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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_04_NaNLVQ.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_04_NaNLVQ.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_01_basic_usage_plot_04_NaNLVQ.py:


=========================
Not a Number LVQ (NaNLVQ)
=========================

NanLVQ `[1]`_ refers to a extension that can be implemented for various distance functions. It uses
the partial distance strategy to ignore any NaN values in the data. Another interpretation would be
that it imputes the missing values with those of the prototypes. Hence, the distance will
be zero, which results in a zero update for the feature containing the NaN value.

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.. code-block:: Python


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

    from sklvq import GMLVQ

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

    iris = load_iris()

    data = iris.data
    labels = iris.target

    # Insert some "random" missing values represented by np.nan
    num_missing_values = 50
    num_samples, num_dimensions = data.shape

    i = np.random.choice(num_samples, num_missing_values, replace=False)
    j = np.random.choice(num_dimensions, num_missing_values, replace=True)

    data[i, j] = np.nan








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

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.. code-block:: Python


    # Object 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 = GMLVQ(
        distance_type="adaptive-squared-euclidean",
        activation_type="swish",
        activation_params={"beta": 2},
        solver_type="waypoint-gradient-descent",
        solver_params={"max_runs": 10, "k": 3, "step_size": np.array([0.1, 0.05])},
        random_state=1428,
        force_all_finite="allow-nan",  # This will make the data  validation  and distance function
        # accept and deal with np.nan values.
    )








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The next step is to fit the GMLVQ 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.

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.. code-block:: Python


    # Train the model using the data and labels
    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      0.92      0.94        50
               2       0.92      0.96      0.94        50

        accuracy                           0.96       150
       macro avg       0.96      0.96      0.96       150
    weighted avg       0.96      0.96      0.96       150





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The examples uses GMLVQ but all models and their compatible distance functions support the
`force_all_finite` option.

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References
..........
_`[1]` Rick van Veen (2016). Analysis of Missing Data Imputation Applied to Heart Failure Data (
Master's Thesis, University  of Groningen, Groningen, The Netherlands). Retrieved from
http://fse.studenttheses.ub.rug.nl/id/eprint/14679


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

   **Total running time of the script:** (0 minutes 0.066 seconds)


.. _sphx_glr_download_auto_examples_01_basic_usage_plot_04_NaNLVQ.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_04_NaNLVQ.ipynb <plot_04_NaNLVQ.ipynb>`

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

      :download:`Download Python source code: plot_04_NaNLVQ.py <plot_04_NaNLVQ.py>`

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

      :download:`Download zipped: plot_04_NaNLVQ.zip <plot_04_NaNLVQ.zip>`


.. only:: html

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

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