Metadata-Version: 1.1
Name: seglearn
Version: 1.0.0
Summary: Machine Learning Time Series
Home-page: https://github.com/dmbee/seglearn
Author: David Burns
Author-email: david.mo.burns@gmail.com
License: UNKNOWN
Download-URL: https://github.com/dmbee/seglearn
Description: .. -*- mode: rst -*-
        
        .. _scikit-learn: http://scikit-learn.org/stable/
        
        .. _scikit-learn-contrib: https://github.com/scikit-learn-contrib
        
        |Travis|_ |Pypi|_ |PythonVersion|_ |CircleCI|_ |Coveralls|_
        
        .. |Travis| image:: https://travis-ci.org/dmbee/seglearn.svg?branch=master
        .. _Travis: https://travis-ci.org/dmbee/seglearn
        
        .. |Pypi| image:: https://badge.fury.io/py/seglearn.svg
        .. _Pypi: https://badge.fury.io/py/seglearn
        
        .. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/seglearn.svg
        .. _PythonVersion: https://img.shields.io/pypi/pyversions/seglearn.svg
        
        .. |CircleCI| image:: https://circleci.com/gh/dmbee/seglearn.svg?style=shield&circle-token=:circle-token
        .. _CircleCI: https://circleci.com/gh/dmbee/seglearn/tree/master
        
        .. |Coveralls| image:: https://coveralls.io/repos/github/dmbee/seglearn/badge.svg?branch=master&&service=github
        .. _Coveralls: https://coveralls.io/github/dmbee/seglearn?branch=master&service=github
        
        
        seglearn
        ========
        
        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_.
        
        Documentation
        -------------
        
        Installation documentation, API documentation, and examples can be found on the
        documentation_.
        
        .. _documentation: https://dmbee.github.io/seglearn/
        
        Dependencies
        ~~~~~~~~~~~~
        
        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.
        
        Installation
        ~~~~~~~~~~~~
        
        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
        
        Testing
        ~~~~~~~
        
        After installation, you can use `pytest` to run the test suite from seglearn's root directory::
        
          pytest
        
        Change Log
        ----------
        Version 1.0.0:
        
        * Major API change integrating preprocessing and estimation into single pipeline (Pype)
        * Note this is unfortunately not backwards compatible
        * Added Interp transformer for resampling time series
        
        Version 0.2.0:
        
        * Added transformer for segmenting data set with padding and / or truncation
        * Implemented random_state initialization for SegPipe
        * Corrected documentation for FeatureRep
        
        Version 0.1.6:
        
        * First stable version
        
        
        Development
        -----------
        
        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
        <http://scikit-learn.org/stable/developers>`_.
        
        About
        -----
        
        This package was developed by::
        
            David M. Burns MD, PhD(c)
            Sunnybrook Research Institute
            University of Toronto
            Email: david.mo.burns@gmail.com
        
        
        Citing seglearn
        ~~~~~~~~~~~~~~~
        
        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}
          }
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
