def ground_problog_program(program):
""" Grounds a Problog program using the problog library. """
lf = LogicFormula.create_from(program,
avoid_name_clash=True,
keep_order=True,
label_all=True)
return lf.to_prolog()
def evaluate_custom_weights(self, eval_name=None):
class TestSemiringProbabilityNSP(SemiringProbability):
def is_nsp(self):
return True
program = """
0.25::a.
query(a).
"""
pl = PrologString(program)
lf = LogicFormula.create_from(pl,
label_all=True,
avoid_name_clash=True)
semiring = TestSemiringProbabilityNSP()
kc_class = get_evaluatable(name=eval_name, semiring=semiring)
kc = kc_class.create_from(lf)
a = Term('a')
# without custom weights
results = kc.evaluate(semiring=semiring)
self.assertEqual(0.25, results[a])
# with custom weights
weights = {a: 0.1}
results = kc.evaluate(semiring=semiring, weights=weights)
self.assertEqual(0.1, results[a])
def main_mpe_semiring(args):
inputfile = args.inputfile
init_logger(args.verbose)
if args.web:
result_handler = print_result_json
else:
result_handler = print_result
if args.output is not None:
outf = open(args.output, 'w')
else:
outf = sys.stdout
with Timer("Total"):
try:
pl = PrologFile(inputfile)
lf = LogicFormula.create_from(model, label_all=True)
prob, facts = mpe_semiring(lf, args.verbose)
result_handler((True, (prob, facts)), outf)
except Exception as err:
trace = traceback.format_exc()
err.trace = trace
result_handler((False, err), outf)
def find_all_prob():
ps = ""
with open("prolog/problog_predicates.pl", "r") as f:
for line in f:
ps += line
# Calcolo probabilità tramite problog
ps += "query(infect(_))."
p = PrologString(ps)
dbp = engine.prepare(p)
lf = LogicFormula.create_from(p) # ground the program
dag = LogicDAG.create_from(lf) # break cycles in the ground program
cnf = CNF.create_from(dag) # convert to CNF
ddnnf = DDNNF.create_from(cnf) # compile CNF to ddnnf
r = ddnnf.evaluate()
# Siccome Problog restituisce un dizionario struttrato in questa maniera:
# {query(infect(2)): 0.67, query(infect(3)): 0.8, ...}
# Bisogna estrarre ogni id dalla chiave nel seguente modo
items = []
if len(RedNode.query.all()) > 0:
for key, value in r.items():
start = "infect("
end = ")"
result = str(key)[len(start):-len(end)]
try:
u = User.query.get(int(result))
items.append((u, value))
except ValueError:
continue
return items
def call_theorem_prover(theorem_prover,
instance_id,
question_id,
theory,
assertion,
gold_label,
print_log=True):
"""Function that takes a single theory/assertion example and runs it through the theorem prover
to obtain a label. Returns the obtained label, elapsed time to solve it, and exception returned
by the engine, if any.
"""
obtained_result = False
millisecs_elapsed = 0
if print_log: print("=======ORIGINAL THEORY=========")
theory_as_txt = theory.program(theorem_prover)
if print_log: print(theory_as_txt)
theory.preprocess(theorem_prover)
theory_as_txt = theory.program(theorem_prover)
if theorem_prover == "problog":
assertion_lf = assertion.logical_form(theorem_prover, False)
assertion_lf = f"query({assertion_lf})."
