决策树分类器算法实现

# -*- coding: cp936 -*-
#决策树分类器
my_data=[['slashdot','USA','yes',18,'None'],['google','France','yes',23,'Premium'],
['digg','USA','yes',24,'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

def divideset(rows,column,value):
split_function=None
if isinstance(value,int) or isinstance(value,float):
split_function=lambda row:row[column]>=value
else:
split_function=lambda row:row[column]==value

set1=[row for row in rows if split_function(row)]
set2=[row for row in rows if not split_function(row)]
return (set1,set2)

def uniquecounts(rows):
results={}
for row in rows:
r=row[len(row)-1]
if r not in results:
results[r]=0
results[r]+=1
return results

def entropy(rows):
from math import log
log2=lambda x:log(x)/log(2)
results=uniquecounts(rows)
ent=0.0
for r in results.keys():
p=float(results[r])/len(rows)
ent=ent-p*log2(p)
return ent

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):
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))

def printtree(tree,indent=''):
if tree.results!=None:
print str(tree.results)
else:
print str(tree.col)+':'+str(tree.value)+'?'

print indent+'T->'
printtree(tree.tb,indent+'  ')
print indent+'F->'
printtree(tree.fb,indent+'  ')

def classify(observation,tree):
if tree.results!=None:
return tree.results
else:
v=observation[tree.col]
branch=None
if isinstance(v,int) or isinstance(v,float):
if v>=tree.value:branc=tree.tb
else:branch=tree.fb
else:
if v==tree.value:branch=tree.tb
else:branch=tree.fb
return classify(observation,branch)