.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_signals_weighted_depth.py:


Weighted depths of `AMFRegressor` on several 1D signals.
========================================================

The example below illustrates the weighted depth learned internally by the
AMF algorithm to estimate 1D regression functions. We observe that AMF automatically
adapts to the local regularity of signals, by putting more emphasis on deeper trees
where the regression function is not unsmooth.



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


    *

      .. image:: /auto_examples/images/sphx_glr_plot_signals_weighted_depth_001.png
            :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_signals_weighted_depth_002.png
            :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_signals_weighted_depth_003.png
            :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_signals_weighted_depth_004.png
            :class: sphx-glr-multi-img






.. code-block:: default

    import sys
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.cm import get_cmap
    import logging

    sys.path.extend([".", ".."])

    from onelearn import AMFRegressor
    from onelearn.datasets import get_signal, make_regression

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

    colormap = get_cmap("tab20")

    n_samples_train = 5000
    n_samples_test = 1000
    random_state = 42


    noise = 0.03
    use_aggregation = True
    split_pure = True
    n_estimators = 100
    step = 10.0


    signals = ["heavisine", "bumps", "blocks", "doppler"]


    def plot_weighted_depth(signal):
        X_train, y_train = make_regression(
            n_samples=n_samples_train, signal=signal, noise=noise, random_state=random_state
        )
        X_test = np.linspace(0, 1, num=n_samples_test)

        amf = AMFRegressor(
            random_state=random_state,
            use_aggregation=use_aggregation,
            n_estimators=n_estimators,
            split_pure=split_pure,
            step=step,
        )

        amf.partial_fit(X_train.reshape(n_samples_train, 1), y_train)
        y_pred = amf.predict(X_test.reshape(n_samples_test, 1))
        weighted_depths = amf.weighted_depth(X_test.reshape(n_samples_test, 1))

        fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, sharex=True, figsize=(6, 5))

        plot_samples = ax1.plot(
            X_train, y_train, color=colormap.colors[1], lw=2, label="Samples"
        )[0]
        plot_signal = ax1.plot(
            X_test,
            get_signal(X_test, signal),
            lw=2,
            color=colormap.colors[0],
            label="Signal",
        )[0]
        plot_prediction = ax2.plot(
            X_test.ravel(), y_pred, lw=2, color=colormap.colors[2], label="Prediction"
        )[0]
        ax3.plot(
            X_test,
            weighted_depths[:, 1:],
            lw=1,
            color=colormap.colors[5],
            alpha=0.2,
            label="Weighted depths",
        )
        plot_weighted_depths = ax3.plot(
            X_test, weighted_depths[:, 0], lw=1, color=colormap.colors[5], alpha=0.2
        )[0]

        plot_mean_weighted_depths = ax3.plot(
            X_test,
            weighted_depths.mean(axis=1),
            lw=2,
            color=colormap.colors[4],
            label="Mean weighted depth",
        )[0]
        filename = "weighted_depths_%s.pdf" % signal
        fig.subplots_adjust(hspace=0.1)
        fig.legend(
            (
                plot_signal,
                plot_samples,
                plot_mean_weighted_depths,
                plot_weighted_depths,
                plot_prediction,
            ),
            (
                "Signal",
                "Samples",
                "Average weighted depths",
                "Weighted depths",
                "Prediction",
            ),
            fontsize=12,
            loc="upper center",
            bbox_to_anchor=(0.5, 1.0),
            ncol=3,
        )
        plt.savefig(filename)
        logging.info("Saved the decision functions in '%s'" % filename)


    for signal in signals:
        plot_weighted_depth(signal)


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

   **Total running time of the script:** ( 0 minutes  20.976 seconds)


.. _sphx_glr_download_auto_examples_plot_signals_weighted_depth.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_signals_weighted_depth.py <plot_signals_weighted_depth.py>`



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

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


.. only:: html

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

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