program = f"{theory_as_txt}\n{assertion_lf}"
if print_log:
print("=======PROGRAM FROM PREPROCESSED THEORY=========")
print(program)
print("=======EXPECTED LABEL=========")
print(f" {gold_label}")
start_millisecs = current_milli_time()
try:
lf = LogicFormula.create_from(program) # ground the program
dag = LogicDAG.create_from(
lf) # break cycles in the ground program
sdd = SDD.create_from(dag)
result = sdd.evaluate()
end_millisecs = current_milli_time()
elapsed_millisecs = end_millisecs - start_millisecs
result_tuples = [(k, v) for k, v in result.items()]
obtained_result = result_tuples[0][1] != float(0)
return obtained_result, elapsed_millisecs, None
except (NegativeCycle, NonGroundProbabilisticClause,
UnknownClause) as e:
end_millisecs = current_milli_time()
elapsed_millisecs = end_millisecs - start_millisecs
if print_log:
print(
f"!!!Encountered Exception at instance id {instance_id}, question id {question_id}: {e}"
)
exception_name = str(type(e)).lstrip("<class '").rstrip("'>")
return None, elapsed_millisecs, exception_name
return obtained_result, elapsed_millisecs, None
def ad_atom_duplicate(self, eval_name=None):
"""
This test must pickup the case where during the transformation, additional _extra atoms are created because
add_atom(..., cr_extra=True) is used instead of cr_extra=False.
"""
program = """
0.2::a ; 0.8::b.
query(a).
query(b).
"""
pl = PrologString(program)
lf = LogicFormula.create_from(pl, label_all=True, avoid_name_clash=True)
semiring = SemiringProbability()
kc_class = get_evaluatable(name=eval_name, semiring=semiring)
kc = kc_class.create_from(lf) # type: LogicFormula
self.assertEqual(3, kc.atomcount)
def run_theory_in_problog(theory, assertion):
"""Run the given theory and assertion through ProbLog engine to obtain a True/False label.
If an exception is encountered, return None so that this example will not be part of output."""
theorem_prover = "problog"
try:
program = theory.program(theorem_prover, assertion)
lf = LogicFormula.create_from(program) # ground the program
dag = LogicDAG.create_from(lf) # break cycles in the ground program
sdd = SDD.create_from(dag)
result = sdd.evaluate()
result_tuples = [(k, v) for k, v in result.items()]
if len(result_tuples) == 0:
return False
return result_tuples[0][1] != 0.0
except (NegativeCycle, NonGroundProbabilisticClause, UnknownClause) as e:
return None
return None
def find_user_prob(uid):
ps = ""
with open("prolog/problog_predicates.pl", "r") as f:
for line in f:
ps += line
# Pulizia dei nodi dinamici date/1 all'interno di problog
p = PrologString(ps)
dbp = engine.prepare(p)
query = Term("clean")
res = engine.query(dbp, query)
# Calcolo probabilità tramite problog
ps += "query(infect(" + str(uid) + "))."
p = PrologString(ps)
dbp = engine.prepare(p)
lf = LogicFormula.create_from(p) # ground the program
dag = LogicDAG.create_from(lf) # break cycles in the ground program
cnf = CNF.create_from(dag) # convert to CNF
ddnnf = DDNNF.create_from(cnf) # compile CNF to ddnnf
r = ddnnf.evaluate()
# Salvataggio nel database SQLite della data del nodo rosso più vecchio con cui è stato a contatto
term = Term("date", None)
database = problog_export.database # Database interno di Problog dove vengono salvati i fatti con assertz()
node_key = database.find(term)
if node_key is not None:
node = database.get_node(node_key)
dates = node.children.find(
term.args) # Tutti i fatti date/1 inseriti con assertz/1
vals = []
if dates:
for date in dates:
n = database.get_node(date)
vals.append(int(n.args[0]))
min_val = min(vals) # Trova la data (in millisecondi) minima
u = User.query.get(uid)
u.oldest_risk_date = min_val
db.session.commit()
return r
def main():
p = PrologString("""
increaseOsteoblasts :- calcium.
0.5::\+increaseOsteoblasts :- calcium, bispho.
reduceOsteoclasts :- bispho.
1.0::\+reduceOsteoclasts :- calcium , bispho.
osteoprosis :- initialOsteoprosis.
0.85::\+osteoprosis :- reduceOsteoclasts. % Bisphosphonates
0.15::\+osteoprosis :- increaseOsteoblasts. % Calcium
% Prior probabilities
0.5::calcium. 0.5::bispho. 0.5::initialOsteoprosis.
% Query probability of effect
evidence(initialOsteoprosis, true).
evidence(calcium, true).
evidence(bispho, false).
query(osteoprosis).
