def buildSubTree(self, trainingExamples, currNode):
"""
method to build our tree - this build method uses OO concepts which we use during our prediction later on
"""
if self.attributesAndValues == []:
return currNode
divisionAttribute = self.getDivisionAttribute(trainingExamples)
if currNode == None: # rootptr
currNode = TreeNode(divisionAttribute.attrName)
else:
currNode.name = divisionAttribute.attrName
# divide up our data based on the attribute we got back
subLists = {}
for attrValue in divisionAttribute.attrValues:
subLists[attrValue] = []
for example in trainingExamples:
# if the example attribute matches our division attributes, add training example to correct sublist
for attribute in example.attributes:
if attribute.attrName == divisionAttribute.attrName:
subLists[attribute.attrValues[0]].append(example)
# check if any of the sublists would require us to return a leaf node
for subListKey in subLists:
childNode = TreeNode()
subList = subLists[subListKey]
if subList == []: # no training examples, default to most common target value
childNode.isLeaf = True
childNode.targetValue = "p"
currNode.childrenNodes[subListKey] = childNode
elif self.isLeafNode(subList):
childNode.isLeaf = True
childNode.targetValue = subList[0].targetValue
currNode.childrenNodes[subListKey] = childNode
else:
currNode.childrenNodes[subListKey] = childNode
# recursively build using each sublist
self.buildSubTree(subList, childNode)
#return the root node with everything built on
return currNode
def buildSubTree(self, trainingExamples, currNode):
"""
method to build our tree - this build method uses OO concepts which we use during our prediction later on
"""
if self.attributesAndValues == []:
return currNode
divisionAttribute = self.getDivisionAttribute(trainingExamples)
if currNode == None: # rootptr
currNode = TreeNode(divisionAttribute.attrName)
else:
currNode.name = divisionAttribute.attrName
# divide up our data based on the attribute we got back
subLists = {}
for attrValue in divisionAttribute.attrValues:
subLists[attrValue] = []
for example in trainingExamples:
# if the example attribute matches our division attributes, add training example to correct sublist
for attribute in example.attributes:
if attribute.attrName == divisionAttribute.attrName:
subLists[attribute.attrValues[0]].append(example)
# check if any of the sublists would require us to return a leaf node
for subListKey in subLists:
childNode = TreeNode()
subList = subLists[subListKey]
if subList == []: # no training examples, default to most common target value
childNode.isLeaf = True
childNode.targetValue = "p"
currNode.childrenNodes[subListKey] = childNode
elif self.isLeafNode(subList):
childNode.isLeaf = True
childNode.targetValue = subList[0].targetValue
currNode.childrenNodes[subListKey] = childNode
else:
currNode.childrenNodes[subListKey] = childNode
# recursively build using each sublist
self.buildSubTree(subList, childNode)
#return the root node with everything built on
return currNode
