simplify imports of metric functions (#3292)

ignite/metrics/cohen_kappa.py

@@ -71,23 +71,17 @@ def __init__(
         # initalize weights
         self.weights = weights
 
-        self.cohen_kappa_compute = self.get_cohen_kappa_fn()
-
         super(CohenKappa, self).__init__(
-            self.cohen_kappa_compute,
+            self._cohen_kappa_score,
             output_transform=output_transform,
             check_compute_fn=check_compute_fn,
             device=device,
             skip_unrolling=skip_unrolling,
         )
 
-    def get_cohen_kappa_fn(self) -> Callable[[torch.Tensor, torch.Tensor], float]:
-        """Return a function computing Cohen Kappa from scikit-learn."""
+    def _cohen_kappa_score(self, y_targets: torch.Tensor, y_preds: torch.Tensor) -> float:
         from sklearn.metrics import cohen_kappa_score
 
-        def wrapper(y_targets: torch.Tensor, y_preds: torch.Tensor) -> float:
-            y_true = y_targets.cpu().numpy()
-            y_pred = y_preds.cpu().numpy()
-            return cohen_kappa_score(y_true, y_pred, weights=self.weights)
-
-        return wrapper
+        y_true = y_targets.cpu().numpy()
+        y_pred = y_preds.cpu().numpy()
+        return cohen_kappa_score(y_true, y_pred, weights=self.weights)

ignite/metrics/regression/spearman_correlation.py

@@ -9,16 +9,13 @@
 from ignite.metrics.regression._base import _check_output_shapes, _check_output_types
 
 
-def _get_spearman_r() -> Callable[[Tensor, Tensor], float]:
+def _spearman_r(predictions: Tensor, targets: Tensor) -> float:
     from scipy.stats import spearmanr
 
-    def _compute_spearman_r(predictions: Tensor, targets: Tensor) -> float:
-        np_preds = predictions.flatten().numpy()
-        np_targets = targets.flatten().numpy()
-        r = spearmanr(np_preds, np_targets).statistic
-        return r
-
-    return _compute_spearman_r
+    np_preds = predictions.flatten().numpy()
+    np_targets = targets.flatten().numpy()
+    r = spearmanr(np_preds, np_targets).statistic
+    return r
 
 
 class SpearmanRankCorrelation(EpochMetric):
@@ -92,7 +89,7 @@ def __init__(
         except ImportError:
             raise ModuleNotFoundError("This module requires scipy to be installed.")
 
-        super().__init__(_get_spearman_r(), output_transform, check_compute_fn, device, skip_unrolling)
+        super().__init__(_spearman_r, output_transform, check_compute_fn, device, skip_unrolling)
 
     def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
         y_pred, y = output[0].detach(), output[1].detach()