kormilitzin / seglearn Goto Github PK

Seglearn is a python package for machine learning time series or sequences using a sliding window segmentation. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator. Seglearn provides a flexible approach to multivariate time series and contextual data for classification, regression, and forecasting problems. 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 2.7 and Python 3.5. The dependency requirements are based on the last scikit-learn release:

• scipy(>=0.13.3)
• numpy(>=1.8.2)
• scikit-learn(>=0.19.0)

Additionally, 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:

pytest

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.

This package was developed by:

David M. Burns MD, PhD(c)
Sunnybrook Research Institute
University of Toronto
Email: [email protected]

If you use seglearn 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}
}