class XValFold(object):
'''
Class representing and providing methods for a cross validation fold.
'''
def __init__(self, params):
'''
params being a XValFoldParams object.
'''
self.params = params
self.fold_id = 'Fold-%d' % params.fold_idx
self.confmat = ConfusionMatrix()
# write the training and testing databases into a file
with open(
os.path.join(params.directory,
'train_dbs_%d.db' % params.fold_idx),
'w+') as dbfile:
Database.write_dbs(params.learn_dbs, dbfile)
with open(
os.path.join(params.directory,
'test_dbs_%d.db' % params.fold_idx),
'w+') as dbfile:
Database.write_dbs(params.test_dbs, dbfile)
def eval(self, mln, dbs):
'''
Returns a confusion matrix for the given (learned) MLN evaluated on
the databases given in dbs.
'''
querypred = self.params.querypred
# query_dom = self.params.query_dom
# sig = ['?arg%d' % i for i, _ in enumerate(mln.predicates[query_pred])]
# querytempl = '%s(%s)' % (query_pred, ','.join(sig))
# dbs = map(lambda db: db.copy(mln), dbs)
for db_ in dbs:
# save and remove the query predicates from the evidence
db = db_.copy()
gndtruth = mln.ground(db)
gndtruth.apply_cw()
for atom, _ in db.gndatoms(querypred):
out('removing evidence', repr(atom))
del db.evidence[atom]
db.write()
stop()
try:
resultdb = MLNQuery(config=self.params.queryconf,
mln=mln,
method=InferenceMethods.WCSPInference,
db=db,
cw_preds=[
p.name for p in mln.predicates
if p.name != self.params.querypred
],
multicore=False).run().resultdb
result = mln.ground(db)
result.set_evidence(resultdb)
for variable in result.variables:
if variable.predicate.name != querypred: continue
pvalue = variable.evidence_value()
tvalue = variable.evidence_value()
prediction = [
a for a, v in variable.atomvalues(pvalue) if v == 1
]
truth = [
a for a, v in variable.atomvalues(tvalue) if v == 1
]
prediction = str(prediction[0]) if prediction else None
truth = str(truth[0]) if truth else None
self.confmat.addClassificationResult(prediction, truth)
# sig2 = list(sig)
# entityIdx = mln.predicates[query_pred].argdoms.index(query_dom)
# for entity in db.domains[]:
# sig2[entityIdx] = entity
# query = '%s(%s)' % (queryPred, ','.join(sig2))
# for truth in trueDB.query(query):
# truth = truth.values().pop()
# for pred in resultDB.query(query):
# pred = pred.values().pop()
# self.confMatrix.addClassificationResult(pred, truth)
# for e, v in trueDB.evidence.iteritems():
# if v is not None:
# db.addGroundAtom('%s%s' % ('' if v is True else '!', e))
except:
logger.critical(''.join(
traceback.format_exception(*sys.exc_info())))
def run(self):
'''
Runs the respective fold of the crossvalidation.
'''
logger.info('Running fold %d of %d...' %
(self.params.fold_idx + 1, self.params.folds))
directory = self.params.directory
try:
# # Apply noisy string clustering
# log.debug('Transforming noisy strings...')
# if self.params.noisyStringDomains is not None:
# noisyStrTrans = NoisyStringTransformer(self.params.mln, self.params.noisyStringDomains, True)
# learnDBs_ = noisyStrTrans.materializeNoisyDomains(self.params.learnDBs)
# testDBs_ = noisyStrTrans.transformDBs(self.params.testDBs)
# else:
# learnDBs_ = self.params.learnDBs
# testDBs_ = self.params.testDBs
# train the MLN
mln = self.params.mln
logger.debug('Starting learning...')
learn_dbs = [db.copy() for db in self.params.learn_dbs]
# apply closed world for fuzzy atoms
for db in learn_dbs:
for a, v in db.gndatoms([
p.name for p in mln.predicates
if isinstance(p, FuzzyPredicate)
]):
if v != 1: db[a] = 0
learned = MLNLearn(config=self.params.learnconf,
mln=mln,
db=learn_dbs,
multicore=False).run() #200
# store the learned MLN in a file
learned.tofile(
os.path.join(directory, 'run_%d.mln' % self.params.fold_idx))
logger.debug('Finished learning.')
# evaluate the MLN
logger.debug('Evaluating.')
# learnedMLN.setClosedWorldPred(None)
# if self.params.cwPreds is None:
# self.params.cwPreds = [p for p in mln.predicates if p != self.params.queryPred]
# for pred in [pred for pred in self.params.cwPreds if pred in learnedMLN.predicates]:
# learnedMLN.setClosedWorldPred(pred)
self.eval(learned, self.params.test_dbs)
self.confmat.toFile(
os.path.join(directory,
'conf_matrix_%d.cm' % self.params.fold_idx))
logger.debug('Evaluation finished.')
except (KeyboardInterrupt, SystemExit):
logger.critical("Exiting...")
return None
class XValFold(object):
'''
Class representing and providing methods for a cross validation fold.
