from __future__ import print_function
from time import time
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 100)
y = np.sin(x) ** 2
plt.plot(y, 'o-')
plt.xlabel('x')
plt.ylabel('y = sin(x)')
<matplotlib.text.Text at 0x109a787b8>
import logging
from sklearn.datasets import fetch_lfw_people
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
X = lfw_people.data
n_samples, n_features = X.shape
# 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)
Total dataset size: n_samples: 1288 n_features: 1850 n_classes: 7
# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape
print("height: %d pixels" % h)
print("width: %d pixels" % w)
height: 50 pixels width: 37 pixels
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_gallery(X, target_names[y], h, w)
Split dataset into a training and testing set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25)
Unsupervised feature extraction / dimensionality reduction
from sklearn.decomposition import PCA
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))
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
Extracting the top 150 eigenfaces from 966 faces done in 0.379s
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))
Projecting the input data on the eigenfaces orthonormal basis done in 0.046s
X_train_reconstructed = np.dot(X_train_pca, pca.components_)
plot_gallery(X_train_reconstructed, target_names[y_train], h, w, n_row=1)
plot_gallery(X_train, target_names[y_train], h, w, n_row=1)
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1, 10, 100],
'gamma': [0.0001, 0.001, 0.01, 0.1]}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid, n_jobs=2)
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_)
Fitting the classifier to the training set done in 4.402s Best estimator found by grid search: SVC(C=10, cache_size=200, class_weight='balanced', coef0=0.0, decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
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))
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)
Predicting people's names on the test set done in 0.059s
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred, target_names=target_names))
precision recall f1-score support Ariel Sharon 0.64 0.73 0.68 22 Colin Powell 0.74 0.83 0.79 66 Donald Rumsfeld 0.62 0.84 0.71 25 George W Bush 0.96 0.79 0.87 123 Gerhard Schroeder 0.74 0.83 0.78 24 Hugo Chavez 0.80 0.89 0.84 18 Tony Blair 0.80 0.75 0.78 44 avg / total 0.82 0.80 0.81 322
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
[[16 3 1 0 0 1 1] [ 3 55 3 2 1 1 1] [ 2 0 21 0 0 1 1] [ 3 12 4 97 2 1 4] [ 0 1 2 0 20 0 1] [ 0 1 0 1 0 16 0] [ 1 2 3 1 4 0 33]]