Attack Recipe Implementation - Leap

docs/3recipes/attack_recipes.rst

@@ -36,6 +36,7 @@ Attacks on classification models
 14. TextBugger (TextBugger: Generating Adversarial Text Against Real-world Applications)
 15. Pruthi (Combating Adversarial Misspellings with Robust Word Recognition 2019)
 16. CLARE (Contextualized Perturbation for Textual Adversarial Attack 2020)
+17. LEAP (LEAP: Efficient and Automated Test Method for NLP Software 2023)
 
 
 
@@ -136,7 +137,9 @@ Attacks on classification models
    :members:
    :noindex:
 
-
+.. automodule:: textattack.attack_recipes.leap_2023
+   :members:
+   :noindex:
 
 
 

docs/api/search_methods.rst

@@ -44,3 +44,7 @@ ParticleSwarmOptimization
 .. autoclass:: textattack.search_methods.ParticleSwarmOptimization
    :members:
 
+ParticleSwarmOptimizationLEAP
+--------------------------
+.. autoclass:: textattack.search_methods.ParticleSwarmOptimizationLEAP
+   :members:

textattack/attack.py

@@ -372,9 +372,9 @@ def filter_transformations(
                 uncached_texts.append(transformed_text)
             else:
                 # promote transformed_text to the top of the LRU cache
-                self.constraints_cache[(current_text, transformed_text)] = (
-                    self.constraints_cache[(current_text, transformed_text)]
-                )
+                self.constraints_cache[
+                    (current_text, transformed_text)
+                ] = self.constraints_cache[(current_text, transformed_text)]
                 if self.constraints_cache[(current_text, transformed_text)]:
                     filtered_texts.append(transformed_text)
         filtered_texts += self._filter_transformations_uncached(

textattack/attack_args.py

@@ -37,6 +37,7 @@
     "checklist": "textattack.attack_recipes.CheckList2020",
     "clare": "textattack.attack_recipes.CLARE2020",
     "a2t": "textattack.attack_recipes.A2TYoo2021",
+    "leap": "textattack.attack_recipes.LEAP2023",
 }
 
 

textattack/attack_recipes/__init__.py

@@ -39,6 +39,7 @@
 from .pso_zang_2020 import PSOZang2020
 from .checklist_ribeiro_2020 import CheckList2020
 from .clare_li_2020 import CLARE2020
+from .leap_2023 import LEAP2023
 from .french_recipe import FrenchRecipe
 from .spanish_recipe import SpanishRecipe
 from .chinese_recipe import ChineseRecipe

textattack/attack_recipes/leap_2023.py

@@ -0,0 +1,44 @@
+"""
+
+LEAP
+==================================
+
+(LEAP: Efficient and Automated Test Method for NLP Software)
+
+"""
+from textattack import Attack
+from textattack.constraints.pre_transformation import (
+    MaxModificationRate,
+    StopwordModification,
+)
+from textattack.goal_functions import UntargetedClassification
+from textattack.search_methods import ParticleSwarmOptimizationLEAP
+from textattack.transformations import WordSwapWordNet
+
+from .attack_recipe import AttackRecipe
+
+
+class LEAP2023(AttackRecipe):
+    @staticmethod
+    def build(model_wrapper):
+        #
+        # Swap words with their synonyms extracted based on the WordNet.
+        #
+        transformation = WordSwapWordNet()
+        #
+        # MaxModificationRate = 0.16 in AG's News
+        #
+        constraints = [MaxModificationRate(max_rate=0.16), StopwordModification()]
+        #
+        #
+        # Use untargeted classification for demo, can be switched to targeted one
+        #
+        goal_function = UntargetedClassification(model_wrapper)
+        #
+        # Perform word substitution with LEAP algorithm.
+        #
+        search_method = ParticleSwarmOptimizationLEAP(
+            pop_size=60, max_iters=20, post_turn_check=True, max_turn_retries=20
+        )
+
+        return Attack(goal_function, constraints, transformation, search_method)

textattack/goal_functions/classification/targeted_classification.py

@@ -11,7 +11,7 @@ class TargetedClassification(ClassificationGoalFunction):
     """A targeted attack on classification models which attempts to maximize
     the score of the target label.
 
-    Complete when the arget label is the predicted label.
+    Complete when the target label is the predicted label.
     """
 
     def __init__(self, *args, target_class=0, **kwargs):

textattack/metrics/attack_metrics/words_perturbed.py

@@ -65,9 +65,9 @@ def calculate(self, results):
         self.all_metrics["avg_word_perturbed"] = self.avg_number_word_perturbed_num()
         self.all_metrics["avg_word_perturbed_perc"] = self.avg_perturbation_perc()
         self.all_metrics["max_words_changed"] = self.max_words_changed
-        self.all_metrics["num_words_changed_until_success"] = (
-            self.num_words_changed_until_success
-        )
+        self.all_metrics[
+            "num_words_changed_until_success"
+        ] = self.num_words_changed_until_success
 
         return self.all_metrics
 

textattack/search_methods/__init__.py

@@ -15,3 +15,4 @@
 from .alzantot_genetic_algorithm import AlzantotGeneticAlgorithm
 from .improved_genetic_algorithm import ImprovedGeneticAlgorithm
 from .particle_swarm_optimization import ParticleSwarmOptimization
+from .particle_swarm_optimization_leap import ParticleSwarmOptimizationLEAP

textattack/search_methods/particle_swarm_optimization.py

@@ -120,9 +120,9 @@ def _turn(self, source_text, target_text, prob, original_text):
                 & indices_to_replace
             )
             if "last_transformation" in source_text.attacked_text.attack_attrs:
-                new_text.attack_attrs["last_transformation"] = (
-                    source_text.attacked_text.attack_attrs["last_transformation"]
-                )
+                new_text.attack_attrs[
+                    "last_transformation"
+                ] = source_text.attacked_text.attack_attrs["last_transformation"]
 
             if not self.post_turn_check or (new_text.words == source_text.words):
                 break