40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132 | @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())
problog_args: Optional[Dict[str, Any]] = None
def models(self, **kwargs) -> Iterator[ProbabilisticModel]:
compiler = ProbLogCompiler()
program = compiler.compile(self.base_theory)
p = PrologString(program)
ev = get_evaluatable()
for k, v in DEFAULT_PROBLOG_ARGS.items():
if k not in kwargs:
kwargs[k] = v
if self.problog_args:
for k, v in self.problog_args.items():
if k not in kwargs:
kwargs[k] = v
try:
result = ev.create_from(p, **kwargs).evaluate()
except InconsistentEvidenceError as e:
raise UnsatisfiableEvidenceError(
"ProbLog evidence is inconsistent; no probabilistic model can satisfy the asserted evidence. "
f"ProbLog reported: {e}"
) from e
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 UnsatisfiableEvidenceError(
"ProbLog produced no models; the evidence may be unsatisfiable for this probabilistic theory."
)
if len(models) > 1:
raise AmbiguousModelError(
"ProbLog produced multiple models; ProbLogSolver.model() expects exactly one model. "
"Use ProbLogSolver.models() to inspect all models."
)
return models[0]
def check(self) -> Solution:
try:
models = list(self.models())
except UnsatisfiableEvidenceError:
return Solution(satisfiable=False)
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)
|
