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from datetime import datetime
import logging
import itertools
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.decomposition import PCA
from sklearn.svm import SVC
import PIL
import numpy as np
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
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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: {ns}'.format(ns=n_samples))
print('n_features: {nf}'.format(nf=n_features))
print('n_classes: {}'.format(n_classes))
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# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=np.random.RandomState())
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n_components = 150
print("Extracting the top {nc} eigenfaces from {nf} faces".format(nc=n_components, nf=X_train.shape[0]))
start = datetime.now()
pca = PCA(n_components=n_components, svd_solver='randomized', whiten=True).fit(X_train)
print("done in {dur:.3f}s".format(dur=(datetime.now() - start).total_seconds()))
eigenfaces = pca.components_.reshape((n_components, h, w))
print('Projecting the input data on the eigenfaces orthonormal basis')
start = datetime.now()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in {dur:.3f}s".format(dur=(datetime.now() - start).total_seconds()))
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print('Fitting the classifier to the training set')
start = datetime.now()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print('done in {dur:.3f}s'.format(dur=(datetime.now() - start).total_seconds()))
print('Best estimator found by grid search:')
print(clf.best_estimator_)
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def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
"""
plt.figure(figsize=(7,7))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize=28)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45, fontsize=12)
plt.yticks(tick_marks, classes, fontsize=12)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
verticalalignment="center",
color="white" if cm[i, j] > thresh else "black", fontsize=18)
plt.tight_layout()
plt.ylabel('True label', fontsize=18)
plt.xlabel('Predicted label', fontsize=18)
plt.style.use('seaborn-dark')
plt.show()
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print("Predicting people's names on the test set")
start = datetime.now()
y_pred = clf.predict(X_test_pca)
print("done in {dur:.3f}s".format(dur=(datetime.now() - start).total_seconds()))
print('\nAccuracy: {:.2f}'.format(accuracy_score(y_test, y_pred)))
print(classification_report(y_test, y_pred, target_names=target_names))
cm = confusion_matrix(y_test, y_pred, labels=range(n_classes))
plot_confusion_matrix(cm=cm, classes=np.array(target_names))
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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=.01, right=.99, top=.90, hspace=.35)
plt.style.use('seaborn-dark')
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(())
def title(y_pred, y_test, target_names, i):
"""Helper function to extract the prediction titles"""
pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
return 'predicted: {p}\ntrue: {t}'.format(p=pred_name, t=true_name)
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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)
# plot the gallery of the most significative eigenfaces
eigenface_titles = ["eigenface {}".format(i) for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
plt.show()
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