split variable names for IGA and GA

textattack/search_methods/genetic_algorithm.py

@@ -46,7 +46,7 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
     @abstractmethod
     def _modify_population_member(self, pop_member, new_text, new_result, word_idx):
         """Modify `pop_member` by returning a new copy with `new_text`,
-        `new_result`, and `num_replacements_per_word` altered appropriately for
+        `new_result`, and `word_attr_list` altered appropriately for
         given `word_idx`"""
         raise NotImplementedError()
 
@@ -61,9 +61,10 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
         Returns:
             Perturbed `PopulationMember`
         """
-        num_words = pop_member.num_replacements_per_word.shape[0]
-        num_replacements_per_word = np.copy(pop_member.num_replacements_per_word)
-        non_zero_indices = np.count_nonzero(num_replacements_per_word)
+        num_words = pop_member.attacked_text.num_words
+        # `word_attr_list` contains some attribute of each word that helps us choose which word to transform.
+        word_attr_list = np.copy(getattr(pop_member, self._attr_name))
+        non_zero_indices = np.count_nonzero(word_attr_list)
         if non_zero_indices == 0:
             return pop_member
         iterations = 0
@@ -71,8 +72,8 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
             if index:
                 idx = index
             else:
-                w_select_probs = num_replacements_per_word / np.sum(
-                    num_replacements_per_word
+                w_select_probs = word_attr_list / np.sum(
+                    word_attr_list
                 )
                 idx = np.random.choice(num_words, 1, p=w_select_probs)[0]
 
@@ -104,22 +105,48 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
                 )
                 return pop_member
 
-            num_replacements_per_word[idx] = 0
+            word_attr_list[idx] = 0
             iterations += 1
         return pop_member
 
     @abstractmethod
     def _crossover_operation(self, pop_member1, pop_member2):
-        """Actual operation for generating crossover between pop_member1 and
-        pop_member2.
+        """
+        Actual operation that takes `pop_member1` text and `pop_member2` text and mixes the two 
+        to generate crossover between `pop_member1` and `pop_member2`.
 
         Args:
             pop_member1 (PopulationMember): The first population member.
             pop_member2 (PopulationMember): The second population member.
         Returns:
-            Tuple of `AttackedText` and `np.array` for new text and its corresponding `num_replacements_per_word`.
+            Tuple of `AttackedText` and `np.array` for new text and its corresponding `word_attr_list`.
         """
         raise NotImplementedError()
+    
+    def _post_crossover_check(self, new_text, parent_text1, parent_text2, original_text):
+        """Check if `new_text` that has been produced by performing crossover between
+        `parent_text1` and `parent_text2` aligns with the constraints.
+        Args:
+            new_text (AttackedText): Text produced by crossover operation
+            parent_text1 (AttackedText): Parent text of `new_text`
+            parent_text2 (AttackedText): Second parent text of `new_text`
+            original_text (AttackedText): Original text
+        Returns:
+            `True` if `new_text` meets the constraints. If otherwise, return `False`.
+        """
+        if "last_transformation" in new_text.attack_attrs:
+            previous_text = (
+                parent_text1
+                if "last_transformation" in parent_text1.attack_attrs
+                else parent_text2
+            )
+            passed_constraints = self._check_constraints(
+                new_text, previous_text, original_text=original_text
+            )
+            return passed_constraints
+        else:
+            # `new_text` has not been actually transformed, so return True
+            return True
 
     def _crossover(self, pop_member1, pop_member2, original_text):
         """Generates a crossover between pop_member1 and pop_member2.
@@ -139,7 +166,7 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
         num_tries = 0
         passed_constraints = False
         while num_tries < self.max_crossover_retries + 1:
-            new_text, num_replacements_per_word = self._crossover_operation(
+            new_text, word_attr_list = self._crossover_operation(
                 pop_member1, pop_member2
             )
 
@@ -157,24 +184,10 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
                     "last_transformation"
                 ]
 
-            if not self.post_crossover_check or (
-                new_text.text == x1_text.text or new_text.text == x2_text.text
-            ):
-                break
+            if self.post_crossover_check:
+                passed_constraints = self._post_crossover_check(new_text, x1_text, x2_text, original_text)
 
-            if "last_transformation" in new_text.attack_attrs:
-                previous_text = (
-                    x1_text
-                    if "last_transformation" in x1_text.attack_attrs
-                    else x2_text
-                )
-                passed_constraints = self._check_constraints(
-                    new_text, previous_text, original_text=original_text
-                )
-            else:
-                passed_constraints = True
-
-            if passed_constraints:
+            if not self.post_crossover_check or passed_constraints:
                 break
 
             num_tries += 1
@@ -189,7 +202,7 @@ class GeneticAlgorithm(PopulationBasedSearch, ABC):
             return PopulationMember(
                 new_text,
                 result=new_results[0],
-                num_replacements_per_word=num_replacements_per_word,
+                **{self._attr_name: word_attr_list},
             )
 
     @abstractmethod