.. 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 ` 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_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 Out: .. code-block:: none Total dataset size: n_samples: 1288 n_features: 1850 n_classes: 7 .. 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)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Extracting the top 150 eigenfaces from 966 faces done in 0.088s Projecting the input data on the eigenfaces orthonormal basis done in 0.011s .. 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_) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Fitting the classifier to the training set done in 6.035s Best estimator found by grid search: SVC(C=5387.73239387596, class_weight='balanced', gamma=0.0005355267666024514) .. 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() .. 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 Out: .. code-block:: none Predicting people's names on the test set done in 0.039s precision recall f1-score support Ariel Sharon 0.56 0.69 0.62 13 Colin Powell 0.72 0.87 0.79 60 Donald Rumsfeld 0.75 0.67 0.71 27 George W Bush 0.92 0.88 0.90 146 Gerhard Schroeder 0.78 0.84 0.81 25 Hugo Chavez 0.69 0.60 0.64 15 Tony Blair 0.87 0.75 0.81 36 accuracy 0.82 322 macro avg 0.76 0.76 0.75 322 weighted avg 0.83 0.82 0.82 322 .. 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) .. 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 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() .. 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 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 6.840 seconds) .. _sphx_glr_download_auto_examples_applications_plot_face_recognition.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.0.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_face_recognition.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_face_recognition.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_face_recognition.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_