.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_birch_vs_minibatchkmeans.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _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 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. .. GENERATED FROM PYTHON SOURCE LINES 23-109 .. 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 0.54 seconds n_clusters : 158 BIRCH with global clustering as the final step took 0.56 seconds n_clusters : 100 Time taken to run MiniBatchKMeans 0.25 seconds | .. code-block:: Python # Authors: The scikit-learn developers # 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 # 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.861 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_birch_vs_minibatchkmeans.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/cluster/plot_birch_vs_minibatchkmeans.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_birch_vs_minibatchkmeans.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_birch_vs_minibatchkmeans.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_birch_vs_minibatchkmeans.zip ` .. include:: plot_birch_vs_minibatchkmeans.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_