WIP: refactoring due to KnowledgeBase changes

ontolearn/abstracts.py

@@ -27,12 +27,16 @@
 import logging
 from abc import ABCMeta, abstractmethod
 from typing import Set, List, Tuple, Iterable, TypeVar, Generic, ClassVar, Optional
+from collections import OrderedDict
+
 from owlapy.class_expression import OWLClassExpression
 from owlapy.abstracts import AbstractOWLOntology
+from owlapy.owl_individual import OWLNamedIndividual
 from owlapy.utils import iter_count
+
 from .data_struct import Experience
 from .utils import read_csv
-from collections import OrderedDict
+
 
 _N = TypeVar('_N')  #:
 _KB = TypeVar('_KB', bound='AbstractKnowledgeBase')  #:
@@ -393,15 +397,19 @@ def individuals_set(self, *args, **kwargs) -> Set:
         pass
 
     @abstractmethod
-    def concept_len(self, ce: OWLClassExpression) -> int:
-        """Calculate the length of a concept.
+    def individuals(self, concept: Optional[OWLClassExpression] = None, named_individuals: bool = False) -> Iterable[OWLNamedIndividual]:
+        pass
 
-        Args:
-            ce: The concept to measure the length for.
+    @abstractmethod
+    def abox(self, *args, **kwargs):
+        pass
 
-        Returns:
-            Length of concept.
-        """
+    @abstractmethod
+    def tbox(self, *args, **kwargs):
+        pass
+
+    @abstractmethod
+    def triples(self, *args, **kwargs):
         pass
 
 

ontolearn/concept_learner.py

@@ -24,47 +24,48 @@
 
 """Concept learning algorithms of Ontolearn."""
 
-import logging
 import operator
 import time
-from datetime import datetime
-from contextlib import contextmanager
-from itertools import islice, chain
-from typing import Any, Callable, Dict, FrozenSet, Set, List, Tuple, Iterable, Optional, Union
-
 import pandas as pd
 import numpy as np
 import torch
+import os
+import json
+import glob
+
+from datetime import datetime
+from itertools import chain
+from typing import Any, Callable, Dict, FrozenSet, Set, List, Tuple, Iterable, Optional, Union
+from sortedcontainers import SortedSet
+
+from torch.utils.data import DataLoader
+from torch.functional import F
+from torch.nn.utils.rnn import pad_sequence
+from deap import gp, tools, base, creator
+
 from owlapy.class_expression import OWLClassExpression
 from owlapy.owl_individual import OWLNamedIndividual
 from owlapy.owl_literal import OWLLiteral
 from owlapy.owl_property import OWLDataProperty
 from owlapy.abstracts import AbstractOWLReasoner
-from torch.utils.data import DataLoader
-from torch.functional import F
-from torch.nn.utils.rnn import pad_sequence
-from deap import gp, tools, base, creator
+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, EncodedPosNegLPStandardKind
-from ontolearn.base_concept_learner import BaseConceptLearner, RefinementBasedConceptLearner
-from owlapy.utils import EvaluatedDescriptionSet, ConceptOperandSorter, OperandSetTransform
+from ontolearn.abstracts import AbstractFitness, AbstractScorer, BaseRefinement, AbstractHeuristic
+from ontolearn.base_concept_learner import BaseConceptLearner
 from ontolearn.data_struct import NCESDataLoader, NCESDataLoaderInference, CLIPDataLoader, CLIPDataLoaderInference
 from ontolearn.ea_algorithms import AbstractEvolutionaryAlgorithm, EASimple
 from ontolearn.ea_initialization import AbstractEAInitialization, EARandomInitialization, EARandomWalkInitialization
 from ontolearn.ea_utils import PrimitiveFactory, OperatorVocabulary, ToolboxVocabulary, Tree, escape, ind_to_string, \
     owlliteral_to_primitive_string
 from ontolearn.fitness_functions import LinearPressureFitness
-from ontolearn.heuristics import OCELHeuristic
 from ontolearn.learning_problem import PosNegLPStandard, EncodedPosNegLPStandard
 from ontolearn.metrics import Accuracy
 from ontolearn.refinement_operators import ExpressRefinement
-from ontolearn.search import EvoLearnerNode, NCESNode, HeuristicOrderedNode, LBLNode, OENode, TreeNode, \
-    LengthOrderedNode, \
-    QualityOrderedNode, EvaluatedConcept
-from ontolearn.utils import oplogging
-from ontolearn.utils.static_funcs import init_length_metric, compute_tp_fn_fp_tn, evaluate_concept
+from ontolearn.search import EvoLearnerNode, NCESNode, OENode, TreeNode, QualityOrderedNode
+from ontolearn.utils.static_funcs import init_length_metric, compute_tp_fn_fp_tn
+from ontolearn.quality_funcs import evaluate_concept
 from ontolearn.value_splitter import AbstractValueSplitter, BinningValueSplitter, EntropyValueSplitter
 from ontolearn.base_nces import BaseNCES
 from ontolearn.nces_architectures import LSTM, GRU, SetTransformer
@@ -73,18 +74,12 @@
 from ontolearn.nces_trainer import NCESTrainer, before_pad
 from ontolearn.clip_trainer import CLIPTrainer
 from ontolearn.nces_utils import SimpleSolution
-from owlapy.render import DLSyntaxObjectRenderer
-from owlapy.parser import DLSyntaxParser
-from owlapy.utils import OrderedOWLObject
-from sortedcontainers import SortedSet
-import os
-import json
-import glob
 from ontolearn.lp_generator import LPGen
-from .learners import CELOE
+from ontolearn.learners import CELOE
 
