%global _empty_manifest_terminate_build 0 Name: python-pyts Version: 0.12.0 Release: 1 Summary: A python package for time series classification License: new BSD URL: https://github.com/johannfaouzi/pyts Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8e/88/929f87082e8700d51c663e613c52c3f47f84558c888af689c52500f3ae65/pyts-0.12.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-joblib Requires: python3-numba Requires: python3-docutils Requires: python3-sphinx Requires: python3-sphinx-gallery Requires: python3-numpydoc Requires: python3-matplotlib Requires: python3-pytest Requires: python3-pytest-cov %description [![Build Status](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_apis/build/status/johannfaouzi.pyts?branchName=main)](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) [![Documentation Status](https://readthedocs.org/projects/pyts/badge/?version=latest)](https://pyts.readthedocs.io/) [![Codecov](https://codecov.io/gh/johannfaouzi/pyts/branch/main/graph/badge.svg)](https://codecov.io/gh/johannfaouzi/pyts) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyts.svg)](https://img.shields.io/pypi/pyversions/pyts.svg) [![PyPI version](https://badge.fury.io/py/pyts.svg)](https://badge.fury.io/py/pyts) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pyts.svg)](https://anaconda.org/conda-forge/pyts) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/johannfaouzi/pyts.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) [![Gitter](https://badges.gitter.im/johann-faouzi/community.svg)](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1244152.svg)](https://doi.org/10.5281/zenodo.1244152) ## 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 (>= 3.7) - NumPy (>= 1.17.5) - SciPy (>= 1.3.0) - Scikit-Learn (>=0.22.1) - Joblib (>=0.12) - Numba (>=0.48.0) 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](https://pyts.readthedocs.io/en/stable/changelog.html) 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](https://scikit-learn.org/stable/developers/). 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](https://pyts.readthedocs.io/). ### Citation If you use pyts in a scientific publication, we would appreciate citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): ``` 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, 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](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), [Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), [Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), [Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and [Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). - `bag_of_words`: This module provide tools to transform time series into bags of words. Implemented algorithms are [WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and [BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). - `classification`: This module provides implementations of algorithms that can classify time series. Implemented algorithms are [KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), [SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), [BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), [LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), [TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and [TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). - `datasets`: This module provides utilities to make or load toy datasets, as well as fetching datasets from the [UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). - `decomposition`: This module provides implementations of algorithms that decompose a time series into several time series. The only implemented algorithm is [Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). - `image`: This module provides implementations of algorithms that transform time series into images. Implemented algorithms are [Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), [Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and [Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). - `metrics`: This module provides implementations of metrics that are specific to time series. Implemented metrics are [Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) with several variants and the [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) metric. - `multivariate`: This modules provides utilities to deal with multivariate time series. Available tools are [MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and [MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) to transform and classify multivariate time series using tools for univariate time series respectively, as well as [JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and [WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) of missing values using interpolation. More information is available at the [pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), [ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), [WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and [ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). - `utils`: a simple module with [utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). %package -n python3-pyts Summary: A python package for time series classification Provides: python-pyts BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyts [![Build Status](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_apis/build/status/johannfaouzi.pyts?branchName=main)](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) [![Documentation Status](https://readthedocs.org/projects/pyts/badge/?version=latest)](https://pyts.readthedocs.io/) [![Codecov](https://codecov.io/gh/johannfaouzi/pyts/branch/main/graph/badge.svg)](https://codecov.io/gh/johannfaouzi/pyts) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyts.svg)](https://img.shields.io/pypi/pyversions/pyts.svg) [![PyPI version](https://badge.fury.io/py/pyts.svg)](https://badge.fury.io/py/pyts) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pyts.svg)](https://anaconda.org/conda-forge/pyts) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/johannfaouzi/pyts.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) [![Gitter](https://badges.gitter.im/johann-faouzi/community.svg)](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1244152.svg)](https://doi.org/10.5281/zenodo.1244152) ## 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 (>= 3.7) - NumPy (>= 1.17.5) - SciPy (>= 1.3.0) - Scikit-Learn (>=0.22.1) - Joblib (>=0.12) - Numba (>=0.48.0) 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](https://pyts.readthedocs.io/en/stable/changelog.html) 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](https://scikit-learn.org/stable/developers/). 