🔨 added ocr UQ

src/arc_spice/eval/inference_utils.py

@@ -8,6 +8,7 @@
 from tqdm import tqdm
 
 from arc_spice.data.multieurlex_utils import MultiHot
+from arc_spice.eval.ocr_error import ocr_error
 from arc_spice.eval.translation_error import get_comet_model
 from arc_spice.variational_pipelines.RTC_single_component_pipeline import (
     RTCSingleComponentPipeline,
@@ -63,10 +64,16 @@ def get_results(
             )._asdict()
         return results_dict
 
-    def recognition_results(self, *args, **kwargs):
+    def recognition_results(
+        self,
+        clean_output: dict[str, str | list[dict[str, str | torch.Tensor]]],
+        var_output: dict[str, dict],
+        **kwargs,
+    ):
         # ### RECOGNITION ###
-        # TODO: add this into results_getter : issue #14
-        return RecognitionResults(confidence=None, accuracy=None)
+        charerror = ocr_error(clean_output)
+        confidence = var_output["recognition"]["mean_entropy"]
+        return RecognitionResults(confidence=confidence, accuracy=charerror)
 
     def translation_results(
         self,

src/arc_spice/eval/ocr_error.py

@@ -21,6 +21,7 @@ def ocr_error(ocr_output: dict[Any, Any]) -> float:
                             {
                                 'generated_text': gen text from the ocr model (str)
                                 'target': target text (str)
+                                'entropies': entropies for UQ (torch.Tensor)
                             }
                         ]
                         'outpu': pieced back together full string (str)

src/arc_spice/variational_pipelines/RTC_single_component_pipeline.py

@@ -112,7 +112,7 @@ def __init__(
             step_name="recognition",
             input_key="ocr_data",
             forward_function=self.recognise,
-            confidence_function=self.recognise,  # THIS WILL NEED UPDATING : #issue 14
+            confidence_function=self.get_ocr_confidence,
             n_variational_runs=n_variational_runs,
             **kwargs,
         )

src/arc_spice/variational_pipelines/utils.py

@@ -519,8 +519,8 @@ def get_ocr_confidence(self, var_output: dict) -> dict[str, float]:
         # Adapted for variational methods from: https://arxiv.org/pdf/2412.01221
         stacked_entropies = torch.stack(
             [
-                output["entropies"]
-                for output in var_output["recognition"]["full_output"]
+                [data["entropies"] for data in output["full_output"]]
+                for output in var_output["recognition"]
             ],
             dim=1,
         )