KnowledgeBase changed to AbstractKnowledgeBase where applicable

ontolearn/base_concept_learner.py

@@ -47,7 +47,7 @@
 from owlapy.owl_ontology_manager import OntologyManager
 from owlapy.render import DLSyntaxObjectRenderer
 from .abstracts import BaseRefinement, AbstractScorer, AbstractHeuristic, \
-    AbstractConceptNode, AbstractLearningProblem
+    AbstractConceptNode, AbstractLearningProblem, AbstractKnowledgeBase
 from .utils import oplogging
 
 _N = TypeVar('_N', bound=AbstractConceptNode)  #:
@@ -81,7 +81,7 @@ class BaseConceptLearner(metaclass=ABCMeta):
           ∀  H \\in \\hypotheses: { (K \\wedge H \\models E^+) \\wedge  \\neg( K \\wedge H \\models E^-) }.
 
     Attributes:
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         quality_func (AbstractScorer) The quality function to be used.
         max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested.
         terminate_on_goal (bool): Whether to stop the algorithm if a perfect solution is found.
@@ -96,7 +96,7 @@ class BaseConceptLearner(metaclass=ABCMeta):
 
     name: ClassVar[str]
 
-    kb: KnowledgeBase
+    kb: AbstractKnowledgeBase
     quality_func: Optional[AbstractScorer]
     max_num_of_concepts_tested: Optional[int]
     terminate_on_goal: Optional[bool]
@@ -107,7 +107,7 @@ class BaseConceptLearner(metaclass=ABCMeta):
 
     @abstractmethod
     def __init__(self,
-                 knowledge_base: KnowledgeBase,
+                 knowledge_base: AbstractKnowledgeBase,
                  reasoner: Optional[AbstractOWLReasoner] = None,
                  quality_func: Optional[AbstractScorer] = None,
                  max_num_of_concepts_tested: Optional[int] = None,
@@ -405,7 +405,7 @@ class RefinementBasedConceptLearner(BaseConceptLearner):
     Base class for refinement based Concept Learning approaches.
 
     Attributes:
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         quality_func (AbstractScorer) The quality function to be used.
         max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested.
         terminate_on_goal (bool): Whether to stop the algorithm if a perfect solution is found.
@@ -432,7 +432,7 @@ class RefinementBasedConceptLearner(BaseConceptLearner):
 
     @abstractmethod
     def __init__(self,
-                 knowledge_base: KnowledgeBase,
+                 knowledge_base: AbstractKnowledgeBase,
                  reasoner: Optional[AbstractOWLReasoner] = None,
                  refinement_operator: Optional[BaseRefinement] = None,
                  heuristic_func: Optional[AbstractHeuristic] = None,

ontolearn/concept_learner.py

@@ -51,8 +51,9 @@
 from owlapy.utils import EvaluatedDescriptionSet, ConceptOperandSorter
 from owlapy.parser import DLSyntaxParser
 
-from ontolearn.knowledge_base import KnowledgeBase
-from ontolearn.abstracts import AbstractFitness, AbstractScorer, BaseRefinement, AbstractHeuristic
+from ontolearn.concept_generator import ConceptGenerator
+from ontolearn.abstracts import AbstractFitness, AbstractScorer, BaseRefinement, AbstractHeuristic, \
+    AbstractKnowledgeBase
 from ontolearn.base_concept_learner import BaseConceptLearner
 from ontolearn.data_struct import NCESDataLoader, NCESDataLoaderInference, CLIPDataLoader, CLIPDataLoaderInference
 from ontolearn.ea_algorithms import AbstractEvolutionaryAlgorithm, EASimple
@@ -89,7 +90,7 @@ class EvoLearner(BaseConceptLearner):
         fitness_func (AbstractFitness): Fitness function.
         height_limit (int): The maximum value allowed for the height of the Crossover and Mutation operations.
         init_method (AbstractEAInitialization): The evolutionary algorithm initialization method.
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         max_num_of_concepts_tested (int): Limit to stop the algorithm after n concepts tested.
         max_runtime (int): max_runtime: Limit to stop the algorithm after n seconds.
         mut_uniform_gen (AbstractEAInitialization): The initialization method to create the tree for mutation operation.
@@ -117,11 +118,11 @@ class EvoLearner(BaseConceptLearner):
     __slots__ = 'fitness_func', 'init_method', 'algorithm', 'value_splitter', 'tournament_size', \
         'population_size', 'num_generations', 'height_limit', 'use_data_properties', 'pset', 'toolbox', \
         '_learning_problem', '_result_population', 'mut_uniform_gen', '_dp_to_prim_type', '_dp_splits', \
-        '_split_properties', '_cache', 'use_card_restrictions', 'card_limit', 'use_inverse', 'total_fits'
+        '_split_properties', '_cache', 'use_card_restrictions', 'card_limit', 'use_inverse', 'total_fits', 'generator'
 
