Note

Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder

Feature agglomeration#

These images show how similar features are merged together using feature agglomeration.

Original data, Agglomerated data, Labels

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
fromsklearnimport cluster, datasets
fromsklearn.feature_extraction.imageimport grid_to_graph
digits = datasets.load_digits ()
images = digits.images
X = np.reshape (images, (len(images), -1))
connectivity = grid_to_graph(*images[0].shape)
agglo = cluster.FeatureAgglomeration (connectivity=connectivity, n_clusters=32)
agglo.fit(X)
X_reduced = agglo.transform(X)
X_restored = agglo.inverse_transform(X_reduced)
images_restored = np.reshape (X_restored, images.shape)
plt.figure (1, figsize=(4, 3.5))
plt.clf ()
plt.subplots_adjust (left=0.01, right=0.99, bottom=0.01, top=0.91)
for i in range(4):
 plt.subplot (3, 4, i + 1)
 plt.imshow (images[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
 plt.xticks (())
 plt.yticks (())
 if i == 1:
 plt.title ("Original data")
 plt.subplot (3, 4, 4 + i + 1)
 plt.imshow (images_restored[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
 if i == 1:
 plt.title ("Agglomerated data")
 plt.xticks (())
 plt.yticks (())
plt.subplot (3, 4, 10)
plt.imshow (
 np.reshape (agglo.labels_, images[0].shape),
 interpolation="nearest",
 cmap=plt.cm.nipy_spectral,
)
plt.xticks (())
plt.yticks (())
plt.title ("Labels")
plt.show ()