.. 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

        Click :ref:`here <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 classification
==================================

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

.. GENERATED FROM PYTHON SOURCE LINES 27-37

.. code-block:: default

    from __future__ import annotations

    from gemseo.api import configure_logger
    from gemseo.api import load_dataset
    from gemseo.mlearning.api import create_classification_model
    from numpy import array

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 38-40

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

.. GENERATED FROM PYTHON SOURCE LINES 40-42

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 43-47

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

.. GENERATED FROM PYTHON SOURCE LINES 47-51

.. code-block:: default

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





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

 .. code-block:: none

    KNNClassifier(n_neighbors=5)
       based on the scikit-learn library
       built from 150 learning samples




.. GENERATED FROM PYTHON SOURCE LINES 52-55

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

.. GENERATED FROM PYTHON SOURCE LINES 55-63

.. code-block:: default

    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)
    print(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  0.007 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-python

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

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

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


.. only:: html

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

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