.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_dict_face_patches.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py>`
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_dict_face_patches.py:
Online learning of a dictionary of parts of faces
=================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the scikit-learn to process a very large
dataset by chunks. The way we proceed is that we load an image at a time
and extract randomly 50 patches from this image. Once we have accumulated
500 of these patches (using 10 images), we run the
:func:`~sklearn.cluster.MiniBatchKMeans.partial_fit` method
of the online KMeans object, MiniBatchKMeans.
The verbose setting on the MiniBatchKMeans enables us to see that some
clusters are reassigned during the successive calls to
partial-fit. This is because the number of patches that they represent
has become too low, and it is better to choose a random new
cluster.
.. GENERATED FROM PYTHON SOURCE LINES 25-27
Load the data
-------------
.. GENERATED FROM PYTHON SOURCE LINES 27-32
.. code-block:: default
from sklearn import datasets
faces = datasets.fetch_olivetti_faces()
.. GENERATED FROM PYTHON SOURCE LINES 33-35
Learn the dictionary of images
------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 35-71
.. code-block:: default
import time
import numpy as np
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
print("Learning the dictionary... ")
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True, n_init=3)
patch_size = (20, 20)
buffer = []
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img, patch_size, max_patches=50, random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print("Partial fit of %4i out of %i" % (index, 6 * len(faces.images)))
dt = time.time() - t0
print("done in %.2fs." % dt)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Learning the dictionary...
[MiniBatchKMeans] Reassigning 8 cluster centers.
[MiniBatchKMeans] Reassigning 5 cluster centers.
Partial fit of 100 out of 2400
[MiniBatchKMeans] Reassigning 3 cluster centers.
Partial fit of 200 out of 2400
[MiniBatchKMeans] Reassigning 1 cluster centers.
Partial fit of 300 out of 2400
[MiniBatchKMeans] Reassigning 3 cluster centers.
Partial fit of 400 out of 2400
Partial fit of 500 out of 2400
Partial fit of 600 out of 2400
Partial fit of 700 out of 2400
Partial fit of 800 out of 2400
Partial fit of 900 out of 2400
Partial fit of 1000 out of 2400
Partial fit of 1100 out of 2400
Partial fit of 1200 out of 2400
Partial fit of 1300 out of 2400
Partial fit of 1400 out of 2400
Partial fit of 1500 out of 2400
Partial fit of 1600 out of 2400
Partial fit of 1700 out of 2400
Partial fit of 1800 out of 2400
Partial fit of 1900 out of 2400
Partial fit of 2000 out of 2400
Partial fit of 2100 out of 2400
Partial fit of 2200 out of 2400
Partial fit of 2300 out of 2400
Partial fit of 2400 out of 2400
done in 1.22s.
.. GENERATED FROM PYTHON SOURCE LINES 72-74
Plot the results
----------------
.. GENERATED FROM PYTHON SOURCE LINES 74-92
.. code-block:: default
import matplotlib.pyplot as plt
plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray, interpolation="nearest")
plt.xticks(())
plt.yticks(())
plt.suptitle(
"Patches of faces\nTrain time %.1fs on %d patches" % (dt, 8 * len(faces.images)),
fontsize=16,
)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dict_face_patches_001.png
:alt: Patches of faces Train time 1.2s on 3200 patches
:srcset: /auto_examples/cluster/images/sphx_glr_plot_dict_face_patches_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 2.335 seconds)
.. _sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.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/1.3.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_dict_face_patches.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/cluster/plot_dict_face_patches.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_dict_face_patches.py <plot_dict_face_patches.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_dict_face_patches.ipynb <plot_dict_face_patches.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_