163 lines
4.7 KiB
Python
163 lines
4.7 KiB
Python
"""
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===================================================
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Faces recognition example using eigenfaces and SVMs
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===================================================
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The dataset used in this example is a preprocessed excerpt of the
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"Labeled Faces in the Wild", aka LFW_:
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http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
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.. _LFW: http://vis-www.cs.umass.edu/lfw/
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"""
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# %%
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from time import time
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import matplotlib.pyplot as plt
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from scipy.stats import loguniform
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from sklearn.datasets import fetch_lfw_people
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from sklearn.decomposition import PCA
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from sklearn.metrics import ConfusionMatrixDisplay, classification_report
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from sklearn.model_selection import RandomizedSearchCV, train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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# %%
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# Download the data, if not already on disk and load it as numpy arrays
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lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
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# introspect the images arrays to find the shapes (for plotting)
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n_samples, h, w = lfw_people.images.shape
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# for machine learning we use the 2 data directly (as relative pixel
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# positions info is ignored by this model)
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X = lfw_people.data
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n_features = X.shape[1]
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# the label to predict is the id of the person
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y = lfw_people.target
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target_names = lfw_people.target_names
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n_classes = target_names.shape[0]
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print("Total dataset size:")
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print("n_samples: %d" % n_samples)
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print("n_features: %d" % n_features)
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print("n_classes: %d" % n_classes)
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# %%
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# Split into a training set and a test and keep 25% of the data for testing.
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.25, random_state=42
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)
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# %%
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# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
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# dataset): unsupervised feature extraction / dimensionality reduction
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n_components = 150
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print(
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"Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
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)
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t0 = time()
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pca = PCA(n_components=n_components, svd_solver="randomized", whiten=True).fit(X_train)
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print("done in %0.3fs" % (time() - t0))
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eigenfaces = pca.components_.reshape((n_components, h, w))
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print("Projecting the input data on the eigenfaces orthonormal basis")
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t0 = time()
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X_train_pca = pca.transform(X_train)
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X_test_pca = pca.transform(X_test)
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print("done in %0.3fs" % (time() - t0))
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# %%
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# Train a SVM classification model
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print("Fitting the classifier to the training set")
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t0 = time()
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param_grid = {
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"C": loguniform(1e3, 1e5),
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"gamma": loguniform(1e-4, 1e-1),
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}
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clf = RandomizedSearchCV(
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SVC(kernel="rbf", class_weight="balanced"), param_grid, n_iter=10
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)
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clf = clf.fit(X_train_pca, y_train)
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print("done in %0.3fs" % (time() - t0))
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print("Best estimator found by grid search:")
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print(clf.best_estimator_)
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# %%
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# Quantitative evaluation of the model quality on the test set
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print("Predicting people's names on the test set")
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t0 = time()
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y_pred = clf.predict(X_test_pca)
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print("done in %0.3fs" % (time() - t0))
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print(classification_report(y_test, y_pred, target_names=target_names))
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ConfusionMatrixDisplay.from_estimator(
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clf, X_test_pca, y_test, display_labels=target_names, xticks_rotation="vertical"
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)
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plt.tight_layout()
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plt.show()
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# %%
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# Qualitative evaluation of the predictions using matplotlib
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def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
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"""Helper function to plot a gallery of portraits"""
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plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
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plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.90, hspace=0.35)
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for i in range(n_row * n_col):
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plt.subplot(n_row, n_col, i + 1)
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plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
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plt.title(titles[i], size=12)
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plt.xticks(())
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plt.yticks(())
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# %%
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# plot the result of the prediction on a portion of the test set
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def title(y_pred, y_test, target_names, i):
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pred_name = target_names[y_pred[i]].rsplit(" ", 1)[-1]
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true_name = target_names[y_test[i]].rsplit(" ", 1)[-1]
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return "predicted: %s\ntrue: %s" % (pred_name, true_name)
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prediction_titles = [
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title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
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]
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plot_gallery(X_test, prediction_titles, h, w)
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# %%
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# plot the gallery of the most significative eigenfaces
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eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
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plot_gallery(eigenfaces, eigenface_titles, h, w)
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plt.show()
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# %%
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# Face recognition problem would be much more effectively solved by training
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# convolutional neural networks but this family of models is outside of the scope of
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# the scikit-learn library. Interested readers should instead try to use pytorch or
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# tensorflow to implement such models.
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