Implement Apriori approach for datamining to organize the data items on a shelf using the dataset.csv file of items purchased in a Mall given below

PROGRAM

filename: apriori.py

from collections import defaultdict

def readFile(fn):
f = open(fn,"r")
for line in f:
line = line.strip().rstrip(",")
record = frozenset(line.split(','))
yield record

def returnItemSetTL(data_iter):
_itemSet = set()
TransactionList = []
for record in data_iter:
trans = frozenset(record)
TransactionList.append(trans)
for item in trans:
_itemSet.add(frozenset([item]))
return _itemSet,TransactionList

def returnItemWithMinSupport(itemSet,TransactionList,ms,freqSet):
localSet = defaultdict(int)
_itemSet = set()
for item in itemSet:
for trans in TransactionList:
if item.issubset(trans):
localSet[item]+=1
freqSet[item]+=1
for item,count in localSet.items():
support = float(count)/float(len(TransactionList))
if support>=minSupport:
_itemSet.add(item)
return _itemSet

def joinSet(itemSet,length):
return set([i.union(j) for i in itemSet for j in itemSet if len(i.union(j))==length])

def getSupport(freqSet,item,TransactionList):
return float(freqSet[item])/float(len(TransactionList))

def runApriori(data_iter,ms,mc):
itemSet,TransactionList = returnItemSetTL(data_iter)

freqSet = defaultdict(int)
largeSet = {}
oneCSet = returnItemWithMinSupport(itemSet,TransactionList,ms,freqSet)
k=2
currentLSet = oneCSet
print "L1"
print currentLSet
while(currentLSet!=set([])):
largeSet[k-1]=currentLSet
currentLSet = joinSet(currentLSet,k)
print "L",k
print currentLSet
currentCSet = returnItemWithMinSupport(currentLSet,TransactionList,ms,freqSet)
currentLSet = currentCSet
k+=1
items = []
for key,value in largeSet.items():
items.extend([(tuple(item),getSupport(freqSet,item,TransactionList)) for item in value])
print items
max_value = 0
for item,support in sorted(items,key=lambda(item,support):support):
if max_value<len(item):
max_value=len(item)
for item,support in sorted(items,key=lambda(item,support):support):
if max_value==len(item):
print item


if __name__=='__main__':
inFile = readFile("dataset.csv")
minSupport = 0.6
minConfidence = 1
runApriori(inFile,minSupport,minConfidence)


filename: dataset.csv

Mango,Onion,Jar,Keychain,Eggs,Chocolate
Nuts,Onion,Jar,Keychain,Eggs,Chocolate
Mango,Apple,Keychain,Eggs
Mango,Toothbrush,Corn,Keychain,Chocolate
Corn,Onion,Onion,Keychain,Knife,Eggs

OUTPUT

Amols-MacBook-Air:b10 Darwin$ python apriori.py
L1
set([frozenset(['Onion']), frozenset(['Chocolate']), frozenset(['Keychain']), frozenset(['Eggs']), frozenset(['Mango'])])
L 2
set([frozenset(['Mango', 'Keychain']), frozenset(['Eggs', 'Onion']), frozenset(['Onion', 'Chocolate']), frozenset(['Mango', 'Chocolate']), frozenset(['Keychain', 'Chocolate']), frozenset(['Eggs', 'Chocolate']), frozenset(['Keychain', 'Onion']), frozenset(['Mango', 'Onion']), frozenset(['Eggs', 'Keychain']), frozenset(['Eggs', 'Mango'])])
L 3
set([frozenset(['Eggs', 'Keychain', 'Onion']), frozenset(['Eggs', 'Keychain', 'Chocolate']), frozenset(['Mango', 'Keychain', 'Chocolate']), frozenset(['Mango', 'Keychain', 'Onion']), frozenset(['Eggs', 'Mango', 'Keychain']), frozenset(['Keychain', 'Onion', 'Chocolate'])])
L 4
set([])
[(('Onion',), 0.6), (('Chocolate',), 0.6), (('Keychain',), 1.0), (('Eggs',), 0.8), (('Mango',), 0.6), (('Eggs', 'Keychain'), 0.8), (('Mango', 'Keychain'), 0.6), (('Eggs', 'Onion'), 0.6), (('Keychain', 'Onion'), 0.6), (('Keychain', 'Chocolate'), 0.6), (('Eggs', 'Keychain', 'Onion'), 0.6)]
('Eggs', 'Keychain', 'Onion')