.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_nca_classification.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_neighbors_plot_nca_classification.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_neighbors_plot_nca_classification.py:


=============================================================================
Comparing Nearest Neighbors with and without Neighborhood Components Analysis
=============================================================================

An example comparing nearest neighbors classification with and without
Neighborhood Components Analysis.

It will plot the class decision boundaries given by a Nearest Neighbors
classifier when using the Euclidean distance on the original features, versus
using the Euclidean distance after the transformation learned by Neighborhood
Components Analysis. The latter aims to find a linear transformation that
maximises the (stochastic) nearest neighbor classification accuracy on the
training set.

.. GENERATED FROM PYTHON SOURCE LINES 17-97



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png
         :alt: KNN (k = 1)
         :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png
         :alt: NCA, KNN (k = 1)
         :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python


    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap

    from sklearn import datasets
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.model_selection import train_test_split
    from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    n_neighbors = 1

    dataset = datasets.load_iris()
    X, y = dataset.data, dataset.target

    # we only take two features. We could avoid this ugly
    # slicing by using a two-dim dataset
    X = X[:, [0, 2]]

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, stratify=y, test_size=0.7, random_state=42
    )

    h = 0.05  # step size in the mesh

    # Create color maps
    cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
    cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])

    names = ["KNN", "NCA, KNN"]

    classifiers = [
        Pipeline(
            [
                ("scaler", StandardScaler()),
                ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
            ]
        ),
        Pipeline(
            [
                ("scaler", StandardScaler()),
                ("nca", NeighborhoodComponentsAnalysis()),
                ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
            ]
        ),
    ]

    for name, clf in zip(names, classifiers):
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)

        _, ax = plt.subplots()
        DecisionBoundaryDisplay.from_estimator(
            clf,
            X,
            cmap=cmap_light,
            alpha=0.8,
            ax=ax,
            response_method="predict",
            plot_method="pcolormesh",
            shading="auto",
        )

        # Plot also the training and testing points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20)
        plt.title("{} (k = {})".format(name, n_neighbors))
        plt.text(
            0.9,
            0.1,
            "{:.2f}".format(score),
            size=15,
            ha="center",
            va="center",
            transform=plt.gca().transAxes,
        )

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.865 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/neighbors/plot_nca_classification.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_nca_classification.recommendations


.. only:: html

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

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