def test3():
parsedDat = [line.split() for line in open('kosarak.dat').readlines()]
initSet = fpGrowth.createInitSet(parsedDat)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 10000)
myFreqList = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 10000, set([]), myFreqList)
print myFreqList
def mineTree(inTree, headerTable, minSup, preFix, freqItemList):
"""
利用条件模式基递归查找频繁项集的minTree函数
:param inTree: 事先创建好的FP树
:param headerTable: 头指针列表
:param minSup: 最小支持度
:param preFix: preFix为newFreqSet上一次的存储记录,初始为空。
:param freqItemList: 用来存储频繁项集的列表
:return:
"""
#通过value进行从小到大的排序， 得到频繁项集的key组成的list。
headerDic = {}
for i in headerTable.keys():
headerDic[i] = headerTable[i][0]
# headerDic[i] = (headerTable[i][0] for i in headerTable.keys())
bigL = [v[0] for v in sorted(headerDic.items(), key=lambda p: p[1])]
for basePat in bigL: #循环遍历每个元素
newFreqSet = preFix.copy() #preFix为newFreqSet上一次的存储记录，一旦没有myHead，就不会更新
newFreqSet.add(basePat) #把每一个频繁项添加到频繁项集列表中。
freqItemList.append(newFreqSet)
#调用findPrefixPath()函数来创建条件模式基
condPattBases = findPrefixPath(basePat, headerTable[basePat][1])
#从条件模式基来构建FP树
myCondTree, myHead = fpGrowth.createTree(condPattBases, minSup)
#如果树种有元素项的话，递归调用mineTree()函数
if myHead != None:
print('conditional tree for:', newFreqSet)
myCondTree.disp(1)
mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList)
def test():
simpDat = fpGrowth.loadSimpDat()
initSet = fpGrowth.createInitSet(simpDat)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 3)
myFPtree.disp()
freqItems = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)
def mineTweets(tweetArr, minSup=5):
parsedList = []
for i in range(14):
for j in range(100):
parsedList.append(textParse(tweetArr[i][j].text))
initSet = fpGrowth.createInitSet(parsedList)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, minSup)
myFreqList = []
fpGrowth.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
return myFreqList
def mineTweets(tweetArr, minSup=5):
parsedList = []
for i in range(len(tweetArr)):
for j in range(len(tweetArr[i])):
#twitter爬出的数据是status类，因此需要status.text来找到实际需要的文本
parsedList.append(textParse(tweetArr[i][j].text))
initSet = fpGrowth.createInitSet(parsedList)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, minSup)
myFreqList = []
fpGrowth.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
return (myFreqList)
def test2():
simplDat = fpGrowth.loadSimpleData()
print "Data: ", simplDat
initSet = fpGrowth.createInitSet(simplDat)
print "initSet: ", initSet
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 3)
myFPtree.disp()
condPat = fpGrowth.findPrefixPath('x', myHeaderTab['x'][1])
print "condPat: ", condPat
freqItems = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)
print "freqItems: ", freqItems
def calcFrequentSets(region):
songs = db.lyrics.find({"region":region})
all_verses = []
for song in songs:
verses = song['lyrics']
for verse in verses:
verse = [v for v in set(verse) if v not in stop_words]
all_verses.append(verse)
minSup = 10
initSet = fpGrowth.createInitSet(all_verses)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, minSup)
myFreqList = []
fpGrowth.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
return myFreqList
def find_distinguishing_itemsets(transactions, max_items_list, minSup,
