first

opencompass/configs/datasets/HealthBench/healthbench_model_gen_ca540e.py

@@ -1,47 +1,33 @@
 from opencompass.datasets import HealthBenchDataset, HealthBenchEvaluator
-from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_prompt_template import HealthBenchTemplate
+from opencompass.openicl.icl_inferencer import ChatInferencer
+from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 
 
 # Reader configuration
 reader_cfg = dict(
     input_columns=[
-        'example_tags', 'ideal_completions_data', 'prompt', 'prompt_id', 'rubrics', 'canary'
+        'prompt_trans'
     ],
     output_column='prompt_id', # useless
 )
 
-# Inference configuration
 infer_cfg = dict(
     prompt_template=dict(
-        type=HealthBenchTemplate,
-        key='prompt_trans',
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(
+                    role='HUMAN',
+                    prompt='{prompt}', # prompt mode: zero-shot
+                ),
+            ],
+        ),
     ),
     retriever=dict(type=ZeroRetriever),
-    inferencer=dict(type=GenInferencer),
+    inferencer=dict(type=ChatInferencer),
 )
 
-# infer_cfg = dict(
-#     prompt_template=dict(
-#         type=PromptTemplate,
-#         template=dict(
-#             round=[
-#                 dict(
-#                     role='HUMAN',
-#                     prompt='{prompt_id}', # prompt mode: zero-shot
-#                 ),
-#                 dict(
-#                     role='BOT',
-#                     prompt='{prompt_id}', # prompt mode: zero-shot
-#                 ),
-#             ],
-#         ),
-#     ),
-#     retriever=dict(type=ZeroRetriever),
-#     inferencer=dict(type=GenInferencer),
-# )
-
 # Evaluation configuration
 eval_cfg = dict(
     evaluator=dict(type=HealthBenchEvaluator),

opencompass/datasets/healthbench/healthbench.py

@@ -24,22 +24,8 @@ grading_sampler = ChatCompletionSampler(
     max_tokens=2048,
 )
 def _parse(item):
-    prompt = item['prompt']
-    new_prompts = []
-    for idx in range(len(prompt)):
-        foo = {}
-        content = prompt[idx]['content']
-        foo['prompt'] = content
-        role = prompt[idx]['role']
-        if role == 'user':
-            foo['role'] = 'HUMAN'
-        elif role == 'assistant':
-            foo['role'] = 'BOT'
-        else:
-            raise ValueError()
-        new_prompts.append(foo)
-    item['prompt_trans'] = new_prompts
-    # item["rubrics"] = [RubricItem.from_dict(d) for d in item["rubrics"]]
+    prompt = item['prompt'] + [dict(role='assistant', content='')]
+    item['prompt_trans'] = prompt
     return item
 
 HEALTHBENCH_HTML_JINJA = (
@@ -84,6 +70,7 @@ class HealthBenchDataset(BaseDataset):
         dataset = dataset.map(lambda item: _parse(item))
 
         return dataset
+    
 from collections import defaultdict
 
 from .types import MessageList
@@ -195,6 +182,62 @@ def get_usage_dict(response_usage) -> dict[str, int | None]:
         }
 import hashlib
 
+import numpy as np
+
+from .types import EvalResult, MessageList, SingleEvalResult
+
+
+def _compute_clipped_stats(
+    values: list,
+    stat: str,
+):
+    """Computes the mean (clipped to [0, 1]), bootstrap std for that mean, and
+    n_samples for final HealthBench scoring."""
+    if stat == 'mean':
+        return np.clip(np.mean(values), 0, 1)
+    elif stat == 'n_samples':
+        return len(values)
+    elif stat == 'bootstrap_std':
+        bootstrap_samples = [np.random.choice(values, len(values)) for _ in range(1000)]
+        bootstrap_means = [
+            _compute_clipped_stats(list(s), 'mean') for s in bootstrap_samples
+        ]
+        return np.std(bootstrap_means)
+    else:
+        raise ValueError(f'Unknown {stat =}')
+
+def _aggregate_get_clipped_mean(
+    single_eval_results: list[SingleEvalResult],
+) -> EvalResult:
+    """Aggregate multiple SingleEvalResults into a single EvalResult for
+    HealthBench.
+
+    For each metric, returns the stats in _compute_clipped_stats.
+    """
+    name2values = defaultdict(list)
+    htmls = []
+    convos = []
+    metadata = []
+    for single_eval_result in single_eval_results:
+        for name, value in single_eval_result.metrics.items():
+            name2values[name].append(value)
+        if single_eval_result.score is not None:
+            name2values['score'].append(single_eval_result.score)
+        htmls.append(single_eval_result.html)
+        convos.append(single_eval_result.convo)
+        metadata.append(single_eval_result.example_level_metadata)
+    final_metrics = {}
+    for name, values in name2values.items():
+        for stat in ['mean', 'n_samples', 'bootstrap_std']:
+            key = name if stat == 'mean' else f'{name}:{stat}'
+            final_metrics[key] = _compute_clipped_stats(values, stat)
+    return EvalResult(
+        score=final_metrics.pop('score', None),
+        metrics=final_metrics,
+        htmls=htmls,
+        convos=convos,
+        metadata={'example_level_metadata': metadata},
+    )
 
 class HealthBenchEvaluator(BaseEvaluator):
 
@@ -290,10 +333,8 @@ class HealthBenchEvaluator(BaseEvaluator):
 
     def score(self, predictions, references, test_set):
         results = []
-        ret = []
         if len(predictions) != len(references):
             return {'error': 'preds and refrs have different length'}
-        all_score = 0
         for idx, (i, j) in enumerate(zip(predictions, references)):
             row = test_set[idx]
             prompt_messages = row['prompt']
@@ -328,7 +369,7 @@ class HealthBenchEvaluator(BaseEvaluator):
             convo = actual_queried_prompt_messages + [
                 dict(content=response_text, role='assistant')
             ]
-            ret.append(SingleEvalResult(
+            results.append(SingleEvalResult(
                 html=html,
                 score=score,
                 convo=convo,
@@ -345,13 +386,18 @@ class HealthBenchEvaluator(BaseEvaluator):
                     ).hexdigest(),
                 },
             ))
-            all_score += score
-        avg_score = all_score / float(idx+1)
-        
-        return {
-            'score': avg_score
+        results = _aggregate_get_clipped_mean(results)
+        assert results.metrics is not None
+        metrics = results.metrics | {'score': results.score}
+        metrics = dict(sorted(metrics.items()))
+        result_dict = {
+            'score': results.score,
+            'metrics': results.metrics,
+            'htmls': results.htmls,
+            'convos': results.convos,
+            'metadata': results.metadata,
         }
-
+        return {'accuracy': result_dict['score'],}
 
 
 

opencompass/openicl/icl_prompt_template.py

@@ -257,22 +257,3 @@ class PromptTemplate:
             prompt.append(dict(section='end', pos='end'))
 
         return prompt
-
-
-class HealthBenchTemplate:
-
-    def __init__(
-        self,
-        key: Union[Dict, str],
-    ) -> None:
-        self.key = key
-
-    def generate_item(self, entry: Dict, **kwargs):
-        template = [{'section': 'round', 'pos': 'begin'}]
-        end_template = [{'section': 'round', 'pos': 'end'}]
-        mid = entry[self.key]
-        template = template + mid + end_template
-        ret = PromptList()
-        for item in template:
-            ret.append(item)
-        return ret

root

@@ -0,0 +1 @@
+/root