rename outputs

Author: TomeHirata

dspy/evaluate/evaluate.py

@@ -110,7 +110,7 @@ def __call__(
             
             - score: A float percentage score (e.g., 67.30) representing overall performance
             
-            - all_outputs: a list of (example, prediction, score) tuples for each example in devset
+            - outputs: a list of (example, prediction, score) tuples for each example in devset
         """
         metric = metric if metric is not None else self.metric
         devset = devset if devset is not None else self.devset
@@ -162,7 +162,7 @@ def process_item(example):
 
         return dspy.Prediction(
             score=round(100 * ncorrect / ntotal, 2),
-            all_outputs=results,
+            outputs=results,
         )
 
 

dspy/teleprompt/bootstrap_finetune.py

@@ -222,7 +222,7 @@ def wrapped_program(**kwargs):
         with dspy.context(trace=[]):
             return program(**kwargs), dspy.settings.trace.copy()
 
-    outputs = evaluator(wrapped_program, metric=wrapped_metric).all_outputs
+    outputs = evaluator(wrapped_program, metric=wrapped_metric).outputs
 
     data = []
     for example_ind, (example, prediction, score) in enumerate(outputs):

dspy/teleprompt/random_search.py

@@ -118,7 +118,7 @@ def compile(self, student, *, teacher=None, trainset, valset=None, restrict=None
 
             result = evaluate(program)
 
-            score, subscores = result.score, [output[2] for output in result.all_outputs]
+            score, subscores = result.score, [output[2] for output in result.outputs]
 
             all_subscores.append(subscores)
 

dspy/teleprompt/utils.py

@@ -59,7 +59,7 @@ def eval_candidate_program(batch_size, trainset, candidate_program, evaluate, rn
     except Exception:
         logger.error("An exception occurred during evaluation", exc_info=True)
         # TODO: Handle this better, as -ve scores are possible
-        return dspy.Prediction(score=0.0, all_outputs=[])
+        return dspy.Prediction(score=0.0, outputs=[])
 
 def eval_candidate_program_with_pruning(
     trial, trial_logs, trainset, candidate_program, evaluate, trial_num, batch_size=100,