# else: clusters = self.posclusters
print map(str, self.cluster2data[c])
for d in list(self.cluster2data[c]):
print d
print map(str, self.clusters[d]), '->',
self.clusters[d].remove(c)
print map(str, self.clusters[d])
del self.cluster2data[c]
def strcluster(self, c):
return map(str, self.cluster2data[c])
if __name__ == '__main__':
# s = ['otto', 'otte', 'obama', 'markov logic network', 'markov logic', 'otta', 'markov random field']
#
# print SAHN(s)
from mln.mln import readMLNFromFile
from mln.database import readDBFromFile
mln = readMLNFromFile('/home/nyga/code/pracmln/models/object-detection.mln')
dbs = readDBFromFile(mln, '/home/nyga/code/pracmln/models/scenes.db')
mln.materializeFormulaTemplates(dbs, verbose=True)
print mln.domains['text']
clusters = SAHN(mln.domains['text'])
for c in clusters:
print c
def doXVal(folds,
percent,
verbose,
multicore,
noisy,
predName,
domain,
mlnfile,
dbfiles,
logicLearn,
logicInfer,
inverse=False,
testSetCount=1):
startTime = time.time()
directory = time.strftime(
"%a_%d_%b_%Y_%H:%M:%S_K=" + str(folds) + "_TSC=" + str(testSetCount),
time.localtime())
os.mkdir(directory)
os.mkdir(os.path.join(directory, 'FOL'))
os.mkdir(os.path.join(directory, 'FUZZY'))
# set up the logger
log = logging.getLogger('xval')
fileLogger = FileHandler(os.path.join(directory, 'xval.log'))
fileLogger.setFormatter(praclog.formatter)
log.addHandler(fileLogger)
log.info('Results will be written into %s' % directory)
# preparations: Read the MLN and the databases
mln_ = readMLNFromFile(mlnfile,
verbose=verbose,
logic='FuzzyLogic',
grammar='PRACGrammar')
log.info('Read MLN %s.' % mlnfile)
dbs = []
for dbfile in dbfiles:
db = readDBFromFile(mln_, dbfile)
if type(db) is list:
dbs.extend(db)
else:
dbs.append(db)
log.info('Read %d databases.' % len(dbs))
cwpreds = [pred for pred in mln_.predicates if pred != predName]
# create the partition of data
subsetLen = int(math.ceil(len(dbs) * percent / 100.0))
if subsetLen < len(dbs):
log.info('Using only %d of %d DBs' % (subsetLen, len(dbs)))
dbs = sample(dbs, subsetLen)
if len(dbs) < folds:
log.error(
'Cannot do %d-fold cross validation with only %d databases.' %
(folds, len(dbs)))
exit(0)
shuffle(dbs)
partSize = int(math.ceil(len(dbs) / float(folds)))
partition = []
for i in range(folds):
partition.append(dbs[i * partSize:(i + 1) * partSize])
foldRunnables = []
for foldIdx in range(folds):
partion_ = list(partition)
params = XValFoldParams()
params.mln = mln_.duplicate()
params.testDBs = []
params.learnDBs = []
for i in range(0, testSetCount):
if (foldIdx >= len(partion_)):
params.testDBs.extend(partion_[0])
del partion_[0]
else:
params.testDBs.extend(partion_[foldIdx])
del partion_[foldIdx]
for part in partion_:
params.learnDBs.extend(part)
print 'LEARN DBS :' + str(len(params.learnDBs))
print 'TEST DBS :' + str(len(params.testDBs))
params.foldIdx = foldIdx
params.foldCount = folds
params.noisyStringDomains = noisy
params.directory = directory
params.queryPred = predName
params.queryDom = domain
params.logicInfer = logicInfer
foldRunnables.append(XValFold(params))
if multicore:
# set up a pool of worker processes
try:
workerPool = Pool()
log.info('Starting %d-fold Cross-Validation in %d processes.' %
(folds, workerPool._processes))
result = workerPool.map_async(runFold, foldRunnables).get()
workerPool.close()
workerPool.join()
cm = ConfusionMatrix()
for r in result:
cm.combine(r.confMatrix)
elapsedTimeMP = time.time() - startTime
prepareResults(directory, 'FOL')
prepareResults(directory, 'FUZZY')
except (KeyboardInterrupt, SystemExit, SystemError):
log.critical("Caught KeyboardInterrupt, terminating workers")
workerPool.terminate()
workerPool.join()
exit(1)
except:
log.error('\n' +
''.join(traceback.format_exception(*sys.exc_info())))
exit(1)
# startTime = time.time()
else:
log.info('Starting %d-fold Cross-Validation in 1 process.' % (folds))
for fold in foldRunnables:
runFold(fold)
prepareResults(directory, 'FOL')
prepareResults(directory, 'FUZZY')
elapsedTimeSP = time.time() - startTime
if multicore:
log.info('%d-fold crossvalidation (MP) took %.2f min' %
(folds, elapsedTimeMP / 60.0))
else:
log.info('%d-fold crossvalidation (SP) took %.2f min' %
(folds, elapsedTimeSP / 60.0))
# else: clusters = self.posclusters
print map(str, self.cluster2data[c])
for d in list(self.cluster2data[c]):
print d
print map(str, self.clusters[d]), '->',
self.clusters[d].remove(c)
print map(str, self.clusters[d])
del self.cluster2data[c]
def strcluster(self, c):
return map(str, self.cluster2data[c])
if __name__ == '__main__':
# s = ['otto', 'otte', 'obama', 'markov logic network', 'markov logic', 'otta', 'markov random field']
#
# print SAHN(s)
from mln.mln import readMLNFromFile
from mln.database import readDBFromFile
mln = readMLNFromFile(
'/home/nyga/code/pracmln/models/object-detection.mln')
dbs = readDBFromFile(mln, '/home/nyga/code/pracmln/models/scenes.db')
mln.materializeFormulaTemplates(dbs, verbose=True)
print mln.domains['text']
clusters = SAHN(mln.domains['text'])
for c in clusters:
print c