.. _sphx_glr_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py:


=================================
Compare BIRCH and MiniBatchKMeans
=================================

This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.

If ``n_clusters`` is set to None, the data is reduced from 100,000
samples to a set of 158 clusters. This can be viewed as a preprocessing
step before the final (global) clustering step that further reduces these
158 clusters to 100 clusters.



.. image:: /auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png
    :align: center


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

 Out::

      Birch without global clustering as the final step took 4.80 seconds
    n_clusters : 158
    Birch with global clustering as the final step took 4.86 seconds
    n_clusters : 100
    Time taken to run MiniBatchKMeans 4.99 seconds




|


.. code-block:: python


    # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com
    #          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
    # License: BSD 3 clause

    print(__doc__)

    from itertools import cycle
    from time import time
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.colors as colors

    from sklearn.preprocessing import StandardScaler
    from sklearn.cluster import Birch, MiniBatchKMeans
    from sklearn.datasets.samples_generator import make_blobs


    # Generate centers for the blobs so that it forms a 10 X 10 grid.
    xx = np.linspace(-22, 22, 10)
    yy = np.linspace(-22, 22, 10)
    xx, yy = np.meshgrid(xx, yy)
    n_centres = np.hstack((np.ravel(xx)[:, np.newaxis],
                           np.ravel(yy)[:, np.newaxis]))

    # Generate blobs to do a comparison between MiniBatchKMeans and Birch.
    X, y = make_blobs(n_samples=100000, centers=n_centres, random_state=0)
   

    # Use all colors that matplotlib provides by default.
    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)

    # Compute clustering with Birch with and without the final clustering step
    # and plot.
    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)
        time_ = time() - t
        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.plot(X[mask, 0], X[mask, 1], 'w',
                    markerfacecolor=col, marker='.')
            if birch_model.n_clusters is None:
                ax.plot(this_centroid[0], this_centroid[1], '+', markerfacecolor=col,
                        markeredgecolor='k', markersize=5)
        ax.set_ylim([-25, 25])
        ax.set_xlim([-25, 25])
        ax.set_autoscaley_on(False)
        ax.set_title('Birch %s' % info)

    # Compute clustering with MiniBatchKMeans.
    mbk = MiniBatchKMeans(init='k-means++', n_clusters=100, batch_size=100,
                          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.plot(X[mask, 0], X[mask, 1], 'w', markerfacecolor=col, marker='.')
        ax.plot(this_centroid[0], this_centroid[1], '+', markeredgecolor='k',
                markersize=5)
    ax.set_xlim([-25, 25])
    ax.set_ylim([-25, 25])
    ax.set_title("MiniBatchKMeans")
    ax.set_autoscaley_on(False)
    plt.show()

**Total running time of the script:**
(0 minutes 16.043 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_birch_vs_minibatchkmeans.py <plot_birch_vs_minibatchkmeans.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_birch_vs_minibatchkmeans.ipynb <plot_birch_vs_minibatchkmeans.ipynb>`