working on the federated optimisation

Medfl/LearningManager/client.py

@@ -95,9 +95,12 @@ def fit(self, parameters, config):
         Returns:
             Tuple: Parameters of the local model, number of training examples, and privacy information.
         """
+        print('\n -------------------------------- \n  this is the config of the client')
         print(f"[Client {self.cid}] fit, config: {config}")
+        print(config['epochs'])
+        print('\n -------------------------------- \n  ')
         self.local_model.set_parameters(parameters)
-        for _ in range(params["train_epochs"]):
+        for _ in range(config['epochs']):
             epsilon = self.local_model.train(
                 self.trainloader,
                 epoch=_,
@@ -131,4 +134,5 @@ def evaluate(self, parameters, config):
         )
         self.losses.append(loss)
         self.accuracies.append(accuracy)
+
         return float(loss), len(self.valloader), {"accuracy": float(accuracy)}

Medfl/LearningManager/flpipeline.py

@@ -62,8 +62,8 @@ def validate(self) -> None:
         if not isinstance(self.description, str):
             raise TypeError("description argument must be a string")
 
-        if not isinstance(self.server, FlowerServer):
-            raise TypeError("server argument must be a FlowerServer")
+        # if not isinstance(self.server, FlowerServer):
+        #     raise TypeError("server argument must be a FlowerServer")
 
     def create(self, result: str) -> None:
         """

Medfl/LearningManager/params.yaml

@@ -13,7 +13,7 @@
   num_rounds: 100
   min_evalclient: 2
   # path_to_master_csv : 'D:\ESI\3CS\PFE\last_year\Code\MEDfl\notebooks\sapsii_score_knnimputed_eicu.csv'
-  path_to_master_csv : '/home/local/USHERBROOKE/saho6810/MEDfl/code/MEDfl/notebooks/data/masterDataSet/Mimic_train.csv'
+  path_to_master_csv : '/home/local/USHERBROOKE/saho6810/MEDfl/code/MEDfl/notebooks/data/masterDataSet/Mimic_ouael.csv'
 
   path_to_test_csv : '/home/local/USHERBROOKE/saho6810/MEDfl/code/MEDfl/notebooks/data/masterDataSet/Mimic_train.csv'
 

Medfl/LearningManager/server.py

@@ -86,8 +86,10 @@ def validate(self) -> None:
         if not isinstance(self.global_model, Model):
             raise TypeError("global_model argument must be a Model instance")
 
-        if not isinstance(self.strategy, Strategy):
-            raise TypeError("strategy argument must be a Strategy instance")
+        # if not isinstance(self.strategy, Strategy):
+        #     print(self.strategy)
+        #     print(isinstance(self.strategy, Strategy))
+        #     raise TypeError("strategy argument must be a Strategy instance")
 
         if not isinstance(self.num_clients, int):
             raise TypeError("num_clients argument must be an int")
@@ -145,6 +147,8 @@ def evaluate(
         loss, accuracy = self.global_model.evaluate(testloader, self.device)
         self.losses.append(loss)
         self.accuracies.append(accuracy)
+        if(server_round > 1 ):
+            self.strategy.study.tell(server_round-1 , accuracy)
         print(f"Server-side evaluation loss {loss} / accuracy {accuracy}")
         return loss, {"accuracy": accuracy}
 

Medfl/LearningManager/strategy.py

@@ -56,33 +56,74 @@ def __init__(
         self.min_evaluate_clients = min_evaluate_clients
         self.min_available_clients = min_available_clients
         self.initial_parameters = initial_parameters
-        self.evaluate_fn = None
+        # self.evaluate_fn = None
         self.name = name
-        self.strategy_object = eval(
-            f"fl.server.strategy.{self.name}(\
-                   fraction_fit={self.fraction_fit},\
-                  fraction_evaluate= {self.fraction_evaluate},\
-                  min_fit_clients= {self.min_fit_clients},\
-                  min_evaluate_clients= {self.min_evaluate_clients},\
-                  min_available_clients={self.min_available_clients},\
-                  on_fit_config_fn : {self.fit_config},\
-                  initial_parameters=fl.common.ndarrays_to_parameters(self.initial_parameters),\
-                  evaluate_fn={self.evaluate_fn})"
-        )
 
     def optuna_fed_optimization(self, direction:str , hpo_rate:int , params_config):
         self.study = optuna.create_study(direction=direction)
         self.hpo_rate = hpo_rate
         self.params_config = params_config
+
+    def evaluate_fn(state, round_results):
+        print('\n *********************************  round results ')
+        print(round_results)
+        print('***********************')
 
+        # Return a dictionary containing the evaluation metrics
+        return {}
+
     def fit_config(self, server_round:int)->Dict [ str , fl . common . Scalar ]:
         if(server_round - 1)%(self.hpo_rate) == 0:
-            trial = self.stydy.ask()
+            trial = self.study.ask()
             config = {
                 'learning_rate' :trial.suggest_float('learning_rate', **self.params_config['learning_rate']), 
                 'optimiser' : trial.suggest_categorical('optimizer', self.params_config['optimizer']),
                 'fl_strategy' : trial.suggest_categorical('fl_strategy', self.params_config['fl_strategy']), 
-                'reset': self.resetParams, 
-                'epochs': 10
+                # 'reset': self.resetParams, 
+                'epochs': trial.suggest_int('epochs', **self.params_config['epochs'])
             }
-        return config
+
+            print("\n ======================== \n the fit config is used \n")
+            print(server_round)
+            print(config)
+            print("\n ======================== \n ")
+            if(server_round == 10):
+                print("\n *-*-*-*-*-*-*-*---*-*-*--*-*-*-*-* \n ")
+                print(self.study.best_params)
+                optuna.visualization.plot_parallel_coordinate(self.study)
+                print("\n *--*-*-*-*--*-*-*---*-**-**--*--*-*-*-*-*-*-*-**---*-* \n ")
+            return config
+
+        return {}
+
+    def create_strategy(self):
+        self.strategy_object = self.get_strategy_by_name()(
+            fraction_fit=self.fraction_fit,
+            fraction_evaluate=self.fraction_evaluate,
+            min_fit_clients=self.min_fit_clients,
+            min_evaluate_clients=self.min_evaluate_clients,
+            min_available_clients=self.min_available_clients,
+            on_fit_config_fn=self.fit_config,
+            initial_parameters=fl.common.ndarrays_to_parameters(self.initial_parameters),
+            evaluate_fn=self.evaluate_fn
+        )
+    def get_strategy_by_name(self):
+        return eval(f"fl.server.strategy.{self.name}")
+
+    def plot_param_importances(self):
+        return optuna.visualization.plot_param_importances(self.study)
+
+    def plot_slice(self , params):
+        return optuna.visualization.plot_slice(self.study , params=params)
+
+    def plot_parallel_coordinate(self):
+        return optuna.visualization.plot_parallel_coordinate(self.study)
+
+    def plot_rank(self , params=None):
+        return optuna.visualization.plot_rank(self.study , params=params)
+
+    def plot_optimization_history(self):
+        return optuna.visualization.plot_optimization_history(self.study)
+
+
+