cog_dict):
# 1. arrange the transactions as proper input for createInitSet function
dataSet = creatDataSet(transactions)
# 2. arrange the data as proper input for fptree creation
initSet = fpGrowth.createInitSet(dataSet)
# 3. crate fptree
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, minSup)
if myHeaderTab is None: # tree is empty
return max_items_list
freqItems = []
# 4. apply fpgrowth algorithm for finding frequent itemsets in data. Eventually [freqitems] contains them
fpGrowth.mineTree(myFPtree, myHeaderTab, minSup, set([]), freqItems,
cog_dict)
max_IG = 0
max_item = ""
# 5. calculate entropy values for whole data
s_entropy, label_0_num, label_1_num = calculate_all_DB_entropy(
transactions)
# 6. calculate IG score for each itemset while finding the one with the highest IG score value
for item in freqItems:
Ig = calculate_IG_value(item, transactions, s_entropy, label_0_num,
label_1_num)
if Ig >= max_IG:
max_item = item
max_IG = Ig
str_max_item = itemToStr(max_item)
max_items_list[str_max_item] = [max_IG]
copy_transactions = transactions.copy()
# 7. find all teansactions in data that contain [max_item] and remove them from data base
for bact in copy_transactions:
if max_item.issubset(transactions[bact][0]):
label = transactions[bact][1]
max_items_list[str_max_item].append([bact, label])
del transactions[bact]
# 8. repeat until no transactions left in data base
if len(transactions) > 0:
find_distinguishing_itemsets(transactions, max_items_list, minSup,
cog_dict)
else:
return max_items_list
def subjectFreq(teamList,authorList,subjectList,minFreq):
subjectFreqList = []
for team in teamList:
tempData = []
#添加minFreq个'a'，保证FPTree不为空
for i in range(minFreq):
tempData.append(['a'])
for author,subject in zip(authorList,subjectList):
if(not [False for a in team if a not in author]):
tempData.append(subject)
tempSet = fpGrowth.createInitSet(tempData)
#fpGrowth树建立
subjectFPtree,subjectHeaderTab=fpGrowth.createTree(tempSet,minFreq)
#挖掘主题
subjectFreqItems = []
fpGrowth.mineTree(subjectFPtree,subjectHeaderTab,minFreq,set([]),subjectFreqItems)
#每个团队主题词添加到subjectFreqList
subjectFreqList.append(subjectFreqItems)
return subjectFreqList
row_by_user[u] = sorted(row_by_user[u], key=lambda p: p[1])[:req_num]
user[u] = {}
for row in row_by_user[u]:
if row[0] not in user[u]:
user[u][row[0]] = set()
user[u][row[0]].add(
int(row[NOPOSITION]) if row[NOPOSITION] != "" else -1)
if row[NOPOSITION] != "":
namedict[int(row[NOPOSITION])] = row[NAMEPOSITON]
# user[row[4]][row[0]][row[7]] = user[row[4]][row[0]].get(row[7], 0) + 1
for MINSUP in [2, 4, 8, 16, 32, 64]:
# MINSUP = 2# print(cnt)
for vip, uidset in user.items():
# print(uidset)
data = createInitSet(uidset.values())
# print(data)
myFpTree, myHeaderTab = createTree(data, MINSUP)
if myFpTree is not None:
print("vipno #", vip, "的支持度大于", MINSUP, "的频繁项集如下")
freqItems = []
mineTree(myFpTree, myHeaderTab, MINSUP, set([]), freqItems)
# print(freqItems)
for freq in freqItems:
for x in freq:
print(namedict[x], end=",")
print("\n**************")
else:
# pass
print("vipno #", vip, "并没有支持度大于", MINSUP, "的频繁项集")
#/usr/bin/python # coding:utf-8 import fpGrowth simpDat = fpGrowth.loadSimpDat() dataSet = fpGrowth.createInitSet(simpDat) retTree, headTab = fpGrowth.createTree(dataSet, 3) retTree.disp()
