feat: add zero_division parameter to F1 metric

metrics/f1/f1.py

@@ -39,6 +39,11 @@
         - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
         - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
     sample_weight (`list` of `float`): Sample weights Defaults to None.
+    zero_division (`int` or `"warn"`, optional): Passed directly to sklearn's `f1_score`. Controls behavior when a label has no predicted or true samples. Use `0`, `1`, or `"warn"` (default sklearn behavior).
+
+        - 0: Returns 0 when there is a zero division.
+        - 1: Returns 1 when there is a zero division.
+        - `'warn'`: Raises a warning and then returns 0 when there is a zero division.
 
 Returns:
     f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
@@ -84,6 +89,13 @@
         >>> results = f1_metric.compute(predictions=[[0, 1, 1], [1, 1, 0]], references=[[0, 1, 1], [0, 1, 0]], average="macro")
         >>> print(round(results['f1'], 2))
         0.67
+
+    Example 6-The same multiclass example as in Example 4, but with `zero_division` set to `1` for labels with no predicted or true samples.
+        >>> predictions = [0, 0, 0, 0, 0]
+        >>> references = [0, 1, 0, 1, 2]
+        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None, labels=[0, 1, 2, 3], zero_division=1)
+        >>> print([round(res, 2) for res in results['f1']])
+        [0.57, 0.0, 0.0, 1.0]
 """
 
 
@@ -123,8 +135,17 @@ def _info(self):
             reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
         )
 
-    def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
+    def _compute(
+        self,
+        predictions,
+        references,
+        labels=None,
+        **kwargs,
+    ):
         score = f1_score(
-            references, predictions, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight
+            references,
+            predictions,
+            labels=labels,
+            **kwargs,
         )
         return {"f1": score if getattr(score, "size", 1) > 1 else float(score)}