# -----------------------------------------------------------------------------
# MIT License
#
# Copyright (c) 2024 Ontolearn Team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# -----------------------------------------------------------------------------
from typing import Set
from owlapy.class_expression import OWLClassExpression
from ontolearn.abstracts import EncodedLearningProblem, AbstractScorer, AbstractKnowledgeBase
from ontolearn.metrics import F1 as _F1, Accuracy as _Accuracy
from ontolearn.search import EvaluatedConcept
def _tp_tn_fp_fn(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, fp, fn
[docs]
def f1(*, individuals: Set, pos: Set, neg: Set):
tp, tn, fp, fn = _tp_tn_fp_fn(individuals, pos, neg)
applicable, score = _F1().score2(tp=tp, fn=fn, fp=fp, tn=tn)
return score if applicable else 0
[docs]
def acc(*, individuals: Set, pos: Set, neg: Set):
tp, tn, fp, fn = _tp_tn_fp_fn(individuals, pos, neg)
_, score = _Accuracy().score2(tp=tp, fn=fn, fp=fp, tn=tn)
return score
[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