 _concept_operand_sorter = ConceptOperandSorter()
 
+
 class EvoLearner(BaseConceptLearner):
     """An evolutionary approach to learn concepts in ALCQ(D).
 

ontolearn/knowledge_base.py

@@ -28,6 +28,7 @@
 from collections import Counter
 from typing import Iterable, Optional, Callable, Union, FrozenSet, Set, Dict, cast, Generator
 import owlapy
+from owlapy import OntologyManager
 from owlapy.class_expression import OWLClassExpression, OWLClass, OWLObjectSomeValuesFrom, OWLObjectAllValuesFrom, \
     OWLThing, OWLObjectMinCardinality, OWLObjectOneOf
 from owlapy.iri import IRI
@@ -46,7 +47,7 @@
 from .abstracts import AbstractKnowledgeBase
 from .concept_generator import ConceptGenerator
 from owlapy.owl_hierarchy import ClassHierarchy, ObjectPropertyHierarchy, DatatypePropertyHierarchy
-from .utils.static_funcs import (init_hierarchy_instances, init_named_individuals, init_individuals_from_concepts)
+from .utils.static_funcs import init_hierarchy_instances
 from owlapy.class_expression import OWLDataSomeValuesFrom
 from owlapy.owl_data_ranges import OWLDataRange
 from owlapy.class_expression import OWLDataOneOf
@@ -108,6 +109,7 @@ def __init__(self, *,
             self.manager = ontology.get_owl_ontology_manager()
             self.ontology = ontology
         else:
+            self.manager = OntologyManager()
             self.ontology = self.manager.load_ontology(IRI.create('file://' + self.path))
 
         reasoner: AbstractOWLReasoner
@@ -152,9 +154,9 @@ def individuals(self, concept: Optional[OWLClassExpression] = None, named_indivi
         """
         # named_individuals check must be supported by the reasoner .instances method
         if concept:
-            return self.reasoner.instances(concept)
+            return frozenset(self.reasoner.instances(concept))
         else:
-            return self.ontology.individuals_in_signature()
+            return frozenset(self.ontology.individuals_in_signature())
 
     def abox(self, individual: Union[OWLNamedIndividual, Iterable[OWLNamedIndividual]] = None, mode='native'):  # pragma: no cover
         """

ontolearn/learners/celoe.py

@@ -3,6 +3,7 @@
 
 from ..abstracts import AbstractScorer, BaseRefinement, AbstractHeuristic, EncodedPosNegLPStandardKind
 from ..learning_problem import PosNegLPStandard
+from ..quality_funcs import evaluate_concept
 from ..search import OENode, TreeNode, EvaluatedConcept, HeuristicOrderedNode, QualityOrderedNode, LengthOrderedNode
 
 from typing import Optional, Union, Iterable, Dict
@@ -17,7 +18,6 @@
 from itertools import islice
 from owlapy.render import DLSyntaxObjectRenderer
 
