def GrowTree(dataset, attributes, level):
print "Constructing Decision Tree" , level
max_gain, max_gain_attr, level = 0, None, 0
print "Attributes ", attributes
if not attributes:
common_val = get_max_val(dataset.get("Class"))
root = BTreeNode(str(common_val))
root.left = None
root.right = None
else:
if dataset.has_key('NA'):
return BTreeNode(str(dataset.get('NA')))
else:
class_list = dataset.get("Class")
tmp_negcnt, tmp_poscnt = get_count(class_list)
if tmp_poscnt == 0:
print "class: all negative examples"
return BTreeNode('0')
if tmp_negcnt == 0:
print "class: all positive examples"
return BTreeNode('1')
for val in attributes:
neg_dict, pos_dict = Dataset.split_dataset(dataset, val)
variance_set = Heuristics.Variance_Impurity_Set(class_list)
print "Variance Impurity set for ", val ,variance_set
member_list = dataset.get(val)
variance_member = Heuristics.Variance_Impurity_Members(dataset,neg_dict,pos_dict,member_list)
print "Variance Impurity member for ",val,variance_member
var_gain = Heuristics.gain(variance_set, variance_member)
print "Variance Impurity gain for ",val ,var_gain
print "Bool value - zeros" , bool([a for a in neg_dict.values() if a == []])
print "Bool value - ones" , bool([a for a in pos_dict.values() if a == []])
if bool([a for a in neg_dict.values() if a == []]):
print "Sub values empty - zero dataset"
common_val = get_max_val(dataset.get("Class"))
neg_dict = {}
neg_dict.update({'NA':common_val})
elif bool([a for a in pos_dict.values() if a == []]):
print "Sub values empty - one dataset"
common_val = get_max_val(dataset.get("Class"))
pos_dict = {}
pos_dict.update({'NA':common_val})
if var_gain > max_gain:
max_gain = var_gain
max_gain_attr = val
root_zero_dataset = neg_dict
root_one_dataset = pos_dict
else:
max_gain = var_gain
max_gain_attr = val
root_zero_dataset = neg_dict
root_one_dataset = pos_dict
print "Maximum Information Gain: ",max_gain
print "Node selected" , max_gain_attr
print "Zero Dataset", root_zero_dataset
print "One Dataset", root_one_dataset
root = BTreeNode(max_gain_attr)
if max_gain_attr in attributes:
attributes.remove(max_gain_attr)
if root != None:
root.left = GrowTree(root_zero_dataset,attributes,level)
root.right = GrowTree(root_one_dataset,attributes,level)
level+= 1
return root
def GrowTree(dataset, attributes):
global cnt_nonleaf_nodes,heuristic
max_gain_attr = None
max_gain = 0.0
gain = 0.0
# print "Attributes ", attributes
if not attributes:
common_val = get_max_val(dataset.get("Class"))
root = BTreeNode(str(common_val))
root.left = None
root.right = None
else:
if dataset.has_key('NA'):
return BTreeNode(str(dataset.get('NA')))
else:
class_list = dataset.get("Class")
# print class_list
tmp_negcnt, tmp_poscnt = get_count(class_list)
if tmp_poscnt == 0:
print "class: all negative examples"
# print class_list
return BTreeNode('0')
if tmp_negcnt == 0:
print "class: all positive examples"
# print class_list
return BTreeNode('1')
for val in attributes:
neg_dict, pos_dict = Dataset.split_dataset(dataset, val)
# print "Neg dict class" , neg_dict.get("Class")
# print "Pos dict class" , pos_dict.get("Class")
if heuristic == 0:
entropy_set = Heuristics.Entropy_Set(class_list)
elif heuristic == 1:
variance_set = Heuristics.Variance_Impurity_Set(class_list)
# print "Entropy set for ", val ,entropy_set
member_list = dataset.get(val)
if heuristic == 0:
entropy_member = Heuristics.Entropy_Members(dataset,neg_dict,pos_dict,member_list)
elif heuristic == 1:
variance_member = Heuristics.Variance_Impurity_Members(dataset,neg_dict,pos_dict,member_list)
# print "Entropy member for ",val,entropy_member
if heuristic == 0:
gain = Heuristics.gain(entropy_set, entropy_member)
elif heuristic == 1:
gain = Heuristics.gain(variance_set, variance_member)
print "gain for ",val ,gain
if bool([a for a in neg_dict.values() if a == []]):
print "Sub values empty - zero dataset"
common_val = get_max_val(dataset.get("Class"))
neg_dict = {}
neg_dict.update({'NA':common_val})
elif bool([a for a in pos_dict.values() if a == []]):
print "Sub values empty - one dataset"
common_val = get_max_val(dataset.get("Class"))
pos_dict = {}
pos_dict.update({'NA':common_val})
if gain >= max_gain:
max_gain = gain
max_gain_attr = val
root_zero_dataset = neg_dict
# print "inside max gain cal zeros ",val, neg_dict.get("Class")
root_one_dataset = pos_dict
# print "inside max gain cal ones ",val, pos_dict.get("Class")
neg_dict = {}
pos_dict = {}
# print
print "Maximum Information Gain: ",max_gain
print "Node selected: " , max_gain_attr
print "Zero Dataset: ", root_zero_dataset.get("Class")
print "One Dataset: ", root_one_dataset.get("Class")
root = BTreeNode(max_gain_attr)
cnt_nonleaf_nodes += 1
root.order = cnt_nonleaf_nodes
root.subset = dataset
if max_gain_attr in attributes:
attributes.remove(max_gain_attr)
if root != None:
# if root.left:
root.left = GrowTree(root_zero_dataset,attributes)
# if root.right:
root.right = GrowTree(root_one_dataset,attributes)
return root
