.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/bag_of_words/plot_bow.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. 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 using the
``numerosity_reduction`` parameter.

This example illustrates the transformation in a particular setting: the step
of the sliding window is equal to the size of the sliding window, making the
subseries non-overlapping. It is common to use a step of 1 for the sliding
window, which is the default behavior. It is implemented as
:class:`pyts.bag_of_words.BagOfWords`.

.. GENERATED FROM PYTHON SOURCE LINES 17-54



.. image-sg:: /auto_examples/bag_of_words/images/sphx_glr_plot_bow_001.png
   :alt: Bag-of-words representation for time series
   :srcset: /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 matplotlib.pyplot as plt
    import numpy as np
    from pyts.bag_of_words import BagOfWords
    from pyts.datasets import load_gunpoint

    # Load the dataset and perform the transformation
    X, _, _, _ = load_gunpoint(return_X_y=True)
    window_size, word_size = 30, 5
    bow = BagOfWords(window_size=window_size, word_size=word_size,
                     window_step=window_size, numerosity_reduction=False)
    X_bow = bow.transform(X)

    # Plot the considered subseries
    plt.figure(figsize=(10, 4))
    splits_series = np.linspace(0, X.shape[1], 1 + X.shape[1] // window_size,
                                dtype='int64')
    for start, end in zip(splits_series[:-1],
                          np.clip(splits_series[1:] + 1, 0, X.shape[1])):
        plt.plot(np.arange(start, end), X[0, start:end], 'o-', lw=1, ms=1)

    # Plot the corresponding letters
    splits_letters = np.linspace(0, X.shape[1],
                                 1 + word_size * X.shape[1] // window_size)
    splits_letters = (
        (splits_letters[:-1] + splits_letters[1:]) / 2).astype('int64')
    for i, (x, text) in enumerate(zip(splits_letters, X_bow[0].replace(' ', ''))):
        plt.text(x, X[0, x] + 0.1, text, color="C{}".format(i // 5), fontsize=14)

    plt.ylim((-1, 2.2))
    plt.xlabel('Time', fontsize=12)
    plt.title('Bag-of-words representation for time series', fontsize=16)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.493 seconds)


.. _sphx_glr_download_auto_examples_bag_of_words_plot_bow.py:

.. only:: html

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


    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :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>`_