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Confusion_matrix.py
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Confusion_matrix.py
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# coding: utf-8
# In[3]:
import cPickle as pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
path = '/Users/soledad/Box Sync/Fall 15/I590 - Collective Intelligence/CV Project/Code/svmethnicity/'
f = open(path+ '8svm.pkl', 'rb')
svm = pickle.load(f)
f.close()
train_set = np.load(path + '8train_set.pkl')
test_set = np.load(path + '8test_set.pkl')
labels_train=np.load(path + '8labels_train.pkl')
labels_test=np.load(path + '8labels_test.pkl')
predicted = svm.predict(test_set)
# In[ ]:
names=['Happiness','Suprise', 'Sadness', 'Disgust', 'Fear', 'Anger']
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(names))
plt.xticks(tick_marks, names, rotation=45)
plt.yticks(tick_marks, names)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
cm = confusion_matrix(labels_test, predicted)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plot_confusion_matrix(cm)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
print(cm_normalized)
plt.figure()
plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
plt.show()
# In[5]:
labels_test
# In[ ]:
# In[6]:
plt.show()
# In[ ]: