Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator. Seglearn provides a flexible approach to multivariate time series and related contextual (meta) data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models. It is compatible with scikit-learn.
Installation documentation, API documentation, and examples can be found on the documentation.
seglearn is tested to work under Python 3.5, 3.6, and 3.8. The dependency requirements are:
- scipy(>=0.17.0)
- numpy(>=1.11.0)
- scikit-learn(>=0.21.3)
seglearn is now also compatible with sklearn 1.0+
To run the examples, you need:
- matplotlib(>=2.0.0)
- keras (>=2.1.4) for the neural network examples
- pandas
In order to run the test cases, you need:
- pytest
The neural network examples were tested on keras using the tensorflow-gpu backend, which is recommended.
seglearn-learn is currently available on the PyPi's repository and you can install it via pip:
pip install -U seglearn
or if you use python3:
pip3 install -U seglearn
If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:
git clone https://github.com/dmbee/seglearn.git cd seglearn pip install .
Or install using pip and GitHub:
pip install -U git+https://github.com/dmbee/seglearn.git
After installation, you can use pytest to run the test suite from seglearn's root directory:
python -m pytest
Version history can be viewed in the Change Log.
The development of this scikit-learn-contrib is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide.
Please submit new pull requests on the dev branch with unit tests and an example to demonstrate any new functionality / api changes.
If you use seglearn in a scientific publication, we would appreciate citations to the following paper:
@article{arXiv:1803.08118,
author = {David Burns, Cari Whyne},
title = {Seglearn: A Python Package for Learning Sequences and Time Series},
journal = {arXiv},
year = {2018},
url = {https://arxiv.org/abs/1803.08118}
}
If you use the seglearn test data in a scientific publication, we would appreciate citations to the following paper:
@article{arXiv:1802.01489,
author = {David Burns, Nathan Leung, Michael Hardisty, Cari Whyne, Patrick Henry, Stewart McLachlin},
title = {Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch},
journal = {arXiv},
year = {2018},
url = {https://arxiv.org/abs/1802.01489}
}