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

    Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_forest_importances_faces.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py:


=================================================
Pixel importances with a parallel forest of trees
=================================================

This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more important.

The code below also illustrates how the construction and the computation
of the predictions can be parallelized within multiple jobs.




.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-0.20.0+dfsg/examples/ensemble/plot_forest_importances_faces.py", line 25, in <module>
        data = fetch_olivetti_faces()
      File "/build/scikit-learn-0.20.0+dfsg/.pybuild/cpython3_3.6/build/sklearn/datasets/olivetti_faces.py", line 91, in fetch_olivetti_faces
        data_home = get_data_home(data_home=data_home)
      File "/build/scikit-learn-0.20.0+dfsg/.pybuild/cpython3_3.6/build/sklearn/datasets/base.py", line 56, in get_data_home
        makedirs(data_home)
      File "/usr/lib/python3.6/os.py", line 210, in makedirs
        makedirs(head, mode, exist_ok)
      File "/usr/lib/python3.6/os.py", line 220, in makedirs
        mkdir(name, mode)
    PermissionError: [Errno 13] Permission denied: '/nonexistent'





.. code-block:: python

    print(__doc__)

    from time import time
    import matplotlib.pyplot as plt

    from sklearn.datasets import fetch_olivetti_faces
    from sklearn.ensemble import ExtraTreesClassifier

    # Number of cores to use to perform parallel fitting of the forest model
    n_jobs = 1

    # Load the faces dataset
    data = fetch_olivetti_faces()
    X = data.images.reshape((len(data.images), -1))
    y = data.target

    mask = y < 5  # Limit to 5 classes
    X = X[mask]
    y = y[mask]

    # Build a forest and compute the pixel importances
    print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs)
    t0 = time()
    forest = ExtraTreesClassifier(n_estimators=1000,
                                  max_features=128,
                                  n_jobs=n_jobs,
                                  random_state=0)

    forest.fit(X, y)
    print("done in %0.3fs" % (time() - t0))
    importances = forest.feature_importances_
    importances = importances.reshape(data.images[0].shape)

    # Plot pixel importances
    plt.matshow(importances, cmap=plt.cm.hot)
    plt.title("Pixel importances with forests of trees")
    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_forest_importances_faces.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_forest_importances_faces.py <plot_forest_importances_faces.py>`



  .. container:: sphx-glr-download

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


.. only:: html

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

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