def PRINT_TREE_AT_LEVEL_2():
# A5
print(" ")
print("LEVEL 1:")
print(m.attributes[4])
Att = [None] * 4
for value in range(1, 5):
Att[value - 1] = select(m.monk1, m.attributes[4], value)
print("LEVEL 2:")
for A in Att:
tmp = bestAttribute(A, m.attributes)
print(tmp)
if tmp == m.attributes[0]:
for value in range(1, 4):
print(mostCommon(select(A, tmp, value)))
if tmp == m.attributes[1]:
for value in range(1, 4):
print(mostCommon(select(A, tmp, value)))
if tmp == m.attributes[2]:
for value in range(1, 3):
print(mostCommon(select(A, tmp, value)))
if tmp == m.attributes[3]:
for value in range(1, 4):
print(mostCommon(select(A, tmp, value)))
if tmp == m.attributes[4]:
for value in range(1, 5):
print(mostCommon(select(A, tmp, value)))
if tmp == m.attributes[5]:
for value in range(1, 3):
print(mostCommon(select(A, tmp, value)))
print(" ")
t = buildTree(m.monk1, m.attributes)
drawTree(t)
def informationGainCalculation():
print("Information gain results ", "\n")
for attributeIndex in range(0, 6):
result = d.averageGain(m.monk1, m.attributes[attributeIndex])
print("Monk1| ", attributeIndex + 1, ": ", result, " ")
print("Best attribute: ", d.bestAttribute(m.monk1, m.attributes), "\n")
for attributeIndex in range(0, 6):
result = d.averageGain(m.monk2, m.attributes[attributeIndex])
print("Monk2| ", attributeIndex + 1, ": ", result, " ")
print("Best attribute: ", d.bestAttribute(m.monk2, m.attributes), "\n")
for attributeIndex in range(0, 6):
result = d.averageGain(m.monk3, m.attributes[attributeIndex])
print("Monk3| ", attributeIndex + 1, ": ", result, " ")
print("Best attribute: ", d.bestAttribute(m.monk3, m.attributes), "\n")
def caspersky(dataset):
print("Assignment 3")
a = d.bestAttribute(dataset, m.attributes)
branches = []
for v in a.values:
s = d.select(dataset, a, v)
tf = d.mostCommon(s)
if tf == True:
branches.append((v, d.TreeLeaf(s)))
else:
a2 = d.bestAttribute(s, m.attributes)
branches2 = []
for v2 in a2.values:
s2 = d.select(s, a2, v2)
branches2.append((v2, d.TreeLeaf(d.mostCommon(s2))))
branches.append((v, d.TreeNode(a2, dict(branches2), d.mostCommon(s))))
drawtree.drawTree(d.TreeNode(a, dict(branches), d.mostCommon(dataset)))
def caspersky(dataset):
print("Assignment 3")
a = d.bestAttribute(dataset, m.attributes)
branches = []
for v in a.values:
s = d.select(dataset, a, v)
tf = d.mostCommon(s)
if tf == True:
branches.append((v, d.TreeLeaf(s)))
else:
a2 = d.bestAttribute(s, m.attributes)
branches2 = []
for v2 in a2.values:
s2 = d.select(s, a2, v2)
branches2.append((v2, d.TreeLeaf(d.mostCommon(s2))))
branches.append((v, d.TreeNode(a2, dict(branches2),
d.mostCommon(s))))
drawtree.drawTree(d.TreeNode(a, dict(branches), d.mostCommon(dataset)))
def buildTree(subset,attrs):
global tree
if isLeaf(subset):
tree = (tree + '+') if d.allPositive(subset) else (tree + '-')
return
else:
root = d.bestAttribute(subset,attrs)
tree = tree + str(root) + "("
for value in root.values:
nextSubset = d.select(subset,root,value)
nextAttrs = attrs - set([root])
buildTree(nextSubset,nextAttrs)
tree = tree + ")"
def makeTree(set, level, attributes):
if level >= depth:
return dtree.TreeLeaf(dtree.mostCommon(set))
attr = dtree.bestAttribute(set, attributes)
node = []
branches = []
for val in attr.values:
subset = dtree.select(set, attr, val)
attributes_left = [a for a in attributes if a != attr]
