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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/01_basic_usage/plot_01_glvq.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_01_basic_usage_plot_01_glvq.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_01_basic_usage_plot_01_glvq.py:


.. _GLVQ:

======================
Generalized LVQ (GLVQ)
======================

Example of how to fit the GLVQ `[1]`_ algorithm on the classic iris dataset.

.. GENERATED FROM PYTHON SOURCE LINES 11-30

.. code-block:: Python


    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.metrics import classification_report
    from sklearn.preprocessing import StandardScaler

    from sklvq import GLVQ

    matplotlib.rc("xtick", labelsize="small")
    matplotlib.rc("ytick", labelsize="small")

    # Contains also the target_names and feature_names, which we will use for the plots.
    iris = load_iris()

    data = iris.data
    labels = iris.target








.. GENERATED FROM PYTHON SOURCE LINES 31-36

Fitting the Model
.................
Scale the data and create a GLVQ object with, e.g., custom distance function, activation
function and solver. See the API reference under documentation for defaults and other
possible parameters.

.. GENERATED FROM PYTHON SOURCE LINES 36-52

.. code-block:: Python


    # Sklearn's standardscaler to perform z-transform
    scaler = StandardScaler()

    # Compute (fit) and apply (transform) z-transform
    data = scaler.fit_transform(data)

    # The creation of the model object used to fit the data to.
    model = GLVQ(
        distance_type="squared-euclidean",
        activation_type="swish",
        activation_params={"beta": 2},
        solver_type="steepest-gradient-descent",
        solver_params={"max_runs": 20, "step_size": 0.1},
    )








.. GENERATED FROM PYTHON SOURCE LINES 53-56

The next step is to fit the GLVQ object to the data and use the predict method to make the
predictions. Note that this example only works on the training data and therefor does not say
anything about the generalizability of the fitted model.

.. GENERATED FROM PYTHON SOURCE LINES 56-66

.. code-block:: Python


    # Train the model using the iris dataset
    model.fit(data, labels)

    # Predict the labels using the trained model
    predicted_labels = model.predict(data)

    # To get a sense of the training performance we could print the classification report.
    print(classification_report(labels, predicted_labels))





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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        50
               1       0.96      0.94      0.95        50
               2       0.94      0.96      0.95        50

        accuracy                           0.97       150
       macro avg       0.97      0.97      0.97       150
    weighted avg       0.97      0.97      0.97       150





.. GENERATED FROM PYTHON SOURCE LINES 67-73

Extracting the Prototypes
.........................
The GLVQ model produces prototypes as representations for the different
classes. These prototypes can be accessed and, e.g., plotted for visual inspection. Note that
the prototypes of the model are within the z-score space and are transformed back before they
are plotted.

.. GENERATED FROM PYTHON SOURCE LINES 73-100

.. code-block:: Python


    colors = ["blue", "red", "green"]
    num_prototypes = model.prototypes_.shape[0]
    num_features = model.prototypes_.shape[1]

    fig, ax = plt.subplots(num_prototypes, 1)
    fig.suptitle("Prototype of each class")

    for i, prototype in enumerate(model.prototypes_):
        # Reverse the z-transform to go back to the original feature space.
        prototype = scaler.inverse_transform(np.atleast_2d(prototype)).squeeze()

        ax[i].bar(
            range(num_features),
            prototype,
            color=colors[i],
            label=iris.target_names[model.prototypes_labels_[i]],
        )
        ax[i].set_xticks(range(num_features))
        if i == (num_prototypes - 1):
            ax[i].set_xticklabels([name[:-5] for name in iris.feature_names])
        else:
            ax[i].set_xticklabels([], visible=False)
            ax[i].tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=False)
        ax[i].set_ylabel("cm")
        ax[i].legend()




.. image-sg:: /auto_examples/01_basic_usage/images/sphx_glr_plot_01_glvq_001.png
   :alt: Prototype of each class
   :srcset: /auto_examples/01_basic_usage/images/sphx_glr_plot_01_glvq_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 101-105

References
..........
_`[1]` Sato, A., and Yamada, K. (1996) “Generalized Learning Vector Quantization.” Advances in
Neural Network Information Processing Systems, 423–429, 1996.


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

   **Total running time of the script:** (0 minutes 0.545 seconds)


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.. only:: html

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

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

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      :download:`Download Python source code: plot_01_glvq.py <plot_01_glvq.py>`

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