pylint

baselines/fedavgm/fedavgm/client.py

@@ -10,6 +10,7 @@
 class FlowerClient(fl.client.NumPyClient):
     """Standard Flower client."""
 
+    # pylint: disable=too-many-arguments
     def __init__(self, x_train, y_train, x_val, y_val, model, num_classes) -> None:
         # local model
         self.model = instantiate(model)

baselines/fedavgm/fedavgm/main.py

@@ -19,6 +19,7 @@
 from fedavgm.server import get_evaluate_fn
 
 
+# pylint: disable=too-many-locals
 @hydra.main(config_path="conf", config_name="base", version_base=None)
 def main(cfg: DictConfig) -> None:
     """Run the baseline.

baselines/fedavgm/fedavgm/models.py

@@ -4,7 +4,7 @@
 from keras.optimizers import SGD
 from keras.regularizers import l2
 from tensorflow import keras
-from tensorflow.nn import local_response_normalization
+from tensorflow.nn import local_response_normalization  # pylint: disable=import-error
 
 
 def cnn(input_shape, num_classes, learning_rate):

baselines/fedavgm/fedavgm/server.py

@@ -10,7 +10,7 @@ def get_on_fit_config(config: DictConfig):
     The config dict is sent to the client fit() method.
     """
 
-    def fit_config_fn(server_round: int):
+    def fit_config_fn(server_round: int):  # pylint: disable=unused-argument
         # option to use scheduling of learning rate based on round
         # if server_round > 50:
         #     lr = config.lr / 10
@@ -28,7 +28,9 @@ def get_evaluate_fn(model, x_test, y_test, num_rounds, num_classes):
     The method evaluate_fn runs after global model aggregation.
     """
 
-    def evaluate_fn(server_round: int, parameters, config):
+    def evaluate_fn(
+        server_round: int, parameters, config
+    ):  # pylint: disable=unused-argument
         if server_round == num_rounds:  # evaluates global model just on the last round
             # instantiate the model
             model.set_weights(parameters)

baselines/fedavgm/fedavgm/utils.py

@@ -10,6 +10,8 @@
 
 from fedavgm.dataset import cifar10, partition
 
+# pylint: disable=too-many-locals
+
 
 def plot_concentrations_cifar10():
     """Create a plot with different concentrations for dataset using LDA."""