
.. 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

        Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py>`
        to download the full example code

.. 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()



.. rst-class:: sphx-glr-script-out

.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-Ye5PqW/scikit-learn-1.4.1.post1+dfsg/examples/cluster/plot_dict_face_patches.py", line 30, in <module>
        faces = datasets.fetch_olivetti_faces()
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-Ye5PqW/scikit-learn-1.4.1.post1+dfsg/.pybuild/cpython3_3.12/build/sklearn/utils/_param_validation.py", line 213, in wrapper
        return func(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-Ye5PqW/scikit-learn-1.4.1.post1+dfsg/.pybuild/cpython3_3.12/build/sklearn/datasets/_olivetti_faces.py", line 125, in fetch_olivetti_faces
        mat_path = _fetch_remote(FACES, dirname=data_home)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-Ye5PqW/scikit-learn-1.4.1.post1+dfsg/.pybuild/cpython3_3.12/build/sklearn/datasets/_base.py", line 1432, in _fetch_remote
        raise IOError('Debian Policy Section 4.9 prohibits network access during build')
    OSError: Debian Policy Section 4.9 prohibits network access during build




.. 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)


.. 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()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.001 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. 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>`_
