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

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

.. _sphx_glr_auto_examples_plot_iris_rep.py:


=======================================================
Plot the decision surface of a pruneable tree using REP
=======================================================

Plot the decision surface of a :class:`pruneabletree.prune.PruneableDecisionTreeClassifier` trained on pairs
of features of the iris dataset.

For each pair of iris features, the decision tree learns decision
boundaries made of combinations of simple thresholding rules inferred from
the training samples.

In this example, the tree is pruned using Reduced Error Pruning (REP).




.. image:: /auto_examples/images/sphx_glr_plot_iris_rep_001.png
    :class: sphx-glr-single-img





.. code-block:: python

    print(__doc__)

    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import load_iris

    from pruneabletree import PruneableDecisionTreeClassifier

    # Parameters
    n_classes = 3
    plot_colors = "ryb"
    plot_step = 0.02

    def plot_surface(iris, prune_method):
        for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
                                        [1, 2], [1, 3], [2, 3]]):
            # We only take the two corresponding features
            X = iris.data[:, pair]
            y = iris.target

            # Train
            clf = PruneableDecisionTreeClassifier(prune=prune_method).fit(X, y)

            # Plot the decision boundary
            plt.subplot(2, 3, pairidx + 1)

            x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
            y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
            xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                                np.arange(y_min, y_max, plot_step))
            plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)

            Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
            Z = Z.reshape(xx.shape)
            plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)

            plt.xlabel(iris.feature_names[pair[0]])
            plt.ylabel(iris.feature_names[pair[1]])

            # Plot the training points
            for i, color in zip(range(n_classes), plot_colors):
                idx = np.where(y == i)
                plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                            cmap=plt.cm.RdYlBu, edgecolor='black', s=15)

        plt.suptitle("Decision surface of a decision tree using paired features")
        plt.legend(loc='lower right', borderpad=0, handletextpad=0)
        plt.axis("tight")
        plt.show()

    # Load data
    iris = load_iris()

    # Create plots
    plot_surface(iris, prune_method='rep')

**Total running time of the script:** ( 0 minutes  1.146 seconds)


.. _sphx_glr_download_auto_examples_plot_iris_rep.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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