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|
%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
[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main)
[](https://pyts.readthedocs.io/)
[](https://codecov.io/gh/johannfaouzi/pyts)
[](https://img.shields.io/pypi/pyversions/pyts.svg)
[](https://badge.fury.io/py/pyts)
[](https://anaconda.org/conda-forge/pyts)
[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python)
[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[](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
[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main)
[](https://pyts.readthedocs.io/)
[](https://codecov.io/gh/johannfaouzi/pyts)
[](https://img.shields.io/pypi/pyversions/pyts.svg)
[](https://badge.fury.io/py/pyts)
[](https://anaconda.org/conda-forge/pyts)
[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python)
[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[](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
[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main)
[](https://pyts.readthedocs.io/)
[](https://codecov.io/gh/johannfaouzi/pyts)
[](https://img.shields.io/pypi/pyversions/pyts.svg)
[](https://badge.fury.io/py/pyts)
[](https://anaconda.org/conda-forge/pyts)
[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python)
[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[](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 <Python_Bot@openeuler.org> - 0.12.0-1
- Package Spec generated
|
