.. 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 ` 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 13-17 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 18-30 .. 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 31-32 Download the data, if not already on disk and load it as numpy arrays .. GENERATED FROM PYTHON SOURCE LINES 32-54 .. 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 55-56 Split into a training set and a test and keep 25% of the data for testing. .. GENERATED FROM PYTHON SOURCE LINES 56-65 .. 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 66-68 Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled dataset): unsupervised feature extraction / dimensionality reduction .. GENERATED FROM PYTHON SOURCE LINES 68-87 .. 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 0.095s Projecting the input data on the eigenfaces orthonormal basis done in 0.006s .. GENERATED FROM PYTHON SOURCE LINES 88-89 Train a SVM classification model .. GENERATED FROM PYTHON SOURCE LINES 89-105 .. 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 5.253s Best estimator found by grid search: SVC(C=np.float64(76823.03433306457), class_weight='balanced', gamma=np.float64(0.0034189458230957995)) .. GENERATED FROM PYTHON SOURCE LINES 106-107 Quantitative evaluation of the model quality on the test set .. GENERATED FROM PYTHON SOURCE LINES 107-121 .. 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 0.043s 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 122-123 Qualitative evaluation of the predictions using matplotlib .. GENERATED FROM PYTHON SOURCE LINES 123-137 .. 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 138-139 plot the result of the prediction on a portion of the test set .. GENERATED FROM PYTHON SOURCE LINES 139-152 .. 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 153-154 plot the gallery of the most significative eigenfaces .. GENERATED FROM PYTHON SOURCE LINES 154-160 .. 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 161-165 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 6.078 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/1.6.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/index.html?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 ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_face_recognition.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_face_recognition.zip ` .. include:: plot_face_recognition.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_