.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_decomposition_plot_faces_decomposition.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_decomposition_plot_faces_decomposition.py:


============================
Faces dataset decompositions
============================

This example applies to :ref:`olivetti_faces_dataset` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .



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


    *

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

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_002.png
          :alt: Eigenfaces - PCA using randomized SVD - Train time 0.0s
          :class: sphx-glr-multi-img

    *

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

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_004.png
          :alt: Independent components - FastICA - Train time 0.1s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_005.png
          :alt: Sparse comp. - MiniBatchSparsePCA - Train time 0.8s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_006.png
          :alt: MiniBatchDictionaryLearning - Train time 0.5s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_007.png
          :alt: Cluster centers - MiniBatchKMeans - Train time 0.1s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_008.png
          :alt: Pixelwise variance
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_009.png
          :alt: Factor Analysis components - FA - Train time 0.2s
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_010.png
          :alt: First centered Olivetti faces
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_011.png
          :alt: Dictionary learning
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_012.png
          :alt: Dictionary learning - positive dictionary
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_013.png
          :alt: Dictionary learning - positive code
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/decomposition/images/sphx_glr_plot_faces_decomposition_014.png
          :alt: Dictionary learning - positive dictionary & code
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none


    Dataset consists of 400 faces
    Extracting the top 6 Eigenfaces - PCA using randomized SVD...
    done in 0.021s
    Extracting the top 6 Non-negative components - NMF...
    done in 0.088s
    Extracting the top 6 Independent components - FastICA...
    /home/circleci/project/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
      warnings.warn('FastICA did not converge. Consider increasing '
    done in 0.146s
    Extracting the top 6 Sparse comp. - MiniBatchSparsePCA...
    done in 0.769s
    Extracting the top 6 MiniBatchDictionaryLearning...
    done in 0.517s
    Extracting the top 6 Cluster centers - MiniBatchKMeans...
    done in 0.079s
    Extracting the top 6 Factor Analysis components - FA...
    done in 0.181s
    Extracting the top 6 Dictionary learning...
    done in 0.492s
    Extracting the top 6 Dictionary learning - positive dictionary...
    done in 0.498s
    Extracting the top 6 Dictionary learning - positive code...
    done in 0.130s
    Extracting the top 6 Dictionary learning - positive dictionary & code...
    done in 0.194s






|


.. code-block:: default

    print(__doc__)

    # Authors: Vlad Niculae, Alexandre Gramfort
    # License: BSD 3 clause

    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.cluster import MiniBatchKMeans
    from sklearn import decomposition

    # 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=16)
        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 = [
        ('Eigenfaces - PCA using randomized SVD',
         decomposition.PCA(n_components=n_components, svd_solver='randomized',
                           whiten=True),
         True),

        ('Non-negative components - NMF',
         decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),
         False),

        ('Independent components - FastICA',
         decomposition.FastICA(n_components=n_components, whiten=True),
         True),

        ('Sparse comp. - MiniBatchSparsePCA',
         decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,
                                          n_iter=100, batch_size=3,
                                          random_state=rng),
         True),

        ('MiniBatchDictionaryLearning',
            decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
                                                      n_iter=50, batch_size=3,
                                                      random_state=rng),
         True),

        ('Cluster centers - MiniBatchKMeans',
            MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,
                            max_iter=50, random_state=rng),
         True),

        ('Factor Analysis components - FA',
         decomposition.FactorAnalysis(n_components=n_components, max_iter=20),
         True),
    ]


    # #############################################################################
    # Plot a sample of the input data

    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()

    # #############################################################################
    # Various positivity constraints applied to dictionary learning.
    estimators = [
        ('Dictionary learning',
            decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
                                                      n_iter=50, batch_size=3,
                                                      random_state=rng),
         True),
        ('Dictionary learning - positive dictionary',
            decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
                                                      n_iter=50, batch_size=3,
                                                      random_state=rng,
                                                      positive_dict=True),
         True),
        ('Dictionary learning - positive code',
            decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
                                                      n_iter=50, batch_size=3,
                                                      fit_algorithm='cd',
                                                      random_state=rng,
                                                      positive_code=True),
         True),
        ('Dictionary learning - positive dictionary & code',
            decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,
                                                      n_iter=50, batch_size=3,
                                                      fit_algorithm='cd',
                                                      random_state=rng,
                                                      positive_dict=True,
                                                      positive_code=True),
         True),
    ]


    # #############################################################################
    # Plot a sample of the input data

    plot_gallery("First centered Olivetti faces", faces_centered[:n_components],
                 cmap=plt.cm.RdBu)

    # #############################################################################
    # 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)
        components_ = estimator.components_
        plot_gallery(name, components_[:n_components], cmap=plt.cm.RdBu)

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  4.746 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_faces_decomposition.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_faces_decomposition.ipynb
      :width: 150 px


  .. 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>`_