class DecisionTreeC45:
def __init__(self, dataset, target, category=None, attribute=None):
self.dataset = dataset
self.target = target
self.category = category
self.attribute = attribute
def build_Tree(self):
self.root = Node(self.dataset)
if (len(self.dataset[self.target].unique()) == 1
): # The result is unique
# print("There is only one type of target for {}, set label = {}".format(self.category, self.dataset[self.target].iloc[0]))
# self.root.setName(self.category)
self.root.setValue(self.dataset[self.target].iloc[0])
return self.root
if (len(self.dataset.columns) <=
2): # There is only one attribute left
# print("There is only one attribute, set label = ", findMostFrequentLabel(self.dataset[self.target]))
# self.root.setName(self.category)
self.root.setValue(findMostFrequentClass(
self.dataset[self.target]))
return self.root
else:
# find the best attribute
self.dataset, best_attribute = findBestAttributeRatio(
self.dataset, self.target)
# print("Chosen best Attribute ", best_attribute)
self.root.setName(best_attribute)
best_attribute_dataGroup = self.dataset.groupby(best_attribute)
# iterate categories of the best attribute, add branches to the root
for category, data in best_attribute_dataGroup:
data = data.drop(best_attribute, axis=1)
subtree = DecisionTreeC45(data, self.target, category,
best_attribute)
self.root.addBranch(category, subtree.build_Tree())
return self.root
def getRoot(self):
return self.root
