1. avoid circular reference

2. add greedy

expo/Greedy.py

@@ -0,0 +1,9 @@
+from expo.MCTS import MCTS
+
+
+class Greedy(MCTS):
+    def best_child(self):
+        if len(self.children) == 0:
+            return self.root_node
+        all_children = [child for children in self.children.values() for child in children]
+        return max(all_children, key=lambda x: x.normalized_reward.get("dev_score", 0))

expo/MCTS.py

@@ -143,6 +143,7 @@ class Node:
         role.state_saved = False
         role.change_next_instruction(self.action)
         mcts_logger.log("MCTS", f"Saving new role: {role.node_id}")
+        role = role.model_copy()
         role.save_state(static_save=True)
 
     async def expand(self, max_children):
@@ -165,18 +166,6 @@ class Node:
             node.save_new_role(new_role)
             self.add_child(node)
 
-    # def evaluate_test(self):
-    #     prediction_fpath = os.path.join(self.state["work_dir"], self.state["task"], "predictions.csv")
-    #     predictions = pd.read_csv(prediction_fpath)["target"]
-    #     # copy predictions.csv to the node_dir
-    #     predictions_node_fpath = os.path.join(self.state["node_dir"], "Node-{self.id}-predictions.csv")
-    #     predictions.to_csv(predictions_node_fpath, index=False)
-    #     # load test_target.csv
-    #     split_datasets_dir = self.state["datasets_dir"]
-    #     gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"]
-    #     metric = self.state["dataset_config"]["metric"]
-    #     return evaluate_score(predictions, gt, metric)
-
     def evaluate_prediction(self, split):
         pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv")
         pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv")
@@ -331,14 +320,10 @@ class MCTS:
             self.children[root] = []
             reward = await self.simulate(root, role)
             self.backpropagate(root, reward)
-            children = await self.expand(root)
-            # 目前是随机选择1个，后续可以改成多个
-            first_leaf = random.choice(children)
-            reward = await self.simulate(first_leaf)
-            self.backpropagate(first_leaf, reward)
+            node, reward = await self.expand_and_simulate(root)
+            # self.backpropagate(node, reward)
         else:
             root = self.root_node
-        # 后续迭代：使用UCT进行选择，expand并模拟和反向传播
         for _ in range(rollouts):  # number of rollouts
             mcts_logger.log("MCTS", f"Start the next rollout {_+1}")
             node = self.select(root)
@@ -350,13 +335,19 @@ class MCTS:
                 mcts_logger.log("MCTS", f"Terminal node's reward: {reward}")
                 self.backpropagate(node, reward)
             else:
-                if node.visited > 0:
-                    children = await self.expand(node)
-                    node = random.choice(children)
-                reward = await self.simulate(node)
-                self.backpropagate(node, reward)
+                node, reward = await self.expand_and_simulate(node)
+                # self.backpropagate(node, reward)
         return self.best_path(root)
 
+    async def expand_and_simulate(self, node):
+        # Expand and randomly select a child node, then simulate it
+        if node.visited > 0:
+            children = await self.expand(node)
+            node = random.choice(children)
+        reward = await self.simulate(node)
+        self.backpropagate(node, reward)
+        return node, reward
+
     def load_tree(self):
         def load_children_node(node):
             mcts_logger.log("MCTS", f"Load node {node.id}'s child: {node.children}")