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

.. only:: html

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

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

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

.. _sphx_glr_examples_preprocessors_frfs.py:


===========================
Feature selection with FRFS
===========================

Sample usage of FRFS feature selection, demonstrated in combination with (strict) FRNN classification.

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.

Two subfigures are displayed: the first represents simple selection of the first two features,
while the second represents selection of two features by FRFS.

.. GENERATED FROM PYTHON SOURCE LINES 16-84



.. image-sg:: /examples/preprocessors/images/sphx_glr_frfs_001.png
   :alt: FRNN applied to iris dataset with ..., ...first two features, ...two features selected by FRFS
   :srcset: /examples/preprocessors/images/sphx_glr_frfs_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 FRNN
    from frlearn.feature_preprocessors import FRFS

    # Import example data.
    iris = datasets.load_iris()
    X_orig = iris.data
    y = iris.target

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

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

    for i, use_frfs in enumerate([False, True]):
        axes = plt.subplot(1, 2, i + 1)

        if use_frfs:
            # Create an instance of the FRFS preprocessor and process the data.
            preprocessor = FRFS(n_features=2)
            model = preprocessor(X_orig, y)
            X = model(X_orig)
        else:
            # Select first two features.
            X = X_orig[:, :2]

        # Create an instance of the FRNN classifier and construct the model.
        clf = FRNN(upper_weights=None, lower_weights=None, upper_k=1, lower_k=1)
        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)

        # 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 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 the subfigures.
        plt.title('...two features selected by FRFS' if use_frfs else '...first two features')

    plt.suptitle('FRNN applied to iris dataset with ...', fontsize=14)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.513 seconds)


.. _sphx_glr_download_examples_preprocessors_frfs.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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