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.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_birch_vs_minibatchkmeans.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_cluster_plot_birch_vs_minibatchkmeans.py:


比较 BIRCH 和 MiniBatchKMeans
=================================

本示例比较了 BIRCH（有全局聚类步骤和无全局聚类步骤）和 MiniBatchKMeans 在一个具有 25,000 个样本和 2 个特征的合成数据集上的时间表现，该数据集使用 make_blobs 生成。

 ``MiniBatchKMeans`` 和 ``BIRCH`` 都是非常可扩展的算法，可以高效地处理数十万甚至数百万的数据点。我们选择限制此示例的数据集大小，以保持我们的持续集成资源使用在合理范围内，但感兴趣的读者可以编辑此脚本，以更大的 `n_samples` 值重新运行它。

如果 ``n_clusters`` 设置为 None，数据将从 25,000 个样本减少到 158 个聚类。这可以视为最终（全局）聚类步骤之前的预处理步骤，该步骤将这 158 个聚类进一步减少到 100 个聚类。

.. GENERATED FROM PYTHON SOURCE LINES 12-97



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png
   :alt: BIRCH without global clustering, BIRCH with global clustering, MiniBatchKMeans
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    BIRCH without global clustering as the final step took 0.22 seconds
    n_clusters : 158
    BIRCH with global clustering as the final step took 0.21 seconds
    n_clusters : 100
    Time taken to run MiniBatchKMeans 0.19 seconds






|

.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause

    from itertools import cycle
    from time import time

    import matplotlib.colors as colors
    import matplotlib.pyplot as plt
    import numpy as np
    from joblib import cpu_count

    from sklearn.cluster import Birch, MiniBatchKMeans
    from sklearn.datasets import make_blobs

    # 生成斑点的中心，使其形成一个10 X 10的网格。
    xx = np.linspace(-22, 22, 10)
    yy = np.linspace(-22, 22, 10)
    xx, yy = np.meshgrid(xx, yy)
    n_centers = np.hstack((np.ravel(xx)[:, np.newaxis], np.ravel(yy)[:, np.newaxis]))

    # 生成斑点以比较 MiniBatchKMeans 和 BIRCH。
    X, y = make_blobs(n_samples=25000, centers=n_centers, random_state=0)

    # 使用 matplotlib 默认提供的所有颜色。
    colors_ = cycle(colors.cnames.keys())

    fig = plt.figure(figsize=(12, 4))
    fig.subplots_adjust(left=0.04, right=0.98, bottom=0.1, top=0.9)

    # 计算使用BIRCH算法的聚类结果（包括和不包括最终聚类步骤），并绘制图表。
    birch_models = [
        Birch(threshold=1.7, n_clusters=None),
        Birch(threshold=1.7, n_clusters=100),
    ]
    final_step = ["without global clustering", "with global clustering"]

    for ind, (birch_model, info) in enumerate(zip(birch_models, final_step)):
        t = time()
        birch_model.fit(X)
        print("BIRCH %s as the final step took %0.2f seconds" % (info, (time() - t)))

        # Plot result
        labels = birch_model.labels_
        centroids = birch_model.subcluster_centers_
        n_clusters = np.unique(labels).size
        print("n_clusters : %d" % n_clusters)

        ax = fig.add_subplot(1, 3, ind + 1)
        for this_centroid, k, col in zip(centroids, range(n_clusters), colors_):
            mask = labels == k
            ax.scatter(X[mask, 0], X[mask, 1], c="w", edgecolor=col, marker=".", alpha=0.5)
            if birch_model.n_clusters is None:
                ax.scatter(this_centroid[0], this_centroid[1], marker="+", c="k", s=25)
        ax.set_ylim([-25, 25])
        ax.set_xlim([-25, 25])
        ax.set_autoscaley_on(False)
        ax.set_title("BIRCH %s" % info)

    # 使用MiniBatchKMeans进行聚类计算。
    mbk = MiniBatchKMeans(
        init="k-means++",
        n_clusters=100,
        batch_size=256 * cpu_count(),
        n_init=10,
        max_no_improvement=10,
        verbose=0,
        random_state=0,
    )
    t0 = time()
    mbk.fit(X)
    t_mini_batch = time() - t0
    print("Time taken to run MiniBatchKMeans %0.2f seconds" % t_mini_batch)
    mbk_means_labels_unique = np.unique(mbk.labels_)

    ax = fig.add_subplot(1, 3, 3)
    for this_centroid, k, col in zip(mbk.cluster_centers_, range(n_clusters), colors_):
        mask = mbk.labels_ == k
        ax.scatter(X[mask, 0], X[mask, 1], marker=".", c="w", edgecolor=col, alpha=0.5)
        ax.scatter(this_centroid[0], this_centroid[1], marker="+", c="k", s=25)
    ax.set_xlim([-25, 25])
    ax.set_ylim([-25, 25])
    ax.set_title("MiniBatchKMeans")
    ax.set_autoscaley_on(False)
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.592 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py:

.. only:: html

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

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      .. image:: images/binder_badge_logo.svg
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      :download:`Download Python source code: plot_birch_vs_minibatchkmeans.py <plot_birch_vs_minibatchkmeans.py>`

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