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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_classification_plot_learning_shapelets.py:


==============================
Learning Time-Series Shapelets
==============================

This example illustrates what the LearningShapelets algorithm learns in the
training phase.
A shapelet is defined as a contiguous subsequence of a time series.
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 identical
length extracted from this time series.
This estimator consists of two steps: computing the distances between the
shapelets and the time series, then computing a logistic regression using
these distances as features. This algorithm learns the shapelets as well as
the coefficients of the logistic regression.

This example highlights two learned shapelets and the distances between the
time series and both shapelets. Note that the tolerance parameter is set to a
high value so that the algorithm converges early (and the example runs faster).
It is implemented as :class:`pyts.classification.LearningShapelets`.

.. GENERATED FROM PYTHON SOURCE LINES 22-59



.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_learning_shapelets_001.png
   :alt: Two learned shapelets, Distances between the time series and both shapelets
   :srcset: /auto_examples/classification/images/sphx_glr_plot_learning_shapelets_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    import matplotlib.pyplot as plt
    import numpy as np
    from pyts.classification import LearningShapelets
    from pyts.datasets import load_gunpoint
    from pyts.utils import windowed_view

    # Load the data set and fit the classifier
    X, _, y, _ = load_gunpoint(return_X_y=True)
    clf = LearningShapelets(random_state=42, tol=0.01)
    clf.fit(X, y)

    # Select two shapelets
    shapelets = np.asarray([clf.shapelets_[0, -9], clf.shapelets_[0, -12]])

    # Derive the distances between the time series and the shapelets
    shapelet_size = shapelets.shape[1]
    X_window = windowed_view(X, window_size=shapelet_size, window_step=1)
    X_dist = np.mean(
        (X_window[:, :, None] - shapelets[None, :]) ** 2, axis=3).min(axis=1)

    plt.figure(figsize=(14, 4))

    # Plot the two shapelets
    plt.subplot(1, 2, 1)
    plt.plot(shapelets[0])
    plt.plot(shapelets[1])
    plt.title('Two learned shapelets', fontsize=14)

    # Plot the distances
    plt.subplot(1, 2, 2)
    for color, label in zip('br', (1, 2)):
        plt.scatter(X_dist[y == label, 0], X_dist[y == label, 1],
                    c=color, label='Class {}'.format(label))
    plt.title('Distances between the time series and both shapelets',
              fontsize=14)
    plt.legend()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  18.861 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_learning_shapelets.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_learning_shapelets.py <plot_learning_shapelets.py>`

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

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


.. only:: html

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

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