.. _sphx_glr_auto_examples_linear_model_plot_ols_3d.py:


=========================================================
Sparsity Example: Fitting only features 1  and 2
=========================================================

Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature 2
has a strong coefficient on the full model, it does not
give us much regarding `y` when compared to just feature 1



.. code-block:: python

    print(__doc__)


    # Code source: Gaƫl Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np
    from mpl_toolkits.mplot3d import Axes3D

    from sklearn import datasets, linear_model

    diabetes = datasets.load_diabetes()
    indices = (0, 1)

    X_train = diabetes.data[:-20, indices]
    X_test = diabetes.data[-20:, indices]
    y_train = diabetes.target[:-20]
    y_test = diabetes.target[-20:]

    ols = linear_model.LinearRegression()
    ols.fit(X_train, y_train)








Plot the figure


.. code-block:: python

    def plot_figs(fig_num, elev, azim, X_train, clf):
        fig = plt.figure(fig_num, figsize=(4, 3))
        plt.clf()
        ax = Axes3D(fig, elev=elev, azim=azim)

        ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+')
        ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]),
                        np.array([[-.1, .15], [-.1, .15]]),
                        clf.predict(np.array([[-.1, -.1, .15, .15],
                                              [-.1, .15, -.1, .15]]).T
                                    ).reshape((2, 2)),
                        alpha=.5)
        ax.set_xlabel('X_1')
        ax.set_ylabel('X_2')
        ax.set_zlabel('Y')
        ax.w_xaxis.set_ticklabels([])
        ax.w_yaxis.set_ticklabels([])
        ax.w_zaxis.set_ticklabels([])

    #Generate the three different figures from different views
    elev = 43.5
    azim = -110
    plot_figs(1, elev, azim, X_train, ols)

    elev = -.5
    azim = 0
    plot_figs(2, elev, azim, X_train, ols)

    elev = -.5
    azim = 90
    plot_figs(3, elev, azim, X_train, ols)

    plt.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png
            :scale: 47

    *

      .. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png
            :scale: 47

    *

      .. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png
            :scale: 47




**Total running time of the script:**
(0 minutes 0.374 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_ols_3d.py <plot_ols_3d.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_ols_3d.ipynb <plot_ols_3d.ipynb>`