app.py

from __future__ import print_function

from time import time
import logging
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.decomposition import PCA
from sklearn.svm import SVC


print(__doc__)

# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')


# #############################################################################
# Download the data, if not already on disk and load it as numpy arrays

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
print(target_names)
# target_names = ['James Brown', 'Michael Jordan', 'Michael Jackson', 'Barack Obama']
# print(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)
#
#
# # #############################################################################
# # Split into a training set and a test set using a stratified k fold
#
# # 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=42)
#
#
# # #############################################################################
# # Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# # dataset): unsupervised feature extraction / dimensionality reduction
# 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))
#
#
# # #############################################################################
# # Train a SVM classification model
#
# print("Fitting the classifier to the training set")
# t0 = time()
# 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 %0.3fs" % (time() - t0))
# print("Best estimator found by grid search:")
# print(clf.best_estimator_)
#
#
# # #############################################################################
# # Quantitative evaluation of the model quality on the test set
#
# 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))
# print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
#
#
# # #############################################################################
# # Qualitative evaluation of the predictions using matplotlib
#
# 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)
#     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(())
#
#
# # plot the result of the prediction on a portion of the test set
#
# 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)
#
# # plot the gallery of the most significative eigenfaces
#
# eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
# plot_gallery(eigenfaces, eigenface_titles, h, w)
#
# plt.show()