#调用findPrefixPath()函数来创建条件模式基
condPattBases = findPrefixPath(basePat, headerTable[basePat][1])
#从条件模式基来构建FP树
myCondTree, myHead = fpGrowth.createTree(condPattBases, minSup)
#如果树种有元素项的话，递归调用mineTree()函数
if myHead != None:
print('conditional tree for:', newFreqSet)
myCondTree.disp(1)
mineTree(myCondTree, myHead, minSup, newFreqSet, freqItemList)
if __name__ == '__main__':
# 测试给定元素项返回的条件模式基
myDat = fpGrowth.loadSimpDat()
myDic = fpGrowth.createInitSet(myDat)
myFPtree, myHeaderTab = fpGrowth.createTree(myDic, 3)
print(myHeaderTab)
# condPatsX= findPrefixPath('x',myHeaderTab['x'][1])
# print(condPatsX)
# condPatsZ = findPrefixPath('z', myHeaderTab['z'][1])
# print(condPatsZ)
# condPatsR = findPrefixPath('r', myHeaderTab['r'][1])
# print(condPatsR)
# 测试利用条件模式基递归查找频繁项集
freqItems = []
myMinTree = mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)
print(freqItems)
"""
正如我们所期望的那样，返回项集与条件FP树相匹配，到现在为止，完整的FP-growth算法以及可以运行。
"""
import fpGrowth
rootNode = fpGrowth.treeNode('pyramid', 9, None)
rootNode.children['eye'] = fpGrowth.treeNode('eye', 13, None)
rootNode.disp()
rootNode.children['phoenix'] = fpGrowth.treeNode('phoenix', 3, None)
rootNode.disp()
from importlib import reload
reload(fpGrowth)
simpDat = fpGrowth.loadSimpDat()
simpDat
initSet = fpGrowth.createInitSet(simpDat)
initSet
# 创建FP树
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 3)
myFPtree.disp()
reload(fpGrowth)
fpGrowth.findPrefixPath('x', myHeaderTab['x'][1])
fpGrowth.findPrefixPath('z', myHeaderTab['z'][1])
fpGrowth.findPrefixPath('r', myHeaderTab['r'][1])
reload(fpGrowth)
freqItems = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)
freqItems
# 示例：从新闻网站点击流中挖掘
parsedDat = [line.split() for line in open('kosarak.dat').readlines()]
initSet = fpGrowth.createInitSet(parsedDat)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 100000)
# -*- coding: UTF-8 -*-
import fpGrowth
rootNode = fpGrowth.treeNode("pyramid", 9, None) # 这个调用的就是class的__init__函数来声明第一个结点
rootNode.children["eye"] = fpGrowth.treeNode("eye", 13, None) # rootNode的['eye']孩子结点也是新声明的一个treeNode
rootNode.display()
rootNode.children["phoenix"] = fpGrowth.treeNode("phoenix", 3, rootNode.children["eye"])
rootNode.display()
simpDat = fpGrowth.loadSimpDat()
print(simpDat)
initSet = fpGrowth.createInitSet(simpDat)
print("createTree with this initSet:", initSet)
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 3)
myFPtree.display()
Result_x = fpGrowth.findPrefixPath("x", myHeaderTab["x"][1])
Result_z = fpGrowth.findPrefixPath("z", myHeaderTab["z"][1])
Result_r = fpGrowth.findPrefixPath("r", myHeaderTab["r"][1])
print(Result_x)
print(Result_z)
print(Result_r)
freqItems = []
Result = fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems)
print(Result)
print("100万记录用FP-growth来处理：\n")
parsedDat = [line.split() for line in open("kosarak.dat").readlines()]
initSet = fpGrowth.createInitSet(parsedDat)
data = []
for i in range(len(values)):
temp = []
for j in range(len(values[0])):
if values[i][j] == 1:
temp.append(j)
data.append(temp)
counts = []
for index in columns:
line = df[index]
count = 0
for i in range(len(line)):
if line[i] == 1:
count += 1
counts.append((float)(count) / 10000)
