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

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

.. _sphx_glr_auto_examples_bag_of_words_plot_bow.py:


============
Bag of Words
============

Time series are often transformed into sequences of symbols. Bag-of-words
approaches are then used to extract features from these sequences.
Identical back-to-back words can be discarded or kept using the
``numerosity_reduction`` parameter.
This example illustrates the transformation and the impact of this parameter.
It is implemented as :class:`pyts.bag_of_words.BagOfWords`.



.. image:: /auto_examples/bag_of_words/images/sphx_glr_plot_bow_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.bag_of_words import BagOfWords

    # Parameters
    n_samples, n_timestamps = 100, 48
    n_bins = 4

    # Toy dataset
    rng = np.random.RandomState(42)
    alphabet = np.array(['a', 'b', 'c', 'd'])
    X_ordinal = rng.randint(n_bins, size=(n_samples, n_timestamps))
    X_alphabet = alphabet[X_ordinal]

    # Bag-of-words transformation
    bow = BagOfWords(window_size=2, numerosity_reduction=False)
    X_bow = bow.transform(X_alphabet)
    words = np.asarray(X_bow[0].split(' '))
    different_words_idx = np.r_[True, words[1:] != words[:-1]]

    # Show the results
    plt.figure(figsize=(16, 7))
    plt.suptitle('Transforming a discretized time series into a bag of words',
                 fontsize=20, y=0.9)

    plt.subplot(121)
    plt.plot(X_ordinal[0], 'o', scalex=0.2)
    plt.yticks(np.arange(4), alphabet)
    plt.xticks([], [])
    plt.yticks(fontsize=16)
    plt.title('Without numerosity reduction', fontsize=16)

    for i, word in enumerate(words):
        plt.text(i, - 0.4 - (i % 5) / 4, word, fontsize=17, color='C0')

    plt.subplot(122)
    plt.plot(X_ordinal[0], 'o')
    plt.yticks(np.arange(4), alphabet)
    plt.xticks([], [])
    plt.yticks(fontsize=16)
    plt.title('With numerosity reduction', fontsize=16)

    for i, (word, different_word) in enumerate(zip(words, different_words_idx)):
        if different_word:
            plt.text(i, - 0.4 - (i % 5) / 4, word, fontsize=17, color='C0')
        else:
            plt.text(i, - 0.4 - (i % 5) / 4, word, fontsize=17, color='C0',
                     alpha=0.2)

    plt.tight_layout()
    plt.subplots_adjust(bottom=0.3, top=0.8)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.674 seconds)


.. _sphx_glr_download_auto_examples_bag_of_words_plot_bow.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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