Metadata-Version: 2.1
Name: sequentia
Version: 0.11.1
Summary: A machine learning interface for isolated sequence classification algorithms in Python.
Home-page: https://github.com/eonu/sequentia
Author: Edwin Onuonga
Author-email: ed@eonu.net
License: MIT
Project-URL: Documentation, https://sequentia.readthedocs.io/en/latest
Project-URL: Bug Tracker, https://github.com/eonu/sequentia/issues
Project-URL: Source Code, https://github.com/eonu/sequentia
Description: <p align="center">
          <h1 align="center">
            <img src="https://i.ibb.co/42GkhfR/sequentia.png" width="275px" alt="Sequentia">
          </h1>
        </p>
        
        <p align="center">
          <em>A machine learning interface for isolated sequence classification algorithms in Python.</em>
        </p>
        
        <p align="center">
          <div align="center">
            <a href="https://pypi.org/project/sequentia">
              <img src="https://img.shields.io/pypi/v/sequentia?style=flat-square" alt="PyPI"/>
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              <img src="https://img.shields.io/pypi/l/sequentia?style=flat-square" alt="PyPI - License"/>
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            <a href="https://sequentia.readthedocs.io/en/latest">
              <img src="https://readthedocs.org/projects/sequentia/badge/?version=latest&style=flat-square" alt="Read The Docs - Documentation">
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            <a href="https://travis-ci.org/eonu/sequentia">
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        ## Introduction
        
        Sequential data is often observed in many different forms such as audio signals and stock prices, to even brain and heart signals. Such data is of particular interest in machine learning, as changing patterns over time naturally provide many interesting opportunities and challenges for classification.
        
        **Sequentia is a Python package that implements various classification algorithms for sequential data.**
        
        Some examples of how Sequentia can be used in isolated sequence classification include:
        
        - determining a spoken word based on its audio signal or some other representation such as MFCCs,
        - identifying potential heart conditions such as arrhythmia from ECG signals,
        - predicting motion intent for gesture control from electrical muscle activity,
        - classifying hand-written characters according to their pen-tip trajectories,
        - classifying hand or head gestures from rotation or movement signals,
        - classifying the sentiment of a phrase or sentence in natural language from word embeddings.
        
        ## Features
        
        Sequentia provides the following algorithms, all supporting multivariate sequences with different durations.
        
        ### Classification algorithms
        
        - [x] [Hidden Markov Models](https://sequentia.readthedocs.io/en/latest/sections/classifiers/gmmhmm.html) (via [`hmmlearn`](https://github.com/hmmlearn/hmmlearn))<br/><em>Learning with the Baum-Welch algorithm</em> [[1]](#references)
          - [x] Gaussian Mixture Model emissions
          - [x] Linear, left-right and ergodic topologies
          - [x] Multi-processed predictions
        - [x] [Dynamic Time Warping k-Nearest Neighbors](https://sequentia.readthedocs.io/en/latest/sections/classifiers/knn.html) (via [`dtaidistance`](https://github.com/wannesm/dtaidistance))
          - [x] Sakoe–Chiba band global warping constraint
          - [x] Dependent and independent feature warping (DTWD & DTWI)
          - [x] Custom distance-weighted predictions
          - [x] Multi-processed predictions
        
        <p align="center">
          <img src="/docs/_static/classifier.svg" width="60%"/><br/>
          Example of a classification algorithm: <em>a multi-class HMM sequence classifier</em>
        </p>
        
        ### Preprocessing methods
        
        - [x] Centering, standardization and min-max scaling
        - [x] Decimation and mean downsampling
        - [x] Mean and median filtering
        
        ## Installation
        
        ```console
        pip install sequentia
        ```
        
        ## Documentation
        
        Documentation for the package is available on [Read The Docs](https://sequentia.readthedocs.io/en/latest).
        
        ## Tutorials and examples
        
        For detailed tutorials and examples on the usage of Sequentia, [see the notebooks here](https://nbviewer.jupyter.org/github/eonu/sequentia/tree/master/notebooks/).
        
        Below are some basic examples of univariate and multivariate sequences can be used in Sequentia.
        
