def model_config(self,predicate,formula,database,mln_path,db_path):
"""
Returns the database and mln objects in MLN format
--Inputs--
predicate: predicate object with parsed predicates
formula: formula object with parsed predicates
database:.txt file containing the database(s)
mln_path: .mln file name to save the learned weights per formula
db_path: .db file to save the progress of the database learning
"""
base_path = os.getcwd()
mln = MLN(grammar='PRACGrammar',logic='FirstOrderLogic') #Parsing with PRACGrammar since we are using clusters
for i in predicate:
mln << i
print('input predicate successful:'+i)
for i in formula:
mln << i
print('input formula successful :'+i)
mln.write()
mln.tofile(base_path + '/'+ mln_path)
db = Database.load(mln,database)
#db.write()
#db.tofile(base_path + '/'+ db_path)
return (db,mln)
def test_learning_taxonomies():
p = '$PRACMLN_HOME/examples/taxonomies/taxonomies.pracmln'
mln = MLN(mlnfile=('%s:senses_and_roles.mln' % p), grammar='PRACGrammar')
mln.write()
dbs = Database.load(mln, dbfiles='%s:training.db' % p)
for method in ('DPLL', 'DBPLL_CG', 'DCLL'):
for multicore in (True, False):
print '=== LEARNING TEST:', method, '==='
learn(method=method, mln=mln, db=dbs, verbose=True, multicore=multicore, epreds='is_a', discr_preds=EVIDENCE_PREDS).run()
def test_GSMLN():
# mln = MLN(grammar='GSMLNGrammar')
# mln << 'residue(id, profile)'
# mln << 'partners(id, id)'
# f = "residue(a, $pa) v residue(b, $pb) => partners(a,b)"
# # f = "((a(x) ^ b(x)) v (c(x) ^ !(d(x) ^ e(x) ^ g(x)))) => f(x)"
# # f = "(a(x) v (b(x) ^ c(x))) => f(x)"
# f = mln.logic.grammar.parse_formula(f)
# f.print_structure()
# print(list(f.literals()))
# g = "partners(id, id)"
# g = mln.logic.grammar.parse_predicate(g)
# print(g)
# print(mln.predicates)
mln = MLN(mlnfile='smokers.mln', grammar='GSMLNGrammar')
# mln.write()
# print(mln.predicates)
dbs = Database.load(mln, dbfiles='smokers.db')
# dbs[0].write()
# print(mln.formulas[0].neural)
# print(mln.formulas[0].cnf())
# print(mln.nnformulas[0].idx)
# print(mln.domains)
# print(dbs[0].domains)
# mln.formulas[1].print_structure()
# mrf = mln.ground(dbs[0])
# grounder = DefaultGroundingFactory(mrf, simplify=False, unsatfailure=True, verbose=False, cache=0)
# for f in grounder.itergroundings():
# print(f)
# print((mrf.gndatoms))
# mln = MLN(grammar='GSMLNGrammar')
# mln << 'Cancer(&person)'
# mln << 'Friends(&person,&person)'
# mln << 'Smokes(&person)'
# f = 'Smokes($x) => Cancer($x)'
# g = 'Friends($x,$y) => (Smokes($x) <=> Smokes($y))'
# print(mln.logic.grammar.parse_formula(f))
# mln.formula(f)
# mln.formula(g)
# print(mln.predicates)
# print(mln.formulas)
# mln.formulas[0].print_structure()
# print(mln.domains)
# print(mln.formulas[0].cnf())
# this uses the method from base.py
learned_mln = mln.learn(databases=dbs, method=GSMLN_L, verbose=True)
def test_learning_taxonomies():
p = os.path.join(locs.examples, 'taxonomies', 'taxonomies.pracmln')
mln = MLN(mlnfile=('%s:senses_and_roles.mln' % p), grammar='PRACGrammar')
mln.write()
dbs = Database.load(mln, dbfiles='%s:training.db' % p)
for method in ('DPLL', 'DBPLL_CG', 'DCLL'):
for multicore in (True, False):
print('=== LEARNING TEST:', method, '===')
learn(method=method,
mln=mln,
db=dbs,
verbose=True,
multicore=multicore,
epreds='is_a',
discr_preds=EVIDENCE_PREDS).run()
def train(self, praclearning):
print prac_heading('Training knowledgebase')
mlnName = praclearning.otherParams.get('mln', None)
mlnLogic = praclearning.otherParams.get('logic', None)
objName = praclearning.otherParams.get('concept', None)
onTheFly = praclearning.otherParams.get('onthefly', False)
mln = MLN(mlnfile=os.path.abspath(mlnName), logic=mlnLogic,
grammar='PRACGrammar')
pracTrainingDBS = praclearning.training_dbs
trainingDBS = []
if len(pracTrainingDBS) >= 1 and type(
pracTrainingDBS[0]) is str: # db from file
logger.info('Learning from db files...')
