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)
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 buildTreeRec(dataset, attributes, depthtodo):
defaultvalue = d.mostCommon(dataset)
if d.allPositive(dataset):
return d.TreeLeaf(True)
elif d.allNegative(dataset):
return d.TreeLeaf(False)
elif (depthtodo <= 0):
return d.TreeLeaf(defaultvalue)
else:
gainziplist = calculateGainTuplesForAllAttributes(dataset, attributes)
maxgain, maxgainattribute = getTupleWithMaxGainValue(gainziplist)
subnodes = []
for attrbutevalue in attributes[maxgainattribute].values:
newdataset = d.select(dataset, attributes[maxgainattribute], attrbutevalue)
subnode = buildTreeRec(newdataset, attributes, depthtodo - 1)
subnodes.append((attrbutevalue, subnode))
return d.TreeNode(attributes[maxgainattribute], dict(subnodes), defaultvalue)
def isLeaf(subset): return d.allPositive(subset) or d.allNegative(subset)
print("Information gains MONK-2:", gains)
print("Best attribute for split:", max(gains, key=gains.get))
print()
gains = dict(
zip(md.attributes, [dt.averageGain(md.monk3, a) for a in md.attributes]))
print("Information gains MONK-3:", gains)
print("Best attribute for split:", max(gains, key=gains.get))
print()
# building tree
print("\n----------DECISION TREE MONK-1 DEPTH 2----------\n")
for v in selected.values:
print(selected, "=", v)
subset = dt.select(md.monk1, selected, v)
if dt.allPositive(subset) or dt.allNegative(subset):
print(selected, "=", v, "->", dt.mostCommon(subset))
else:
attributes_left = [a for a in md.attributes if a != selected]
gains = dict(
zip(attributes_left,
[dt.averageGain(subset, a) for a in attributes_left]))
print("Information gains:", gains)
best = max(gains, key=gains.get)
print("Best attribute for split:", best)
for v2 in best.values:
print(best, "=", v2, "->",
dt.mostCommon(dt.select(subset, best, v2)))
print()
# dr.drawTree(dt.buildTree(md.monk1, md.attributes, 2))
print(dt.buildTree(md.monk1, md.attributes, 2))
