.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_padtrunc.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_padtrunc.py:


=================================================
Pipeline using Time Series Padding and Truncation
=================================================

This is a basic example using the pipeline to learn a feature representation of the time series data
using padding and truncation instead of sliding window segmentation.




.. image:: /auto_examples/images/sphx_glr_plot_padtrunc_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    N series in train:  105
    N series in test:  35
    N segments in train:  105
    N segments in test:  35
    Accuracy score:  0.8285714285714286

    <matplotlib.image.AxesImage object at 0x7f4c55d8d7f0>





|


.. code-block:: default

    # Author: David Burns
    # License: BSD


    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import LinearSVC

    from seglearn.datasets import load_watch
    from seglearn.pipe import Pype
    from seglearn.transform import FeatureRep, PadTrunc

    # load the data
    data = load_watch()
    X = data['X']
    y = data['y']

    # create a feature representation pipeline with PadTrunc segmentation
    # the time series are between 20-40 seconds
    # this truncates them all to the first 5 seconds (sampling rate is 50 Hz)

    pipe = Pype([('trunc', PadTrunc(width=250)),
                 ('features', FeatureRep()),
                 ('scaler', StandardScaler()),
                 ('svc', LinearSVC())])

    # split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, shuffle=True,
                                                        random_state=42)

    pipe.fit(X_train, y_train)
    score = pipe.score(X_test, y_test)

    print("N series in train: ", len(X_train))
    print("N series in test: ", len(X_test))
    print("N segments in train: ", pipe.N_train)
    print("N segments in test: ", pipe.N_test)
    print("Accuracy score: ", score)

    img = mpimg.imread('trunk.jpg')
    plt.imshow(img)


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

   **Total running time of the script:** ( 0 minutes  0.201 seconds)


.. _sphx_glr_download_auto_examples_plot_padtrunc.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

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



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

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


.. only:: html

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

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