""")
#1.3: Create the CNF of the problog
lf = LogicFormula.create_from(p,avoid_name_clash=True, keep_order=True, label_all=True) # ground the program
print("Ground program")
print(LogicFormula.to_prolog(lf))
dag = LogicDAG.create_from(lf,avoid_name_clash=True, keep_order=True, label_all=True) # break cycles in the ground program
cnf = CNF.create_from(dag) # convert to CNF
print(CNF.to_dimacs(cnf))
ddnnf = DDNNF.create_from(cnf) # compile CNF to ddnnf
test = DDNNF.get_weights(ddnnf)
print(test)
print(ddnnf.evaluate())
#3.1: Create 4 interpretations
print("--Create 4 interpretations--")
interpretations = create_interpretations(p_without_evidence, 4)
for i in interpretations: print(i)
#3.2: Create 100, 1000, 10000 interpretations and estimate p_n
print("--Estimate parameters--")
estimate_parameters(100)
estimate_parameters(1000)
estimate_parameters(10000)
#
p2 = PrologString("""
person(a).
person(b).
person(c).
0.2::stress(X) :- person(X).
0.1::friends(X,Y) :- person(X), person(Y).
0.3::smokes(X) :- stress(X).
0.4::smokes(X) :- friends(X,Y), smokes(Y).
evidence(friends(a,b), true).
evidence(friends(a,c), true).
query(smokes(a)).
""")
lf2 = LogicFormula.create_from(p2,
avoid_name_clash=True,
keep_order=True,
label_all=True)
# print(LogicFormula.to_prolog(lf2))
dag2 = LogicDAG.create_from(lf2,
avoid_name_clash=False,
keep_order=True,
label_all=True)
# # print(dag2)
# # print(LogicFormula.to_prolog(dag2))
cnf2 = CNF.create_from(dag2)
# # print(cnf2.to_dimacs(weighted=True, invert_weights=True))
ddnnf2 = DDNNF.create_from(cnf2)
#print(ddnnf2.evaluate())
#
# import PyBool_public_interface as Bool
m = model.read()
times = []
door_num = range(3, 10)
for i in door_num:
start = timeit.default_timer()
model = m.format(door_num=i)
p = PrologString(model)
formula = get_evaluatable().create_from(p)
print(formula.evaluate())
stop = timeit.default_timer()
times.append(stop - start)
for i in door_num:
model = m.format(door_num=i)
p = PrologString(model)
lf = LogicFormula.create_from(p)
lfs.append(lf)
dag = LogicDAG.create_from(lf)
dags.append(dag)
cnf = CNF.create_from(dag)
cnfs.append(cnf)
for i in door_num:
model = m.format(door_num=i)
p = PrologString(model)
lf = LogicFormula.create_from(p)
lfs.append(lf)
dag = LogicDAG.create_from(lf)
dags.append(dag)
cnf = CNF.create_from(dag)
cnfs.append(cnf)
from problog.program import PrologString
from problog.formula import LogicFormula, LogicDAG
from problog.logic import Term
from problog.ddnnf_formula import DDNNF
from problog.cnf_formula import CNF
p = PrologString("""
coin(c1). coin(c2).
0.4::heads(C); 0.6::tails(C) :- coin(C).
win :- heads(C).
evidence(heads(c1), false).
query(win).
query(coin(X)).
""")
lf = LogicFormula.create_from(p) # ground the program
dag = LogicDAG.create_from(lf) # break cycles in the ground program
cnf = CNF.create_from(dag) # convert to CNF
ddnnf = DDNNF.create_from(cnf) # compile CNF to ddnnf
results = ddnnf.evaluate()
print(results)
def evaluate_custom_weights(self, eval_name=None):
class TestSemiringProbabilityNSP(SemiringProbability):
def is_nsp(self):
return True
def pos_value(self, a, key=None):
if isinstance(a, tuple):
return float(a[0])
else:
return float(a)
def neg_value(self, a, key=None):
if isinstance(a, tuple):
return float(a[1])
else:
return 1 - float(a)
program = """
0.25::a.
query(a).