'''
def __init__(self, params):
'''
params being a XValFoldParams object.
'''
self.params = params
self.fold_id = 'Fold-%d' % params.fold_idx
self.confmat = ConfusionMatrix()
# write the training and testing databases into a file
with open(os.path.join(params.directory, 'train_dbs_%d.db' % params.fold_idx), 'w+') as dbfile:
Database.write_dbs(params.learn_dbs, dbfile)
with open(os.path.join(params.directory, 'test_dbs_%d.db' % params.fold_idx), 'w+') as dbfile:
Database.write_dbs(params.test_dbs, dbfile)
def eval(self, mln, dbs):
'''
Returns a confusion matrix for the given (learned) MLN evaluated on
the databases given in dbs.
'''
querypred = self.params.querypred
# query_dom = self.params.query_dom
# sig = ['?arg%d' % i for i, _ in enumerate(mln.predicates[query_pred])]
# querytempl = '%s(%s)' % (query_pred, ','.join(sig))
# dbs = map(lambda db: db.copy(mln), dbs)
for db_ in dbs:
# save and remove the query predicates from the evidence
db = db_.copy()
gndtruth = mln.ground(db)
gndtruth.apply_cw()
for atom, _ in db.gndatoms(querypred):
out('removing evidence', repr(atom))
del db.evidence[atom]
db.write()
stop()
try:
resultdb = MLNQuery(config=self.params.queryconf, mln=mln, method=InferenceMethods.WCSPInference, db=db,
cw_preds=[p.name for p in mln.predicates if p.name != self.params.querypred], multicore=False).run().resultdb
result = mln.ground(db)
result.set_evidence(resultdb)
for variable in result.variables:
if variable.predicate.name != querypred: continue
pvalue = variable.evidence_value()
tvalue = variable.evidence_value()
prediction = [a for a, v in variable.atomvalues(pvalue) if v == 1]
truth = [a for a, v in variable.atomvalues(tvalue) if v == 1]
prediction = str(prediction[0]) if prediction else None
truth = str(truth[0]) if truth else None
self.confmat.addClassificationResult(prediction, truth)
# sig2 = list(sig)
# entityIdx = mln.predicates[query_pred].argdoms.index(query_dom)
# for entity in db.domains[]:
# sig2[entityIdx] = entity
# query = '%s(%s)' % (queryPred, ','.join(sig2))
# for truth in trueDB.query(query):
# truth = truth.values().pop()
# for pred in resultDB.query(query):
# pred = pred.values().pop()
# self.confMatrix.addClassificationResult(pred, truth)
# for e, v in trueDB.evidence.iteritems():
# if v is not None:
# db.addGroundAtom('%s%s' % ('' if v is True else '!', e))
except:
logger.critical(''.join(traceback.format_exception(*sys.exc_info())))
def run(self):
'''
Runs the respective fold of the crossvalidation.
'''
logger.info('Running fold %d of %d...' % (self.params.fold_idx + 1, self.params.folds))
directory = self.params.directory
try:
# # Apply noisy string clustering
# log.debug('Transforming noisy strings...')
# if self.params.noisyStringDomains is not None:
# noisyStrTrans = NoisyStringTransformer(self.params.mln, self.params.noisyStringDomains, True)
# learnDBs_ = noisyStrTrans.materializeNoisyDomains(self.params.learnDBs)
# testDBs_ = noisyStrTrans.transformDBs(self.params.testDBs)
# else:
# learnDBs_ = self.params.learnDBs
# testDBs_ = self.params.testDBs
# train the MLN
mln = self.params.mln
logger.debug('Starting learning...')
learn_dbs = [db.copy() for db in self.params.learn_dbs]
# apply closed world for fuzzy atoms
for db in learn_dbs:
for a, v in db.gndatoms([p.name for p in mln.predicates if isinstance(p, FuzzyPredicate)]):
if v != 1: db[a] = 0
learned = MLNLearn(config=self.params.learnconf, mln=mln, db=learn_dbs, multicore=False).run()#200
# store the learned MLN in a file
learned.tofile(os.path.join(directory, 'run_%d.mln' % self.params.fold_idx))
logger.debug('Finished learning.')
# evaluate the MLN
logger.debug('Evaluating.')
# learnedMLN.setClosedWorldPred(None)
# if self.params.cwPreds is None:
# self.params.cwPreds = [p for p in mln.predicates if p != self.params.queryPred]
# for pred in [pred for pred in self.params.cwPreds if pred in learnedMLN.predicates]:
# learnedMLN.setClosedWorldPred(pred)
self.eval(learned, self.params.test_dbs)
self.confmat.toFile(os.path.join(directory, 'conf_matrix_%d.cm' % self.params.fold_idx))
logger.debug('Evaluation finished.')
except (KeyboardInterrupt, SystemExit):
logger.critical("Exiting...")
return None
