.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_forest_effect.py:


Illustration of the forest effect
=================================

In this example we show that the decision function of a forest is the average of
independent trees, and that averaging allows to produce smooth decision functions.



.. image:: /auto_examples/images/sphx_glr_plot_forest_effect_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    2020-04-30 21:08:19 Building the graph...
    2020-04-30 21:08:28 Saved the forest effect plot in forest_effect.pdf






|


.. code-block:: default

    import sys
    import warnings

    warnings.filterwarnings("ignore")

    import logging
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_moons


    sys.path.extend([".", ".."])
    from onelearn import AMFClassifier
    from experiments.plot import (
        plot_contour_binary_classif,
        plot_scatter_binary_classif,
        get_mesh,
    )


    logging.basicConfig(
        level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
    )


    norm = plt.Normalize(vmin=0.0, vmax=1.0)
    levels = 30


    def plot_forest_effect(forest, dataset):
        n_estimators = forest.n_estimators
        _ = plt.figure(figsize=(2 * (n_estimators / 2 + 1), 4))

        X, y = dataset
        xx, yy, X_mesh = get_mesh(X)

        # Plot the training points
        ax = plt.subplot(2, n_estimators / 2 + 1, 1)
        plot_scatter_binary_classif(ax, xx, yy, X, y, title="Input data")

        forest.partial_fit(X, y)

        for idx_tree in range(n_estimators):
            ax = plt.subplot(2, n_estimators / 2 + 1, idx_tree + 2)
            Z = forest.predict_proba_tree(X_mesh, idx_tree)[:, 1].reshape(xx.shape)
            plot_contour_binary_classif(
                ax, xx, yy, Z, title="Tree #%d" % (idx_tree + 1), norm=norm, levels=levels
            )

        ax = plt.subplot(2, n_estimators / 2 + 1, n_estimators + 2)
        Z = forest.predict_proba(X_mesh)[:, 1].reshape(xx.shape)
        plot_contour_binary_classif(ax, xx, yy, Z, title="Forest", norm=norm, levels=levels)
        plt.tight_layout()


    n_samples = 100
    n_features = 2
    n_classes = 2
    random_state = 42
    dataset = make_moons(n_samples=n_samples, noise=0.15, random_state=random_state)

    n_estimators = 10
    amf = AMFClassifier(
        n_classes=n_classes,
        n_estimators=n_estimators,
        random_state=random_state,
        use_aggregation=True,
        split_pure=True,
    )

    logging.info("Building the graph...")
    plot_forest_effect(amf, dataset)

    plt.savefig("forest_effect.pdf")
    logging.info("Saved the forest effect plot in forest_effect.pdf")


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

   **Total running time of the script:** ( 0 minutes  9.546 seconds)


.. _sphx_glr_download_auto_examples_plot_forest_effect.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_forest_effect.py <plot_forest_effect.py>`



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

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


.. only:: html

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

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