.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/classifiers/fronec.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_fronec.py>`
        to download the full example code.

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

.. _sphx_glr_examples_classifiers_fronec.py:


=====================================
Multilabel classification with FRONEC
=====================================

The figures contain training instances within a section of the 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.

There are three labels, encoded by the colours red, blue and yellow,
while green means both blue and yellow, and white means no label.

In total, six subfigures are displayed, to illustrate the effect of the `Q_type` and `R_d_type` parameters.

.. GENERATED FROM PYTHON SOURCE LINES 16-87



.. image-sg:: /examples/classifiers/images/sphx_glr_fronec_001.png
   :alt: Q^1, Q^2, Q^3
   :srcset: /examples/classifiers/images/sphx_glr_fronec_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 BoundaryNorm, ListedColormap
    from sklearn import datasets

    from frlearn.base import discretise
    from frlearn.classifiers import FRONEC

    # Import example data, reduce to 2 dimensions and manipulate to create multilabel problem.
    iris = datasets.load_iris()
    X = iris.data[:, :2]
    y = iris.target
    Y = (y[:, None] == np.arange(3)).astype(int)
    Y[[109, 117, 131], 2] = 0
    Y[((X[:, 0] >= 6) & (y == 1)), 2] = 1
    Y[((X[:, 0] <= 6) & (y == 2)), 1] = 1

    # Create a mesh of points in the attribute space.
    step_size = .1
    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))

    # Define light and dark colour maps for label combinations.
    light_colours = ['#FFFFFF', '#FF8080', '#8080FF', '#C080C0', '#FFFF80', '#FFC080', '#80C080', '#C0C0C0']
    light_cmap = ListedColormap(light_colours)
    dark_colours = ['#FFFFFF', '#FF0000', '#0000FF', '#800080', '#FFFF00', '#FF8000', '#008000', '#808080']
    dark_cmap = ListedColormap(dark_colours)
    norm = BoundaryNorm(np.arange(9), 8)

    # Initialise figure with wide aspect for two side-by-side subfigures.
    plt.figure(figsize=(8, 4))

    for i, R_d_type in enumerate([1, 2]):
        for j, Q_type in enumerate([1, 2, 3]):
            axes = plt.subplot(2, 3, i*3 + j + 1)

            # Create an instance of the FRONEC classifier and construct the model.
            clf = FRONEC(k=10, Q_type=Q_type, R_d_type=R_d_type)
            model = clf(X, Y)

            # Query mesh points to obtain label values and convert into discrete predictions.
            Z = model(np.c_[xx.ravel(), yy.ravel()])
            Z = discretise(Z)

            # Encode label combinations as unique numbers, reshape into mesh and plot with light colour map.
            Z = np.sum(Z * [1, 2, 4], axis=-1)
            Z = Z.reshape(xx.shape)
            plt.pcolormesh(xx, yy, Z, cmap=light_cmap, norm=norm)

            # Encode training instance label combinations as unique numbers and plot.
            C = np.sum(Y * [1, 2, 4], axis=-1)
            plt.scatter(X[:, 0], X[:, 1], c=C, cmap=dark_cmap, norm=norm, edgecolor='k', s=20)

            # Set subplot aspect to standard aspect ratio.
            axes.set_aspect(1.0 / axes.get_data_ratio() * .75)

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

            # Describe columns and rows.
            if axes.get_subplotspec().is_first_col():
                plt.ylabel('R_d^{}'.format(R_d_type), rotation=0, size='large', ha='right')
            if axes.get_subplotspec().is_first_row():
                plt.title('Q^{}'.format(Q_type))

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.844 seconds)


.. _sphx_glr_download_examples_classifiers_fronec.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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