counts.sort()
minSupport = counts[len(counts) * 1 / 5]
#use apriori
L, supportData = ap.apriori(data, minSupport)
rules = ap.generateRules(L, supportData, minConf=0.4)
#use fpGrowth
minSup = minSupport * 10000
simpDat = data
initSet = fp.createInitSet(simpDat)
myFPtree, myHeaderTab = fp.createTree(initSet, minSup)
myFreqList = []
fp.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
print myFreqList
# #@ myFPtree：FP树的根节点
# #@ myHeaderTab：头指针表（字典）
# #@
# #递归查找频繁项集（每一个频繁项集都要创建一颗FP树）
# fpGrowth.mineTree(myFPtree, myHeaderTab, 3, set([]), freqItems) #发现频繁项集并显示前缀路径
# print "\nfreqItems:\n", freqItems #显示频繁项集
#### 例子:在Twitter源中发现一些共现词
# lotsOtweets = fpGrowth.getLotsOfTweets('RIMM')
# print lotsOtweets
parsedDat = [line.split() for line in open('kosarak.dat').readlines()] #加载数据存放在列表中
initSet = fpGrowth.createInitSet(parsedDat) #数据存放形式从列表转化成字典
myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 100000) #创建FP树和头指针表
myFreqList = []
fpGrowth.mineTree(myFPtree, myHeaderTab, 100000, set([]), myFreqList) #生成频繁项集
print "\nlength of 'myFreqList' is:", len(myFreqList)
print "myFreqList is：", myFreqList
end = time.clock()
print "The run time of the program is：",end-start, "seconds"
#读取主题数据 titleRd = readData(fileName,confName,"title") titleRd.openFile() titleData = titleRd.readFile() #查询活跃研究者 lastYear = "2015" activeAuthorList = activeAuthor(authorData,yearData,lastYear) #频繁模式查询团队，利用fpGrowth方法进行 #数据预处理 minMem = 3 authorSet = fpGrowth.createInitSet(authorData) #fpGrowth树建立 authorFPtree,authoryHeaderTab=fpGrowth.createTree(authorSet,minMem) #挖掘团队 teamList = [] fpGrowth.mineTree(authorFPtree,authoryHeaderTab,minMem,set([]),teamList) teamList = fpGrowth.teamSelect(minMem,teamList) #主题List查询 removeList = ['a','an','the','for','of','with','and','in','to'] subjectList = titleInit(titleData,removeList) #团队主题 minFreq = 2 subjectFreqList = [] subjectFreqList = subjectFreq(teamList,authorData,subjectList,minFreq)
rootNode = fpGrowth.treeNode('pyramid', 9, None)
rootNode.children['eye'] = fpGrowth.treeNode('eye', 13, None)
rootNode.children['phoenix'] = fpGrowth.treeNode('phoenix', 3, None)
rootNode.disp()
'''
simData = fpGrowth.loadSimpleData()
print('simData : ' , simData)
initSet = fpGrowth.createInitSet(simData)
print('initSet : ', initSet)
simFPTree, simHeaderTable = fpGrowth.createTree(initSet, 3)
simFPTree.disp()
'''
print('========= prefix path : ')
print(fpGrowth.findPrefixPath('x', simHeaderTable['x'][1]))
print(fpGrowth.findPrefixPath('z', simHeaderTable['z'][1]))
print(fpGrowth.findPrefixPath('r', simHeaderTable['r'][1]))
'''
# print('sorted(simHeaderTable.items()) : ', sorted(simHeaderTable.items()))
# bigL = [v[0] for v in sorted(simHeaderTable.items(), key = lambda p : p[0])]
# print(' test bigL = ', bigL)
freqItems = []
if __name__ == "__main__":
path="./../item/"
arr = getData(path,3)
dataSet = arr;
print(dataSet)
inputData={}
for line in dataSet:
inputData[frozenset(line)] = 1
print(len(inputData))
#print(inputData)
fp_items=[]
fp_items_temp=[]
Fptree,headerTable=fpg.createTree(inputData, 0.1*len(inputData))
Fptree.display()
fpg.mineTree(Fptree, headerTable, 0.1*len(inputData), set([]), fp_items_temp)
print(headerTable)