     name = 'evolearner'
 
-    kb: KnowledgeBase
+    kb: AbstractKnowledgeBase
     fitness_func: AbstractFitness
     init_method: AbstractEAInitialization
     algorithm: AbstractEvolutionaryAlgorithm
@@ -135,6 +136,7 @@ class EvoLearner(BaseConceptLearner):
     population_size: int
     num_generations: int
     height_limit: int
+    generator: ConceptGenerator
 
     pset: gp.PrimitiveSetTyped
     toolbox: base.Toolbox
@@ -146,7 +148,7 @@ class EvoLearner(BaseConceptLearner):
     _cache: Dict[str, Tuple[float, float]]
 
     def __init__(self,
-                 knowledge_base: KnowledgeBase,
+                 knowledge_base: AbstractKnowledgeBase,
                  reasoner: Optional[AbstractOWLReasoner] = None,
                  quality_func: Optional[AbstractScorer] = None,
                  fitness_func: Optional[AbstractFitness] = None,
@@ -174,7 +176,7 @@ def __init__(self,
                                 Defaults to 17.
             init_method (AbstractEAInitialization): The evolutionary algorithm initialization method. Defaults
                                                     to EARandomWalkInitialization.
-            knowledge_base (KnowledgeBase): The knowledge base that the concept learner is using.
+            knowledge_base (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
             max_runtime (int): max_runtime: Limit to stop the algorithm after n seconds. Defaults to 5.
             mut_uniform_gen (AbstractEAInitialization): The initialization method to create the tree for mutation
                                                         operation. Defaults to
@@ -217,6 +219,7 @@ def __init__(self,
         self.num_generations = num_generations
         self.height_limit = height_limit
         self.total_fits = 0
+        self.generator = ConceptGenerator()
         self.__setup()
 
     def __setup(self):
@@ -251,7 +254,7 @@ def __build_primitive_set(self) -> gp.PrimitiveSetTyped:
         intersection = factory.create_intersection()
 
         pset = gp.PrimitiveSetTyped("concept_tree", [], OWLClassExpression)
-        pset.addPrimitive(self.kb.generator.negation, [OWLClassExpression], OWLClassExpression,
+        pset.addPrimitive(self.generator.negation, [OWLClassExpression], OWLClassExpression,
                           name=OperatorVocabulary.NEGATION)
         pset.addPrimitive(union, [OWLClassExpression, OWLClassExpression], OWLClassExpression,
                           name=OperatorVocabulary.UNION)
@@ -323,10 +326,10 @@ class Bool(object):
         for class_ in self.kb.get_concepts():
             pset.addTerminal(class_, OWLClassExpression, name=escape(class_.iri.get_remainder()))
 
