add random tree search

expo/Greedy.py

@@ -1,3 +1,5 @@
+import random
+
 from expo.MCTS import MCTS
 
 
@@ -7,3 +9,11 @@ class Greedy(MCTS):
             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))
+
+
+class Random(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 random.choice(all_children)

expo/data/dataset.py

@@ -23,7 +23,7 @@ DI_INSTRUCTION = """\
 2. Test set does not have the target column.
 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition).
 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions.
-5. You could split the training set further to make cross-validation and hyperparameter tuning.
+5. You could utilize dev set to improve the model.
 
 ## Saving Dev and Test Predictions
 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. 

expo/experimenter/mcts.py

@@ -1,19 +1,21 @@
 from expo.evaluation.visualize_mcts import get_tree_text
 from expo.experimenter.experimenter import Experimenter
-from expo.Greedy import Greedy
+from expo.Greedy import Greedy, Random
 from expo.MCTS import MCTS
 
 
 class MCTSExperimenter(Experimenter):
     result_path: str = "results/mcts"
 
-    def __init__(self, args, greedy=False, **kwargs):
+    def __init__(self, args, tree_mode=None, **kwargs):
         super().__init__(args, **kwargs)
-        self.greedy = greedy
+        self.tree_mode = tree_mode
 
     async def run_experiment(self):
-        if self.greedy:
+        if self.tree_mode == "greedy":
             mcts = Greedy(root_node=None, max_depth=5)
+        elif self.tree_mode == "random":
+            mcts = Random(root_node=None, max_depth=5)
         else:
             mcts = MCTS(root_node=None, max_depth=5)
         best_nodes = await mcts.search(

expo/run_experiment.py

@@ -10,7 +10,9 @@ from expo.experimenter.mcts import MCTSExperimenter
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--name", type=str, default="")
-    parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy"])
+    parser.add_argument(
+        "--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy", "random"]
+    )
     get_di_args(parser)
     get_mcts_args(parser)
     get_aug_exp_args(parser)
@@ -42,7 +44,9 @@ async def main(args):
     if args.exp_mode == "mcts":
         experimenter = MCTSExperimenter(args)
     elif args.exp_mode == "greedy":
-        experimenter = MCTSExperimenter(args, greedy=True)
+        experimenter = MCTSExperimenter(args, tree_mode="greedy")
+    elif args.exp_mode == "random":
+        experimenter = MCTSExperimenter(args, tree_mode="random")
     elif args.exp_mode == "aug":
         experimenter = AugExperimenter(args)
     elif args.exp_mode == "base":