
.. 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 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()





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

 .. code-block:: none

    downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /root/scikit_learn_data




.. 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 0.84s.




.. 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 0.8s 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 29.199 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:: 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>`_