-        pset.addTerminal(self.kb.generator.thing, OWLClassExpression,
-                         name=escape(self.kb.generator.thing.iri.get_remainder()))
-        pset.addTerminal(self.kb.generator.nothing, OWLClassExpression,
-                         name=escape(self.kb.generator.nothing.iri.get_remainder()))
+        pset.addTerminal(self.generator.thing, OWLClassExpression,
+                         name=escape(self.generator.thing.iri.get_remainder()))
+        pset.addTerminal(self.generator.nothing, OWLClassExpression,
+                         name=escape(self.generator.nothing.iri.get_remainder()))
         return pset
 
     def __build_toolbox(self) -> base.Toolbox:
@@ -527,7 +530,7 @@ class CLIP(CELOE):
         heuristic_func (AbstractHeuristic): Function to guide the search heuristic.
         heuristic_queue (SortedSet[OENode]): A sorted set that compares the nodes based on Heuristic.
         iter_bound (int): Limit to stop the algorithm after n refinement steps are done.
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         max_child_length (int): Limit the length of concepts generated by the refinement operator.
         max_he (int): Maximal value of horizontal expansion.
         max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested.
@@ -553,7 +556,7 @@ class CLIP(CELOE):
     name = 'clip'
 
     def __init__(self,
-                 knowledge_base: KnowledgeBase,
+                 knowledge_base: AbstractKnowledgeBase,
                  knowledge_base_path='',
                  reasoner: Optional[AbstractOWLReasoner] = None,
                  refinement_operator: Optional[BaseRefinement[OENode]] = ExpressRefinement,

ontolearn/ea_initialization.py

@@ -34,8 +34,8 @@
 from owlapy.owl_literal import OWLLiteral
 from owlapy.owl_property import OWLDataProperty, OWLObjectProperty
 
+from ontolearn.abstracts import AbstractKnowledgeBase
 from ontolearn.ea_utils import OperatorVocabulary, Tree, escape, owlliteral_to_primitive_string
-from ontolearn.knowledge_base import KnowledgeBase
 import random
 from abc import ABCMeta, abstractmethod
 from typing import Any, Callable, Dict, Final, List, Set, Union
@@ -153,7 +153,7 @@ class EARandomWalkInitialization(AbstractEAInitialization):
     type_counts: Dict[OWLClass, int]
     dp_to_prim_type: Dict[OWLDataProperty, Any]
     dp_splits: Dict[OWLDataProperty, List[OWLLiteral]]
-    kb: KnowledgeBase
+    kb: AbstractKnowledgeBase
 
     def __init__(self, max_t: int = 2, jump_pr: float = 0.5):
         """
@@ -175,7 +175,7 @@ def get_population(self, container: Callable,
                        pos: List[OWLNamedIndividual] = None,
                        dp_to_prim_type: Dict[OWLDataProperty, Any] = None,
                        dp_splits: Dict[OWLDataProperty, List[OWLLiteral]] = None,
-                       kb: KnowledgeBase = None) -> List[Tree]:
+                       kb: AbstractKnowledgeBase = None) -> List[Tree]:
         assert pos is not None
         assert kb is not None
         assert dp_to_prim_type is not None

ontolearn/executor.py

@@ -47,7 +47,7 @@
 from ontolearn.learning_problem import PosNegLPStandard
 from ontolearn.refinement_operators import ModifiedCELOERefinement
 from ontolearn.metrics import Accuracy, F1, Recall, Precision, WeightedAccuracy
-from ontolearn.triple_store import TripleStoreKnowledgeBase
+from ontolearn.triple_store import TripleStore
 from ontolearn.value_splitter import BinningValueSplitter, EntropyValueSplitter
 
 logger = logging.getLogger(__name__)
@@ -200,7 +200,7 @@ def execute(args): # pragma: no cover
     learner_type = models[args.model]
     optargs = {}
     if args.sparql_endpoint:
-        kb = TripleStoreKnowledgeBase(args.sparql_endpoint)
+        kb = TripleStore(args.sparql_endpoint)
     else:
         kb = KnowledgeBase(path=args.knowledge_base_path)
 

ontolearn/learners/celoe.py

@@ -1,7 +1,7 @@
 from ..base_concept_learner import RefinementBasedConceptLearner
-from ..knowledge_base import KnowledgeBase
 
