.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_logo.py:


Logo example
============

This is a small example that produces the logo of the ``onelearn`` library.



.. image:: /auto_examples/images/sphx_glr_plot_logo_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    Plotting iterations:   0%|          | 0/4 [00:00<?, ?it/s]
    Plotting iterations:  25%|##5       | 1/4 [00:02<00:06,  2.04s/it]
    Plotting iterations:  50%|#####     | 2/4 [00:04<00:04,  2.11s/it]
    Plotting iterations:  75%|#######5  | 3/4 [00:07<00:02,  2.31s/it]
    Plotting iterations: 100%|##########| 4/4 [00:09<00:00,  2.42s/it]
    Plotting iterations: 100%|##########| 4/4 [00:09<00:00,  2.44s/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
    )

    n_samples = 100
    n_features = 2
    n_classes = 2
    random_state = 123
    save_iterations = [5, 10, 30, 70]

    logging.info("Simulation of the data")
    X, y = make_moons(n_samples=n_samples, noise=0.25, 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]

    plt.figure(figsize=(3, 3))

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

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

    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)
        n_fig += 1
        Z = clf.predict_proba(X_mesh)[:, 1].reshape(xx.shape)
        ax = plt.subplot(2, 2, n_fig)
        score = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
        plot_contour_binary_classif(ax, xx, yy, Z, levels=5, norm=norm)

        plot_scatter_binary_classif(
            ax, xx, yy, X_train[:end], y_train[:end], s=15, norm=norm
        )
        bar.update(1)

    bar.close()
    plt.subplots_adjust(wspace=0, hspace=0)
    plt.savefig("logo.png", transparent=True)
    logging.info("Saved logo in file logo.png")


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

   **Total running time of the script:** ( 0 minutes  9.866 seconds)


.. _sphx_glr_download_auto_examples_plot_logo.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_logo.py <plot_logo.py>`



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

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


.. only:: html

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

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