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

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

.. _sphx_glr_auto_examples_plot_benchmark.py:


==============
GLVQ Benchmark
==============
This example shows the differences between the 4 different GLVQ implementations and LMNN.
The Image Segmentation dataset is used for training and test. Each plot shows the projection
and classification from each implementation. Because Glvq can't project the data on its own
a PCA is used.





.. image:: /auto_examples/images/sphx_glr_plot_benchmark_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    ('variance coverd by projection:', 97.5655488246322)
    ('variance coverd by projection:', 98.6644813167321)




|


.. code-block:: python

    from __future__ import with_statement
    import numpy as np
    import matplotlib.pyplot as plt
    from metric_learn import LMNN
    from sklearn.decomposition import PCA

    from sklearn_lvq import GlvqModel, GrlvqModel, LgmlvqModel, GmlvqModel
    from sklearn_lvq.utils import _to_tango_colors, _tango_color

    print(__doc__)


    def plot(data, target, target_p, prototype, prototype_label, p):
        p.scatter(data[:, 0], data[:, 1], c=_to_tango_colors(target, 0), alpha=0.5)
        p.scatter(data[:, 0], data[:, 1], c=_to_tango_colors(target_p, 0),
                  marker='.')
        p.scatter(prototype[:, 0], prototype[:, 1],
                  c=_tango_color('aluminium', 5), marker='D')
        try:
            p.scatter(prototype[:, 0], prototype[:, 1], s=60,
                      c=_to_tango_colors(prototype_label, 0), marker='.')
        except:
            p.scatter(prototype[:, 0], prototype[:, 1], s=60,
                      c=_tango_color(prototype_label), marker='.')
        p.axis('equal')


    y = []
    x = []
    with open('segmentation.data') as f:
        for line in f:
            v = line.split(',')
            y.append(v[0])
            x.append(v[1:])
    x = np.asarray(x, dtype='float64')
    y = np.asarray(y)

    lmnn = LMNN(k=5, learn_rate=1e-6)
    lmnn.fit(x, y)
    x_t = lmnn.transform(x)

    p1 = plt.subplot(231)
    p1.scatter(x_t[:, 0], x_t[:, 1], c=_to_tango_colors(y, 0))
    p1.axis('equal')
    p1.set_title('LMNN')

    # GLVQ
    glvq = GlvqModel()
    glvq.fit(x, y)
    p2 = plt.subplot(232)
    p2.set_title('GLVQ')
    plot(PCA().fit_transform(x), y, glvq.predict(x), glvq.w_, glvq.c_w_, p2)

    # GRLVQ
    grlvq = GrlvqModel()
    grlvq.fit(x, y)
    p3 = plt.subplot(233)
    p3.set_title('GRLVQ')
    plot(grlvq.project(x, 2),
         y, grlvq.predict(x), grlvq.project(grlvq.w_, 2),
         grlvq.c_w_, p3)

    # GMLVQ
    gmlvq = GmlvqModel()
    gmlvq.fit(x, y)
    p4 = plt.subplot(234)
    p4.set_title('GMLVQ')
    plot(gmlvq.project(x, 2),
         y, gmlvq.predict(x), gmlvq.project(gmlvq.w_, 2),
         gmlvq.c_w_, p4)

    # LGMLVQ
    lgmlvq = LgmlvqModel()
    lgmlvq.fit(x, y)
    p5 = plt.subplot(235)
    elem_set = list(set(lgmlvq.c_w_))
    p5.set_title('LGMLVQ 1')
    plot(lgmlvq.project(x, 1, 2, True),
         y, lgmlvq.predict(x), lgmlvq.project(np.array([lgmlvq.w_[1]]), 1, 2),
         elem_set.index(lgmlvq.c_w_[1]), p5)
    p6 = plt.subplot(236)
    p6.set_title('LGMLVQ 2')
    plot(lgmlvq.project(x, 6, 2, True),
         y, lgmlvq.predict(x), lgmlvq.project(np.array([lgmlvq.w_[6]]), 6, 2),
         elem_set.index(lgmlvq.c_w_[6]), p6)

    plt.show()

**Total running time of the script:** ( 0 minutes  16.844 seconds)


.. _sphx_glr_download_auto_examples_plot_benchmark.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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