GitLocker: The Coding Marketplace

Description:

pyts 0.13.0

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.