Oct-22-2017, 07:42 PM
I have the following problem. I have a written code attach below. The code constructs a tree based on the information in "my_data". The main idea is to construct different trees and see how they align the data. What I need is to construct more "trees" given "my_data". For example, I need Multi-Level Node-Leaf Tree, Dictionary-based Tree, Indented Text-based Tree. Please, advise me how can I proceed with the code.
my_data=[['slashdot','USA','yes',18,'None'],
['google','France','yes',23,'Premium'],
['digg','USA','yes',24,'Basic'],
['kiwitobes','France','yes',23,'Basic'],
['google','UK','no',21,'Premium'],
['(direct)','New Zealand','no',12,'None'],
['(direct)','UK','no',21,'Basic'],
['google','USA','no',24,'Premium'],
['slashdot','France','yes',19,'None'],
['digg','USA','no',18,'None'],
['google','UK','no',18,'None'],
['kiwitobes','UK','no',19,'None'],
['digg','New Zealand','yes',12,'Basic'],
['slashdot','UK','no',21,'None'],
['google','UK','yes',18,'Basic'],
['kiwitobes','France','yes',19,'Basic']]
class decisionnode:
def __init__(self,col=-1,value=None,results=None,tb=None,fb=None):
self.col=col
self.value=value
self.results=results
self.tb=tb
self.fb=fb
# Divides a set on a specific column. Can handle numeric or nominal values
def divideset(rows,column,value):
# Make a function that tells us if a row is in
# the first group (true) or the second group (false)
split_function=None
if isinstance(value,int) or isinstance(value,float): #value int or float?
split_function=lambda row:row[column]>=value
else:
split_function=lambda row:row[column]==value
# Divide the rows into two sets and return them
set1=[row for row in rows if split_function(row)]
set2=[row for row in rows if not split_function(row)]
return (set1,set2)
#Split the sample into subgroups
print("Subgroups of Yes/No")
a = divideset(my_data,2,'yes')
print(a)
def uniquecounts(rows):
results={}
for row in rows:
# The result is the last column
r=row[len(row)-1]
if r not in results: results[r]=0
results[r]+=1
return results
print("")
print('The data set has the following characteristics:')
print(uniquecounts(my_data))
# Entropy is the sum of p(x)log(p(x)) across all the different possible results
def entropy(rows):
from math import log
log2=lambda x:log(x)/log(2)
results=uniquecounts(rows)
# Now calculate the entropy
ent=0.0
for r in results.keys():
p=float(results[r])/len(rows)
ent=ent-p*log2(p)
return ent
set1,set2=divideset(my_data,3,20)
print("")
a = entropy(my_data)
print("Total entropy is %.2f" % a)
def buildtree(rows,scoref=entropy):
if len(rows)==0: return decisionnode()
current_score=scoref(rows)
best_gain=0.0
best_criteria=None
best_sets=None
column_count=len(rows[0])-1
for col in range(0,column_count):
global column_values
column_values={}
for row in rows:
column_values[row[col]]=1
for value in column_values.keys():
(set1,set2)=divideset(rows,col,value)
p=float(len(set1))/len(rows)
gain=current_score-p*scoref(set1)-(1-p)*scoref(set2)
if gain>best_gain and len(set1)>0 and len(set2)>0:
best_gain=gain
best_criteria=(col,value)
best_sets=(set1,set2)
if best_gain>0:
trueBranch=buildtree(best_sets[0])
falseBranch=buildtree(best_sets[1])
return decisionnode(col=best_criteria[0],value=best_criteria[1], tb=trueBranch,fb=falseBranch)
else:
return decisionnode(results=uniquecounts(rows))
tree=buildtree(my_data)
def printtree(tree,indent=''):
# Is this a leaf node?
if tree.results!=None:
print(str(tree.results))
else:
print(str(tree.col)+':'+str(tree.value)+'? ')
# Print the branches
print(indent+'T->', end=" ")
printtree(tree.tb,indent+' ')
print(indent+'F->', end=" ")
printtree(tree.fb,indent+' ')
printtree(tree)
def getwidth(tree):
if tree.tb==None and tree.fb==None: return 1
return getwidth(tree.tb)+getwidth(tree.fb)
def getdepth(tree):
if tree.tb==None and tree.fb==None: return 0
return max(getdepth(tree.tb),getdepth(tree.fb))+1
from PIL import Image,ImageDraw
def drawtree(tree,jpeg='tree.jpg'):
w=getwidth(tree)*100
h=getdepth(tree)*100+120
img=Image.new('RGB',(w,h),(255,255,255))
draw=ImageDraw.Draw(img)
drawnode(draw,tree,w/2,20)
img.save(jpeg,'JPEG')
def drawnode(draw,tree,x,y):
if tree.results==None:
# Get the width of each branch
w1=getwidth(tree.fb)*100
w2=getwidth(tree.tb)*100
# Determine the total space required by this node
left=x-(w1+w2)/2
right=x+(w1+w2)/2
# Draw the condition string
draw.text((x-20,y-10),str(tree.col)+':'+str(tree.value),(0,0,0))
# Draw links to the branches
draw.line((x,y,left+w1/2,y+100),fill=(255,0,0))
draw.line((x,y,right-w2/2,y+100),fill=(255,0,0))
# Draw the branch nodes
drawnode(draw,tree.fb,left+w1/2,y+100)
drawnode(draw,tree.tb,right-w2/2,y+100)
else:
txt=' \n'.join(['%s:%d'%v for v in tree.results.items()])
draw.text((x-20,y),txt,(0,0,0))
drawtree(tree,jpeg='treeview.jpg')
