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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_transformation_plot_weasel.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_transformation_plot_weasel.py:


=======================================================
Word ExtrAction for time SEries cLassification (WEASEL)
=======================================================

Bag-of-words approaches are common in time series classification. The
Word ExtrAction for time SEries cLassification (WEASEL) 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.WEASEL`.

.. GENERATED FROM PYTHON SOURCE LINES 14-51



.. image-sg:: /auto_examples/transformation/images/sphx_glr_plot_weasel_001.png
   :alt: WEASEL transformation
   :srcset: /auto_examples/transformation/images/sphx_glr_plot_weasel_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 WEASEL

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

    # WEASEL transformation
    weasel = WEASEL(word_size=2, n_bins=2, window_sizes=[12, 36], sparse=False)
    X_weasel = weasel.fit_transform(X_train, y_train)

    # Visualize the transformation for the first time series
    plt.figure(figsize=(6, 4))
    vocabulary_length = len(weasel.vocabulary_)
    width = 0.3
    plt.bar(np.arange(vocabulary_length) - width / 2, X_weasel[y_train == 1][0],
            width=width, label='First time series in class 1')
    plt.bar(np.arange(vocabulary_length) + width / 2, X_weasel[y_train == 2][0],
            width=width, label='First time series in class 2')
    plt.xticks(np.arange(vocabulary_length),
               np.vectorize(weasel.vocabulary_.get)(np.arange(X_weasel[0].size)),
               fontsize=12, rotation=60)
    y_max = np.max(np.concatenate([X_weasel[y_train == 1][0],
                                   X_weasel[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("WEASEL transformation", fontsize=16)
    plt.legend(loc='best', fontsize=10)

    plt.subplots_adjust(bottom=0.27)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.208 seconds)


.. _sphx_glr_download_auto_examples_transformation_plot_weasel.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_weasel.py <plot_weasel.py>`

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

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


.. only:: html

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

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