.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_iterations.py:


Plot iterations of AMFClassifier
================================

In this examples we illustrate the evolution of the decision function produced by
:obj:`AMFClassifier` along iterations (repeated calls to ``partial_fit``).



.. image:: /auto_examples/images/sphx_glr_plot_iterations_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    Plotting iterations:   0%|          | 0/6 [00:00<?, ?it/s]
    Plotting iterations:  17%|#6        | 1/6 [00:01<00:09,  1.83s/it]
    Plotting iterations:  33%|###3      | 2/6 [00:03<00:07,  1.90s/it]
    Plotting iterations:  50%|#####     | 3/6 [00:06<00:06,  2.02s/it]
    Plotting iterations:  67%|######6   | 4/6 [00:08<00:04,  2.22s/it]
    Plotting iterations:  83%|########3 | 5/6 [00:11<00:02,  2.46s/it]
    Plotting iterations: 100%|##########| 6/6 [00:15<00:00,  2.72s/it]
    Plotting iterations: 100%|##########| 6/6 [00:15<00:00,  2.54s/it]






|


.. code-block:: default

    import sys
    import logging
    import matplotlib.pyplot as plt
    from tqdm import trange
    from sklearn.datasets import make_moons
    from sklearn.metrics import roc_auc_score
    from sklearn.model_selection import train_test_split

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

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

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

    n_samples = 400
    n_features = 2
    n_classes = 2
    seed = 123
    random_state = 42
    levels = 30

    save_iterations = [5, 10, 20, 50, 100, 200]
    output_filename = "iterations.pdf"

    logging.info("Simulation of the data")
    X, y = make_moons(n_samples=n_samples, noise=0.2, random_state=random_state)

    logging.info("Train/Test splitting")
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.5, random_state=random_state
    )

    logging.info("Computation of the meshgrid")
    xx, yy, X_mesh = get_mesh(X)


    clf = AMFClassifier(
        n_classes=n_classes,
        n_estimators=100,
        random_state=random_state,
        split_pure=True,
        use_aggregation=True,
    )


    n_plots = len(save_iterations)
    n_fig = 0
    save_iterations = [0, *save_iterations]

    fig, axes = plt.subplots(nrows=2, ncols=n_plots, figsize=(3 * n_plots, 6))

    logging.info("Launching iterations")
    bar = trange(n_plots, desc="Plotting iterations", leave=True)


    for start, end in zip(save_iterations[:-1], save_iterations[1:]):
        X_iter = X_train[start:end]
        y_iter = y_train[start:end]
        clf.partial_fit(X_iter, y_iter)

        ax = axes[0, n_fig]
        plot_scatter_binary_classif(
            ax,
            xx,
            yy,
            X_train[:end],
            y_train[:end],
            s=50,
            title="t = %d" % end,
            fontsize=20,
            noaxes=False,
        )

        Z = clf.predict_proba(X_mesh)[:, 1].reshape(xx.shape)
        score = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
        ax = axes[1, n_fig]
        plot_contour_binary_classif(ax, xx, yy, Z, score=score, levels=levels, norm=norm)
        n_fig += 1
        bar.update(1)

    bar.close()

    plt.tight_layout()
    plt.savefig(output_filename)
    logging.info("Saved result in file %s" % output_filename)


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

   **Total running time of the script:** ( 0 minutes  16.269 seconds)


.. _sphx_glr_download_auto_examples_plot_iterations.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_iterations.py <plot_iterations.py>`



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

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


.. only:: html

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

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