Add sklearn template for flwr new (#3251)

src/py/flwr/cli/new/new.py

@@ -36,6 +36,7 @@ class MlFramework(str, Enum):
     NUMPY = "NumPy"
     PYTORCH = "PyTorch"
     TENSORFLOW = "TensorFlow"
+    SKLEARN = "sklearn"
 
 
 class TemplateNotFound(Exception):

src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl

@@ -0,0 +1,94 @@
+"""$project_name: A Flower / Scikit-Learn app."""
+
+import warnings
+
+import numpy as np
+from flwr.client import NumPyClient, ClientApp
+from flwr_datasets import FederatedDataset
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import log_loss
+
+
+def get_model_parameters(model):
+    if model.fit_intercept:
+        params = [
+            model.coef_,
+            model.intercept_,
+        ]
+    else:
+        params = [model.coef_]
+    return params
+
+
+def set_model_params(model, params):
+    model.coef_ = params[0]
+    if model.fit_intercept:
+        model.intercept_ = params[1]
+    return model
+
+
+def set_initial_params(model):
+    n_classes = 10  # MNIST has 10 classes
+    n_features = 784  # Number of features in dataset
+    model.classes_ = np.array([i for i in range(10)])
+
+    model.coef_ = np.zeros((n_classes, n_features))
+    if model.fit_intercept:
+        model.intercept_ = np.zeros((n_classes,))
+
+
+class FlowerClient(NumPyClient):
+    def __init__(self, model, X_train, X_test, y_train, y_test):
+        self.model = model
+        self.X_train = X_train
+        self.X_test = X_test
+        self.y_train = y_train
+        self.y_test = y_test
+
+    def get_parameters(self, config):
+        return get_model_parameters(self.model)
+
+    def fit(self, parameters, config):
+        set_model_params(self.model, parameters)
+
+        # Ignore convergence failure due to low local epochs
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            self.model.fit(self.X_train, self.y_train)
+
+        return get_model_parameters(self.model), len(self.X_train), {}
+
+    def evaluate(self, parameters, config):
+        set_model_params(self.model, parameters)
+
+        loss = log_loss(self.y_test, self.model.predict_proba(self.X_test))
+        accuracy = self.model.score(self.X_test, self.y_test)
+
+        return loss, len(self.X_test), {"accuracy": accuracy}
+
+fds = FederatedDataset(dataset="mnist", partitioners={"train": 2})
+
+def client_fn(cid: str):
+    dataset = fds.load_partition(int(cid), "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)) :]
+
+    # Create LogisticRegression Model
+    model = LogisticRegression(
+        penalty="l2",
+        max_iter=1,  # local epoch
+        warm_start=True,  # prevent refreshing weights when fitting
+    )
+
+    # Setting initial parameters, akin to model.compile for keras models
+    set_initial_params(model)
+
+    return FlowerClient(model, X_train, X_test, y_train, y_test).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(client_fn=client_fn)

src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl

@@ -0,0 +1,17 @@
+"""$project_name: A Flower / Scikit-Learn app."""
+
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg
+
+
+strategy = FedAvg(
+    fraction_fit=1.0,
+    fraction_evaluate=1.0,
+    min_available_clients=2,
+)
+
+# Create ServerApp
+app = ServerApp(
+    config=ServerConfig(num_rounds=3),
+    strategy=strategy,
+)

src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl

@@ -0,0 +1,20 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+authors = [
+    { name = "The Flower Authors", email = "[email protected]" },
+]
+license = {text = "Apache License (2.0)"}
+dependencies = [
+    "flwr[simulation]>=1.8.0,<2.0",
+    "flwr-datasets[vision]>=0.0.2,<1.0.0",
+    "scikit-learn>=1.1.1",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]