"""
pl = PrologString(program)
lf = LogicFormula.create_from(pl,
label_all=True,
avoid_name_clash=True)
semiring = TestSemiringProbabilityNSP()
kc_class = get_evaluatable(name=eval_name, semiring=semiring)
kc = kc_class.create_from(lf)
a = Term('a')
# without custom weights
results = kc.evaluate(semiring=semiring)
self.assertEqual(0.25, results[a])
# with custom weights
weights = {a: 0.1}
results = kc.evaluate(semiring=semiring, weights=weights)
self.assertEqual(0.1, results[a])
# with custom weights
weights = {a: (0.1, 0.1)}
results = kc.evaluate(semiring=semiring, weights=weights)
self.assertEqual(0.5, results[a])
# with custom weights based on index
weights = {kc.get_node_by_name(a): 0.2}
results = kc.evaluate(semiring=semiring, weights=weights)
self.assertEqual(0.2, results[a])
# Testing with weight on node 0 (True)
weights = {0: 0.3, a: (0.1, 0.1)}
results = kc.evaluate(semiring=semiring, weights=weights)
self.assertEqual(0.5, results[a])
# Testing query on node 0 (True)
class TestSemiringProbabilityIgnoreNormalize(SemiringProbabilityNSPCopy
):
def normalize(self, a, z):
return a
weights = {0: (0.3, 0.7), a: (0.1, 0.1)}
results = kc.evaluate(
index=0,
semiring=TestSemiringProbabilityIgnoreNormalize(),
weights=weights)
self.assertEqual(0.06, results)
def main():
program = PrologFile('model.txt')
formula = LogicFormula.create_from(program)
modelEvaluatable = get_evaluatable().create_from(formula)
print modelEvaluatable.evaluate()
return new_weights, iters
# Read in the progam with tunable parameters
#ground_progam = file_to_string("test_learn.pl")
# Assign to each tunable parameter a random initialization value
#ground_progam_tunable = init_tunable(ground_progam)
# Write back the prolog file with random initialized tunable probabilities
#f = open('test_learn_tunable.pl', 'w')
#f.write(ground_progam_tunable)
pf = PrologFile("test_learn_tunable.pl")
formula = LogicFormula.create_from(pf)
sdd = SDD.create_from(formula)
queries = [
Term('stress', Term('a')),
Term('stress', Term('b')),
Term('stress', Term('c')),
Term('smokes', Term('a')),
Term('smokes', Term('b')),
Term('smokes', Term('c'))
]
# File which holds all the evidence examples
examples = file_to_string('data.pl')
# Retrieve the amount of interpretations that has been specified
# 0.4::b.
# 0.3::a :- b.
# 0.5::b :- a.
# query(a).
# evidence(b).
# """)
p = PrologString("""
0.4::heads(1). 0.7::heads(2). 0.5::heads(3).
win :- heads(1).
win :- heads(2), heads(3).
query(win).
""")
lf = LogicFormula.create_from(p,
avoid_name_clash=True,
keep_order=True,
label_all=True) # ground the program
# print(lf)
# print(LogicFormula.to_prolog(lf))
dag = LogicDAG.create_from(
lf, avoid_name_clash=True, keep_order=True,
label_all=True) # break cycles in the ground program
# print(dag)
# print(LogicFormula.to_prolog(dag))
cnf = CNF.create_from(dag) # convert to CNF
# for clause in cnf._clauses:
# print(clause)
ddnnf = DDNNF.create_from(cnf) # compile CNF to ddnnf
# Outcome for the a/b thing with query(a) is 0,2+(0,8*0,3*0,4)
# but if evidence(b) : (0,2*0,4+0,2*0,5*0,6+0,4*0,3*0,8) / (0,4+0,6*0,5*0,2) [Formula for conditional probability P(a|b) = ...]
# For coin thing : 1-0,6*0,3*0,5-0,6*0,3*0,5-0,6*0,7*0,5 = 0,61
