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

.. only:: html

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

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

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

.. _sphx_glr_examples_data_descriptors_one_class_classification.py:


========================
One class classification
========================

Data descriptors generalise knowledge about a target class of data to the whole attribute space.
This can be used to predict whether new data instances belong to the target class or not,
or to identify which new instances should be subjected to further inspection.
This type of binary classification is known as *one-class classification*,
*semi-supervised outlier detection*, *semi-supervised anomaly detection*, or *novelty detection*.
In principle, there is no good or bad way to generalise the target class, this can only be evaluated empirically.
In practice, we want a good balance between variance and bias.

The following graphs illustrate the behaviour of the data descriptors in fuzzy-rough-learn,
with their default hyperparameter values as established in [1]_.
Note that the predicted scores have been converted to quantiles
to obtain clear contour lines that illustrate how the predicted scores taper off.

References
----------
.. [1] `Lenz OU, Peralta D, Cornelis C (2021).
   Average Localised Proximity: A new data descriptor with good default one-class classification performance.
   Pattern Recognition, vol 118, no 107991.
   doi: 10.1016/j.patcog.2021.107991
   <https://www.sciencedirect.com/science/article/abs/pii/S0031320321001783>`_

.. GENERATED FROM PYTHON SOURCE LINES 27-95



.. image-sg:: /examples/data_descriptors/images/sphx_glr_one_class_classification_001.png
   :alt: ALP, CD, IF, LNND, LOF, MD, NND, SVM
   :srcset: /examples/data_descriptors/images/sphx_glr_one_class_classification_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from frlearn.data_descriptors import ALP, CD, IF, MD, NND, LNND, LOF, SVM


    # Sample attribute space, to use as test data
    xx, yy = np.meshgrid(np.linspace(-6, 6, 300), np.linspace(-6, 6, 300))

    # Generate training data
    rng = np.random.default_rng(0)
    X = rng.standard_normal((100, 2))
    X_train = np.r_[1 * X + 2, 0.75*X, 0.5 * X - 2]

    # Initialise data descriptors to include
    data_descriptors = [
        ('ALP', ALP()),
        ('CD', CD()),
        ('IF', IF()),
        ('LNND', LNND()),
        ('LOF', LOF()),
        ('MD', MD()),
        ('NND', NND()),
        ('SVM', SVM()),
    ]

    # Calculate number of rows
    cols = 3
    rows = (len(data_descriptors) + (cols - 1)) // cols

    # Create plot layout with square subplots
    fig, axs = plt.subplots(rows, cols, figsize=(3*cols, 3*rows), subplot_kw=dict(box_aspect=1), )

    # Iterate over data descriptors
    for i, (name, clf) in enumerate(data_descriptors):
        ax = axs[i // cols][i % cols]

        # Create model and query for scores
        model = clf(X_train)
        Z = model(np.c_[xx.ravel(), yy.ravel()])

        # Transform scores into their respective centile
        centiles = np.quantile(Z, np.linspace(0, 1, 101))
        Z = np.searchsorted(centiles, Z)/100
        Z = Z.reshape(xx.shape)

        # Plot contours
        ax.contourf(xx, yy, Z, levels=np.linspace(0, 1, 12), cmap=plt.cm.PuBu)

        # Plot training data
        c = ax.scatter(X_train[:, 0], X_train[:, 1], c='white', s=10, edgecolors='k')

        # Set axis limits and delete ticks and legends
        plt.xlim((-6, 6))
        plt.ylim((-6, 6))
        c.axes.get_xaxis().set_visible(False)
        c.axes.get_yaxis().set_visible(False)

        ax.set_title(name)

    # Delete spare subfigures
    for i in range((-len(data_descriptors)) % cols):
        fig.delaxes(axs[-1, -(i + 1)])

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.844 seconds)


.. _sphx_glr_download_examples_data_descriptors_one_class_classification.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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