update compute_fn style

ignite/metrics/clustering/silhouette_score.py

@@ -8,18 +8,6 @@
 __all__ = ["SilhouetteScore"]
 
 
-def _get_silhouette_score(**kwargs: Any) -> Callable[[Tensor, Tensor], float]:
-    from sklearn.metrics import silhouette_score
-
-    def _silhouette_score(features: Tensor, labels: Tensor) -> float:
-        np_features = features.numpy()
-        np_labels = labels.numpy()
-        score = silhouette_score(np_features, np_labels, **kwargs)
-        return score
-
-    return _silhouette_score
-
-
 class SilhouetteScore(_ClusteringMetricBase):
     r"""Calculates the
     `silhouette score <https://en.wikipedia.org/wiki/Silhouette_(clustering)>`_.
@@ -66,7 +54,7 @@ class SilhouetteScore(_ClusteringMetricBase):
         skip_unrolling: specifies whether output should be unrolled before being fed to update method. Should be
             true for multi-output model, for example, if ``y_pred`` contains multi-ouput as ``(y_pred_a, y_pred_b)``
             Alternatively, ``output_transform`` can be used to handle this.
-        **silhouette_kwargs: additional arguments passed to ``sklearn.metrics.silhouette_score``.
+        silhouette_kwargs: additional arguments passed to ``sklearn.metrics.silhouette_score``.
 
     Examples:
         To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
@@ -109,13 +97,21 @@ def __init__(
         check_compute_fn: bool = True,
         device: Union[str, torch.device] = torch.device("cpu"),
         skip_unrolling: bool = False,
-        **silhouette_kwargs: Any,
+        silhouette_kwargs: dict | None = None,
     ) -> None:
         try:
             from sklearn.metrics import silhouette_score  # noqa: F401
         except ImportError:
             raise ModuleNotFoundError("This module requires scikit-learn to be installed.")
 
-        super().__init__(
-            _get_silhouette_score(**silhouette_kwargs), output_transform, check_compute_fn, device, skip_unrolling
-        )
+        self._silhouette_kwargs = {} if silhouette_kwargs is None else silhouette_kwargs
+
+        super().__init__(self._silhouette_score, output_transform, check_compute_fn, device, skip_unrolling)
+
+    def _silhouette_score(self, features: Tensor, labels: Tensor) -> float:
+        from sklearn.metrics import silhouette_score
+
+        np_features = features.numpy()
+        np_labels = labels.numpy()
+        score = silhouette_score(np_features, np_labels, **self._silhouette_kwargs)
+        return score