Merge pull request #551 from dice-group/FixIssue550

Fix issue550

Author: Demirrr

ontolearn/learners/drill.py

@@ -706,61 +706,79 @@ def generate_learning_problems(self,
 
  Time complexity: O(n^2) n = named concepts
  """
- counter = 0
- size_of_examples = 3
- examples = []
- # C: Iterate over all named OWL concepts
+ # Initialize counters and containers
+ counter = 0 
+ size_of_examples = 3 # Minimum number of examples required for positive/negative sets
+ examples = [] 
+ # C: Iterate over all named OWL concepts
  for i in self.kb.get_concepts():
- # Retrieve(C)
+ # Retrieve all individuals that belong to concept i (positive examples)
  individuals_i = set(self.kb.individuals(i, True))
+ 
+ # Skip concepts with insufficient individuals for sampling
  if len(individuals_i) < size_of_examples:
  continue
+ 
  for j in self.kb.get_concepts():
+ # Skip if same concept (can't use same concept for both positive and negative examples)
  if i == j:
  continue
  str_dl_concept_i = owl_expression_to_dl(i)
+ 
+ # Retrieve all individuals that belong to concept j (negative examples)
  individuals_j = set(self.kb.individuals(j, True))
+ 
+ # Skip concepts with insufficient individuals for sampling
  if len(individuals_j) < size_of_examples:
  continue
 
  # Generate Learning problems from a single target
  for _ in range(num_of_target_concepts):
+ # Randomly sample positive examples from concept i
  sampled_positives = set(random.sample(individuals_i, size_of_examples))
+ 
+ # Randomly sample negative examples from concept j
  sampled_negatives = set(random.sample(individuals_j, size_of_examples))
- if sampled_negatives== sampled_positives:
+ 
+ # Validate that positive and negative examples are different
+ if sampled_negatives == sampled_positives:
  print("Sampled Positives and negatives are same. We need to ignore this example")
  continue
- lp = (str_dl_concept_i,sampled_positives,sampled_negatives)
+ 
+ lp = (str_dl_concept_i, sampled_positives, sampled_negatives)
  examples.append(lp)
  counter += 1
+ 
+ # Check if we've generated enough learning problems
  if counter == num_learning_problems:
  break
 
+ # Early termination if we've reached the desired number of learning problems
  if counter == num_learning_problems:
  break
-
- return examples
- """
- # if |Retrieve(C|>3
- if len(individuals_i) > size_of_examples:
- str_dl_concept_i = owl_expression_to_dl(i)
- for j in self.kb.get_concepts():
- if i == j:
- continue
- individuals_j = set(self.kb.individuals(j))
- if len(individuals_j) > size_of_examples:
- for _ in range(num_learning_problems):
- lp = (str_dl_concept_i,
- set(random.sample(individuals_i, size_of_examples)),
- set(random.sample(individuals_j, size_of_examples)))
- yield lp
-
- counter += 1
- if counter == num_of_target_concepts:
- break
+ # Return the generated learning problems
+ return examples
+ """
+ # if |Retrieve(C|>3
+ if len(individuals_i) > size_of_examples:
+ str_dl_concept_i = owl_expression_to_dl(i)
+ for j in self.kb.get_concepts():
+ if i == j:
+ continue
+ individuals_j = set(self.kb.individuals(j))
+ if len(individuals_j) > size_of_examples:
+ for _ in range(num_learning_problems):
+ lp = (str_dl_concept_i,
+ set(random.sample(individuals_i, size_of_examples)),
+ set(random.sample(individuals_j, size_of_examples)))
+ yield lp
+
+ counter += 1
  if counter == num_of_target_concepts:
  break
- """
+ if counter == num_of_target_concepts:
+ break
+ """
 
  def learn_from_illustration(self, sequence_of_goal_path: List[RL_State]):
  """