File Content: 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. """ # 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() Submit