0530

opencompass/datasets/srbench.py

@@ -18,7 +18,7 @@ import sympy as sp
 class SRbenchDataset(BaseDataset):
     @staticmethod
     def load(path: str,local_mode=True):
-        base_path = get_data_path(path,local_mode=local_mode) # Resolve base path if necessary
+        base_path = get_data_path(path,local_mode=local_mode) 
         formula_csv_path = os.path.join(base_path, f'FeynmanEquation_23.csv')
         data_files_base_dir = os.path.join(base_path, 'Feynman_with_units')
         dataset = load_dataset('csv', data_files=formula_csv_path)['train']
@@ -46,6 +46,8 @@ class SRbenchDataset(BaseDataset):
             else:
                 prompt_1 = '\n'.join([f'x0={x1:.4f}, x1={x2:.4f}, x2={x3:.4f},y={y:.4f}' for x1, x2,x3, y in sampled_data_i[:-1]])
                 prompt_2=f'x0={sampled_data_i[-1, 0]:.4f}, x1={sampled_data_i[-1, 1]:.4f},x3={sampled_data_i[-1, 2]:.4f}, y={sampled_data_i[-1, 3]:.4f}'
+
+
             prompt_1_out.append(prompt_1)
             prompt_2_out.append(prompt_2)
         dataset=dataset.add_column(name="prompt1",column=prompt_1_out)
@@ -55,13 +57,18 @@ class SRbenchDataset(BaseDataset):
         return dataset
 
 def mydataset_postprocess(formula_str):
-
+    # 1. 删除 Markdown 残留符号
     formula_str = formula_str.replace('×', '*').replace('·', '*').replace('÷', '/')
     formula_str = formula_str.replace('−', '-').replace('^', '**')
     formula_str = formula_str.replace('“', '"').replace('”', '"').replace('’', "'")
+
+    # 2. 去除 markdown 反引号 ``` 和 $ 符号
     formula_str = formula_str.replace('`', '').replace('$', '').strip()
 
+    # 3. 提取第一行公式（防止有多行解释性输出）
     formula_str = formula_str.split('\n')[0].strip()
+
+    # 4. 用正则去除非合法字符（保留基本数学表达式）
     formula_str = re.sub(r'[^\w\s\+\-\*/\^\=\.\(\)]', '', formula_str)
 
     # 5. 确保左右去空格
@@ -70,7 +77,7 @@ def mydataset_postprocess(formula_str):
 class SRbenchDatasetEvaluator(BaseEvaluator):
     def __init__(self,
             local_mode: bool = True,path=""):
-            self.dataset=SRbenchDataset.load(path="",local_mode=local_mode)
+            self.dataset=SRbenchDataset.load(path,local_mode=local_mode)
     def parse_formula(self,formula_str, n_var=2):
         try:
             if '=' in formula_str:
@@ -115,22 +122,22 @@ class SRbenchDatasetEvaluator(BaseEvaluator):
             'R2': pd.Series(dtype=float),
             'SymbolicMatch': pd.Series(dtype=bool)
         })
+        # 结构评分（用 LLM）
         for row in range(len(references)):
             #metrics['LLM_Score'] = float(self.llm_evaluate(predictions[row], references[row], mllm='gpt-4o'))
-            print(self.dataset[row]["n_var"])
             n_var=self.dataset[row]["n_var"]
-            y_true=references[row]
+            data_sample=self.dataset[row]["data_samples_list"]
+            data_sample = np.array(data_sample)
+            x=data_sample[:,:n_var]
+            y_true=data_sample[:,-1]
             func = self.parse_formula(predictions[row], n_var=n_var)
             if func is not None:
-                try:
-                    x_vars = [x[:, i] for i in range(n_var)]
-                    y_pred = func(*x_vars)
-                    if np.isscalar(y_pred):
-                        y_pred = np.full_like(y_true, y_pred)
-                    metrics['RMSE'] = root_mean_squared_error(y_true, y_pred)
-                    metrics['R2'] = r2_score(y_true, y_pred)
-                except Exception:
-                    pass
+                x_vars = [x[:, i] for i in range(n_var)]
+                y_pred = func(*x_vars)
+                if np.isscalar(y_pred):
+                    y_pred = np.full_like(y_true, y_pred)
+                metrics['RMSE'] = root_mean_squared_error(y_true, y_pred)
+                metrics['R2'] = r2_score(y_true, y_pred)
             else:
                 metrics["R2"]=0
                 metrics["RMSE"]= np.inf
@@ -156,5 +163,3 @@ class SRbenchDatasetEvaluator(BaseEvaluator):
 
         return metrics_out
 
-
-