.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_caching_nearest_neighbors.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_caching_nearest_neighbors.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_caching_nearest_neighbors.py:


=========================
缓存最近邻
=========================

本示例演示了如何在使用KNeighborsClassifier之前预先计算k个最近邻。KNeighborsClassifier可以内部计算最近邻，但预先计算它们可以有几个好处，例如更精细的参数控制、缓存以供多次使用或自定义实现。

在这里，我们使用管道的缓存属性在多次拟合KNeighborsClassifier之间缓存最近邻图。第一次调用较慢，因为它计算了邻居图，而后续调用较快，因为它们不需要重新计算图。这里的时间较短，因为数据集较小，但当数据集变大或需要搜索的参数网格较大时，收益可能会更显著。

.. GENERATED FROM PYTHON SOURCE LINES 11-57



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_caching_nearest_neighbors_001.png
   :alt: Classification accuracy, Fit time (with caching)
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_caching_nearest_neighbors_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause
    from tempfile import TemporaryDirectory

    import matplotlib.pyplot as plt

    from sklearn.datasets import load_digits
    from sklearn.model_selection import GridSearchCV
    from sklearn.neighbors import KNeighborsClassifier, KNeighborsTransformer
    from sklearn.pipeline import Pipeline

    X, y = load_digits(return_X_y=True)
    n_neighbors_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]

    # 变换器使用网格搜索中所需的最大邻居数计算最近邻图。分类器模型根据其自身的n_neighbors参数过滤最近邻图。
    graph_model = KNeighborsTransformer(n_neighbors=max(n_neighbors_list), mode="distance")
    classifier_model = KNeighborsClassifier(metric="precomputed")

    # 请注意，我们为 `memory` 提供了一个目录，用于缓存图计算，这将在调整分类器的超参数时多次使用。
    with TemporaryDirectory(prefix="sklearn_graph_cache_") as tmpdir:
        full_model = Pipeline(
            steps=[("graph", graph_model), ("classifier", classifier_model)], memory=tmpdir
        )

        param_grid = {"classifier__n_neighbors": n_neighbors_list}
        grid_model = GridSearchCV(full_model, param_grid)
        grid_model.fit(X, y)

    # 绘制网格搜索的结果。
    fig, axes = plt.subplots(1, 2, figsize=(8, 4))
    axes[0].errorbar(
        x=n_neighbors_list,
        y=grid_model.cv_results_["mean_test_score"],
        yerr=grid_model.cv_results_["std_test_score"],
    )
    axes[0].set(xlabel="n_neighbors", title="Classification accuracy")
    axes[1].errorbar(
        x=n_neighbors_list,
        y=grid_model.cv_results_["mean_fit_time"],
        yerr=grid_model.cv_results_["std_fit_time"],
        color="r",
    )
    axes[1].set(xlabel="n_neighbors", title="Fit time (with caching)")
    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.639 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_caching_nearest_neighbors.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_caching_nearest_neighbors.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_caching_nearest_neighbors.recommendations


.. only:: html

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

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