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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_applications_plot_face_recognition.py:


===================================================
Faces recognition example using eigenfaces and SVMs
===================================================

The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:

  http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

.. _LFW: http://vis-www.cs.umass.edu/lfw/

.. GENERATED FROM PYTHON SOURCE LINES 15-29

.. code-block:: default

    from time import time
    import matplotlib.pyplot as plt

    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import RandomizedSearchCV
    from sklearn.datasets import fetch_lfw_people
    from sklearn.metrics import classification_report
    from sklearn.metrics import ConfusionMatrixDisplay
    from sklearn.preprocessing import StandardScaler
    from sklearn.decomposition import PCA
    from sklearn.svm import SVC
    from sklearn.utils.fixes import loguniform









.. GENERATED FROM PYTHON SOURCE LINES 30-31

Download the data, if not already on disk and load it as numpy arrays

.. GENERATED FROM PYTHON SOURCE LINES 31-53

.. code-block:: default


    lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

    # introspect the images arrays to find the shapes (for plotting)
    n_samples, h, w = lfw_people.images.shape

    # for machine learning we use the 2 data directly (as relative pixel
    # positions info is ignored by this model)
    X = lfw_people.data
    n_features = X.shape[1]

    # the label to predict is the id of the person
    y = lfw_people.target
    target_names = lfw_people.target_names
    n_classes = target_names.shape[0]

    print("Total dataset size:")
    print("n_samples: %d" % n_samples)
    print("n_features: %d" % n_features)
    print("n_classes: %d" % n_classes)




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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/examples/applications/plot_face_recognition.py", line 32, in <module>
        lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/.pybuild/cpython3_3.11/build/sklearn/datasets/_lfw.py", line 328, in fetch_lfw_people
        lfw_home, data_folder_path = _check_fetch_lfw(
                                     ^^^^^^^^^^^^^^^^^
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/.pybuild/cpython3_3.11/build/sklearn/datasets/_lfw.py", line 88, in _check_fetch_lfw
        _fetch_remote(target, dirname=lfw_home)
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/.pybuild/cpython3_3.11/build/sklearn/datasets/_base.py", line 1323, 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 54-55

Split into a training set and a test and keep 25% of the data for testing.

.. GENERATED FROM PYTHON SOURCE LINES 55-64

.. code-block:: default


    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.25, random_state=42
    )

    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)


.. GENERATED FROM PYTHON SOURCE LINES 65-67

Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
dataset): unsupervised feature extraction / dimensionality reduction

.. GENERATED FROM PYTHON SOURCE LINES 67-86

.. code-block:: default


    n_components = 150

    print(
        "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
    )
    t0 = time()
    pca = PCA(n_components=n_components, svd_solver="randomized", whiten=True).fit(X_train)
    print("done in %0.3fs" % (time() - t0))

    eigenfaces = pca.components_.reshape((n_components, h, w))

    print("Projecting the input data on the eigenfaces orthonormal basis")
    t0 = time()
    X_train_pca = pca.transform(X_train)
    X_test_pca = pca.transform(X_test)
    print("done in %0.3fs" % (time() - t0))



.. GENERATED FROM PYTHON SOURCE LINES 87-88

Train a SVM classification model

.. GENERATED FROM PYTHON SOURCE LINES 88-104

.. code-block:: default


    print("Fitting the classifier to the training set")
    t0 = time()
    param_grid = {
        "C": loguniform(1e3, 1e5),
        "gamma": loguniform(1e-4, 1e-1),
    }
    clf = RandomizedSearchCV(
        SVC(kernel="rbf", class_weight="balanced"), param_grid, n_iter=10
    )
    clf = clf.fit(X_train_pca, y_train)
    print("done in %0.3fs" % (time() - t0))
    print("Best estimator found by grid search:")
    print(clf.best_estimator_)



.. GENERATED FROM PYTHON SOURCE LINES 105-106

Quantitative evaluation of the model quality on the test set

.. GENERATED FROM PYTHON SOURCE LINES 106-120

.. code-block:: default


    print("Predicting people's names on the test set")
    t0 = time()
    y_pred = clf.predict(X_test_pca)
    print("done in %0.3fs" % (time() - t0))

    print(classification_report(y_test, y_pred, target_names=target_names))
    ConfusionMatrixDisplay.from_estimator(
        clf, X_test_pca, y_test, display_labels=target_names, xticks_rotation="vertical"
    )
    plt.tight_layout()
    plt.show()



.. GENERATED FROM PYTHON SOURCE LINES 121-122

Qualitative evaluation of the predictions using matplotlib

.. GENERATED FROM PYTHON SOURCE LINES 122-136

.. code-block:: default



    def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
        """Helper function to plot a gallery of portraits"""
        plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
        plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35)
        for i in range(n_row * n_col):
            plt.subplot(n_row, n_col, i + 1)
            plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
            plt.title(titles[i], size=12)
            plt.xticks(())
            plt.yticks(())



.. GENERATED FROM PYTHON SOURCE LINES 137-138

plot the result of the prediction on a portion of the test set

.. GENERATED FROM PYTHON SOURCE LINES 138-151

.. code-block:: default



    def title(y_pred, y_test, target_names, i):
        pred_name = target_names[y_pred[i]].rsplit(" ", 1)[-1]
        true_name = target_names[y_test[i]].rsplit(" ", 1)[-1]
        return "predicted: %s\ntrue:      %s" % (pred_name, true_name)


    prediction_titles = [
        title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
    ]

    plot_gallery(X_test, prediction_titles, h, w)

.. GENERATED FROM PYTHON SOURCE LINES 152-153

plot the gallery of the most significative eigenfaces

.. GENERATED FROM PYTHON SOURCE LINES 153-159

.. code-block:: default


    eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
    plot_gallery(eigenfaces, eigenface_titles, h, w)

    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 160-164

Face recognition problem would be much more effectively solved by training
convolutional neural networks but this family of models is outside of the scope of
the scikit-learn library. Interested readers should instead try to use pytorch or
tensorflow to implement such models.


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

   **Total running time of the script:** ( 0 minutes  0.003 seconds)


.. _sphx_glr_download_auto_examples_applications_plot_face_recognition.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_face_recognition.py <plot_face_recognition.py>`



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

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


.. only:: html

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

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