-from ..abstracts import AbstractScorer, BaseRefinement, AbstractHeuristic, EncodedPosNegLPStandardKind
+from ..abstracts import AbstractScorer, BaseRefinement, AbstractHeuristic, EncodedPosNegLPStandardKind, \
+    AbstractKnowledgeBase
 from ..learning_problem import PosNegLPStandard
 from ..quality_funcs import evaluate_concept
 from ..search import OENode, TreeNode, EvaluatedConcept, HeuristicOrderedNode, QualityOrderedNode, LengthOrderedNode
@@ -22,6 +22,7 @@
 
 _concept_operand_sorter = ConceptOperandSorter()
 
+
 class CELOE(RefinementBasedConceptLearner):
     """Class Expression Learning for Ontology Engineering.
     Attributes:
@@ -31,7 +32,7 @@ class CELOE(RefinementBasedConceptLearner):
         heuristic_func (AbstractHeuristic): Function to guide the search heuristic.
         heuristic_queue (SortedSet[OENode]): A sorted set that compares the nodes based on Heuristic.
         iter_bound (int): Limit to stop the algorithm after n refinement steps are done.
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         max_child_length (int): Limit the length of concepts generated by the refinement operator.
         max_he (int): Maximal value of horizontal expansion.
         max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested.
@@ -55,7 +56,7 @@ class CELOE(RefinementBasedConceptLearner):
     name = 'celoe_python'
 
     def __init__(self,
-                 knowledge_base: KnowledgeBase=None,
+                 knowledge_base: AbstractKnowledgeBase = None,
                  reasoner: Optional[owlapy.abstracts.AbstractOWLReasoner] = None,
                  refinement_operator: Optional[BaseRefinement[OENode]] = None,
                  quality_func: Optional[AbstractScorer] = None,
@@ -78,7 +79,7 @@ def __init__(self,
                                                         Defaults to `ModifiedCELOERefinement`.
             heuristic_func (AbstractHeuristic): Function to guide the search heuristic. Defaults to `CELOEHeuristic`.
             iter_bound (int): Limit to stop the algorithm after n refinement steps are done. Defaults to 10'000.
-            knowledge_base (KnowledgeBase): The knowledge base that the concept learner is using.
+            knowledge_base (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
             max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested. Defaults to 10'000.
             max_runtime (int): Limit to stop the algorithm after n seconds. Defaults to 5.
             max_results (int): Maximum hypothesis to store. Defaults to 10.
@@ -124,7 +125,6 @@ def next_node_to_expand(self, step: int) -> OENode:  # pragma: no cover
             # from reimplementation, pick without quality criterion
             return self.heuristic_queue[-1]
 
-
     def best_hypotheses(self, n: int = 1, return_node: bool = False) -> Union[
         OWLClassExpression | Iterable[OWLClassExpression],
         OENode | Iterable[OENode]]:

ontolearn/learners/drill.py

@@ -28,7 +28,7 @@
 from owlapy import owl_expression_to_dl
 from ontolearn.base_concept_learner import RefinementBasedConceptLearner
 from ontolearn.refinement_operators import LengthBasedRefinement
-from ontolearn.abstracts import AbstractNode
+from ontolearn.abstracts import AbstractNode, AbstractKnowledgeBase
 from ontolearn.search import RL_State
 from typing import Set, List, Tuple, Optional, Generator, SupportsFloat, Iterable, FrozenSet, Callable, Union
 from ontolearn.learning_problem import PosNegLPStandard
@@ -56,7 +56,7 @@
 class Drill(RefinementBasedConceptLearner):  # pragma: no cover
     """ Neuro-Symbolic Class Expression Learning (https://www.ijcai.org/proceedings/2023/0403.pdf)"""
 