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](https://pyts.readthedocs.io/). ### Citation If you use pyts in a scientific publication, we would appreciate citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): ``` 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, 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](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), [Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), [Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), [Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and [Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). - `bag_of_words`: This module provide tools to transform time series into bags of words. Implemented algorithms are [WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and [BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). - `classification`: This module provides implementations of algorithms that can classify time series. Implemented algorithms are [KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), [SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), [BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), [LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), [TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and [TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). - `datasets`: This module provides utilities to make or load toy datasets, as well as fetching datasets from the [UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). - `decomposition`: This module provides implementations of algorithms that decompose a time series into several time series. The only implemented algorithm is [Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). - `image`: This module provides implementations of algorithms that transform time series into images. Implemented algorithms are [Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), [Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and [Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). - `metrics`: This module provides implementations of metrics that are specific to time series. Implemented metrics are [Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) with several variants and the [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) metric. - `multivariate`: This modules provides utilities to deal with multivariate time series. Available tools are [MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and [MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) to transform and classify multivariate time series using tools for univariate time series respectively, as well as [JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and [WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) of missing values using interpolation. More information is available at the [pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), [ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), [WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and [ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). - `utils`: a simple module with [utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). %package help Summary: Development documents and examples for pyts Provides: python3-pyts-doc %description help [![Build Status](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_apis/build/status/johannfaouzi.pyts?branchName=main)](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) [![Documentation Status](https://readthedocs.org/projects/pyts/badge/?version=latest)](https://pyts.readthedocs.io/) [![Codecov](https://codecov.io/gh/johannfaouzi/pyts/branch/main/graph/badge.svg)](https://codecov.io/gh/johannfaouzi/pyts) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyts.svg)](https://img.shields.io/pypi/pyversions/pyts.svg) [![PyPI version](https://badge.fury.io/py/pyts.svg)](https://badge.fury.io/py/pyts) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pyts.svg)](https://anaconda.org/conda-forge/pyts) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/johannfaouzi/pyts.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) [![Gitter](https://badges.gitter.im/johann-faouzi/community.svg)](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1244152.svg)](https://doi.org/10.5281/zenodo.1244152) ## 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 (>= 3.7) - NumPy (>= 1.17.5) - SciPy (>= 1.3.0) - Scikit-Learn (>=0.22.1) - Joblib (>=0.12) - Numba (>=0.48.0) 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](https://pyts.readthedocs.io/en/stable/changelog.html) 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](https://scikit-learn.org/stable/developers/). 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](https://pyts.readthedocs.io/). ### Citation If you use pyts in a scientific publication, we would appreciate citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): ``` 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, 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](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), [Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), [Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), [Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and [Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). - `bag_of_words`: This module provide tools to transform time series into bags of words. Implemented algorithms are [WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and [BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). - `classification`: This module provides implementations of algorithms that can classify time series. Implemented algorithms are [KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), [SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), [BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), [LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), [TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and [TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). - `datasets`: This module provides utilities to make or load toy datasets, as well as fetching datasets from the [UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). - `decomposition`: This module provides implementations of algorithms that decompose a time series into several time series. The only implemented algorithm is [Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). - `image`: This module provides implementations of algorithms that transform time series into images. Implemented algorithms are [Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), [Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and [Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). - `metrics`: This module provides implementations of metrics that are specific to time series. Implemented metrics are [Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) with several variants and the [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) metric. - `multivariate`: This modules provides utilities to deal with multivariate time series. Available tools are [MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and [MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) to transform and classify multivariate time series using tools for univariate time series respectively, as well as [JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and [WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) of missing values using interpolation. More information is available at the [pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). - `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](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), [BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), [ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), [WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and [ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). - `utils`: a simple module with [utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). %prep %autosetup -n pyts-0.12.0 %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-pyts -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 0.12.0-1 - Package Spec generated