.. 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 Binder

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

.. _sphx_glr_auto_examples_neighbors_plot_nca_classification.py:


=============================================================================
比较有无邻域成分分析的最近邻分类
=============================================================================

一个比较有无邻域成分分析的最近邻分类的示例。

它将绘制在使用原始特征的欧几里得距离时由最近邻分类器给出的类决策边界，与使用邻域成分分析学习的变换后的欧几里得距离时的类决策边界。后者旨在找到一种线性变换，以最大化训练集上的（随机）最近邻分类准确性。

.. GENERATED FROM PYTHON SOURCE LINES 11-90



.. 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


    # SPDX-License-Identifier: 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

    # 我们只取两个特征。我们可以通过使用一个二维数据集来避免这种丑陋的切片操作。
    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

    # 创建色彩映射
    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",
        )

        # 还要绘制训练点和测试点
        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 0.338 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:: 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>`

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

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


.. include:: plot_nca_classification.recommendations


.. only:: html

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

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