def deserialize_bernoulli_nb(model_dict):
model = BernoulliNB(model_dict['params'])
model.classes_ = np.array(model_dict['classes_'])
model.class_count_ = np.array(model_dict['class_count_'])
model.class_log_prior_ = np.array(model_dict['class_log_prior_'])
model.feature_count_ = np.array(model_dict['feature_count_'])
model.feature_log_prob_ = np.array(model_dict['feature_log_prob_'])
return model
def leave_one_out(training_images, test_image, vocab):
X_training = occurrance_matrix(training_images, vocab)
Y_training = np.array([img.rating for img in training_images])
X_testing = occurrance_matrix([test_image], vocab)
from sklearn.naive_bayes import BernoulliNB
classifier = BernoulliNB()
classifier.fit(X_training, Y_training)
classifier.classes_ = np.array([-1, 1])
estimates = classifier.predict(X_testing)
if estimates[0] == test_image.rating:
return 1.0
else:
return 0.0
