def learning(self):
mln = MLN(mlnfile=self.mln_path, grammar='StandardGrammar')
db = Database(mln, dbfile=self.db_path)
for method in ('BPLL', 'BPLL_CG', 'CLL'):
print('=== LEARNING TEST:', method, '===')
learn(method=method, mln=mln, db=db, verbose=True,
multicore=False).run()
def main(arg='.'):
#if (len(sys))
pth = os.path.join(arg, 'wts.pybpll.smoking-train-smoking.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
pth = os.path.join(arg, 'smoking-test-smaller.db')
db = Database(mln, dbfile=pth)
#with open(os.path.join(arg, 'man_performance.txt'), 'a') as fl:
start = time.time()
#t = 1000* timeit.timeit("test(False)", setup="from __main__ import test, set; set()", number=10)
query(queries='Cancer,Smokes,Friends',
method='MC-SAT',
mln=mln,
db=db,
verbose=False,
multicore=True).run()
t1 = time.time() - start
#print('single core exact inference test: {}'.format(t))
#fl.write(str(t))
#fl.write('\t(SingleCore)\n')
start = time.time()
#t = 1000* timeit.timeit("test(True)", setup="from __main__ import test, set; set()", number=10)
query(queries='Cancer,Smokes,Friends',
method='MC-SAT',
mln=mln,
db=db,
verbose=False,
multicore=False).run()
t2 = time.time() - start
#print('multi core exact inference test: {}'.format(t))
print('Inference, MC-SAT, {}, {}'.format(t1, t2))
def train(self, prac_learning):
training_dbs = []
if hasattr(prac_learning,
'training_dbs') and prac_learning.training_dbs is not None:
for dbfile in prac_learning.training_dbs:
training_dbs.extend(
Database(self.mln,
dbfile=dbfile,
ignore_unknown_preds=True))
else:
for dbfile in self.prac.training_dbs():
db = Database(self.mln,
dbfile=dbfile,
ignore_unknown_preds=True)
training_dbs.append(db)
def main(arg='.'): #(arg='..'):
#if (len(sys))
pth = os.path.join(arg, 'wts.pybpll.smoking-train-smoking.mln')
#pth = os.path.join(arg, 'learnt.cll.scenes-new-object-detection.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
#mln = MLN(mlnfile=pth, grammar='PRACGrammar')
pth = os.path.join(arg, 'smoking-test.db')
#pth = os.path.join(arg, 'test.db')
db = Database(mln, dbfile=pth)
#with open(os.path.join(arg, 'man_performance.txt'), 'a') as fl:
#start = time.time()
#t = 1000* timeit.timeit("test(False)", setup="from __main__ import test, set; set()", number=10)
#MULTICORE:#
#query(queries='Cancer,Smokes,Friends', method='EnumerationAsk', mln=mln, db=db, verbose=False, multicore=True).run()
#t1 = time.time()-start
#print('single core exact inference test: {}'.format(t))
#fl.write(str(t))
#fl.write('\t(SingleCore)\n')
start = time.time()
#t = 1000* timeit.timeit("test(True)", setup="from __main__ import test, set; set()", number=10)
query(method='EnumerationAsk',
mln=mln,
db=db,
verbose=False,
multicore=False).run()
t2 = time.time() - start
print('exact inference test: {}'.format(t2))
def model_config(predicate, formula, database, mln_path,
db_path): # mln_path,db_path 為string
base_path = os.getcwd()
mln = MLN(grammar='StandardGrammar', logic='FirstOrderLogic')
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) #把谓语数据储存成 mln_path.mln 档案
db = Database(mln)
try:
for i in enumerate(database):
db << i[1][1]
print('input database successful : ' + i[1][0] + ' : ' +
i[1][1])
except:
for j in database[i[0]::]:
db << j[1]
db.write()
db.tofile(base_path + '\\' + db_path) #把证据数据储存成 db_path.db 档案
return (db, mln)
def test_db():
mln = test_mln()
db = Database(mln)
db << 'foo(X)'
db['bar(Y)'] = .0
print('db write:')
db.write()
del db['bar(Y)']
print('db write:')
db.write()
return (mln, db)
def test_learning_smokers():
p = os.path.join(locs.examples, 'smokers', 'smokers.pracmln')
mln = MLN(mlnfile=('%s:smoking.mln' % p), grammar='StandardGrammar')
mln.write()
db = Database(mln, dbfile='%s:smoking-train.db' % p)
for method in ('BPLL', 'BPLL_CG', 'CLL'):
for multicore in (True, False):
print('=== LEARNING TEST:', method, '===')
learn(method=method,
mln=mln,
db=db,
verbose=True,
multicore=multicore).run()
def main(arg='.'):
pth = os.path.join(arg, 'wts.pybpll.smoking-train-smoking.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
pth = os.path.join(arg, 'smoking-test-tiny.db')
db = Database(mln, dbfile=pth)
start = time.time()
query(method='EnumerationAsk',
mln=mln,
db=db,
verbose=False,
multicore=False).run()
t2 = time.time() - start
print('exact inference test: {}'.format(t2))
def infer(self, pracinference):
inf_step = PRACInferenceStep(pracinference, self)
for db in pracinference.get_inference_steps_of_module(
'nl_parsing').output_dbs:
database = Database(self.prac.mln)
for truth, gndLit in db.iterGroundLiteralStrings():
database << (gndLit, truth)
logger.info(gndLit)
logger.info('Adding all similarities...')