-    def __init__(self, knowledge_base,
+    def __init__(self, knowledge_base: AbstractKnowledgeBase,
                  path_embeddings: str = None,
                  refinement_operator: LengthBasedRefinement = None,
                  use_inverse: bool = True,

ontolearn/learners/ocel.py

@@ -2,7 +2,7 @@
 from ..knowledge_base import KnowledgeBase
 from typing import Optional
 import owlapy
-from ..abstracts import AbstractScorer, BaseRefinement, AbstractHeuristic
+from ..abstracts import AbstractScorer, BaseRefinement, AbstractHeuristic, AbstractKnowledgeBase
 from ..search import OENode, LBLNode
 from owlapy.class_expression import OWLClassExpression
 from ..heuristics import OCELHeuristic
@@ -19,7 +19,7 @@ class OCEL(CELOE):
         heuristic_func (AbstractHeuristic): Function to guide the search heuristic.
         heuristic_queue (SortedSet[OENode]): A sorted set that compares the nodes based on Heuristic.
         iter_bound (int): Limit to stop the algorithm after n refinement steps are done.
-        kb (KnowledgeBase): The knowledge base that the concept learner is using.
+        kb (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
         max_child_length (int): Limit the length of concepts generated by the refinement operator.
         max_he (int): Maximal value of horizontal expansion.
         max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested.
@@ -41,7 +41,7 @@ class OCEL(CELOE):
     name = 'ocel_python'
 
     def __init__(self,
-                 knowledge_base: KnowledgeBase,
+                 knowledge_base: AbstractKnowledgeBase,
                  reasoner: Optional[owlapy.abstracts.AbstractOWLReasoner] = None,
                  refinement_operator: Optional[BaseRefinement[OENode]] = None,
                  quality_func: Optional[AbstractScorer] = None,
@@ -64,7 +64,7 @@ def __init__(self,
                                                         Defaults to `ModifiedCELOERefinement`.
             heuristic_func (AbstractHeuristic): Function to guide the search heuristic. Defaults to `OCELHeuristic`.
             iter_bound (int): Limit to stop the algorithm after n refinement steps are done. Defaults to 10'000.
-            knowledge_base (KnowledgeBase): The knowledge base that the concept learner is using.
+            knowledge_base (AbstractKnowledgeBase): The knowledge base that the concept learner is using.
             max_num_of_concepts_tested (int) Limit to stop the algorithm after n concepts tested. Defaults to 10'000.
             max_runtime (int): Limit to stop the algorithm after n seconds. Defaults to 5.
             max_results (int): Maximum hypothesis to store. Defaults to 10.

ontolearn/learning_problem.py

@@ -25,13 +25,10 @@
 """Learning problem in Ontolearn."""
 import logging
 import random
-from typing import Set, Optional, TYPE_CHECKING
-
+from typing import Set, Optional
 from owlapy.render import DLSyntaxObjectRenderer
-
-if TYPE_CHECKING:
-    from ontolearn.knowledge_base import KnowledgeBase
-from ontolearn.abstracts import AbstractLearningProblem, EncodedLearningProblem, EncodedPosNegLPStandardKind
+from ontolearn.abstracts import AbstractLearningProblem, EncodedLearningProblem, EncodedPosNegLPStandardKind, \
+    AbstractKnowledgeBase
 from owlapy.owl_individual import OWLNamedIndividual
 
 logger = logging.getLogger(__name__)
@@ -103,7 +100,7 @@ def __init__(self,
     # def encode_kb(self, knowledge_base: 'KnowledgeBase') -> EncodedPosNegLPStandard:
     #     return knowledge_base.encode_learning_problem(self)
 
-    def encode_kb(self, kb: 'KnowledgeBase') -> EncodedPosNegLPStandard:
+    def encode_kb(self, kb: 'AbstractKnowledgeBase') -> EncodedPosNegLPStandard:
         """
         Provides the encoded learning problem (lp), i.e. the class containing the set of OWLNamedIndividuals
         as follows:
@@ -117,9 +114,9 @@ def encode_kb(self, kb: 'KnowledgeBase') -> EncodedPosNegLPStandard:
             EncodedPosNegLPStandard: The encoded learning problem.
         """
         if self.all is None:
-            kb_all = kb.individuals()
+            kb_all = set(kb.individuals())
         else:
-            kb_all = kb.individuals_set(self.all)
+            kb_all = set(kb.individuals_set(self.all))
 
         assert 0 < len(self.pos) < len(kb_all) and len(kb_all) > len(self.neg)
         if logger.isEnabledFor(logging.INFO):