        ### Univariate sequences
        
        ```python
        import numpy as np, sequentia as seq
        
        # Generate training observation sequences and labels
        X, y = [
          np.array([1, 0, 5, 3, 7, 2, 2, 4, 9, 8, 7]),
          np.array([2, 1, 4, 6, 5, 8]),
          np.array([5, 8, 0, 3, 1, 0, 2, 7, 9])
        ], ['good', 'good', 'bad']
        
        # Create and fit the classifier
        clf = seq.KNNClassifier(k=1, classes=('good', 'bad'))
        clf.fit(X, y)
        
        # Make a prediction for a new observation sequence
        x_new = np.array([0, 3, 2, 7, 9, 1, 1])
        y_new = clf.predict(x_new)
        ```
        
        ### Multivariate sequences
        
        ```python
        import numpy as np, sequentia as seq
        
        # Generate training observation sequences and labels
        X, y = [
          np.array([[1, 0, 5, 3, 7, 2, 2, 4, 9, 8, 7],
                    [3, 8, 4, 0, 7, 1, 1, 3, 4, 2, 9]]).T,
          np.array([[2, 1, 4, 6, 5, 8],
                    [5, 3, 9, 0, 8, 2]]).T,
          np.array([[5, 8, 0, 3, 1, 0, 2, 7, 9],
                    [0, 2, 7, 1, 2, 9, 5, 8, 1]]).T
        ], ['good', 'good', 'bad']
        
        # Create and fit the classifier
        clf = seq.KNNClassifier(k=1, classes=('good', 'bad'))
        clf.fit(X, y)
        
        # Make a prediction for a new observation sequence
        x_new = np.array([[0, 3, 2, 7, 9, 1, 1],
                          [2, 5, 7, 4, 2, 0, 8]]).T
        y_new = clf.predict(x_new)
        ```
        
        ## Acknowledgments
        
        In earlier versions of the package (<0.10.0), an approximate dynamic time warping algorithm implementation ([`fastdtw`](https://github.com/slaypni/fastdtw)) was used in hopes of speeding up k-NN predictions, as the authors of the original FastDTW paper [[2]](#references) claim that approximated DTW alignments can be computed in linear memory and time - compared to the O(N^2) runtime complexity of the usual exact DTW implementation.
        
        However, I was recently contacted by [Prof. Eamonn Keogh](https://www.cs.ucr.edu/~eamonn/) (at _University of California, Riverside_), whose recent work [[3]](#references) makes the surprising revelation that FastDTW is generally slower than the exact DTW algorithm that it approximates. Upon switching from the `fastdtw` package to [`dtaidistance`](https://github.com/wannesm/dtaidistance) (a very solid implementation of exact DTW with fast pure C compiled functions), DTW k-NN prediction times were indeed reduced drastically.
        
        I would like to thank Prof. Eamonn Keogh for directly reaching out to me regarding this finding!
        
        ## References
        
        <table>
          <tbody>
            <tr>
              <td>[1]</td>
              <td>
                <a href=https://web.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf">Lawrence R. Rabiner. <b>"A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition"</b> <em>Proceedings of the IEEE 77 (1989)</em>, no. 2, pp. 257-86.</a>
              </td>
            </tr>
            <tr>
              <td>[2]</td>
              <td>
                <a href="https://pdfs.semanticscholar.org/05a2/0cde15e172fc82f32774dd0cf4fe5827cad2.pdf">Stan Salvador & Philip Chan. <b>"FastDTW: Toward accurate dynamic time warping in linear time and space."</b> <em>Intelligent Data Analysis 11.5 (2007)</em>, 561-580.</a>
              </td>
            </tr>
            <tr>
              <td>[3]</td>
              <td>
                <a href="https://arxiv.org/ftp/arxiv/papers/2003/2003.11246.pdf">Renjie Wu & Eamonn J. Keogh. <b>"FastDTW is approximate and Generally Slower than the Algorithm it Approximates"</b> <em>IEEE Transactions on Knowledge and Data Engineering (2020)</em>, 1–1.</a>
              </td>
            </tr>
          </tbody>
        </table>
        
        ## Contributors
        
        All contributions to this repository are greatly appreciated. Contribution guidelines can be found [here](/CONTRIBUTING.md).
        
        <table>
        	<thead>
        		<tr>
        			<th align="center">
                <a href="https://github.com/eonu">
                  <img src="https://avatars0.githubusercontent.com/u/24795571?s=460&v=4" alt="Edwin Onuonga" width="60px">
                  <br/><sub><b>Edwin Onuonga</b></sub>
                </a>
                <br/>
                <a href="mailto:ed@eonu.net">✉️</a>
                <a href="https://eonu.net">🌍</a>
        			</th>
              <th align="center">
                <a href="https://github.com/Prhmma">
                  <img src="https://avatars0.githubusercontent.com/u/16954887?s=460&v=4" alt="Prhmma" width="60px">
                  <br/><sub><b>Prhmma</b></sub>
                </a>
        			</th>
        			<!-- Add more <th></th> blocks for more contributors -->
        		</tr>
        	</thead>
        </table>
        
        ---
        
        <p align="center">
          <b>Sequentia</b> &copy; 2019-2022, Edwin Onuonga - Released under the <a href="https://opensource.org/licenses/MIT">MIT</a> License.<br/>
          <em>Authored and maintained by Edwin Onuonga.</em>
        </p>
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