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add experimenter

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This commit is contained in:
Yizhou Chi 2024-08-30 19:55:40 +08:00
parent d14f07f9b1
commit 32759f031c
14 changed files with 196 additions and 51 deletions
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24
expo/MCTS.py
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@ -169,8 +169,8 @@ class Node():
# 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")
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")
gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"])
preds = pd.read_csv(pred_path)["target"]
preds.to_csv(pred_node_path, index=False)
@ -201,12 +201,12 @@ class Node():
score_dict = await role.get_score()
score_dict = self.evaluate_simulation(score_dict)
self.raw_reward = score_dict
if self.state["low_is_better"]:
# normalized the score to be between 0 and 1, and higher is better
def normalize_score(score):
return 1 / (1 + score)
score_dict = {k: normalize_score(v) for k, v in score_dict.items()}
self.normalized_reward = score_dict
return score_dict
@ -262,19 +262,23 @@ class MCTS():
def best_path(self, root : Node):
best_child = root
best_score = 0
def bfs(node : Node, best_score, best_child : Node):
def bfs(node : Node, best_score, best_child : Node, split):
assert split in ["test_score", "dev_score"]
if node not in self.children:
return best_score, best_child
for child in self.children[node]:
print(child.id, child.raw_value)
if child.raw_value > best_score:
best_score = child.raw_value
score = child.normalized[split]
print(child.id, score)
if score > best_score:
best_score = score
best_child = child
best_score, best_child = bfs(child, best_score, best_child)
return best_score, best_child
best_score, best_child = bfs(root, best_score, best_child)
mcts_logger.log("MCTS", f"Best Score: {best_score}, Best Node ID: {best_child.id}")
return best_child
_, best_child = bfs(root, best_score, best_child, "test_score")
_, dev_best_child = bfs(root, best_score, best_child, "dev_score")
return {"dev_best": dev_best_child,
"global_best": best_child}
def get_num_simulations(self):
return self.root_node.visited