-from ..utils.static_funcs import evaluate_concept
 
 _concept_operand_sorter = ConceptOperandSorter()
 

ontolearn/learning_problem.py

@@ -103,7 +103,7 @@ def __init__(self,
     # def encode_kb(self, knowledge_base: 'KnowledgeBase') -> EncodedPosNegLPStandard:
     #     return knowledge_base.encode_learning_problem(self)
 
-    def encode_learning_problem(self, kb: 'KnowledgeBase') -> EncodedPosNegLPStandard:
+    def encode_kb(self, kb: 'KnowledgeBase') -> EncodedPosNegLPStandard:
         """
         Provides the encoded learning problem (lp), i.e. the class containing the set of OWLNamedIndividuals
         as follows:

ontolearn/quality_funcs.py

@@ -23,6 +23,10 @@
 # -----------------------------------------------------------------------------
 
 from typing import Set
+from owlapy.class_expression import OWLClassExpression
+from ontolearn.abstracts import EncodedLearningProblem, AbstractScorer
+from ontolearn.knowledge_base import KnowledgeBase
+from ontolearn.search import EvaluatedConcept
 
 
 def f1(*, individuals: Set, pos: Set, neg: Set):
@@ -64,3 +68,26 @@ def acc(*, individuals: Set, pos: Set, neg: Set):
     fp = len(neg.intersection(individuals))
     fn = len(pos.difference(individuals))
     return (tp + tn) / (tp + tn + fp + fn)
+
+
+def evaluate_concept(kb: KnowledgeBase, 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

ontolearn/utils/static_funcs.py

@@ -21,37 +21,25 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 # -----------------------------------------------------------------------------
-from itertools import chain
-from typing import Optional, Callable, Tuple, Generator, List, Union, Final
 import pandas
 import matplotlib.pyplot as plt
 import sklearn
 import numpy as np
-from owlapy.class_expression import OWLClass, OWLClassExpression
+import traceback
+
+from itertools import chain
+from typing import Optional, Callable, Tuple, Generator, List, Union, Final
+from tqdm import tqdm
+from typing import Set, Iterable
+
 from owlapy.iri import IRI
 from owlapy.owl_axiom import OWLEquivalentClassesAxiom
 from owlapy.abstracts import AbstractOWLOntology, AbstractOWLOntologyManager
 from owlapy.owl_ontology_manager import OntologyManager
 from owlapy.owl_hierarchy import ClassHierarchy, ObjectPropertyHierarchy, DatatypePropertyHierarchy
 from owlapy.utils import OWLClassExpressionLengthMetric, LRUCache
-import traceback
-from tqdm import tqdm
-from ontolearn.abstracts import EncodedLearningProblem, AbstractScorer
-from ontolearn.search import EvaluatedConcept
-from typing import Set, Iterable
-from owlapy.class_expression import (
-    OWLQuantifiedObjectRestriction,
-    OWLObjectCardinalityRestriction,
-)
-from owlapy.class_expression import (
-    OWLObjectUnionOf,
-    OWLObjectIntersectionOf,
-    OWLObjectSomeValuesFrom,
-    OWLObjectAllValuesFrom,
-    OWLObjectMinCardinality,
-    OWLObjectMaxCardinality,
-    OWLObjectOneOf,
-)
+from owlapy.class_expression import OWLQuantifiedObjectRestriction, OWLObjectCardinalityRestriction, \
+                                    OWLObjectMinCardinality, OWLObjectMaxCardinality, OWLClass, OWLClassExpression
 
 
 def f1_set_similarity(y: Set[str], yhat: Set[str]) -> float:
@@ -257,6 +245,7 @@ def compute_f1_score_from_confusion_matrix(confusion_matrix:dict)->float:
     f_1 = 2 * ((precision * recall) / (precision + recall))
     return f_1
 
+
 def plot_umap_reduced_embeddings(X: pandas.DataFrame, y: List[float], name: str = "umap_visualization.pdf") -> None:  # pragma: no cover
     # TODO:AB: 'umap' is not part of the dependencies !?
     import umap
@@ -414,26 +403,3 @@ def verbalize(predictions_file_path: str):  # pragma: no cover
         print("Image generated successfully!")
     else:
         print("Images generated successfully!")
-
-
-def evaluate_concept(kb: 'KnowledgeBase', 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