.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/transformation/plot_shapelet_transform.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_shapelet_transform.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_transformation_plot_shapelet_transform.py:


==================
Shapelet Transform
==================

The Shapelet Transform algorithm extracts shapelets from a data set of
time series and returns the distances between the shapelets and the time
series. A shapelet is defined as a subset of a time series, that is a
set of values from consecutive time points. The distance between a shapelet
and a time series is defined as the minimum of the distances between this
shapelet and all the shapelets of same length extracted from this time series.
The most discriminative shapelets are selected.
This example illustrates the transformation of this algorithm and highlights
the most discriminative shapelets that have been selected. It is implemented
as :class:`pyts.transformation.ShapeletTransform`.

.. GENERATED FROM PYTHON SOURCE LINES 17-44



.. image-sg:: /auto_examples/transformation/images/sphx_glr_plot_shapelet_transform_001.png
   :alt: The four most discriminative shapelets
   :srcset: /auto_examples/transformation/images/sphx_glr_plot_shapelet_transform_001.png
   :class: sphx-glr-single-img





.. code-block:: default


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

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

    # Shapelet transformation
    st = ShapeletTransform(window_sizes=[12, 24, 36, 48],
                           random_state=42, sort=True)
    X_new = st.fit_transform(X_train, y_train)

    # Visualize the four most discriminative shapelets
    plt.figure(figsize=(6, 4))
    for i, index in enumerate(st.indices_[:4]):
        idx, start, end = index
        plt.plot(X_train[idx], color='C{}'.format(i),
                 label='Sample {}'.format(idx))
        plt.plot(np.arange(start, end), X_train[idx, start:end],
                 lw=5, color='C{}'.format(i))

    plt.xlabel('Time', fontsize=12)
    plt.title('The four most discriminative shapelets', fontsize=14)
    plt.legend(loc='best', fontsize=8)
    plt.show()


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

   **Total running time of the script:** ( 1 minutes  7.010 seconds)


.. _sphx_glr_download_auto_examples_transformation_plot_shapelet_transform.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_shapelet_transform.py <plot_shapelet_transform.py>`

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

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


.. only:: html

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

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