format

examples/sklearn-logreg-mnist/README.md

@@ -72,6 +72,7 @@ python3 client.py --node-id 1 # or any integer in {0-9}
 ```
 
 Alternatively, you can run all of it in one shell as follows:
+
 ```bash
 bash run.sh
 ```

examples/sklearn-logreg-mnist/client.py

@@ -28,8 +28,8 @@
     dataset = fds.load_partition(partition_id, "train").with_format("numpy")
     X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
     # Split the on edge data: 80% train, 20% test
-    X_train, X_test = X[:int(0.8 * len(X))], X[int(0.8 * len(X)):]
-    y_train, y_test = y[:int(0.8 * len(y))], y[int(0.8 * len(y)):]
+    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
 
     # Create LogisticRegression Model
     model = LogisticRegression(
@@ -41,7 +41,6 @@
     # Setting initial parameters, akin to model.compile for keras models
     utils.set_initial_params(model)
 
-
     # Define Flower client
     class MnistClient(fl.client.NumPyClient):
         def get_parameters(self, config):  # type: ignore
@@ -62,6 +61,5 @@ def evaluate(self, parameters, config):  # type: ignore
             accuracy = model.score(X_test, y_test)
             return loss, len(X_test), {"accuracy": accuracy}
 
-
     # Start Flower client
     fl.client.start_numpy_client(server_address="0.0.0.0:8080", client=MnistClient())

examples/sklearn-logreg-mnist/utils.py

@@ -18,9 +18,7 @@ def get_model_parameters(model: LogisticRegression) -> NDArrays:
     return params
 
 
-def set_model_params(
-        model: LogisticRegression, params: NDArrays
-) -> LogisticRegression:
+def set_model_params(model: LogisticRegression, params: NDArrays) -> LogisticRegression:
     """Sets the parameters of a sklean LogisticRegression model."""
     model.coef_ = params[0]
     if model.fit_intercept: