Added verbalize method for CL models. #292

ontolearn/base_concept_learner.py

@@ -4,10 +4,10 @@
 import time
 from abc import ABCMeta, abstractmethod
 from typing import List, Tuple, Dict, Optional, Iterable, Generic, TypeVar, ClassVar, Final, Union, cast, Callable, Type
-
+import xml.etree.ElementTree as ET
 import numpy as np
 import pandas as pd
-
+import os
 from ontolearn.heuristics import CELOEHeuristic
 from ontolearn.knowledge_base import KnowledgeBase
 from ontolearn.metrics import F1, Accuracy
@@ -358,6 +358,67 @@ def load_hypotheses(self, path: str) -> Iterable[OWLClassExpression]:
                     if equivalent_c != c:
                         yield equivalent_c
 
+    @staticmethod
+    def verbalize(predictions_file_path: str):
+
+        tree = ET.parse(predictions_file_path)
+        root = tree.getroot()
+        tmp_file = 'tmp_file_' + predictions_file_path
+        owl = 'http://www.w3.org/2002/07/owl#'
+        ontology_elem = root.find(f'{{{owl}}}Ontology')
+        ontology_elem.remove(ontology_elem.find(f'{{{owl}}}imports'))
+
+        # The commented lines below are needed if you want to use `verbaliser.verbalise_class_expression`
+        # They assign labels to classes and properties.
+
+        # rdf = 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'
+        # rdfs = 'http://www.w3.org/2000/01/rdf-schema#'
+        # for element in root.iter():
+        #     resource = None
+        #     if f'{{{rdf}}}about' in element.attrib:
+        #         resource = element.attrib[f'{{{rdf}}}about']
+        #     elif f'{{{rdf}}}resource' in element.attrib:
+        #         resource = element.attrib[f'{{{rdf}}}resource']
+        #     if resource is not None:
+        #         label = resource.split('#')
+        #         if len(label) > 1:
+        #             element.set(f'{{{rdfs}}}label', label[1])
+        #         else:
+        #             element.set(f'{{{rdfs}}}label', resource)
+
+        tree.write(tmp_file)
+
+        try:
+            from deeponto.onto import Ontology, OntologyVerbaliser
+            from anytree.dotexport import RenderTreeGraph
+            from IPython.display import Image
+        except Exception as e:
+            print("You need to install deeponto to use this feature (pip install deeponto). If you have already, check "
+                  "whether it's installed properly. \n   ----> Error: " + f'{e}')
+            if os.path.exists(tmp_file):
+                os.remove(tmp_file)
+            return
+
+        onto = Ontology(tmp_file)
+        verbalizer = OntologyVerbaliser(onto)
+        complex_concepts = onto.get_asserted_complex_classes()
+        try:
+            for i, ce in enumerate(complex_concepts):
+                tree = verbalizer.parser.parse(str(ce))
+                tree.render_image()
+                os.rename("range_node.png", f"Prediction_{i}.png")
+        except Exception as e:
+            print("If you have not installed graphviz, please do so at https://graphviz.org/download/ to make the "
+                  "verbalization possible. Otherwise check the error message: \n" + f'{e}')
+        if os.path.exists(tmp_file):
+            os.remove(tmp_file)
+        if len(complex_concepts) == 0:
+            print("No complex classes found!")
+        elif len(complex_concepts) == 1:
+            print("Image generated successfully!")
+        else:
+            print("Images generated successfully!")
+
 
 class RefinementBasedConceptLearner(BaseConceptLearner[_N]):
     """