added to comments, renamed metric classes

Author: neel

eureka_ml_insights/data_utils/__init__.py

@@ -69,6 +69,7 @@
     PrependStringTransform,
     ExtractAnswerGrid,
     ExtractAnswerSpatialMapAndMaze,
+    ExtractQuestionOptions,
     ShuffleColumnsTransform,
     ColumnMatchMapTransform,
     TokenCounterTransform,

eureka_ml_insights/metrics/__init__.py

@@ -2,12 +2,13 @@
 from .geomtric_reasoning_metrics import GeoMCQMetric
 from .metrics_base import (
     CaseInsensitiveMatch,
-    CaseInsensitiveOrMatch,
     ClassicMetric,
     CompositeMetric,
     ExactMatch,
     IdentityMetric,
     Metric,
+    MultiCandidateAnyExactMatch,
+    MultiCandidateAnyCaseInsensitiveMatch,
     SubstringExistsMatch,
 )
 from .mmmu_metrics import MMMUMetric

eureka_ml_insights/metrics/metrics_base.py

@@ -144,8 +144,14 @@ def __evaluate__(self, answer_text, target_text, is_valid):
         else:
             return "incorrect"
 
-class ExactOrMatch(ExactMatch):
-    """This class checks for a case-sensitive, but otherwise exact match, and returns the or of them."""
+class MultiCandidateAnyExactMatch(ExactMatch):
+    """
+    This class checks for a case-sensitive match for a list of answers from the model output, 
+    and returns the or of the list of metric results.
+
+    This is required for answers to multiple-choice questions.  As many models sometimes give the letter answer 
+    and sometimes the full word answer. This allows one to consider the answer correct if either one was correct.
+    """
 
     def __evaluate__(self, answer_texts, target_text, is_valid):
 
@@ -170,8 +176,14 @@ class CaseInsensitiveMatch(ExactMatch):
     def __evaluate__(self, answer_text, target_text, is_valid):
         return super().__evaluate__(str(answer_text).lower(), str(target_text).lower(), is_valid)
 
-class CaseInsensitiveOrMatch(ExactOrMatch):
-    """This class checks for a case-insensitive, but otherwise exact or match."""
+class MultiCandidateAnyCaseInsensitiveMatch(MultiCandidateAnyExactMatch):
+    """
+    This class checks for a case-insensitive match for a list of answers from the model output, 
+    and returns the or of the list of metric results.
+
+    This is required for answers to multiple-choice questions.  As many models sometimes give the letter answer 
+    and sometimes the full word answer. This allows one to consider the answer correct if either one was correct.
+    """
 
     def __evaluate__(self, answer_texts, target_text, is_valid):
         answer_texts = [str(answer_text).lower() for answer_text in answer_texts]

eureka_ml_insights/user_configs/vision_language/maze.py

@@ -13,7 +13,7 @@
     PrependStringTransform,
     SequenceTransform,
 )
-from eureka_ml_insights.metrics import CaseInsensitiveOrMatch, CountAggregator
+from eureka_ml_insights.metrics import MultiCandidateAnyCaseInsensitiveMatch, CountAggregator
 
 from eureka_ml_insights.configs import (
     AggregatorConfig,
@@ -96,7 +96,7 @@ def configure_pipeline(self, model_config: ModelConfig, resume_from: str = None)
                     ),
                 },
             ),
-            metric_config=MetricConfig(CaseInsensitiveOrMatch),
+            metric_config=MetricConfig(MultiCandidateAnyCaseInsensitiveMatch),
             aggregator_configs=[
                 AggregatorConfig(CountAggregator, {"column_names": ["CaseInsensitiveOrMatch_result"], "normalize": True}),
                 AggregatorConfig(

eureka_ml_insights/user_configs/vision_language/spatial_map.py

@@ -13,7 +13,7 @@
     PrependStringTransform,
     SequenceTransform,
 )
-from eureka_ml_insights.metrics import CaseInsensitiveOrMatch, CountAggregator
+from eureka_ml_insights.metrics import MultiCandidateAnyCaseInsensitiveMatch, CountAggregator
 
 from eureka_ml_insights.configs import (
     AggregatorConfig,
@@ -97,7 +97,7 @@ def configure_pipeline(self, model_config: ModelConfig, resume_from: str = None)
                     ),
                 },
             ),
-            metric_config=MetricConfig(CaseInsensitiveOrMatch),
+            metric_config=MetricConfig(MultiCandidateAnyCaseInsensitiveMatch),
             aggregator_configs=[
                 AggregatorConfig(CountAggregator, {"column_names": ["CaseInsensitiveOrMatch_result"], "normalize": True}),
                 AggregatorConfig(