self.addPossibleWordSensesToDBs(database)
inf_step.output_dbs.append(database)
return inf_step
def test_inference_taxonomies():
p = os.path.join(locs.examples, 'taxonomies', 'taxonomies.pracmln')
mln = MLN(mlnfile=('%s:wts.learned.taxonomy.mln' % p),
grammar='PRACGrammar',
logic='FuzzyLogic')
db = Database(mln, dbfile='%s:evidence.db' % p)
for method in ('EnumerationAsk', 'WCSPInference'):
print('=== INFERENCE TEST:', method, '===')
query(queries='has_sense, action_role',
method=method,
mln=mln,
db=db,
verbose=False,
cw=True).run().write()
def test_inference_smokers():
p = os.path.join(locs.examples, 'smokers', 'smokers.pracmln')
mln = MLN(mlnfile=('%s:wts.pybpll.smoking-train-smoking.mln' % p),
grammar='StandardGrammar')
db = Database(mln, dbfile='%s:smoking-test-smaller.db' % p)
for method in ('EnumerationAsk', 'MC-SAT', 'WCSPInference',
'GibbsSampler'):
for multicore in (False, True):
print('=== INFERENCE TEST:', method, '===')
query(queries='Cancer,Smokes,Friends',
method=method,
mln=mln,
db=db,
verbose=True,
multicore=multicore).run()
def test_inference_smokers():
p = os.path.join(locs.examples, 'smokers', 'smokers')
# mln = MLN(mlnfile=('%s:wts.pybpll.smoking-train-smoking.mln' % p),
# grammar='StandardGrammar')
# db = Database(mln, dbfile='%s:smoking-test-smaller.db' % p)
print(p)
mln = MLN(mlnfile=('%s.mln' % p), grammar='StandardGrammar')
db = Database(mln, dbfile='%s.db' % p)
for method in ('GibbsSampler', ):
print('=== INFERENCE TEST:', method, '===')
query(queries='Cancer,Smokes,Friends',
method=method,
mln=mln,
db=db,
verbose=True,
multicore=False).run()
def main(arg='.'): #(arg='..'):
#if (len(sys))
pth = os.path.join(arg, 'wts.pybpll.smoking-train-smoking.mln')
#pth = os.path.join(arg, 'learnt.cll.scenes-new-object-detection.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
#mln = MLN(mlnfile=pth, grammar='PRACGrammar')
pth = os.path.join(arg, 'smoking-test-small.db')
#pth = os.path.join(arg, 'test.db')
db = Database(mln, dbfile=pth)
start = time.time()
query(method='EnumerationAsk',
mln=mln,
db=db,
verbose=False,
multicore=False).run()
t2 = time.time() - start
print('exact inference test: {}'.format(t2))
def get_similarities(self, *dbs):
'''
Returns a database containing all possible similarities for the senses
of the input databases.
