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

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

.. _sphx_glr_auto_examples_classification_plot_time_series_forest.py:


==================
Time Series Forest
==================

This example illustrates which information is considered important
by the algorithm in order to classify time series. The index of
the most important window is retrieved via the ``feature_importance_``
and ``indices_`` attributes. The first time series for both classes
are plotted and the most important window is highlighted with a
larger line width.

It is implemented as :class:`pyts.classification.TimeSeriesForest`.

.. GENERATED FROM PYTHON SOURCE LINES 15-47



.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_time_series_forest_001.png
   :alt: The most important window according to the feature importance scores
   :srcset: /auto_examples/classification/images/sphx_glr_plot_time_series_forest_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
    from pyts.datasets import load_gunpoint
    from pyts.classification import TimeSeriesForest
    import matplotlib.pyplot as plt

    X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
    clf = TimeSeriesForest(random_state=43)
    clf.fit(X_train, y_train)

    start_idxmax, end_idxmax = clf.indices_[
        np.argmax(clf.feature_importances_) // 3]

    plt.figure(figsize=(12, 5))
    plt.plot(X_train[y_train == 1][0], label='First sample in class 1')
    plt.plot(np.arange(start_idxmax, end_idxmax),
             X_train[y_train == 1][0, start_idxmax:end_idxmax],
             color='C0', lw=4)

    plt.plot(X_train[y_train == 2][0], label='First sample in class 2')
    plt.plot(np.arange(start_idxmax, end_idxmax),
             X_train[y_train == 2][0, start_idxmax:end_idxmax],
             color='C1', lw=4)

    plt.legend(loc='best', fontsize=14)
    plt.title('The most important window according to the feature importance '
              'scores', fontsize=16)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.733 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_time_series_forest.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_time_series_forest.py <plot_time_series_forest.py>`

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

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


.. only:: html

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

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