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.. "auto_examples/classification/plot_tsbf.py"
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.. only:: html

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

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

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

.. _sphx_glr_auto_examples_classification_plot_tsbf.py:


===========================
Time Series Bag-of-Features
===========================

This example illustrates the feature importance scores of the final random
forest classifier of the time series bag-of-features (TSBF) algorithm. Fitting
this algorithm consists of the following steps:

* Random subsequences are extracted from each input time series.
* Each subsequence is split into several intervals.
* Three features are extracted from each interval: the mean, the standard
  deviation and the slope.
* Four features are also extracted from the whole subsequence:
  the mean, the standard deviation and the start and end indices.
* A first random forest classifier is fitted on this dataset of subsequences,
  and the label of a subsequence is given by the label of the time series from
  which this subsequence was extracted.
* Out-of-bag probabilities for each class are binned across all the
  subsequences extracted from a given time series; the mean probability for
  each class is also computed. They are the features extracted from the
  original data set.
* A second random forest classifier is finally fitted using the extracted
  features.

The number of extracted features is ``(n_bins + 1) * n_classes``, where
``n_bins`` is the number of bins used to bin the probabilities output by the
first random forest classifier, and ``n_classes`` is the number of classes. In
this example, the number of classes is 2 and the number of bins is set to 5
(meaning that the bin edges are [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), resulting in
12 features.

Plotting the feature importance scores, we can see that the second random
forest classifier finds the mean probabilities and most of
the extreme bins important:

* feature 0: bin [0.0, 0.2) for the first class,
* feature 4: bin [0.8, 1.0] for the first class,
* feature 5: mean probability for the first class,
* feature 9: bin [0.6, 0.8) for the second class,
* feature 10: bin [0.8, 1.0] for the second class,
* feature 11: mean probability for the second class.

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

.. GENERATED FROM PYTHON SOURCE LINES 46-67



.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_tsbf_001.png
   :alt: Feature importance scores
   :srcset: /auto_examples/classification/images/sphx_glr_plot_tsbf_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 TSBF
    import matplotlib.pyplot as plt

    X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
    clf = TSBF(random_state=43, bins=5)
    clf.fit(X_train, y_train)

    plt.bar(np.arange(clf.n_features_in_), clf.feature_importances_)
    plt.title('Feature importance scores')
    plt.xticks(np.arange(clf.n_features_in_),
               ['feature {}'.format(i) for i in range(clf.n_features_in_)],
               rotation=90)
    plt.ylabel("Mean decrease in impurity")
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  4.504 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_tsbf.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_tsbf.py <plot_tsbf.py>`

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

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


.. only:: html

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

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