.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_model_selection1.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_model_selection1.py:


==========================
Hyperparameter Selection 1
==========================

This example demonstrates how to do model selection in a feature representation pipeline using a grid search



.. image:: /auto_examples/images/sphx_glr_plot_model_selection1_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    /home/david/Code/seglearn/examples/plot_model_selection1.py:72: 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
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import GridSearchCV, GroupKFold
    from sklearn.preprocessing import StandardScaler

    import seglearn as sgl


    def plot_grid_search(cv_results, grid_param_1, grid_param_2, name_param_1, name_param_2):
        # plotting grid results from David Alvarez on Stack Overflow

        # Get Test Scores Mean and std for each grid search
        scores_mean = cv_results['mean_test_score']
        scores_mean = np.array(scores_mean).reshape(len(grid_param_2), len(grid_param_1))
        scores_sd = cv_results['std_test_score']
        scores_sd = np.array(scores_sd).reshape(len(grid_param_2), len(grid_param_1))

        # Plot Grid search scores
        _, ax = plt.subplots(1, 1)

        # Param1 is the X-axis, Param 2 is represented as a different curve (color line)
        for idx, val in enumerate(grid_param_2):
            ax.plot(grid_param_1, scores_mean[idx, :], '-o', label=name_param_2 + ': ' + str(val))

        ax.set_title("Grid Search Scores", fontsize=20, fontweight='bold')
        ax.set_xlabel(name_param_1, fontsize=16)
        ax.set_ylabel('CV Average Score', fontsize=16)
        ax.legend(loc="best", fontsize=15)
        ax.grid(True)


    # load the data
    data = sgl.load_watch()
    X = data['X']
    y = data['y']
    g = data['subject']

    # use subject id to group folds
    splitter = GroupKFold(n_splits=3)
    cv = splitter.split(X, y, groups=g)

    # create a feature representation pipeline
    pipe = sgl.Pype([('seg', sgl.Segment()),
                     ('features', sgl.FeatureRep()),
                     ('scaler', StandardScaler()),
                     ('rf', RandomForestClassifier())])

    # create a parameter dictionary using the sklearn API
    # note that if you want to set a parameter to a single value, it will still need to be as a list

    par_grid = {'seg__width': [50, 100, 200],
                'seg__overlap': [0., 0.5],
                'rf__n_estimators': [20]}

    clf = GridSearchCV(pipe, par_grid, cv=cv)
    clf.fit(X, y)

    plot_grid_search(clf.cv_results_, par_grid['seg__width'],
                     par_grid['seg__overlap'], 'width', 'overlap')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  8.050 seconds)


.. _sphx_glr_download_auto_examples_plot_model_selection1.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_model_selection1.py <plot_model_selection1.py>`



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

     :download:`Download Jupyter notebook: plot_model_selection1.ipynb <plot_model_selection1.ipynb>`


.. only:: html

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

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