解读python如何实现决策树算法
数据描述
每条数据项储存在列表中，最后一列储存结果
多条数据项形成数据集
data=[[d1,d2,d3...dn,result],
[d1,d2,d3...dn,result],
.
.
[d1,d2,d3...dn,result]]
决策树数据结构
class DecisionNode:
'''决策树节点
'''
def __init__(self,col=-1,value=None,results=None,tb=None,fb=None):
'''初始化决策树节点
args：
col -- 按数据集的col列划分数据集
value -- 以value作为划分col列的参照
result -- 只有叶子节点有，代表最终划分出的子数据集结果统计信息。｛‘结果'：结果出现次数｝
rb,fb -- 代表左右子树
'''
self.col=col
self.value=value
self.results=results
self.tb=tb
self.fb=fb
决策树分类的最终结果是将数据项划分出了若干子集，其中每个子集的结果都一样，所以这里采用｛‘结果'：结果出现次数｝的方式表达每个子集
def pideset(rows,column,value):
'''依据数据集rows的column列的值，判断其与参考值value的关系对数据集进行拆分
返回两个数据集
'''
split_function=None
#value是数值类型
if isinstance(value,int) or isinstance(value,float):
#定义lambda函数当row[column]>=value时返回true
split_function=lambda row:row[column]>=value
#value是字符类型
else:
#定义lambda函数当row[column]==value时返回true
split_function=lambda row:row[column]==value
#将数据集拆分成两个
set1=[row for row in rows if split_function(row)]
set2=[row for row in rows if not split_function(row)]
#返回两个数据集
return (set1,set2)
def uniquecounts(rows):
'''计算数据集rows中有几种最终结果，计算结果出现次数，返回一个字典
'''
results={}
for row in rows:
r=row[len(row)-1]
if r not in results: results[r]=0
results[r]+=1
return results
def giniimpurity(rows):
'''返回rows数据集的基尼不纯度
'''
total=len(rows)
counts=uniquecounts(rows)
imp=0
for k1 in counts:
p1=float(counts[k1])/total
for k2 in counts:
if k1==k2: continue
p2=float(counts[k2])/total
imp+=p1*p2
return imp
def entropy(rows):
'''返回rows数据集的熵
'''
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 build_tree(rows,scoref=entropy):
'''构造决策树
'''
if len(rows)==0: return DecisionNode()
current_score=scoref(rows)
# 最佳信息增益
best_gain=0.0
#
best_criteria=None
#最佳划分
best_sets=None
column_count=len(rows[0])-1
#遍历数据集的列，确定分割顺序
for col in range(0,column_count):
column_values={}
# 构造字典
for row in rows:
column_values[row[col]]=1
for value in column_values.keys():
(set1,set2)=pideset(rows,col,value)
p=float(len(set1))/len(rows)
# 计算信息增益
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)
# 如果划分的两个数据集熵小于原数据集，进一步划分它们
if best_gain>0:
trueBranch=build_tree(best_sets[0])
falseBranch=build_tree(best_sets[1])
return DecisionNode(col=best_criteria[0],value=best_criteria[1],
tb=trueBranch,fb=falseBranch)
# 如果划分的两个数据集熵不小于原数据集，停止划分
else:
return DecisionNode(results=uniquecounts(rows))
def print_tree(tree,indent=''):
if tree.results!=None:
print(str(tree.results))
else:
print(str(tree.col)+':'+str(tree.value)+'? ')
print(indent+'T->',end='')
print_tree(tree.tb,indent+' ')
print(indent+'F->',end='')
print_tree(tree.fb,indent+' ')
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
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))
对测试数据进行分类（附带处理缺失数据）
def mdclassify(observation,tree):
'''对缺失数据进行分类
args：
observation -- 发生信息缺失的数据项
tree -- 训练完成的决策树
返回代表该分类的结果字典
'''
# 判断数据是否到达叶节点
if tree.results!=None:
# 已经到达叶节点，返回结果result
return tree.results
else:
# 对数据项的col列进行分析
v=observation[tree.col]
# 若col列数据缺失
if v==None:
#对tree的左右子树分别使用mdclassify，tr是左子树得到的结果字典，fr是右子树得到的结果字典
tr,fr=mdclassify(observation,tree.tb),mdclassify(observation,tree.fb)
# 分别以结果占总数比例计算得到左右子树的权重
tcount=sum(tr.values())
fcount=sum(fr.values())
tw=float(tcount)/(tcount+fcount)
fw=float(fcount)/(tcount+fcount)
result={}
# 计算左右子树的加权平均
for k,v in tr.items():
result[k]=v*tw
for k,v in fr.items():
# fr的结果k有可能并不在tr中，在result中初始化k
if k not in result:
result[k]=0
# fr的结果累加到result中
result[k]+=v*fw
return result
# col列没有缺失，继续沿决策树分类
else:
if isinstance(v,int) or isinstance(v,float):
if v>=tree.value: branch=tree.tb
else: branch=tree.fb
else:
if v==tree.value: branch=tree.tb
else: branch=tree.fb
return mdclassify(observation,branch)
tree=build_tree(my_data)
print(mdclassify(['google',None,'yes',None],tree))
print(mdclassify(['google','France',None,None],tree))
决策树剪枝
def prune(tree,mingain):
'''对决策树进行剪枝
args：
tree -- 决策树
mingain -- 最小信息增益
返回
'''
# 修剪非叶节点
if tree.tb.results==None:
prune(tree.tb,mingain)
if tree.fb.results==None:
prune(tree.fb,mingain)
#合并两个叶子节点
if tree.tb.results!=None and tree.fb.results!=None:
tb,fb=[],[]
for v,c in tree.tb.results.items():
tb+=[[v]]*c
for v,c in tree.fb.results.items():
fb+=[[v]]*c
#计算熵减少情况
delta=entropy(tb+fb)-(entropy(tb)+entropy(fb)/2)
#熵的增加量小于mingain，可以合并分支
if delta<mingain:
tree.tb,tree.fb=None,None
tree.results=uniquecounts(tb+fb)