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# 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 25,000 samples and 2 features generated using make_blobs.

Both `MiniBatchKMeans` and `BIRCH` are very scalable algorithms and could run efficiently on hundreds of thousands or even millions of datapoints. We chose to limit the dataset size of this example in the interest of keeping our Continuous Integration resource usage reasonable but the interested reader might enjoy editing this script to rerun it with a larger value for `n_samples`.

If `n_clusters` is set to None, the data is reduced from 25,000 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.

    BIRCH without global clustering as the final step took 0.39 seconds
    n_clusters : 158
    BIRCH with global clustering as the final step took 0.40 seconds
    n_clusters : 100
    Time taken to run MiniBatchKMeans 0.59 seconds

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

    # 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_centers = 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=25000, centers=n_centers, 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)
        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)

    # Compute clustering with MiniBatchKMeans.
    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()

Total running time of the script: (0 minutes 2.935 seconds)

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