if dtree.allPositive(subset):
node = dtree.TreeLeaf(True)
elif dtree.allNegative(subset):
node = dtree.TreeLeaf(False)
else:
node = makeTree(subset, level + 1, attributes_left)
branches.append((val, node))
node = dtree.TreeNode(attr, dict(branches), dtree.mostCommon(set))
return node
def Tree(dataset, attributes, maxdepth=3):
def Branch(dataset, default, attributes):
if not dataset:
return dtree.TreeLeaf(default)
if dtree.allPositive(dataset):
return dtree.TreeLeaf(True)
if dtree.allNegative(dataset):
return dtree.TreeLeaf(False)
return Tree(dataset, attributes, maxdepth - 1)
default = dtree.mostCommon(dataset)
if maxdepth < 1:
return dtree.TreeLeaf(default)
a = dtree.bestAttribute(dataset, attributes)
attributesLeft = [x for x in attributes if x != a]
branches = [(v, Branch(dtree.select(dataset, a, v), default,
attributesLeft)) for v in a.values]
return dtree.TreeNode(a, dict(branches), default)
def buildTreeCustom(dataset, depth):
if (depth > 0):
bestAttr = dt.bestAttribute(dataset, m.attributes)
print(str(bestAttr), end='')
# Select datasets splits for each value of the bestAttr
splits = []
for value in bestAttr.values:
splits.append(dt.select(dataset, bestAttr, value))
for split in splits:
# If entropy of the split > 0, the split is impure and we can further split it. Recursive call with reduced depth
if (dt.entropy(split) > 0):
buildTreeCustom(split, depth - 1)
else:
print('+' if dt.mostCommon(split) else '-', end='')
else:
print('+' if dt.mostCommon(dataset) else '-', end='')
def calc_next_level():
#print "\nAverage gain when a5 is choosen"
print "\nA5\t a1\t\t a2\t\t a3\t\t a4\t\t a5\t\t a6"
s = "A5("
for val in data.attributes[4].values:
subset = dt.select(data.monk1, data.attributes[4], val)
t = "\t"
for attr in data.attributes:
t = t + "%.6f\t" % (dt.averageGain(subset, attr))
print val , t
best = dt.bestAttribute(subset, data.attributes)
s = s + best.name + "("
#print "best attribute: ", best.name
for value in best.values:
#print "choose: ", value, "mostCommon: ", dt.mostCommon(dt.select(subset, best, value))
if(dt.mostCommon(dt.select(subset, best, value))):
s = s + "+"
else:
s = s + "-"
s = s + ")"
s = s + ")"
print "\nOur tree:\t", s
print "Build tree:\t", dt.buildTree(data.monk1, data.attributes, 2)
# gain1=dtree.averageGain(mdata.monk1 , mdata.attributes[index])
# gain2=dtree.averageGain(mdata.monk2 , mdata.attributes[index])
# gain3=dtree.averageGain(mdata.monk3 , mdata.attributes[index])
# ag1.append(gain1)
# ag2.append(gain2)
# ag3.append(gain3)
# print(ag1) #a5
# print(ag2) #a5
# print(ag3) #a2 a5
#**********************************
# Assignment 5
a = dtree.bestAttribute(mdata.monk1, mdata.attributes)
attributesLeft = [x for x in mdata.attributes if x != a]
#print(a,attributesLeft) #a5
subsets = []
for v in a.values:
temp = dtree.select(mdata.monk1, a, v)
subsets.append(temp)
ag_in2level = []
subsets_ag = []
#print(len(a.values))
for subset in subsets:
for i in range(len(attributesLeft)):
gain1 = dtree.averageGain(subset, attributesLeft[i])
ag_in2level.append(gain1)
print("Monk3 has entropy: ", dt.entropy(m.monk3))
print("Monk1, attribute a1 has information gain: ",
dt.averageGain(m.monk1, m.attributes[0]))
print("Monk1, attribute a2 has information gain: ",
dt.averageGain(m.monk1, m.attributes[1]))
