.. _sphx_glr_auto_examples_plot_weasel.py:


==============================================
Word ExtrAction for time SEries cLassification
==============================================

This example shows how the WEASEL algorithm transforms a time series of real
numbers into a sequence of frequencies of words. It is implemented as
:class:`pyts.transformation.WEASEL`.




.. image:: /auto_examples/images/sphx_glr_plot_weasel_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.transformation import WEASEL

    # Parameters
    n_samples, n_features = 100, 144
    n_classes = 2

    # Toy dataset
    rng = np.random.RandomState(41)
    X = rng.randn(n_samples, n_features)
    y = rng.randint(n_classes, size=n_samples)

    # WEASEL transformation
    weasel = WEASEL(n_coefs=2, window_sizes=[12, 24, 36], pvalue_threshold=0.2)
    X_weasel = weasel.fit_transform(X, y).toarray()

    # Visualize the transformation for the first time series
    plt.figure(figsize=(12, 8))
    plt.bar(np.arange(X_weasel[0].size), X_weasel[0])
    plt.xticks(np.arange(X_weasel[0].size),
               np.vectorize(weasel.vocabulary_.get)(np.arange(X_weasel[0].size)),
               fontsize=12, rotation=60)
    plt.xlabel("Words", fontsize=18)
    plt.ylabel("Frequencies", fontsize=18)
    plt.show()

**Total running time of the script:** ( 0 minutes  4.152 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :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.readthedocs.io>`_