.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_transformation_plot_boss.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_transformation_plot_boss.py:


=========================
Bag-of-SFA Symbols (BOSS)
=========================

Bag-of-words approaches are common in time series classification. The
Bag-of-SFA Symbols (BOSS) algorithm extracts words from time series and
builds features representing frequencies of each word for each time series.
This example illustrates the words and the frequencies of these words that
have been learned by this algorithm.
It is implemented as :class:`pyts.transformation.BOSS`.



.. image:: /auto_examples/transformation/images/sphx_glr_plot_boss_001.png
    :class: sphx-glr-single-img






.. code-block:: default


    # Author: Johann Faouzi <johann.faouzi@gmail.com>
    # License: BSD-3-Clause

    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.datasets import load_gunpoint
    from pyts.transformation import BOSS

    # Toy dataset
    X_train, _, y_train, _ = load_gunpoint(return_X_y=True)

    # BOSS transformation
    boss = BOSS(word_size=2, n_bins=4, window_size=12, sparse=False)
    X_boss = boss.fit_transform(X_train)

    # Visualize the transformation for the first time series
    plt.figure(figsize=(6, 4))
    vocabulary_length = len(boss.vocabulary_)
    width = 0.3
    plt.bar(np.arange(vocabulary_length) - width / 2, X_boss[y_train == 1][0],
            width=width, label='First time series in class 1')
    plt.bar(np.arange(vocabulary_length) + width / 2, X_boss[y_train == 2][0],
            width=width, label='First time series in class 2')
    plt.xticks(np.arange(vocabulary_length),
               np.vectorize(boss.vocabulary_.get)(np.arange(X_boss[0].size)),
               fontsize=12)
    y_max = np.max(np.concatenate([X_boss[y_train == 1][0],
                                   X_boss[y_train == 2][0]]))
    plt.yticks(np.arange(y_max + 1), fontsize=12)
    plt.xlabel("Words", fontsize=14)
    plt.ylabel("Frequencies", fontsize=14)
    plt.title("BOSS transformation", fontsize=16)
    plt.legend(loc='best', fontsize=10)
    plt.tight_layout()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.247 seconds)


.. _sphx_glr_download_auto_examples_transformation_plot_boss.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_boss.py <plot_boss.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_boss.ipynb <plot_boss.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_