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【Python】 apriori算法
作者:sherlockzoom / 发布于2015/1/4/ 894

	"""
	Description : Simple Python implementation of the Apriori Algorithm
	
	Usage:
	 $python apriori.py -f DATASET.csv -s minSupport -c minConfidence
	
	 $python apriori.py -f DATASET.csv -s 0.15 -c 0.6
	"""
	
	import sys
	
	from itertools import chain, combinations
	from collections import defaultdict
	from optparse import OptionParser
	
	
	def subsets(arr):
	 """ Returns non empty subsets of arr"""
	 return chain(*[combinations(arr, i + 1) for i, a in enumerate(arr)])
	
	
	def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):
	 """calculates the support for items in the itemSet and returns a subset
	 of the itemSet each of whose elements satisfies the minimum support"""
	 _itemSet = set()
	 localSet = defaultdict(int)
	
	 for item in itemSet:
	 for transaction in transactionList:
	 if item.issubset(transaction):
	 freqSet[item] += 1
	 localSet[item] += 1
	
	 for item, count in localSet.items():
	 support = float(count)/len(transactionList)
	
	 if support >= minSupport:
	 _itemSet.add(item)
	
	 return _itemSet
	
	
	def joinSet(itemSet, length):
	 """Join a set with itself and returns the n-element itemsets"""
	 return set([i.union(j) for i in itemSet for j in itemSet if len(i.union(j)) == length])
	
	
	def getItemSetTransactionList(data_iterator):
	 transactionList = list()
	 itemSet = set()
	 for record in data_iterator:
	 transaction = frozenset(record)
	 transactionList.append(transaction)
	 for item in transaction:
	 itemSet.add(frozenset([item])) # Generate 1-itemSets
	 return itemSet, transactionList
	
	
	def runApriori(data_iter, minSupport, minConfidence):
	 """
	 run the apriori algorithm. data_iter is a record iterator
	 Return both:
	 - items (tuple, support)
	 - rules ((pretuple, posttuple), confidence)
	 """
	 itemSet, transactionList = getItemSetTransactionList(data_iter)
	
	 freqSet = defaultdict(int)
	 largeSet = dict()
	 # Global dictionary which stores (key=n-itemSets,value=support)
	 # which satisfy minSupport
	
	 assocRules = dict()
	 # Dictionary which stores Association Rules
	
	 oneCSet = returnItemsWithMinSupport(itemSet,
	 transactionList,
	 minSupport,
	 freqSet)
	
	 currentLSet = oneCSet
	 k = 2
	 while(currentLSet != set([])):
	 largeSet[k-1] = currentLSet
	 currentLSet = joinSet(currentLSet, k)
	 currentCSet = returnItemsWithMinSupport(currentLSet,
	 transactionList,
	 minSupport,
	 freqSet)
	 currentLSet = currentCSet
	 k = k + 1
	
	 def getSupport(item):
	 """local function which Returns the support of an item"""
	 return float(freqSet[item])/len(transactionList)
	
	 toRetItems = []
	 for key, value in largeSet.items():
	 toRetItems.extend([(tuple(item), getSupport(item))
	 for item in value])
	
	 toRetRules = []
	 for key, value in largeSet.items()[1:]:
	 for item in value:
	 _subsets = map(frozenset, [x for x in subsets(item)])
	 for element in _subsets:
	 remain = item.difference(element)
	 if len(remain) > 0:
	 confidence = getSupport(item)/getSupport(element)
	 if confidence >= minConfidence:
	 toRetRules.append(((tuple(element), tuple(remain)),
	 confidence))
	 return toRetItems, toRetRules
	
	
	def printResults(items, rules):
	 """prints the generated itemsets and the confidence rules"""
	 for item, support in items:
	 print "item: %s , %.3f" % (str(item), support)
	 print "\n------------------------ RULES:"
	 for rule, confidence in rules:
	 pre, post = rule
	 print "Rule: %s ==> %s , %.3f" % (str(pre), str(post), confidence)
	
	
	def dataFromFile(fname):
	 """Function which reads from the file and yields a generator"""
	 file_iter = open(fname, 'rU')
	 for line in file_iter:
	 line = line.strip().rstrip(',') # Remove trailing comma
	 record = frozenset(line.split(','))
	 yield record
	
	
	if __name__ == "__main__":
	
	 optparser = OptionParser()
	 optparser.add_option('-f', '--inputFile',
	 dest='input',
	 help='filename containing csv',
	 default=None)
	 optparser.add_option('-s', '--minSupport',
	 dest='minS',
	 help='minimum support value',
	 default=0.15,
	 type='float')
	 optparser.add_option('-c', '--minConfidence',
	 dest='minC',
	 help='minimum confidence value',
	 default=0.6,
	 type='float')
	
	 (options, args) = optparser.parse_args()
	
	 inFile = None
	 if options.input is None:
	 inFile = sys.stdin
	 elif options.input is not None:
	 inFile = dataFromFile(options.input)
	 else:
	 print 'No dataset filename specified, system with exit\n'
	 sys.exit('System will exit')
	
	 minSupport = options.minS
	 minConfidence = options.minC
	
	 items, rules = runApriori(inFile, minSupport, minConfidence)
	
	 printResults(items, rules)
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