# for item in fp_items_temp:
# item2=sorted(item)
# fp_items.append(set(item2))
#
#
# supportdata={}
# for fp in fp_items:
# for transaction in inputData:
# if fp.issubset(transaction):
# if frozenset(fp) in supportdata:
# supportdata[frozenset(fp)] += 1
# else:supportdata[frozenset(fp)] = 1
import fpGrowth
simpDat = fpGrowth.loadSimpDat()
# print simpDat
initSet = fpGrowth.createInitSet(simpDat)
# print initSet
myFpTree, myHeaderTab = fpGrowth.createTree(initSet, 3)
# print myFpTree.disp()
# print myHeaderTab
myCondPat = fpGrowth.findPrefixPath('x', myHeaderTab['x'][1])
# print myCondPat
freqItems = []
myfpGrowth = fpGrowth.mineTree(myFpTree, myHeaderTab, 3, set([]), freqItems)
# print myFpTree.disp()
# #12.5 示例：从新闻网站点击流中挖掘 # parsedDat=[line.split() for line in open(homedir+'kosarak.dat').readlines()] # # print "parsedDat:",parsedDat # initSet=fpGrowth.createInitSet(parsedDat) # # print "initSet:",initSet # # print ":", # myFPtree,myHeaderTab=fpGrowth.createTree(initSet,100000) # myFPtree.disp() # print "myHeaderTab:",myHeaderTab # myFreqList=[] # fpGrowth.mineTree(myFPtree,myHeaderTab,100000,set([]),myFreqList) # # print "len(myFreqList):",len(myFreqList) # print "myFreqList:",myFreqList # #12.6 示例：从图灵书籍中挖掘 import sys # print sys.getdefaultencoding() # print sys.stdout.encoding parsedDat = [line.split() for line in open(homedir + 'anjuke.dat').readlines()] # print "parsedDat:",parsedDat initSet = fpGrowth.createInitSet(parsedDat) # print "initSet:",initSet # print ":", myFPtree, myHeaderTab = fpGrowth.createTree(initSet, 20) # myFPtree.disp() #print "myHeaderTab:",myHeaderTab myFreqList = [] fpGrowth.mineTree(myFPtree, myHeaderTab, 20, set([]), myFreqList) # print "len(myFreqList):",len(myFreqList) #print "myFreqList:",myFreqList
import fpGrowth as fp
import pandas as pd
import numpy as np
# simpDat = fp.loadSimpDat()
# initset = fp.createInitSet(simpDat)
# myfptree,myheaderTab = fp.createTree(initset,3)
# freqItems = []
# fp.mineTree(myfptree,myheaderTab,3,set[()],freqItems)
# print freqItems
df = pd.read_excel("Transactions.xls")
values = df.values
data = []
for i in range(len(values)):
temp = []
for j in range(len(values[0])):
if values[i][j] == 1:
temp.append(j)
data.append(temp)
minSup = 313.0
#simpDat = fp.loadSimpDat()
simpDat = data
initSet = fp.createInitSet(simpDat)
myFPtree, myHeaderTab = fp.createTree(initSet, minSup)
myFreqList = []
fp.mineTree(myFPtree, myHeaderTab, minSup, set([]), myFreqList)
print myFreqList
import fpGrowth
reload(fpGrowth)
rootNode = fpGrowth.treeNode('pyramid' , 9 , None) # 创建一个单节点
rootNode.children['eye'] = fpGrowth.treeNode('eye',13,None) # 增加一个子节点
rootNode.disp() # 显示
rootNode.children['phoenix'] = fpGrowth.treeNode('phoenix',3,None) # 增加一个子节点
rootNode.disp() # 显示
reload(fpGrowth)
simpDat = fpGrowth.loadSimpDat() # 获取数据集
simpDat
initSet = fpGrowth.createInitSet(simpDat) # 格式化处理数据集
initSet
myFPtree,myHeaderTab = fpGrowth.createTree(initSet,3) # 生成树
myFPtree.disp() # 显示
reload(fpGrowth)
fpGrowth.findPrefixPath('x',myHeaderTab['x'][1]) # 给定元素，生成条件基
fpGrowth.findPrefixPath('z',myHeaderTab['z'][1])
fpGrowth.findPrefixPath('r',myHeaderTab['r'][1])
reload(fpGrowth)
freqItems = [] # 空列表存储频繁项集
fpGrowth.mineTree(myFPtree,myHeaderTab,3,set([]),freqItems)
freqItems
