我一直在尝试使用this数据开发apriori算法。我能够获得对和三元组的关联和置信度，但是在格式化输出和提取正确的元素时遇到了问题。
我在这个test数据上运行算法。它只是原始数据集的一个子集。目前输出如下：
[[frozenset({'GRO73461'}), frozenset({'ELE17451'}), 1.0],
[frozenset({'GRO99222'}), frozenset({'ELE17451'}), 0.8125], [frozenset({'ELE17451'}), frozenset({'GRO99222'}), 0.5], [frozenset({'ELE17451'}), frozenset({'GRO73461'}), 0.38461538461538464]]
frozenset({'GRO73461', 'ELE17451'}), 0.8], [frozenset({'GRO73461'}), frozenset({'DAI22896', 'ELE17451'}), 0.8]
你可以看到它的一团糟。该列表是根据降序的置信度排序的。我想将频繁对与频繁三元组分开并排列输出，使其看起来像这样：
OUTPUT A
FRO11987 FRO12685 0.4325
FRO11987 ELE11375 0.4225
FRO11987 GRO94758 0.4125
FRO11987 SNA80192 0.4025
FRO11987 FRO18919 0.4015
OUTPUT B
FRO11987 FRO12685 DAI95741 0.4325
FRO11987 ELE11375 GRO73461 0.4225
FRO11987 GRO94758 ELE26917 0.4125
FRO11987 SNA80192 ELE28189 0.4025
FRO11987 FRO18919 GRO68850 0.4015
以上是前5名频繁对，前5名基于信心的频繁三元组。
我遇到问题的主要区域是在频繁对和三元组之间辨别，然后从frozensets中提取项目，使它们采用上述格式。
from numpy import *
import pandas as pd
from operator import itemgetter
def loadDataSet(data=None):
return pd.read_csv(data, sep = ' ', error_bad_lines=False)
def createCandidateSet(data):
C1 = []
for transaction in data:
for item in transaction:
if not [item] in C1:
C1.append([item])
C1.sort()
return list(map(frozenset, C1))
def scanData(dataset, Ck, support):
ssCount = {}
for tID in dataset:
for candidate in Ck:
if candidate.issubset(tID):
if not candidate in ssCount:
ssCount[candidate] = 1
else:
ssCount[candidate]+=1
# numItems = float(len(dataset))
res = []
supportData ={}
for key in ssCount:
#Support is a proportion or a integer; the occurrence of the item in relation to the data set
# currSupport = ssCount[key]/numItems
currSupport = ssCount[key]
if currSupport >= support:
res.insert(0, key)
supportData[key] = currSupport
return res, supportData
def aprioriHelper(Lk, k): #creates candidate itemsets
res = []
freqItemLen = len(Lk)
for i in range(freqItemLen):
for j in range(i+1, freqItemLen):
L1 = list(Lk[i])[:k-2]
L2 = list(Lk[j])[:k-2]
L1.sort()
L2.sort()
if L1 == L2:
res.append(Lk[i] | Lk[j])
return res
def apriori(dataset, minSupport=100):
C1 = createCandidateSet(dataset)
D = list(map(set, dataset))
L1, supportData = scanData(D, C1, minSupport)
L = [L1]
k = 2
while (len(L[k-2]) > 0):
Ck = aprioriHelper(L[k-2], k)
Lk, supportK = scanData(D, Ck, minSupport) #scan dataset to get frequent items sets, now the itemsets are bigger
supportData.update(supportK)
L.append(Lk)
k+=1
return L, supportData
def generateRules(L, supportData, conf = 0.7): #support data is data on each item sets support, comes from scanData
rules = [] #takes tuples of associations, consequences, and confidence
for i in range(1, len(L)): #get itemsets with number of items >=2
for freq in L[i]:
association = [frozenset([item]) for item in freq]
if i > 1:
rulesFromConsequences(freq, association, supportData, rules, conf)
else:
calculateConfidence(freq, association, supportData, rules, conf)
return rules
def calculateConfidence(freq, association, supportData, rules, conf=0.7):
filteredAssociations = []
for consequence in association:
#confidence(I -> J) = Support(I U J)/Support(I)
confidence = supportData[freq]/supportData[freq - consequence] #calculate confidence
if confidence >= conf:
# print(freq-consequence, ' ', consequence, ' ', confidence) #print out association rule and confidence
rules.append((freq-consequence, consequence, confidence))
filteredAssociations.append(consequence)
return filteredAssociations
def rulesFromConsequences(freq, association, supportData, rules, conf=0.7):
#generate more rules when frequent itemsets become larger
a_len = len(association[0])
if (len(freq) > (a_len+1)): #try to merge into a bigger itemset that is frequent
association_p1 = aprioriHelper(association, a_len+1) #create association+1 new candidates- create bigger itemset and get more candidates for association rules
association_p1 = calculateConfidence(freq, association_p1, supportData, rules, conf)
if len(association_p1) > 1: #need to have at least two sets in order to merge
rulesFromConsequences(freq, association_p1, supportData, rules, conf) #recursively call to build bigger itemset and get more rules
def main():
dataset = [line.split() for line in open('datatest.txt')]
L, supportData = apriori(dataset, minSupport=8)
rules = generateRules(L, supportData, conf=0)
rules = sorted(rules, key = itemgetter(2), reverse=True)
triples = []
doubles = []
i = 0
while len(triples) < 5:
if i == len(rules):
break
if len(rules[i][1]) == 2:
triples.append(rules[i])
i+=1
j = 0
while len(doubles) < 5:
if j == len(rules):
break
if len(rules[j][1]) == 1:
doubles.append(rules[j])
j+=1
if __name__ == '__main__':
main()
对此问题的任何建议表示赞赏。如果您对代码或思考过程有任何疑问，请告诉我们。如果有任何粗心的错误，请提前道歉。
感谢您阅读