.. 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
:ref:`Go to the end <sphx_glr_download_auto_examples_applications_plot_face_recognition.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. 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 15-27
.. code-block:: Python
from time import time
import matplotlib.pyplot as plt
from scipy.stats import loguniform
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import PCA
from sklearn.metrics import ConfusionMatrixDisplay, classification_report
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
.. GENERATED FROM PYTHON SOURCE LINES 28-29
Download the data, if not already on disk and load it as numpy arrays
.. GENERATED FROM PYTHON SOURCE LINES 29-51
.. code-block:: Python
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:: none
Total dataset size:
n_samples: 1288
n_features: 1850
n_classes: 7
.. GENERATED FROM PYTHON SOURCE LINES 52-53
Split into a training set and a test and keep 25% of the data for testing.
.. GENERATED FROM PYTHON SOURCE LINES 53-62
.. code-block:: Python
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 63-65
Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
dataset): unsupervised feature extraction / dimensionality reduction
.. GENERATED FROM PYTHON SOURCE LINES 65-84
.. code-block:: Python
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))
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Extracting the top 150 eigenfaces from 966 faces
done in 0.076s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.007s
.. GENERATED FROM PYTHON SOURCE LINES 85-86
Train a SVM classification model
.. GENERATED FROM PYTHON SOURCE LINES 86-102
.. code-block:: Python
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_)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Fitting the classifier to the training set
done in 5.682s
Best estimator found by grid search:
SVC(C=76823.03433306456, class_weight='balanced', gamma=0.0034189458230957995)
.. GENERATED FROM PYTHON SOURCE LINES 103-104
Quantitative evaluation of the model quality on the test set
.. GENERATED FROM PYTHON SOURCE LINES 104-118
.. code-block:: Python
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()
.. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_001.png
:alt: plot face recognition
:srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Predicting people's names on the test set
done in 0.044s
precision recall f1-score support
Ariel Sharon 0.75 0.69 0.72 13
Colin Powell 0.72 0.87 0.79 60
Donald Rumsfeld 0.77 0.63 0.69 27
George W Bush 0.88 0.95 0.91 146
Gerhard Schroeder 0.95 0.80 0.87 25
Hugo Chavez 0.90 0.60 0.72 15
Tony Blair 0.93 0.75 0.83 36
accuracy 0.84 322
macro avg 0.84 0.75 0.79 322
weighted avg 0.85 0.84 0.84 322
.. GENERATED FROM PYTHON SOURCE LINES 119-120
Qualitative evaluation of the predictions using matplotlib
.. GENERATED FROM PYTHON SOURCE LINES 120-134
.. code-block:: Python
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 135-136
plot the result of the prediction on a portion of the test set
.. GENERATED FROM PYTHON SOURCE LINES 136-149
.. code-block:: Python
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)
.. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_002.png
:alt: predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Blair true: Blair, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Schroeder true: Schroeder, predicted: Powell true: Powell, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush, predicted: Bush true: Bush
:srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_002.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 150-151
plot the gallery of the most significative eigenfaces
.. GENERATED FROM PYTHON SOURCE LINES 151-157
.. code-block:: Python
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
plt.show()
.. image-sg:: /auto_examples/applications/images/sphx_glr_plot_face_recognition_003.png
:alt: eigenface 0, eigenface 1, eigenface 2, eigenface 3, eigenface 4, eigenface 5, eigenface 6, eigenface 7, eigenface 8, eigenface 9, eigenface 10, eigenface 11
:srcset: /auto_examples/applications/images/sphx_glr_plot_face_recognition_003.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 158-162
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 22.070 seconds)
.. _sphx_glr_download_auto_examples_applications_plot_face_recognition.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_face_recognition.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/applications/plot_face_recognition.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_face_recognition.ipynb <plot_face_recognition.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_face_recognition.py <plot_face_recognition.py>`
.. include:: plot_face_recognition.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_