Bases: Solver
A solver that uses problog.
problog is a
probabilistic logic programming language.
Example:
>>> from typedlogic.integrations.frameworks.pydantic import FactBaseModel
>>> from typedlogic import SentenceGroup, PredicateDefinition, Forall, Variable
>>> solver = ProbLogSolver()
>>> solver.add_predicate_definition(PredicateDefinition(predicate="AncestorOf", arguments={'ancestor': 'str', 'descendant': 'str'}))
>>> x = Variable('x')
>>> y = Variable('y')
>>> z = Variable('z')
>>> tr_axiom = Forall([x, y, z], (Term('AncestorOf', x, y) & Term('AncestorOf', y, z)) >> Term('AncestorOf', x, z))
>>> solver.add_sentence(tr_axiom)
>>> solver.add_probabilistic_fact(Term('AncestorOf', 'p1', 'p1a'), 0.5)
>>> solver.add_probabilistic_fact(Term('AncestorOf', 'p1a', 'p1aa'), 0.5)
>>> model = solver.model()
>>> print(model.term_probabilities[Term('AncestorOf', 'p1', 'p1aa')])
0.25
Source code in src/typedlogic/integrations/solvers/problog/problog_solver.py
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93 | @dataclass
class ProbLogSolver(Solver):
"""
A solver that uses problog.
[problog](https://dtai.cs.kuleuven.be/problog/index.html) is a
probabilistic logic programming language.
Example:
>>> from typedlogic.integrations.frameworks.pydantic import FactBaseModel
>>> from typedlogic import SentenceGroup, PredicateDefinition, Forall, Variable
>>> solver = ProbLogSolver()
>>> solver.add_predicate_definition(PredicateDefinition(predicate="AncestorOf", arguments={'ancestor': 'str', 'descendant': 'str'}))
>>> x = Variable('x')
>>> y = Variable('y')
>>> z = Variable('z')
>>> tr_axiom = Forall([x, y, z], (Term('AncestorOf', x, y) & Term('AncestorOf', y, z)) >> Term('AncestorOf', x, z))
>>> solver.add_sentence(tr_axiom)
>>> solver.add_probabilistic_fact(Term('AncestorOf', 'p1', 'p1a'), 0.5)
>>> solver.add_probabilistic_fact(Term('AncestorOf', 'p1a', 'p1aa'), 0.5)
>>> model = solver.model()
>>> print(model.term_probabilities[Term('AncestorOf', 'p1', 'p1aa')])
0.25
"""
exec_name: str = field(default="problog")
profile: ClassVar[Profile] = MixedProfile(Probabilistic(), AllowsComparisonTerms(), MultipleModelSemantics())
def models(self) -> Iterator[ProbabilisticModel]:
compiler = ProbLogCompiler()
program = compiler.compile(self.base_theory)
p = PrologString(program)
ev = get_evaluatable()
result = ev.create_from(p).evaluate()
m = ProbabilisticModel()
for term, prob in result.items():
plt_term = compiler.decompile_term(term)
reified = Probability(prob, That(plt_term))
reified_as_term = reified.to_model_object()
if not isinstance(reified_as_term, Term):
raise ValueError(f"Expected a term, got {reified_as_term}")
m.ground_terms.append(reified_as_term)
m.term_probabilities[plt_term] = prob
yield m
def model(self) -> ProbabilisticModel:
models = list(self.models())
if len(models) == 0:
raise NotInProfileError("No models found")
if len(models) > 1:
raise NotInProfileError("Multiple models found")
return models[0]
def check(self) -> Solution:
models = list(self.models())
sat = len(models) > 0
return Solution(satisfiable=sat)
def dump(self) -> str:
compiler = ProbLogCompiler()
return compiler.compile(self.base_theory)
def add_probabilistic_fact(self, fact: Sentence, probability: float) -> None:
pr_sent = Probability(probability, That(fact))
self.add(pr_sent)
def add_evidence(self, fact: Sentence, truth_value: bool) -> None:
ev = Evidence(fact, truth_value)
self.add(ev)
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