In [1]:

    

from sklearn.datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces()


    

In [2]:

    

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)


    

In [4]:

    

from sklearn.svm import SVC
svc = SVC(kernel='linear').fit(X_train, y_train)


    

In [10]:

    

from sklearn.metrics import precision_score
from sklearn.metrics import classification_report

print(precision_score(y_test, svc.predict(X_test), average="micro"))
print(classification_report(y_test, svc.predict(X_test)))


    

    




0.99
             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

In [16]:

    

k = 5
plt.imshow(X_test[k:(k+1), :].reshape(64,64), cmap=plt.cm.bone)
plt.grid(False)
y_test[5], svc.predict(X_test[5])


    

    




/home/dockeruser/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  DeprecationWarning)






    
Out[16]:





(6, array([6]))






    

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