Fix flake8 check

Signed-off-by: Beat Buesser <[email protected]>

Author: Beat Buesser

tests/estimators/classification/test_query_efficient_bb.py

@@ -25,7 +25,7 @@
 from art.estimators.classification.keras import KerasClassifier
 from art.defences.preprocessor import FeatureSqueezing
 from art.utils import load_dataset, get_labels_np_array
-from art.estimators.classification.query_efficient_bb import QueryEfficientBBGradientEstimation
+from art.estimators.classification.query_efficient_bb import QueryEfficientGradientEstimationClassifier
 
 from tests.utils import master_seed, get_image_classifier_kr, get_tabular_classifier_kr
 
@@ -57,7 +57,9 @@ def test_without_defences(self):
         (x_train, y_train), (x_test, y_test) = self.mnist
 
         # Get the ready-trained Keras model and wrap it in query efficient gradient estimator
-        classifier = QueryEfficientBBGradientEstimation(self.classifier_k, 20, 1 / 64.0, round_samples=1 / 255.0)
+        classifier = QueryEfficientGradientEstimationClassifier(
+            self.classifier_k, 20, 1 / 64.0, round_samples=1 / 255.0
+        )
 
         attack = FastGradientMethod(classifier, eps=1)
         x_train_adv = attack.generate(x_train)
@@ -81,7 +83,7 @@ def test_with_defences(self):
         classifier = KerasClassifier(model=model, clip_values=(0, 1), preprocessing_defences=fs)
 
         # Create the classifier
-        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+        classifier = QueryEfficientGradientEstimationClassifier(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
 
         attack = FastGradientMethod(classifier, eps=1)
         x_train_adv = attack.generate(x_train)
@@ -111,7 +113,7 @@ def test_iris_clipped(self):
         (_, _), (x_test, y_test) = self.iris
 
         classifier = get_tabular_classifier_kr()
-        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+        classifier = QueryEfficientGradientEstimationClassifier(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
 
         # Test untargeted attack
         attack = FastGradientMethod(classifier, eps=0.1)
@@ -129,7 +131,7 @@ def test_iris_unbounded(self):
 
         # Recreate a classifier without clip values
         classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
-        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+        classifier = QueryEfficientGradientEstimationClassifier(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
         attack = FastGradientMethod(classifier, eps=1)
         x_test_adv = attack.generate(x_test)
         self.assertFalse((x_test == x_test_adv).all())