.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_regression.py:


============================================
Continuous Target and Time Series Regression
============================================

In this example, we use the pipeline to learn a continuous time series target with a regressor




.. image:: /auto_examples/images/sphx_glr_plot_regression_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    N series in train:  1
    N series in test:  1
    N segments in train:  74
    N segments in test:  24
    Score:  0.973886266451577
    /home/david/Code/seglearn/examples/plot_regression.py:61: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      plt.show()






|


.. code-block:: default

    # Author: David Burns
    # License: BSD


    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import cross_validate

    from seglearn.pipe import Pype
    from seglearn.split import temporal_split, TemporalKFold
    from seglearn.transform import FeatureRep, Segment, last

    # for a single time series, we need to make it a list
    X = [np.arange(10000) / 100.]
    y = [np.sin(X[0]) * X[0] * 3 + X[0] * X[0]]

    # split the data along the time axis (our only option since we have only 1 time series)
    X_train, X_test, y_train, y_test = temporal_split(X, y)

    # setting y_func = last, selects the last value from each y segment as the target
    # other options include transform.middle, or you can make your own function
    # see the API documentation for further details

    pipe = Pype([('seg', Segment(width=200, overlap=0.5, y_func=last)),
                 ('features', FeatureRep()),
                 ('lin', LinearRegression())])

    # fit and score
    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("Score: ", score)

    # generate some predictions
    ytr, ytr_p = pipe.transform_predict(X_train, y_train)  # training predictions
    yte, yte_p = pipe.transform_predict(X_test, y_test)  # test predictions
    xtr = np.arange(len(ytr))  # segment number
    xte = np.arange(len(yte)) + len(xtr)

    # plot the amazing results
    plt.plot(xtr, ytr, '.', label="training")
    plt.plot(xte, yte, '.', label="actual")
    plt.plot(xte, yte_p, label="predicted")
    plt.xlabel("Segment Number")
    plt.ylabel("Target")
    plt.legend()
    plt.show()

    # # now try a cross validation
    # X = [np.arange(4000) / 100.]
    # y = [np.sin(X[0])]
    #
    # tkf = TemporalKFold()
    # X, y, cv = tkf.split(X, y)
    # cv_scores = cross_validate(pipe, X, y, cv=cv, return_train_score=True)
    # print("CV Scores: ", pd.DataFrame(cv_scores))


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

   **Total running time of the script:** ( 0 minutes  0.077 seconds)


.. _sphx_glr_download_auto_examples_plot_regression.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_regression.py <plot_regression.py>`



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

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


.. only:: html

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

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