inputdbs = Database.load(mln, dbfile=pracTrainingDBS,
ignore_unknown_preds=True)
trainingDBS += inputdbs
elif len(pracTrainingDBS) > 1:
logger.info('Learning from db files (xfold)...')
trainingDBS = pracTrainingDBS
else: # db from inference result
logger.info('Learning from inference result...')
inputdbs = pracTrainingDBS
for db in inputdbs:
db << 'object(cluster, {})'.format(objName)
trainingDBS.append(db)
outputfile = '{}_trained.mln'.format(mlnName.split('.')[0])
# learning mln
trainedMLN = mln.learnWeights(trainingDBS, LearningMethods.DCLL,
evidencePreds=possibleProps, partSize=1,
gaussianPriorSigma=10, useMultiCPU=0,
optimizer='cg', learningRate=0.9)
print prac_heading('Learnt Formulas')
trainedMLN.printFormulas()
trainedMLN.write(file(outputfile, "w"))
return trainedMLN
from pracmln import MLN
from pracmln import Database
from pracmln import MLNQuery
mln = MLN(mlnfile='./data/smokers/mlns/smoking_trained.mln',grammar='PRACGrammar', logic='FirstOrderLogic')
mln.write()
db = Database.load(mln,'./data/smokers/dbs/smoking-test.db')[0]
db.write()
print("Running Query...")
result = MLNQuery(mln=mln, db=db).run()
print(result)
Takes a sequence of databases and performs a multi-dimensional scaling on
the synsets given by the merged set of 'concept' domains in them.
'''
domains = mergedom(*map(lambda d: d.domains, dbs))
concepts = domains.get('concept', None)
if concepts is None:
logger.error('Domain "concepts" not found in databases.')
return
if 'null' in concepts: # remove the null concept
del concepts[concepts.index('null')]
synsets = map(lambda x: wn.synset(x), concepts) # @UndefinedVariable
distance = zeros((len(synsets),len(synsets)))
for (i, pointi) in enumerate(synsets):
for (j, pointj) in enumerate(synsets):
sys.stdout.write('{:f} / {:f} \r'.format(i, len(synsets)))
sim = synsets[i].path_similarity(synsets[j])
if sim is None: sim = 0
distance[i,j] = 1. - sim
Y, eig = doMDS(distance, dimensions=2)
pylab.figure(1)
for i, s in enumerate(synsets):
text(Y[i,0],Y[i,1], s.name, fontsize=8)
pylab.plot(Y[:,0],Y[:,1],'.')
