WIP

examples/validation/_03_custom_scorer.py

@@ -517,9 +517,8 @@ def score(
 # There we have full control over the results and can calculate the precision, recall and f1-score across all
 # datapoints and remove the raw matches from the final results.
 
-def score(
-    pipeline: MyPipeline, datapoint: ECGExampleData, *, tolerance_s: float
-):
+
+def score(pipeline: MyPipeline, datapoint: ECGExampleData, *, tolerance_s: float):
     # We use the `safe_run` wrapper instead of just run. This is always a good idea.
     # We don't need to clone the pipeline here, as GridSearch will already clone the pipeline internally and `run`
     # will clone it again.
@@ -537,7 +536,10 @@ def score(
         "_raw": no_agg(matches),
     }
 
-def final_aggregator(agg_results: dict[str, float], raw_results: dict[str, list], pipeline: MyPipeline, dataset:ECGExampleData):
+
+def final_aggregator(
+    agg_results: dict[str, float], raw_results: dict[str, list], pipeline: MyPipeline, dataset: ECGExampleData
+):
     # We use pop, as we don't want to have the raw matches in our final results, but this is up to you.
     raw_matches = raw_results.pop("_raw")
     matches = np.vstack(raw_matches)
@@ -547,7 +549,10 @@ def final_aggregator(agg_results: dict[str, float], raw_results: dict[str, list]
     agg_results["per_sample__f1_score"] = f1_score
     return agg_results, raw_results
 
-agg_final_agg, raw_final_agg = Scorer(partial(score, tolerance_s=0.02), final_aggregator=final_aggregator)(pipe, example_data)
+
+agg_final_agg, raw_final_agg = Scorer(partial(score, tolerance_s=0.02), final_aggregator=final_aggregator)(
+    pipe, example_data
+)
 
 agg_final_agg