def cross_validate(folds, method):
if folds < 2:
print 'Must have at least 2 folds.. evaluating 2-fold cross validation'
folds = 2
test_size = 100/folds
training_size = 100 - test_size
songs_by_class = split_by_class()
sentiment_accuracy_sum = 0.0
emotion_accuracy_sum = 0.0
for f in range(0,folds):
test_set = songs_by_class['+'][int(test_size*f):int(test_size+test_size*f)] + songs_by_class['0'][int(test_size*f):int(test_size+test_size*f)] +songs_by_class['-'][int(test_size*f):int(test_size+test_size*f)]
training_set = songs_by_class['+'][int(test_size+test_size*f):] + songs_by_class['+'][:int(test_size*f)] + songs_by_class['0'][int(test_size+test_size*f):] + songs_by_class['0'][:int(test_size*f)] + songs_by_class['-'][int(test_size+test_size*f):] + songs_by_class['-'][:int(test_size*f)]
if method == 'nb':
nb = NaiveBayes()
nb.train_model(training_set)
sentiment_accuracy, emotion_accuracy = nb.evaluate_model(test_set, len(training_set))
emotion_accuracy_sum += emotion_accuracy
sentiment_accuracy_sum += sentiment_accuracy
elif method == 'sa':
sa = SimpleAveraging()
avgs = sa.train(training_set)
sentiment_accuracy, emotion_accuracy = sa.evaluate(test_set, avgs)
emotion_accuracy_sum += emotion_accuracy
sentiment_accuracy_sum += sentiment_accuracy
# elif method == 'pool':
# pool = AffectPool(NaiveBayes(), SimpleAveraging())
# pool.simple_train(training_set)
elif method =='r':
nb = NaiveBayes()
nb.train_model(test_set + training_set)
print "EMOTION ACCURACY ", emotion_accuracy_sum / folds, " SENTIMENT ACCURACY: ", sentiment_accuracy_sum / folds
