def test_inference_taxonomies():
p = '$PRACMLN_HOME/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 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 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 test_inference_smokers():
p = '$PRACMLN_HOME/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 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 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 choose_action(self, state, valid_action):
state_list = []
for item in state:
if item.predicate.name == "empty":
state_list.append("Empty({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Mine({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Opponent({0},{1})".format(item.terms[0], item.terms[1]))
elif item.predicate.name == "mine":
state_list.append("Mine({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Empty({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Opponent({0},{1})".format(item.terms[0], item.terms[1]))
elif item.predicate.name == "opponent":
state_list.append("Opponent({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Mine({0},{1})".format(item.terms[0], item.terms[1]))
state_list.append("!Empty({0},{1})".format(item.terms[0], item.terms[1]))
self.state_list = state_list
if random.random() < self.EPSILON:
# act = "Place{0}".format(valid_action[random.randint(0, len(valid_action) - 1)])
act = self.action_list[random.randint(0, 2)][random.randint(0, 2)]
print("random choice")
else:
# state_list = [state_list[:20], state_list[20:]]
data, self.mln = self.model_config(self.predicate, self.formula, state_list, 'TicTacToe.mln', 'TicTacToe.db')
reults = query(queries='Place(x,y)', method='MC-SAT', mln=self.mln, db=data, verbose=False, multicore=True).run().results
probs = np.array(list(reults.values()))
acts = np.array(list(reults.keys()))
act = acts[probs.argmax(-1)]
original_act = act
act = act.replace(" ", "")
# if (int(act[-4]), int(act[-2])) not in valid_action:
# act = "Place{0}".format(valid_action[random.randint(0, len(valid_action) - 1)])
print("valid action:", valid_action)
print("act:", act)
return (int(act[-4]), int(act[-2])), str(act)
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 inference_str(string, result, data, mln):
print(
query(queries=string,
method='EnumerationAsk',
mln=mln,
db=data,
verbose=True,
multicore=False,
save=True,
output_filename=
r'learnt.dbpll_cg.student-new-train-student-new-2.mln').run(
).results)
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()
def inference(path, result, data, mln): #推理查询未知的命题
query_list = []
base_path = os.getcwd()
file = open(base_path + '\\' + path, 'r', encoding='utf8')
query_list = file.read()
query_list = query_list.split('\n')
query_list = [x for x in query_list if x != '']
for i in query_list:
print(
query(queries=i,
method='MC-SAT',
mln=mln,
db=data,
verbose=False,
multicore=True).run().results)
def inference(self, path,data, mln):
"""
Returns the queries and probabilities
--Inputs--
path: .txt file containing the Queries
result: the output object from the learned mln
data: the inference data object
mln: the mln object
"""
query_list = []
base_path = os.getcwd()
file = open(base_path + '/' + path,'r',encoding = 'ascii')
query_list = file.read()
query_list = query_list.split('\n')
query_list = [x for x in query_list if x !='']
for i in query_list:
print(query(queries=i.encode("ascii"), method='GibbsSampler', mln=mln, db=data, verbose=False, multicore=True).run().results) # check to change the method
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 []
