def main():
dataset_file, model = sys.argv[1], sys.argv[2]
classes = load_classes(dataset_file)
train, test = split_dataset(classes, 0.6)
classifier = NaiveBayes()
classifier.train(train)
accuracy, recall, f1 = classifier.perfomance(test)
print("Total perfomance")
print("================")
print("Accuracy: %f" % accuracy)
print("Recall: %f" % recall)
print("F1: %f" % f1)
print("\n")
class_accuracy, class_recall, class_f1 = classifier.class_perfomance(test)
print("Class accuracy")
print("================")
for klass in class_accuracy:
print("%s: %f" % (klass, class_accuracy[klass]))
print("\n")
print("Class recall")
print("================")
for klass in class_recall:
print("%s: %f" % (klass, class_recall[klass]))
print("\n")
print("Class F1")
print("================")
for klass in class_f1:
print("%s: %f" % (klass, class_f1[klass]))
print("\n")
classifier.save(model)