print("Monk1, attribute a3 has information gain: ",
dt.averageGain(m.monk1, m.attributes[2]))
print("Monk1, attribute a4 has information gain: ",
dt.averageGain(m.monk1, m.attributes[3]))
print("Monk1, attribute a5 has information gain: ",
dt.averageGain(m.monk1, m.attributes[4]))
print("Monk1, attribute a6 has information gain: ",
dt.averageGain(m.monk1, m.attributes[5]))
print("Monk1's best attribute is: ", dt.bestAttribute(m.monk1, m.attributes))
print("\n")
print("Monk2, attribute a1 has information gain: ",
dt.averageGain(m.monk2, m.attributes[0]))
print("Monk2, attribute a2 has information gain: ",
dt.averageGain(m.monk2, m.attributes[1]))
print("Monk2, attribute a3 has information gain: ",
dt.averageGain(m.monk2, m.attributes[2]))
print("Monk2, attribute a4 has information gain: ",
dt.averageGain(m.monk2, m.attributes[3]))
print("Monk2, attribute a5 has information gain: ",
dt.averageGain(m.monk2, m.attributes[4]))
print("Monk2, attribute a6 has information gain: ",
dt.averageGain(m.monk2, m.attributes[5]))
def getAverageInformationGain(dataset,name):
print("Information Gain of:"+name+":")
for i in range (len(m.attributes)):
print("InformationGain of "+name +" "+m.attributes[i].name+" : "+ str(d.averageGain(dataset,m.attributes[i])))
print("The Best Attribute for splitting the result "+ name +":"+ str(d.bestAttribute(dataset,m.attributes)))
def get_gain(monk):
gain_list = []
for attribute in range(6):
gain_list.append(dtree.averageGain(monk, m.attributes[attribute]))
return gain_list
gain_monk1 = get_gain(monk1)
gain_monk2 = get_gain(monk2)
gain_monk3 = get_gain(monk3)
print(gain_monk1)
print(gain_monk2)
print(gain_monk3)
BestAttribute = dtree.bestAttribute(monk3, m.attributes)
print(BestAttribute)
#monk1_A5_1 = dtree.select(monk1,m.attributes[5],1)
#print(monk1_A5_1)
monk1_tree = dtree.buildTree(monk1, m.attributes)
#graf1 = dt.drawTree(monk1_tree)
monk2_tree = dtree.buildTree(monk2, m.attributes)
#graf2 = dt.drawTree(monk2_tree)
monk3_tree = dtree.buildTree(monk3, m.attributes)
#graf3 = dt.drawTree(monk3_tree)
print(dtree.check(monk1_tree, m.monk1))
def printInformationGainOfDataset(dataset, name):
print("\nInformation gain of " + name + ":")
for i in range(len(m.attributes)):
print(m.attributes[i].name + ": " +
str(d.averageGain(dataset, m.attributes[i])))
print("Best attribute is: " + str(d.bestAttribute(dataset, m.attributes)))
print("MONK 3:", dt.entropy(m.monk3))
print("")
# ASSIGNMENT 3
monks = [m.monk1, m.monk2, m.monk3]
for monk_id, monk in enumerate(monks):
print("Monk:", monk_id+1)
for i in range(6):
gain = dt.averageGain(monk, m.attributes[i])
print("A" + str(i+1) + ": " + str(gain))
print("")
# ASSIGNMENT 4
for monk_id, monk in enumerate(monks):
print("Monk:", monk_id+1)
best_atribute = dt.bestAttribute(monk, m.attributes)
print("Best attribute: " + str(best_atribute))
for value in best_atribute.values:
subset = dt.select(monk, best_atribute, value)
entropy = dt.entropy(subset)
print("Entropy " + str(value) + ": " + str(entropy))
# print("Next level information gains:")
# for i in range(6):
# gain = dt.averageGain(monk, m.attributes[i])
# print("A" + str(i+1) + ": " + str(gain))
print("")
best_atribute = dt.bestAttribute(m.monk1, m.attributes)
for value in best_atribute.values:
subset = dt.select(m.monk1, best_atribute, value)