:param dbs: the input databases to be queried
:return: a generator yielding an instance of Database
'''
wordnet = self.prac.wordnet
full_domain = mergedom(*[db.domains for db in dbs])
for db in dbs:
db_ = Database(self.mln)
for q in db.query('has_sense(?w, ?s) ^ is_a(?s, ?c)'):
sense = q['?s']
concept = q['?c']
for c in full_domain['concept']:
sim = wordnet.similarity(c, concept)
db_ << ('is_a({},{})'.format(sense, c), sim)
yield db_
def finalgraph(self, filename=None):
finaldb = Database(self.prac.mln)
for step in self.inference_steps:
for db in step.output_dbs:
for atom, truth in list(db.evidence.items()):
if truth == 0: continue
_, predname, args = self.prac.mln.logic.parseLiteral(atom)
if predname in self.prac.roles.union(
['has_sense', 'action_core', 'achieved_by']):
finaldb << atom
# finaldb.write(sys.stdout, color=True)
g = Digraph(format='svg', engine='dot')
g.attr('node', shape='box', style='filled')
for res in finaldb.query('action_core(?w, ?a) ^ has_sense(?w, ?s)'):
actioncore = res['?a']
sense = res['?s']
predname = 'action_core'
g.node(actioncore, fillcolor='#bee280')
g.node(sense)
g.edge(actioncore, sense, label='is_a')
roles = self.prac.actioncores[actioncore].roles
for role in roles:
for res in db.query('{}(?w, {}) ^ has_sense(?w, ?s)'.format(
role, actioncore)):
sense = res['?s']
g.node(sense)
g.edge(actioncore, sense, label=role)
for res in finaldb.query('achieved_by(?a1, ?a2)'):
a1 = res['?a1']
a2 = res['?a2']
g.node(a1, fillcolor='#bee280')
g.node(a2, fillcolor='#bee280')
g.edge(a1, a2, label='achieved_by')
actioncore = a2
roles = self.prac.actionroles[actioncore].roles
for role in roles:
for res in db.query('{}(?w, {}) ^ has_sense(?w, ?s)'.format(
role, actioncore)):
sense = res['?s']
g.node(sense)
g.edge(actioncore, sense, label=role)
return render_gv(g, filename)
def main():
path = os.path.join('Vessel Traffic Data sub-areas April 2020',
'cts_sub-areas_04_2020_pt',
'cts_bass_st_04_2020_pt.shp')
df = gpd.read_file(path)
mln = MLN()
populatePredicates(mln)
populateFormulas(mln)
mln.write()
dbs = []
for i in range(100):
collection = df.sample(n=3)
lons = collection['LON'].tolist()
lats = collection['LAT'].tolist()
points = list(zip(lons, lats))
evidence = ThreePointEvidence(*points)
db = Database(mln)
evidence.generateEvidence(db)
dbs.append(db)
mln.learn(dbs)
def train(self, praclearning):
prac = praclearning.prac
# get all the relevant training databases
db_files = prac.training_dbs()
nl_module = prac.module('nl_parsing')
syntactic_preds = nl_module.mln.predicates
logger.debug(db_files)
dbs = [
x for x in [
Database(self.mln, dbfile=name, ignore_unknown_preds=True)
for name in db_files
] if type(x) is Database
]
logger.debug(dbs)
new_dbs = []
training_dbs = []
known_concepts = []
logger.debug(self.mln.domains)
for db in dbs:
if not 'actioncore' in db.domains: continue
if not 'concept' in db.domains: continue
for c in db.domains['concept']:
known_concepts.append(c)
new_dbs.append(db)
wordnet = prac.wordnet
for db in new_dbs:
new_db = db.duplicate()
for sol in db.query('has_sense(?w, ?s) ^ is_a(?s, ?c)'):
word = sol['?w']
sense = sol['?s']
concept = sol['?c']
synset = wordnet.synset(concept)
for known_concept in known_concepts:
known_synset = wordnet.synset(known_concept)
if known_synset is None or synset is None: sim = 0
else: sim = wordnet.wup_similarity(synset, known_synset)
new_db << ('is_a(%s,%s)' % (sense, known_concept), sim)
training_dbs.append(new_db)
logger.info('Starting training with %d databases'.format(
len(training_dbs)))
def inference(self, inference_query):
mln = MLN(mlnfile=self.mln_path, grammar='StandardGrammar')
db = Database(mln, dbfile=self.db_path)
for method in [
'EnumerationAsk'
# 'MC-SAT',
# 'WCSPInference',
# 'GibbsSampler'
]:
print('=== INFERENCE TEST:', method, '===')
result = query(queries=inference_query,
method=method,
mln=mln,
db=db,
verbose=True,
multicore=False).run()
print(result)
if result:
return result
else:
return []
def test_learning_smokers(arg='.', disp=False):
pth = os.path.join(arg, 'smoking.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
pth = os.path.join(arg, 'smoking-train.db')
db = Database(mln, dbfile=pth)
for method in ('BPLL', 'BPLL_CG', 'CLL'):
for multicore in (True, False):
print('=== LEARNING TEST:', method, '===')
if disp:
query(queries='Cancer,Smokes,Friends',
method=method,
mln=mln,
db=db,
verbose=disp,
multicore=multicore).run().write()
else:
learn(method=method,
mln=mln,
db=db,
verbose=disp,
multicore=multicore).run()
def test_inference_smokers(arg='.', disp=False):
pth = os.path.join(arg, 'wts.pybpll.smoking-train-smoking.mln')
mln = MLN(mlnfile=pth, grammar='StandardGrammar')
pth = os.path.join(arg, 'smoking-test-smaller.db')
db = Database(mln, dbfile=pth)
for method in ('EnumerationAsk', 'MC-SAT', 'WCSPInference',
'GibbsSampler'):
for multicore in (False, True):
print('=== INFERENCE TEST:', method, '===')
if disp:
query(queries='Cancer,Smokes,Friends',
method=method,
mln=mln,
db=db,
verbose=disp,
multicore=multicore).run().write()
else:
query(queries='Cancer,Smokes,Friends',
method=method,
mln=mln,
db=db,
verbose=disp,
multicore=multicore).run()