.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/classifiers/frovoco.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_classifiers_frovoco.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_classifiers_frovoco.py:


=================================================
Imbalanced multiclass classification with FROVOCO
=================================================

The figures contain the training instances within a section of the selected feature space.
The training instances are coloured according to their true labels,
while the feature space is coloured according to predictions on the basis of the training instances,
making the decision boundaries visible.

.. GENERATED FROM PYTHON SOURCE LINES 11-65



.. image-sg:: /examples/classifiers/images/sphx_glr_frovoco_001.png
   :alt: FROVOCO applied to an imbalanced selection of iris dataset
   :srcset: /examples/classifiers/images/sphx_glr_frovoco_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap
    from sklearn import datasets

    from frlearn.base import select_class
    from frlearn.classifiers import FROVOCO

    # Import example data, reduce to 2 dimensions, and create imbalanced selection.
    iris = datasets.load_iris()
    X = iris.data[:, :2]
    y = iris.target
    X = np.concatenate([X[:5], X[50:67], X[100:]], axis=0)
    y = np.concatenate([y[:5], y[50:67], y[100:]], axis=0)

    # Define color maps.
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

    # Create an instance of the FROVOCO classifier and construct the model.
    clf = FROVOCO()
    model = clf(X, y)

    # Create a mesh of points in the attribute space.
    step_size = .02
    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, step_size), np.arange(y_min, y_max, step_size))

    # Query mesh points to obtain class values and select highest valued class.
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = select_class(Z, labels=model.classes)

    # Initialise figure.
    plt.figure()

    # Plot mesh.
    Z = Z.reshape(xx.shape)
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot training instances.
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                edgecolor='k', s=20)

    # Set plot dimensions.
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())

    plt.title('FROVOCO applied to an imbalanced selection of iris dataset')

    plt.show()



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

   **Total running time of the script:** (0 minutes 0.495 seconds)


.. _sphx_glr_download_examples_classifiers_frovoco.py:

.. only:: html

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: frovoco.zip <frovoco.zip>`


.. only:: html

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

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