add feature importance to xgb algo

federatedscope/core/monitors/monitor.py

@@ -699,3 +699,10 @@ def update_best_result(self, best_results, new_results, results_type):
                     logger.error(
                         "cfg.wandb.use=True but not install the wandb package")
                     exit()
+
+    def add_items_to_best_result(self, best_results, new_results,
+                                 results_type):
+        """
+        Add a new key: value item (results-type: new_results) to best_result
+        """
+        best_results[results_type] = new_results

federatedscope/vertical_fl/worker/vertical_server.py

@@ -90,7 +90,7 @@ def callback_funcs_for_encryped_gradient(self, message: Message):
             formatted_logs = self._monitor.format_eval_res(
                 metrics,
                 rnd=self.state,
-                role='Global-Eval-Server #',
+                role='Server #',
                 forms=self._cfg.eval.report)
             logger.info(formatted_logs)
 

federatedscope/vertical_fl/xgb_base/baseline/xgb_base_on_adult.yaml

@@ -26,7 +26,7 @@ train:
     max_tree_depth: 3
 xgb_base:
   use: True
-  use_bin: False
+  use_bin: True
   dims: [7, 14]
 eval:
   freq: 5

federatedscope/vertical_fl/xgb_base/worker/Feature_sort_base.py

@@ -130,6 +130,7 @@ def callback_func_for_split(self, message: Message):
         tree_num, node_num, split_ref = message.content
         feature_idx = split_ref['feature_idx'] - self.client.feature_list[
             self.client.ID - 1]
+        self.client.feature_importance[feature_idx] += 1
         value_idx = split_ref['value_idx']
         # feature_value = sorted(self.client.x[:, feature_idx])[value_idx]
         feature_value = self.client.x[:, feature_idx][

federatedscope/vertical_fl/xgb_base/worker/Feature_sort_by_bin.py

@@ -205,6 +205,7 @@ def callback_func_for_split(self, message: Message):
         feature_idx = split_ref['feature_idx'] - self.client.feature_list[
             self.client.ID - 1]
         bin_idx = split_ref['bin_idx']
+        self.client.feature_importance[feature_idx] += 1
         # feature_value = sorted(self.x[:, feature_idx])[value_idx]
         if bin_idx == 0:
             feature_value = self._min(

federatedscope/vertical_fl/xgb_base/worker/XGBClient.py

@@ -70,6 +70,7 @@ def __init__(self,
 
         self.feature_order = [0] * self.my_num_of_feature
 
+        self.feature_importance = [0] * self.my_num_of_feature
         # self.ss = AdditiveSecretSharing(shared_party_num=self.num_of_parties)
         # self.ns = Node_split()
         # self.fs = Feature_sort()
@@ -91,9 +92,10 @@ def __init__(self,
         self.register_handlers('model_para', self.callback_func_for_model_para)
         self.register_handlers('data_sample',
                                self.callback_func_for_data_sample)
-
         self.register_handlers('compute_next_node',
                                self.callback_func_for_compute_next_node)
+        self.register_handlers('send_feature_importance',
+                               self.callback_func_for_send_feature_importance)
 
     # save the order of values in each feature
     def order_feature(self, data):
@@ -125,6 +127,8 @@ def callback_func_for_model_para(self, message: Message):
             # init y_hat
             self.y_hat = np.random.uniform(low=0.0, high=1.0, size=len(self.y))
             # self.y_hat = np.zeros(len(self.y))
+            logger.info(f'----------- Starting a new training round (Round '
+                        f'#{self.state}) -------------')
             self.comm_manager.send(
                 Message(
                     msg_type='data_sample',
@@ -200,6 +204,23 @@ def compute_weight(self, tree_num, node_num):
                             state=self.state,
                             receiver=self.server_id,
                             content=None))
+                self.comm_manager.send(
+                    Message(msg_type='send_feature_importance',
+                            sender=self.ID,
+                            state=self.state,
+                            receiver=[
+                                each for each in list(
+                                    self.comm_manager.neighbors.keys())
+                                if each != self.server_id
+                            ],
+                            content=None))
+                self.comm_manager.send(
+                    Message(msg_type='feature_importance',
+                            sender=self.ID,
+                            state=self.state,
+                            receiver=self.server_id,
+                            content=self.feature_importance))
+
             else:
                 self.state += 1
                 logger.info(
@@ -214,3 +235,11 @@ def compute_weight(self, tree_num, node_num):
                     node_num].weight * self.tree_list[tree_num][
                         node_num].indicator
             self.compute_weight(tree_num, node_num + 1)
+
+    def callback_func_for_send_feature_importance(self, message: Message):
+        self.comm_manager.send(
+            Message(msg_type='feature_importance',
+                    sender=self.ID,
+                    state=self.state,
+                    receiver=self.server_id,
+                    content=self.feature_importance))

federatedscope/vertical_fl/xgb_base/worker/XGBServer.py

@@ -45,10 +45,13 @@ def __init__(self,
         self.tree_list = [
             Tree(self.max_tree_depth).tree for _ in range(self.num_of_trees)
         ]
+        self.feature_importance_dict = dict()
 
         self.register_handlers('test', self.callback_func_for_test)
         self.register_handlers('test_result',
                                self.callback_func_for_test_result)
+        self.register_handlers('feature_importance',
+                               self.callback_func_for_feature_importance)
 
     def trigger_for_start(self):
         if self.check_client_join_in():
@@ -64,6 +67,14 @@ def broadcast_model_para(self):
                     content=(self.lambda_, self.gamma, self.num_of_trees,
                              self.max_tree_depth)))
 
+    def callback_func_for_feature_importance(self, message: Message):
+        feature_importance = message.content
+        self.feature_importance_dict[message.sender] = feature_importance
+        if len(self.feature_importance_dict) == self.num_of_parties:
+            self.feature_importance_dict = dict(
+                sorted(self.feature_importance_dict.items(),
+                       key=lambda x: x[0]))
+
     def callback_func_for_test(self, message: Message):
         test_x = self.data['test']['x']
         test_y = self.data['test']['y']
@@ -88,6 +99,10 @@ def callback_func_for_test_result(self, message: Message):
         self._monitor.update_best_result(self.best_results,
                                          metrics,
                                          results_type='server_global_eval')
+        self._monitor.add_items_to_best_result(
+            self.best_results,
+            self.feature_importance_dict,
+            results_type='feature_importance')
         formatted_logs = self._monitor.format_eval_res(
             metrics,
             rnd=self.state,