.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_faces_decomposition.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

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

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_faces_decomposition.py:


Faces Decomposition
===================

Implemented based on scikit-learn's decomposition example.

Authors: Federico Raimondo, Vlad Niculae, Alexandre Gramfort

License: BSD 3 clause

.. GENERATED FROM PYTHON SOURCE LINES 12-107



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_faces_decomposition_001.png
          :alt: First centered Olivetti faces
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_faces_decomposition_002.png
          :alt: Non-negative components - NMF - Train time 0.2s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_faces_decomposition_003.png
          :alt: Orthogonal Projected Non-negative components - OPNMF - Train time 0.1s
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    Dataset consists of 400 faces
    Extracting the top 6 Non-negative components - NMF...
    done in 0.219s
    Extracting the top 6 Orthogonal Projected Non-negative components - OPNMF...
    2021-09-02 13:05:21,694 INFO Initializing using nndsvd
    2021-09-02 13:05:21,754 INFO iter=0 diff=0.9636634588241577, obj=155.62796020507812
    2021-09-02 13:05:21,801 INFO Converged in 80 iterations
    done in 0.113s






|

.. code-block:: default

    import logging
    from time import time

    from numpy.random import RandomState
    import matplotlib.pyplot as plt

    from sklearn.datasets import fetch_olivetti_faces
    from sklearn import decomposition

    from opnmf import OPNMF

    # Display progress logs on stdout
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s %(levelname)s %(message)s')
    n_row, n_col = 2, 3
    n_components = n_row * n_col
    image_shape = (64, 64)
    rng = RandomState(0)


    # #############################################################################
    # Load faces data
    faces, _ = fetch_olivetti_faces(return_X_y=True, shuffle=True,
                                    random_state=rng)
    n_samples, n_features = faces.shape

    # global centering
    faces_centered = faces - faces.mean(axis=0)

    # local centering
    faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)

    print("Dataset consists of %d faces" % n_samples)


    def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):
        plt.figure(figsize=(2. * n_col, 2.26 * n_row))
        plt.suptitle(title, size=11)
        for i, comp in enumerate(images):
            plt.subplot(n_row, n_col, i + 1)
            vmax = max(comp.max(), -comp.min())
            plt.imshow(comp.reshape(image_shape), cmap=cmap,
                       interpolation='nearest',
                       vmin=-vmax, vmax=vmax)
            plt.xticks(())
            plt.yticks(())
        plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)


    # #############################################################################
    # List of the different estimators, whether to center and transpose the
    # problem, and whether the transformer uses the clustering API.
    estimators = [
        ('Non-negative components - NMF',
         decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),
         False),

        ('Orthogonal Projected Non-negative components - OPNMF',
         OPNMF(n_components=n_components, init='nndsvd', tol=5e-3),
         False),
    ]

    plot_gallery("First centered Olivetti faces", faces_centered[:n_components])

    # #############################################################################
    # Do the estimation and plot it

    for name, estimator, center in estimators:
        print("Extracting the top %d %s..." % (n_components, name))
        t0 = time()
        data = faces
        if center:
            data = faces_centered
        estimator.fit(data)
        train_time = (time() - t0)
        print("done in %0.3fs" % train_time)
        if hasattr(estimator, 'cluster_centers_'):
            components_ = estimator.cluster_centers_
        else:
            components_ = estimator.components_

        # Plot an image representing the pixelwise variance provided by the
        # estimator e.g its noise_variance_ attribute. The Eigenfaces estimator,
        # via the PCA decomposition, also provides a scalar noise_variance_
        # (the mean of pixelwise variance) that cannot be displayed as an image
        # so we skip it.
        if (hasattr(estimator, 'noise_variance_') and
                estimator.noise_variance_.ndim > 0):  # Skip the Eigenfaces case
            plot_gallery("Pixelwise variance",
                         estimator.noise_variance_.reshape(1, -1), n_col=1,
                         n_row=1)
        plot_gallery('%s - Train time %.1fs' % (name, train_time),
                     components_[:n_components])

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.014 seconds)


.. _sphx_glr_download_auto_examples_plot_faces_decomposition.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_faces_decomposition.py <plot_faces_decomposition.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_faces_decomposition.ipynb <plot_faces_decomposition.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_