Update Hotpotqa

examples/ags/benchmark/hotpotqa.py

@@ -4,8 +4,12 @@ import aiofiles
 import pandas as pd
 import numpy as np
 from typing import List, Tuple, Callable, Set
+from collections import Counter
 from tqdm.asyncio import tqdm_asyncio
 from scipy.optimize import linear_sum_assignment
+import string
+import re
+
 
 from examples.ags.benchmark.utils import generate_random_indices
 
@@ -16,9 +20,10 @@ def is_number(text: str) -> bool:
     except ValueError:
         return False
 
-def normalize_answer(text):
-    import re
-    import string
+def normalize_answer(s):
+    """
+    Normalize answers for evaluation.
+    """
 
     def remove_articles(text):
         return re.sub(r"\b(a|an|the)\b", " ", text)
@@ -33,77 +38,24 @@ def normalize_answer(text):
     def lower(text):
         return text.lower()
 
-    def tokenize(text):
-        return re.split(" |-", text)
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
 
-    def normalize_number(text: str) -> str:
-        if is_number(text):
-            return str(float(text))
-        else:
-            return text
-
-    parts = [
-        white_space_fix(remove_articles(normalize_number(remove_punc(lower(token)))))
-        for token in tokenize(text)
-    ]
-    parts = [part for part in parts if part.strip()]
-    normalized = " ".join(parts).strip()
-    return normalized
-
-def answer_to_bags(answer: str) -> Set[str]:
-    raw_spans = [answer]
-
-    normalized_spans = []
-    token_bags = []
-    for raw_span in raw_spans:
-        normalized_span = normalize_answer(raw_span)
-        normalized_spans.append(normalized_span)
-        token_bags.append(set(normalized_span.split()))
-    return normalized_spans, token_bags
-
-def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> List[float]:
+def f1_score(prediction, ground_truth):
     """
-    Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
-    between them and gets maximum metric values over all the answers.
+    Compute the F1 score between prediction and ground truth answers.
     """
-    scores = np.zeros([len(gold), len(predicted)])
-    for gold_index, gold_item in enumerate(gold):
-        for pred_index, pred_item in enumerate(predicted):
-            if match_numbers_if_present(gold_item, pred_item):
-                scores[gold_index, pred_index] = f1_score(pred_item, gold_item)
-    row_ind, col_ind = linear_sum_assignment(-scores)
-
-    max_scores = np.zeros([max(len(gold), len(predicted))])
-    for row, column in zip(row_ind, col_ind):
-        max_scores[row] = max(max_scores[row], scores[row, column])
-    return max_scores
-
-def match_numbers_if_present(gold_bag: Set[str], predicted_bag: Set[str]) -> bool:
-    gold_numbers = set()
-    predicted_numbers = set()
-    for word in gold_bag:
-        if is_number(word):
-            gold_numbers.add(word)
-    for word in predicted_bag:
-        if is_number(word):
-            predicted_numbers.add(word)
-    if (not gold_numbers) or gold_numbers.intersection(predicted_numbers):
-        return True
-    return False
-
-def f1_score(predicted_bag: Set[str], gold_bag: Set[str]) -> float:
-    intersection = len(gold_bag.intersection(predicted_bag))
-    if not predicted_bag:
-        precision = 1.0
-    else:
-        precision = intersection / float(len(predicted_bag))
-    if not gold_bag:
-        recall = 1.0
-    else:
-        recall = intersection / float(len(gold_bag))
-    f1 = (2 * precision * recall) / (precision + recall) if not (precision == 0.0 and recall == 0.0) else 0.0
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
     return f1
 
+
 async def load_data(file_path: str, samples=20, total_length=1000) -> List[dict]:
     data = []
     async with aiofiles.open(file_path, mode="r") as file:
@@ -120,12 +72,8 @@ async def evaluate_problem(input: str, context_str: str, graph: Callable, expect
 
     while retries < max_retries:
         try:
-            prediction, supporting_sentences = await graph(input, context_str) if graph else "None"
-            predicted_bags = answer_to_bags(prediction)
-            gold_bags = answer_to_bags(expected_output)
-
-            f1_per_bag = _align_bags(predicted_bags[1], gold_bags[1])
-            score = np.mean(f1_per_bag)
+            prediction = await graph(input, context_str) if graph else "None"
+            score = f1_score(prediction, expected_output)
 
             break
         except Exception as e:
@@ -135,11 +83,10 @@ async def evaluate_problem(input: str, context_str: str, graph: Callable, expect
             if retries == max_retries:
                 print("Maximum retries reached. Skipping this sample.")
                 prediction = None
-                supporting_sentences = None
                 score = 0
                 break
 
-    return input, prediction, expected_output, supporting_sentences, score
+    return input, prediction, expected_output, score
 
 async def evaluate_all_problems(data: List[dict], graph: Callable, max_concurrent_tasks: int = 50):
     semaphore = asyncio.Semaphore(max_concurrent_tasks)
@@ -156,9 +103,9 @@ async def evaluate_all_problems(data: List[dict], graph: Callable, max_concurren
 
     return await tqdm_asyncio.gather(*tasks, desc="Evaluating HotpotQA problems", total=len(data))
 
-def save_results_to_csv(results: List[Tuple[str, str, str, str, float]], path: str) -> float:
+def save_results_to_csv(results: List[Tuple[str, str, str, float]], path: str) -> float:
     df = pd.DataFrame(
-        results, columns=["question", "prediction", "expected_output", "supporting_sentences", "score"]
+        results, columns=["question", "prediction", "expected_output", "score"]
     )
     average_score = df["score"].mean()
 

examples/ags/experiments/baselines/cot_hotpotqa.py

@@ -19,7 +19,6 @@ HOTPOTQA_PROMPT = """
 
 class GenerateOp(BaseModel):
     answer: str = Field(default="", description="问题的答案")
-    supporting_sentences: str = Field(default="", description="支持性句子")
 
 class CoTGenerate(Operator):
     def __init__(self, llm: LLM, name: str = "Generate"):
@@ -32,7 +31,7 @@ class CoTGenerate(Operator):
             fill_kwargs["mode"] = mode
         node = await ActionNode.from_pydantic(GenerateOp).fill(**fill_kwargs)
         response = node.instruct_content.model_dump()
-        return response["answer"], response["supporting_sentences"]
+        return response["answer"]
 
 class CoTSolveGraph(SolveGraph):
     def __init__(self, name: str, llm_config, dataset: str):
@@ -40,8 +39,8 @@ class CoTSolveGraph(SolveGraph):
         self.cot_generate = CoTGenerate(self.llm)
 
     async def __call__(self, question: str, context: str) -> Tuple[str, str]:
-        answer, supporting_sentences = await self.cot_generate(question, context, mode="context_fill")
-        return answer, supporting_sentences
+        answer = await self.cot_generate(question, context, mode="context_fill")
+        return answer
 
 if __name__ == "__main__":
     async def main():