Note

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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, BIRCH with global clustering, MiniBatchKMeans

BIRCH without global clustering as the final step took 0.45 seconds
n_clusters : 158
BIRCH with global clustering as the final step took 0.45 seconds
n_clusters : 100
Time taken to run MiniBatchKMeans 0.22 seconds

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
fromitertoolsimport cycle
fromtimeimport time
importmatplotlib.colorsascolors
importmatplotlib.pyplotasplt
importnumpyasnp
fromjoblibimport cpu_count
fromsklearn.clusterimport Birch , MiniBatchKMeans
fromsklearn.datasetsimport 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 ()