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buildARfromlogs.py
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buildARfromlogs.py
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from collections import defaultdict
import sys
def parseLine(line):
"""
Returns a DFS code edge from a string
"""
line = line.replace('(','').replace(')','')
line = line.split(',')
v_from = int(line[0])
v_to = int(line[1])
label_from = line[2].replace(' \'','').replace('\'','')
label_to = line[3].replace(' \'','').replace('\'','')
edge_label = line[4].replace(' \'','').replace('\'','')
return (v_from,v_to,label_from,label_to,edge_label)
def getPatterns(filename):
"""
Returns a list of patterns (DFS codes) from an output file created by classificationWorkflow.logPatterns
"""
patterns = {}
with open(filename,'r') as log:
current = ''
for line in log:
line = line.replace('\n','')
if line == '' or 'time taken' in line:
continue
if 'Pattern' in line:
current = line
patterns[line] = []
else:
patterns[current].append(parseLine(line))
return patterns.values()
class AR:
"""
A class representing an Association Rule
"""
def __init__(self,lhs='',rhs='',conf=0):
self.confidence = conf
self.lhs = lhs
self.rhs = rhs
def __str__(self):
return str(self.lhs) + " ==> " + str(self.rhs) + " : " + str(self.confidence)
def buildARfromlogs(logfiledict,graphs,outputfile=None):
"""
Params:
- dictionary with as key the label, and as value a list of logfiles with their support
- dictionary with as key the label, value is a list of graphs
- optionally the name of an output file
Returns: list of AR Association Rules
"""
label2pattern2sup = defaultdict(list)
for label, logfiles in logfiledict.items():
#from high to low support to make sure only highest support is saved
logfiles = sorted(logfiles,key=lambda x: x[1],reverse=True)
for (filename,support) in logfiles:
patterns = getPatterns(filename)
for pattern in patterns:
if len(filter(lambda x: x[0] == pattern,label2pattern2sup[label])) == 0:
label2pattern2sup[label].append((pattern,support))
#list of association rules
ARs = []
for label, pattern2sup in label2pattern2sup.items():
print "Building association rules for " + label + "..."
i = 0
for pattern, localsupport in pattern2sup:
#pretty output
sys.stdout.write('\r \r')
sys.stdout.write( label + " finished " + str(round(float(i)/float(len(pattern2sup)) * 10000) / 100.0) + "%")
sys.stdout.flush()
totalsupport = localsupport
for label2, label2graphs in graphs.items():
if label2 != label:
for g in label2graphs:
if g.subgraphIsomorphism(pattern):
totalsupport += 1
confidence = float(localsupport)/float(totalsupport)
ARs.append(AR(pattern,label,confidence))
i += 1
print "\r \r" + label + " finished 100%"
if outputfile != None:
with open(outputfile,'w') as output:
for rule in ARs:
output.write(str(rule) + "\n")
return ARs