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

.. only:: html

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

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

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

.. _sphx_glr_examples_mlearning_classification_model_plot_knn.py:


k-nearest neighbors
===================

A :class:`.KNNClassifier` is a k-nearest neighbors model
based on `scikit-learn <https://scikit-learn.org/>`__.

We want to classify the Iris dataset using a KNN classifier.

.. GENERATED FROM PYTHON SOURCE LINES 30-42

.. code-block:: Python


    from __future__ import annotations

    from numpy import array

    from gemseo import configure_logger
    from gemseo import create_benchmark_dataset
    from gemseo.mlearning import create_classification_model

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 43-45

Load Iris dataset
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 45-47

.. code-block:: Python

    iris = create_benchmark_dataset("IrisDataset", as_io=True)








.. GENERATED FROM PYTHON SOURCE LINES 48-52

Create the classification model
-------------------------------
Then, we build the k-NN classification model from the discipline cache and
displays this model.

.. GENERATED FROM PYTHON SOURCE LINES 52-56

.. code-block:: Python

    model = create_classification_model("KNNClassifier", data=iris)
    model.learn()
    model






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='margin: 1em;'>KNNClassifier(input_names=(), n_neighbors=5, output_names=(), parameters={}, transformer={&#x27;inputs&#x27;: &lt;gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x704937e17c50&gt;})<br/><ul><li>based on the scikit-learn library</li><li>built from 150 learning samples</li></ul></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 57-60

Predict output
--------------
Once it is built, we can use it for prediction.

.. GENERATED FROM PYTHON SOURCE LINES 60-68

.. code-block:: Python

    input_value = {
        "sepal_length": array([4.5]),
        "sepal_width": array([3.0]),
        "petal_length": array([1.0]),
        "petal_width": array([0.2]),
    }
    output_value = model.predict(input_value)
    output_value




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

 .. code-block:: none


    {'specy': array([0])}




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

   **Total running time of the script:** (0 minutes 0.026 seconds)


.. _sphx_glr_download_examples_mlearning_classification_model_plot_knn.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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