def mineTweets(inputfile, minSup=5):
parsedList = []
for line in open(inputfile).readlines():
parsedList.append(textParse(line.strip()))
initSet = fpgrowth.createInitSet(parsedList)
myFPTree, myHeaderTab = fpgrowth.createTree(initSet, minSup)
myFreqList = []
fpgrowth.mineTree(myFPTree, myHeaderTab, minSup, set([]), myFreqList)
return myFreqList
def gen_rules(filepath, args):
if not os.path.exists(filepath):
print(filepath, 'not exists,please set the filepath')
print('\n\n\n')
print(
'------------------------------处理文件%s-----------------------------------'
% (os.path.basename(filepath)))
with open(filepath, encoding='utf-8') as f:
dataSet = [line.split() for line in f.readlines()]
if len(dataSet) <= 100: ##如果交易数据少于100条，返回空
print(
'-----------------------交易数据小于100条，不生成rules-------------------------'
)
return []
n = args.support * len(dataSet)
initSet = fpgrowth.createInitSet(dataSet)
myFPtree, myHeaderTab = fpgrowth.createFPtree(initSet, n)
freqItems = []
fpgrowth.mineFPtree(myFPtree, myHeaderTab, n, set([]), freqItems)
# for x in freqItems:
# print(x)
# compute support values of freqItems
suppData = fpgrowth.calSuppData(myHeaderTab, freqItems, len(dataSet))
suppData[frozenset([])] = 1.0
# for x, v in suppData.items():
# print(x, v)
# freqItems = [frozenset(x) for x in freqItems]
# print(freqItems)
rules = fpgrowth.generateRules(freqItems,
suppData,
minConf=args.confidence)
filter_rules = [rule for rule in rules if len(rule[1]) == 1]
filter_rules = sorted(filter_rules, key=lambda p: p[2], reverse=True)
print('number of association rules:\n', len(filter_rules))
return filter_rules
# myFPtree.disp()
# print fpgrowth.findPrefixPath('z', myHeaderTab)
# print fpgrowth.findPrefixPath('r', myHeaderTab)
# print fpgrowth.findPrefixPath('t', myHeaderTab)
# freqItems = []
# fpgrowth.mineFPtree(myFPtree, myHeaderTab, 3, set([]), freqItems)
# for x in freqItems:
# print x
'''kosarak data'''
start = time.time()
n = 20000
with open("E:\dvancedos\database\webdocs.dat", "rb") as f:
parsedDat = [line.split() for line in f.readlines()]
initSet = fpgrowth.createInitSet(parsedDat)
myFPtree, myHeaderTab = fpgrowth.createFPtree(initSet, n)
freqItems = []
fpgrowth.mineFPtree(myFPtree, myHeaderTab, n, set([]), freqItems)
for x in freqItems:
print(x)
print(time.time() - start, 'sec')
# compute support values of freqItems
suppData = fpgrowth.calSuppData(myHeaderTab, freqItems, len(parsedDat))
suppData[frozenset([])] = 1.0
for x, v in suppData.items():
print(x, v)
freqItems = [frozenset(x) for x in freqItems]
fpgrowth.generateRules(freqItems, suppData)
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 11 20:29:01 2018
@author: Admin
"""
import fpgrowth
import generaterules
#导入数据，转化为相应的形式
testdata = fpgrowth.loadSimpData()
print(testdata)
itemset = fpgrowth.createInitSet(testdata)
#设置参数，minSup为最小支持度，itemnumber为频繁集中的项数
minSup = 0.1
itemnumber = 3
#生成树和项头表
retTree, headerTable = fpgrowth.createTree(itemset, sum(itemset.values()), minSup=minSup)
print(itemset)
#生成频繁项
freqItems = {}
fpgrowth.mineTree(inTree=retTree, headerTable=headerTable, dataSetLen=sum(itemset.values()), itemnumber = itemnumber, minSup = minSup, freqItemList = freqItems)
#从频繁集中挖掘相关规则
minConf = 0.8 #最小置信度
minLift = 1.3 #最小提升度
ruleset = generaterules.generateRules(freqItems = freqItems, dataSetLen = sum(itemset.values()), minConf = minConf, minLift = minLift)
import fpgrowth
import time
#simple data
simDat = fpgrowth.loadSimpDat()
initSet = fpgrowth.createInitSet(simDat)
myFPtree, myHeaderTab = fpgrowth.createFPtree(initSet, 3)
myFPtree.disp() #FP树
print(fpgrowth.findPrefixPath('z', myHeaderTab))
print(fpgrowth.findPrefixPath('r', myHeaderTab))
print(fpgrowth.findPrefixPath('x', myHeaderTab))
freqItems = []
fpgrowth.mineFPtree(myFPtree, myHeaderTab, 3, set([]), freqItems)
for x in freqItems:
print(x)
#kosarak data
'''
start = time.time()
n = 100000
with open("H:/data/kosarak.dat/kosarak.dat", "rb") as f:
parsedDat = [line.split() for line in f.readlines()]
initSet = fpgrowth.createInitSet(parsedDat)
ruleset = []
temp = []
for i in range(5422):
if(float(content[i].split(',')[-1]) > float(content[i+1].split(',')[-1])):
temp.append(content[i])
ruleset.append(temp)
temp = []
else:
temp.append(content[i])
fitdata = []
for j in range(len(ruleset)):
datamid = []
for i in range(len(ruleset[j])):
datamid.append(ruleset[j][i].split(',')[:-1])
fitdata.append(datamid)
###fitdata contains the data for every week
itemset = fpgrowth.createInitSet(fitdata[2])
minSup = 0.1
itemnumber = 3
retTree, headerTable = fpgrowth.createTree(itemset, sum(itemset.values()), minSup=minSup)
freqItems = {}
fpgrowth.mineTree(inTree=retTree, headerTable=headerTable, dataSetLen=sum(itemset.values()),
itemnumber = itemnumber, minSup = minSup, freqItemList = freqItems)
minConf = 0.7
minLift = 0.9
ruleset = generaterules.generateRules(freqItems = freqItems, dataSetLen = sum(itemset.values()),
minConf = minConf, minLift = minLift)
print(ruleset)
