Flake8 for LambdaCallbackTest

keras_core/callbacks/lambda_callback_test.py

@@ -12,11 +12,11 @@
 
 class LambdaCallbackTest(testing.TestCase):
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback(self):
+    def test_lambda_callback(self):
         """Test standard LambdaCallback functionalities with training."""
-        BATCH_SIZE = 4
+        batch_size = 4
         model = Sequential(
-            [layers.Input(shape=(2,), batch_size=BATCH_SIZE), layers.Dense(1)]
+            [layers.Input(shape=(2,), batch_size=batch_size), layers.Dense(1)]
         )
         model.compile(
             optimizer=optimizers.SGD(), loss=losses.MeanSquaredError()
@@ -35,27 +35,23 @@ def test_LambdaCallback(self):
             model.fit(
                 x,
                 y,
-                batch_size=BATCH_SIZE,
+                batch_size=batch_size,
                 validation_split=0.2,
                 callbacks=[lambda_log_callback],
                 epochs=5,
                 verbose=0,
             )
-            self.assertTrue
-            (any("on_train_begin" in log for log in logs.output))
-            self.assertTrue
-            (any("on_epoch_begin" in log for log in logs.output))
-            self.assertTrue
-            (any("on_epoch_end" in log for log in logs.output))
-            self.assertTrue
-            (any("on_train_end" in log for log in logs.output))
+            self.assertTrue(any("on_train_begin" in log for log in logs.output))
+            self.assertTrue(any("on_epoch_begin" in log for log in logs.output))
+            self.assertTrue(any("on_epoch_end" in log for log in logs.output))
+            self.assertTrue(any("on_train_end" in log for log in logs.output))
 
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback_with_batches(self):
+    def test_lambda_callback_with_batches(self):
         """Test LambdaCallback's behavior with batch-level callbacks."""
-        BATCH_SIZE = 4
+        batch_size = 4
         model = Sequential(
-            [layers.Input(shape=(2,), batch_size=BATCH_SIZE), layers.Dense(1)]
+            [layers.Input(shape=(2,), batch_size=batch_size), layers.Dense(1)]
         )
         model.compile(
             optimizer=optimizers.SGD(), loss=losses.MeanSquaredError()
@@ -74,7 +70,7 @@ def test_LambdaCallback_with_batches(self):
             model.fit(
                 x,
                 y,
-                batch_size=BATCH_SIZE,
+                batch_size=batch_size,
                 validation_split=0.2,
                 callbacks=[lambda_log_callback],
                 epochs=5,
@@ -88,19 +84,19 @@ def test_LambdaCallback_with_batches(self):
             )
 
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback_with_kwargs(self):
+    def test_lambda_callback_with_kwargs(self):
         """Test LambdaCallback's behavior with custom defined callback."""
-        BATCH_SIZE = 4
+        batch_size = 4
         model = Sequential(
-            [layers.Input(shape=(2,), batch_size=BATCH_SIZE), layers.Dense(1)]
+            [layers.Input(shape=(2,), batch_size=batch_size), layers.Dense(1)]
         )
         model.compile(
             optimizer=optimizers.SGD(), loss=losses.MeanSquaredError()
         )
         x = np.random.randn(16, 2)
         y = np.random.randn(16, 1)
         model.fit(
-            x, y, batch_size=BATCH_SIZE, epochs=1, verbose=0
+            x, y, batch_size=batch_size, epochs=1, verbose=0
         )  # Train briefly for evaluation to work.
 
         def custom_on_test_begin(logs):
@@ -113,7 +109,7 @@ def custom_on_test_begin(logs):
             model.evaluate(
                 x,
                 y,
-                batch_size=BATCH_SIZE,
+                batch_size=batch_size,
                 callbacks=[lambda_log_callback],
                 verbose=0,
             )
@@ -125,13 +121,13 @@ def custom_on_test_begin(logs):
             )
 
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback_no_args(self):
+    def test_lambda_callback_no_args(self):
         """Test initializing LambdaCallback without any arguments."""
         lambda_callback = callbacks.LambdaCallback()
         self.assertIsInstance(lambda_callback, callbacks.LambdaCallback)
 
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback_with_additional_kwargs(self):
+    def test_lambda_callback_with_additional_kwargs(self):
         """Test initializing LambdaCallback with non-predefined kwargs."""
 
         def custom_callback(logs):
@@ -143,11 +139,11 @@ def custom_callback(logs):
         self.assertTrue(hasattr(lambda_callback, "custom_method"))
 
     @pytest.mark.requires_trainable_backend
-    def test_LambdaCallback_during_prediction(self):
+    def test_lambda_callback_during_prediction(self):
         """Test LambdaCallback's functionality during model prediction."""
-        BATCH_SIZE = 4
+        batch_size = 4
         model = Sequential(
-            [layers.Input(shape=(2,), batch_size=BATCH_SIZE), layers.Dense(1)]
+            [layers.Input(shape=(2,), batch_size=batch_size), layers.Dense(1)]
         )
         model.compile(
             optimizer=optimizers.SGD(), loss=losses.MeanSquaredError()
@@ -162,7 +158,7 @@ def custom_on_predict_begin(logs):
         )
         with self.assertLogs(level="WARNING") as logs:
             model.predict(
-                x, batch_size=BATCH_SIZE, callbacks=[lambda_callback], verbose=0
+                x, batch_size=batch_size, callbacks=[lambda_callback], verbose=0
             )
             self.assertTrue(
                 any("on_predict_begin_executed" in log for log in logs.output)