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from typing import Set
from owlapy.class_expression import OWLClassExpression
from ontolearn.abstracts import EncodedLearningProblem, AbstractScorer, AbstractKnowledgeBase
from ontolearn.search import EvaluatedConcept
[docs]
def f1(*, individuals: Set, pos: Set, neg: Set):
assert isinstance(individuals, set)
assert isinstance(pos, set)
assert isinstance(neg, set)
tp = len(pos.intersection(individuals))
tn = len(neg.difference(individuals))
fp = len(neg.intersection(individuals))
fn = len(pos.difference(individuals))
try:
recall = tp / (tp + fn)
except ZeroDivisionError:
return 0
try:
precision = tp / (tp + fp)
except ZeroDivisionError:
return 0
if precision == 0 or recall == 0:
return 0
f_1 = 2 * ((precision * recall) / (precision + recall))
return f_1
[docs]
def acc(*, individuals: Set, pos: Set, neg: Set):
assert isinstance(individuals, set)
assert isinstance(pos, set)
assert isinstance(neg, set)
tp = len(pos.intersection(individuals))
tn = len(neg.difference(individuals))
fp = len(neg.intersection(individuals))
fn = len(pos.difference(individuals))
return (tp + tn) / (tp + tn + fp + fn)
[docs]
def evaluate_concept(kb: AbstractKnowledgeBase, concept: OWLClassExpression, quality_func: AbstractScorer,
encoded_learning_problem: EncodedLearningProblem) -> EvaluatedConcept:
"""Evaluates a concept by using the encoded learning problem examples, in terms of Accuracy or F1-score.
Note:
This method is useful to tell the quality (e.q) of a generated concept by the concept learners, to get
the set of individuals (e.inds) that are classified by this concept and the amount of them (e.ic).
Args:
kb: The knowledge base where to evaluate the concept.
concept: The concept to be evaluated.
quality_func: Quality measurement in terms of Accuracy or F1-score.
encoded_learning_problem: The encoded learning problem.
Return:
The evaluated concept.
"""
e = EvaluatedConcept()
e.inds = kb.individuals_set(concept)
e.ic = len(e.inds)
_, e.q = quality_func.score_elp(e.inds, encoded_learning_problem)
return e