pylab.show()
if __name__ == '__main__':
prac = PRAC()
dbs = list(Database.load(prac.mln, os.path.join('/', 'home', 'nyga', 'work', 'nl_corpora', 'wikihow', 'Slicing.db')))
domains = mergedom(*map(lambda d: d.domains, dbs))
concepts = domains.get('concept', None)
args = parser.parse_args()
if args.learn:
print('you chose learn the weights for the mln')
predicate = s.read_predicate('predicate.txt')
formula = s.read_formula('formula.txt',predicate)
data,mln = s.model_config(predicate,formula,'data.txt','results.mln','results.db')
with open('base.mln', 'wb') as base_mln_file:
pickle.dump(mln, base_mln_file)
output = s.activate_model(data,mln)
output.tofile(os.getcwd() + '/' + 'learnt_mln.mln')
elif args.query:
print('you chose to query the mln')
mln = MLN.load(files='learnt_mln.mln')
infer_world = Database.load(mln,'inference_data.txt')
s.inference('query.txt',infer_world,mln)
else:
print ('please input learn (-l) or query (-q) to proceed')
#query(queries='Cancer(x)', method='MC-SAT', mln=mln, db=data, verbose=False, multicore=True).run().results
# =============================================================================
# predicate_list = [(x,x.lower()) for x in predicate_list]
# predicate = [x.replace(' ','').lower() for x in predicate if x !='']
# predicate2 = []
valmap = dict([(val, computeClosestCluster(val, self.clusters[domain])[1][0]) for val in newdb.domains[domain]])
newdb.domains[domain] = valmap.values()
# replace the affected evidences
for ev in newdb.evidence.keys():
truth = newdb.evidence[ev]
_, pred, params = db.mln.logic.parse_literal(ev)
if domain in self.mln.predicate(pred).argdoms: # domain is affected by the mapping
newdb.retract(ev)
newargs = [v if domain != self.mln.predicate(pred).argdoms[i] else valmap[v] for i, v in enumerate(params)]
atom = '%s%s(%s)' % ('' if truth else '!', pred, ','.join(newargs))
newdb << atom
newdbs.append(newdb)
return newdbs
if __name__ == '__main__':
mln = MLN.load('/home/nyga/code/pracmln/examples/object-recognition/object-recognition.pracmln:object-detection.mln')
dbs = Database.load(mln, '/home/nyga/code/pracmln/examples/object-recognition/object-recognition.pracmln:scenes-new.db')
# do some plain clustering on texts
s = ['otto', 'otte', 'obama', 'markov logic network', 'markov logic', 'otta', 'markov random field']
s = set([val for db in Database.load(mln, '/home/nyga/code/pracmln/examples/object-recognition/object-recognition.pracmln:scenes-new.db') for val in db.domains['text']])
clusters = SAHN(s)
for c in clusters:
print(c)
# apply clustering to a set of databases
cluster = NoisyStringClustering(mln, ['text'])
cluster.materialize(dbs)
def test_reasoning():
mln = MLN.load(files='./mln/alarm.mln')
db = Database.load(mln, './mln/alarm.db')
result = MLNQuery(mln=mln, db=db).run()
result.write()
mln << 'instance(cluster, instance)'
mln << 'object(cluster, object!)'
##formulas
mln << '0 shape(?c, +?sha) ^ color(?c, +?col) ^ size(?c, +?size) ^ instance(?c, +?inst) ^ object(?c, +?obj)'
mln << '0 goggles_Logo(?c, +?comp) ^ object(?c, +?obj)'
mln << '0 goggles_Text(?c, +?text) ^ object(?c, +?obj)'
mln << '0 goggles_Product(?c, +?prod) ^ object(?c, +?obj)'
mln << '0 scene(+?s) ^ object(?c, +?obj)'
##unique clusters
mln << '0 scene(+?s) ^ object(?c1, +?t1) ^ object(?c2, +?t2) ^ ?c1 =/= ?c2'
dbFileName = '/home/dominik/python_ws/testDB.txt'
#allDB = Database.load(mln, '/home/dominik/python_ws/testDB.txt')
allDB = Database.load(mln, dbFileName)
predictionList = []
predIdx = 0
groundTruthList = []
gtIdx = 0
i = 0
splits = 10
testArray = np.array(allDB, dtype=Database)
kf = KFold(n_splits=splits, shuffle=True)
for train, test in kf.split(testArray):
print("%s %s" % (train, test))
predList = []
pIdx = 0
gtList = []
