In [1]:

    

# ML testing with Olivetti faces

import sklearn as sk
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces

faces = fetch_olivetti_faces()
print(faces.DESCR)

print(faces.keys())

print(faces.images.shape)

print(faces.data.shape)

print(faces.target.shape)

print(np.max(faces.data))

print(np.min(faces.data))

print(np.mean(faces.data))


    

    




Modified Olivetti faces dataset.

The original database was available from (now defunct)

    http://www.uk.research.att.com/facedatabase.html

The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:

    http://www.cs.nyu.edu/~roweis/

There are ten different images of each of 40 distinct subjects. For some
subjects, the images were taken at different times, varying the lighting,
facial expressions (open / closed eyes, smiling / not smiling) and facial
details (glasses / no glasses). All the images were taken against a dark
homogeneous background with the subjects in an upright, frontal position (with
tolerance for some side movement).

The original dataset consisted of 92 x 112, while the Roweis version
consists of 64x64 images.

['images', 'data', 'target', 'DESCR']
(400, 64, 64)
(400, 4096)
(400,)
1.0
0.0
0.547043

In [2]:

    

def print_faces(images, target, top_n):
    fig = plt.figure(figsize=(12, 12))
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
    
    for i in range(top_n):
        p = fig.add_subplot(20, 20, i+1, xticks=[], yticks=[])
        p.imshow(images[i], cmap=plt.cm.bone)
        
        p.text(0, 14, str(target[i]))
        p.text(0, 60, str(i))
    plt.show()
    
print_faces(faces.images, faces.target, 20)


    

In [3]:

    

from sklearn.svm import SVC

svc_1 = SVC(kernel='linear')

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(faces.data, faces.target, test_size=0.25, random_state=0)

from sklearn.cross_validation import cross_val_score, KFold
from scipy.stats import sem

def evaluate_cross_validation(clf, X, y, K):
    cv = KFold(len(y), K, shuffle=True, random_state=0)
    scores = cross_val_score(clf, X, y, cv=cv)
    
    print(scores)
    print("Mean score: {0: .3f} (+/-{1: .3f})".format(np.mean(scores), sem(scores)))
    
evaluate_cross_validation(svc_1, X_train, y_train, 5)


    

    




[ 0.93333333  0.86666667  0.91666667  0.93333333  0.91666667]
Mean score:  0.913 (+/- 0.012)

In [5]:

    

from sklearn import metrics

def train_and_evaluate(clf, X_train, X_test, y_train, y_test):
    clf.fit(X_train, y_train)
    
    print("Accuracy on training set:")
    print(clf.score(X_train, y_train))
    print("Accuracy on testing set:")
    print(clf.score(X_test, y_test))
    
    y_pred = clf.predict(X_test)
    
    print("Classification Report:")
    print(metrics.classification_report(y_test, y_pred))
    print("Confusion Matrix:")
    print(metrics.confusion_matrix(y_test, y_pred))
    
train_and_evaluate(svc_1, X_train, X_test, y_train, y_test)


    

    




Accuracy on training set:
1.0
Accuracy on testing set:
0.99
Classification Report:
             precision    recall  f1-score   support

          0       0.86      1.00      0.92         6
          1       1.00      1.00      1.00         4
          2       1.00      1.00      1.00         2
          3       1.00      1.00      1.00         1
          4       1.00      1.00      1.00         1
          5       1.00      1.00      1.00         5
          6       1.00      1.00      1.00         4
          7       1.00      0.67      0.80         3
          9       1.00      1.00      1.00         1
         10       1.00      1.00      1.00         4
         11       1.00      1.00      1.00         1
         12       1.00      1.00      1.00         2
         13       1.00      1.00      1.00         3
         14       1.00      1.00      1.00         5
         15       1.00      1.00      1.00         3
         17       1.00      1.00      1.00         6
         19       1.00      1.00      1.00         4
         20       1.00      1.00      1.00         1
         21       1.00      1.00      1.00         1
         22       1.00      1.00      1.00         2
         23       1.00      1.00      1.00         1
         24       1.00      1.00      1.00         2
         25       1.00      1.00      1.00         2
         26       1.00      1.00      1.00         4
         27       1.00      1.00      1.00         1
         28       1.00      1.00      1.00         2
         29       1.00      1.00      1.00         3
         30       1.00      1.00      1.00         4
         31       1.00      1.00      1.00         3
         32       1.00      1.00      1.00         3
         33       1.00      1.00      1.00         2
         34       1.00      1.00      1.00         3
         35       1.00      1.00      1.00         1
         36       1.00      1.00      1.00         3
         37       1.00      1.00      1.00         3
         38       1.00      1.00      1.00         1
         39       1.00      1.00      1.00         3

avg / total       0.99      0.99      0.99       100

Confusion Matrix:
[[6 0 0 ..., 0 0 0]
 [0 4 0 ..., 0 0 0]
 [0 0 2 ..., 0 0 0]
 ..., 
 [0 0 0 ..., 3 0 0]
 [0 0 0 ..., 0 1 0]
 [0 0 0 ..., 0 0 3]]

In [7]:

    

# index of people with glasses
glasses = [
    (10, 19), (30, 32), (37, 38), (50, 59), (63, 64),
   (69, 69), (120, 121), (124, 129), (130, 139), (160, 161),
   (164, 169), (180, 182), (185, 185), (189, 189), (190, 192),
   (194, 194), (196, 199), (260, 269), (270, 279), (300, 309),
   (330, 339), (358, 359), (360, 369)
]


def create_target(segments):
    y = np.zeros(faces.target.shape[0])
    
    for (start, end) in segments:
        y[start:end + 1] = 1
    return y

target_glasses = create_target(glasses)

X_train, X_test, y_train, y_test = train_test_split(faces.data, target_glasses, test_size=0.25, random_state=0)

svc_2 = SVC(kernel='linear')

evaluate_cross_validation(svc_2, X_train, y_train, 5)

train_and_evaluate(svc_2, X_train, X_test, y_train, y_test)


    

    




[ 1.          0.95        0.98333333  0.98333333  0.93333333]
Mean score:  0.970 (+/- 0.012)
Accuracy on training set:
1.0
Accuracy on testing set:
0.99
Classification Report:
             precision    recall  f1-score   support

        0.0       1.00      0.99      0.99        67
        1.0       0.97      1.00      0.99        33

avg / total       0.99      0.99      0.99       100

Confusion Matrix:
[[66  1]
 [ 0 33]]

In [8]:

    

# Let's manipulate the data to isolate some of the data
X_test = faces.data[30:40]
y_test = target_glasses[30:40]
print(y_test.shape[0])

select = np.ones(target_glasses.shape[0])
select[30:40] = 0

X_train = faces.data[select == 1]
y_train = target_glasses[select == 1]
print(y_train.shape[0])

svc_3 = SVC(kernel='linear')

train_and_evaluate(svc_3, X_train, X_test, y_train, y_test)


    

    




10
390
Accuracy on training set:
1.0
Accuracy on testing set:
0.9
Classification Report:
             precision    recall  f1-score   support

        0.0       0.83      1.00      0.91         5
        1.0       1.00      0.80      0.89         5

avg / total       0.92      0.90      0.90        10

Confusion Matrix:
[[5 0]
 [1 4]]

In [12]:

    

# Reshape the data from arrays to 64x64 matrices
y_pred = svc_3.predict(X_test)
eval_faces = [np.reshape(a, (64, 64)) for a in X_test]

print_faces(eval_faces, y_pred, 10)


    

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