms are Piecewise Aggregate Approximation, Symbolic Aggregate approXimation, Discrete Fourier Transform, Multiple Coefficient Binning and Symbolic Fourier Approximation.bag_of_words: This module provide tools to transform time series into bags of words. Implemented algorithms are WordExtractor and BagOfWords.classification: This module provides implementations of algorithms that can classify time series. Implemented algorithms are KNeighborsClassifier, SAXVSM, BOSSVS, LearningShapelets, TimeSeriesForest and TSBF.datasets: This module provides utilities to make or load toy datasets, as well as fetching datasets from the UEA & UCR Time Series Classification Repository.decomposition: This module provides implementations of algorithms that decompose a time series into several time series. The only implemented algorithm is Singular Spectrum Analysis.image: This module provides implementations of algorithms that transform time series into images. Implemented algorithms are Recurrence Plot, Gramian Angular Field and Markov Transition Field.metrics: This module provides implementations of metrics that are specific to time series. Implemented metrics are Dynamic Time Warping with several variants and the BOSS metric.multivariate: This modules provides utilities to deal with multivariate time series. Available tools are MultivariateTransformer and MultivariateClassifier to transform and classify multivariate time series using tools for univariate time series respectively, as well as JointRecurrencePlot and WEASEL+MUSE.preprocessing: This module provides most of the scikit-learn preprocessing tools but applied sample-wise (i.e. to each time series independently) instead of feature-wise, as well as an imputer of missing values using interpolation. More information is available at the pyts.preprocessing API documentation.transformation: This module provides implementations of algorithms that transform a data set of time series with shape(n_samples, n_timestamps)into a data set with shape(n_samples, n_extracted_features). Implemented algorithms are BagOfPatterns, BOSS, ShapeletTransform, WEASEL and ROCKET.utils: a simple module with utility functions.
License
The contents of this repository is under a BSD 3-Clause License.
Owner
- Name: Johann Faouzi
- Login: johannfaouzi
- Kind: user
- Location: Paris, France
- Website: https://github.com/johannfaouzi
- Repositories: 1
- Profile: https://github.com/johannfaouzi
Postdoctorate researcher at Paris Brain Institute. Interested in Machine Learning and Python programming with medical applications.
GitHub Events
Total
- Issues event: 6
- Watch event: 87
- Issue comment event: 5
- Push event: 2
- Pull request event: 6
- Fork event: 8
Last Year
- Issues event: 6
- Watch event: 87
- Issue comment event: 5
- Push event: 2
- Pull request event: 6
- Fork event: 8
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Johann Faouzi | j****i@g****m | 162 |
| johann.faouzi | j****i@i****g | 109 |
| Johann Faouzi | j****o@o****r | 69 |
| Hicham Janati | h****i@i****r | 24 |
| Ken Lee | h****a@h****m | 2 |
| Lucas Plagwitz | l****z@u****e | 2 |
| FAOUZI Johann | j****i@u****g | 2 |
| AvisP | a****1@g****m | 1 |
| Bruno P. Kinoshita | k****w | 1 |
| Darigov Research | 3****h | 1 |
| Roman Yurchak | r****k@s****m | 1 |
| Santiago M. Mola | s****i@m****o | 1 |
| SvenBarray | 8****y | 1 |
| Tobias | T****r | 1 |
| lgtm-com[bot] | 4****] | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 68
- Total pull requests: 56
- Average time to close issues: 7 days
- Average time to close pull requests: 16 days
- Total issue authors: 61
- Total pull request authors: 12
- Average comments per issue: 3.74
- Average comments per pull request: 1.89
- Merged pull requests: 50
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 8
- Pull requests: 4
- Average time to close issues: about 23 hours
- Average time to close pull requests: about 17 hours
- Issue authors: 7
- Pull request authors: 3
- Average comments per issue: 0.25
- Average comments per pull request: 1.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Sandy4321 (2)
- fkiraly (2)
- wwjd1234 (2)
- zneha (2)
- jmrichardson (2)
- rgieseke (2)
- aleksejs-fomins (2)
- GiovannaR (1)
- toncho11 (1)
- thunderbug1 (1)
- holderhe (1)
- aglenis (1)
- darigovresearch (1)
- lambdatascience (1)
- BrannonKing (1)
Pull Request Authors
- johannfaouzi (42)
- protti (2)
- smola (2)
- antonioscarinci (2)
- lucasplagwitz (2)
- SvenBarray (2)
- lgtm-com[bot] (1)
- valcarcexyz (1)
- stepanmk (1)
- jmrichardson (1)
- darigovresearch (1)
- kinow (1)
Top Labels
Issue Labels
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Packages
- Total packages: 5
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Total downloads:
- pypi 115,692 last-month
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Total dependent packages: 22
(may contain duplicates) -
Total dependent repositories: 78
(may contain duplicates) - Total versions: 30
- Total maintainers: 2
pypi.org: pyts
A python package for time series classification
- Homepage: https://github.com/johannfaouzi/pyts
- Documentation: https://pyts.readthedocs.io/
- License: new BSD
-
Latest release: 0.13.0
published over 2 years ago
Rankings
Maintainers (1)
proxy.golang.org: github.com/johannfaouzi/pyts
- Documentation: https://pkg.go.dev/github.com/johannfaouzi/pyts#section-documentation
- License: bsd-3-clause
-
Latest release: v0.13.0
published over 2 years ago
Rankings
spack.io: py-pyts
pyts is a Python package for time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.
- Homepage: https://github.com/johannfaouzi/pyts
- License: []
-
Latest release: 0.12.0
published almost 4 years ago
Rankings
Maintainers (1)
conda-forge.org: pyts
pyts is a Python package for time series transformation and classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.
- Homepage: http://github.com/johannfaouzi/pyts
- License: BSD-3-Clause
-
Latest release: 0.12.0
published over 4 years ago
Rankings
anaconda.org: pyts
pyts is a Python package for time series transformation and classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.
- Homepage: https://github.com/johannfaouzi/pyts
- License: BSD-3-Clause
-
Latest release: 0.12.0
published over 2 years ago
Rankings
Dependencies
- joblib >=0.12
- numba >=0.48.0
- numpy >=1.17.5
- scikit-learn >=0.22.1
- scipy >=1.3.0
- actions/checkout v3 composite
- github/codeql-action/analyze v2 composite
- github/codeql-action/autobuild v2 composite
- github/codeql-action/init v2 composite
- actions/checkout v2 composite
- actions/setup-python v1 composite
- numba
- numpy
- scikit-learn
- scipy