beta=1 behaviour fix

seqeval_with_fbeta.py

@@ -96,8 +96,8 @@ Examples:
     >>> seqeval = evaluate.load("seqeval")
     >>> results = seqeval.compute(predictions=predictions, references=references, beta=1.0)
     >>> print(list(results.keys()))
-    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
-    >>> print(results["overall_f1"])
+    ['MISC', 'PER', 'overall']
+    >>> print(results["overall"]["f1"])
     0.5
     >>> print(results["PER"]["f1"])
     1.0
@@ -126,7 +126,7 @@ class Seqeval_With_Beta(evaluate.Metric):
         self,
         predictions,
         references,
-        beta: float = 1.0,
+        beta: float = 1,
         suffix: bool = False,
         scheme: Optional[str] = None,
         mode: Optional[str] = None,
@@ -152,13 +152,12 @@ class Seqeval_With_Beta(evaluate.Metric):
         report.pop("macro avg")
         report.pop("weighted avg")
 
-        if beta != 1.0:
-            beta2 = beta ** 2
-            for k, v in report.items():
-                denom = beta2 * v["precision"] + v["recall"]
-                if denom == 0:
-                    denom += 1
-                v[f"f{beta}-score"] = (1 + beta2) * v["precision"] * v["recall"] / denom
+        beta2 = beta ** 2
+        for k, v in report.items():
+            denom = beta2 * v["precision"] + v["recall"]
+            if denom == 0:
+                denom += 1
+            v[f"f{beta}-score"] = (1 + beta2) * v["precision"] * v["recall"] / denom
 
         overall_score = report.pop("micro avg")
 
@@ -172,11 +171,6 @@ class Seqeval_With_Beta(evaluate.Metric):
             }
             for type_name, score in report.items()
         }
-        # scores["overall_precision"] = overall_score["precision"]
-        # scores["overall_recall"] = overall_score["recall"]
-        # scores["overall_f1"] = overall_score["f1-score"]
-        # scores[f"overall_f{beta}"] = overall_score[f"f{beta}-score"]
-        # scores["overall_accuracy"] = accuracy_score(y_true=references, y_pred=predictions)
 
         scores["overall"] = {
             "precision": overall_score["precision"],