def cross_val(data, labels, k, smote, classifier):
""" Performs k-fold cross validation using the specified classifier, returns number of true/false
positives/negatives """
kf = KFold(n_splits=k)
tp, fp, fn, tn = 0, 0, 0, 0
i = 0
for train_index, test_index in kf.split(data):
test_set, train_set, test_label, train_label = [], [], [], []
# make train and test sets/labels
for i in train_index:
train_set.append(data[i])
train_label.append(labels[i])
for i in test_index:
test_set.append(data[i])
test_label.append(labels[i])
# Apply SMOTEing when smote parameter is True
if smote:
train_set, train_label = SMOTE.SMOTEd(train_set, train_label)
if classifier == 'linear':
predicted = classifiers.lin_reg(train_set, test_set, train_label)
elif classifier == 'logistic':
predicted = classifiers.log_reg(train_set, test_set, train_label)
elif classifier == 'decision tree':
predicted = classifiers.decision_tree(train_set, test_set, train_label)
elif classifier == 'neuralnetwork':
predicted = classifiers.neuralnetwork(train_set, test_set, train_label)
elif classifier == 'naive bayes':
predicted = classifiers.naive_bayes(train_set, test_set, train_label)
elif classifier == 'randomforest':
predicted = classifiers.randomforest(train_set, test_set, train_label)
elif classifier == 'knn':
predicted = classifiers.knn(train_set, test_set, train_label)
else:
print 'Wrong name supplied: %s' % classifier
return [test_label, predicted]
