Move higher_is_better and aggregation into their own functions

lm_eval/base.py

@@ -130,22 +130,8 @@ class Dataset(abc.ABC):
     @abc.abstractmethod
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a 
-        list of dicts, each with the following format:
-
-        {
-            "submetric": str,
-            "value": float,
-            "higher_is_better": bool,
-            "aggregation": ([float] -> float),
-        }
-
-        * `submetric` should be the name of the metric
-        * `value` should be the value of the metric
-        * `higher_is_better` determines whether a higher metric is better
-        * `aggregation` should be a function that takes a list of floats and 
-            aggregates them into one float. This should be the same for all 
-            submetrics of the same name; if it differs, an error should be 
-            raised.
+        dict where keys are the names of submetrics and values are the values of 
+        the metric for that one document
 
         :param doc:
             The document as returned from training_docs, validation_docs, or test_docs.
@@ -154,6 +140,24 @@ class Dataset(abc.ABC):
         """
         pass
 
+    @abc.abstractmethod
+    def aggregation(self):
+        """
+        :returns: {str: [float] -> float}
+            A dictionary where keys are the names of submetrics and values are 
+            functions that aggregate a list of metrics
+        """
+        pass
+
+    @abc.abstractmethod
+    def higher_is_better(self):
+        """
+        :returns: {str: bool}
+            A dictionary where keys are the names of submetrics and values are 
+            whether a higher value of the submetric is better
+        """
+        pass
+
     def fewshot_description(self):
         return ""
 

lm_eval/tasks/sat.py

@@ -90,11 +90,17 @@ class SATAnalogies(Dataset):
 
         acc = 1. if np.argmax(results) == gold else 0.
 
-        return [
-            {
-                "submetric": "acc",
-                "value": acc,
-                "higher_is_better": True,
-                "aggregation": mean
+        return {
+            "acc": acc
         }
-        ]
+    
+    def higher_is_better(self):
+        return {
+            "acc": True
+        }
+    
+    def aggregation(self):
+        return {
+            "acc": mean
+        }
+

lm_eval/tasks/superglue.py

@@ -38,18 +38,22 @@ class BoolQ(HFTask):
     def process_results(self, doc, results):
         ll_yes, ll_no = results
         gold = doc["label"]
-        print(ll_yes > ll_no, gold)
 
         acc = 1. if (ll_yes > ll_no) == gold else 0.
 
-        return [
-            {
-                "submetric": "acc",
-                "value": acc,
-                "higher_is_better": True,
-                "aggregation": mean
+        return {
+            "acc": acc
+        }
+    
+    def higher_is_better(self):
+        return {
+            "acc": True
+        }
+    
+    def aggregation(self):
+        return {
+            "acc": mean
         }
-        ]
 
 
 class CommitmentBank(HFTask):

main.py

@@ -94,20 +94,13 @@ def main():
         doc = docs[(task_name, doc_id)]
 
         metrics = task.process_results(doc, requests)
-        for metric in metrics:
-            results[task_name][metric['submetric']] = {
-                "higher_is_better": metric["higher_is_better"],
-                "aggregation": metric["aggregation"]
-            }
-            vals[(task_name, metric['submetric'])].append(metric['value'])
+        for metric, value in metrics.items():
+            vals[(task_name, metric)].append(value)
     
     # aggregate results
-    for task_name, submetrics in results.items():
-        for k in submetrics.keys():
-            submetrics[k]['value'] = submetrics[k]['aggregation'](vals[(task_name, k)])
-
-            # can't serialize a function
-            del submetrics[k]['aggregation']
+    for (task_name, metric), items in vals.items():
+        task = task_dict[task_name]
+        results[task_name][metric] = task.aggregation()[metric](items)
 
     dumped = json.dumps(results, indent=2)
     print(dumped)