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PiperOrigin-RevId: 824614898

Author: WeatherBenchX authors

weatherbenchX/metrics/categorical.py

@@ -532,7 +532,7 @@ class Reliability(base.PerVariableMetric):
   predictions. It will automatically apply binning to the predictions into 10
   equal-width bins, assuming the predictions are in [0, 1]. You can modify the
   number of bins and the bin edges by passing in `bin_values` and `bin_dim`. For
-  each bin of predicted probabilities, the metric will compute the  probability
+  each bin of predicted probabilities, the metric will compute the probability
   of the positive class according to the ground truth.
   """
 
@@ -552,16 +552,19 @@ def __init__(
           1.
       ),
       bin_dim: str = 'reliability_bin',
+      statistic_suffix: str | None = None,
   ):
     self._bin_values = bin_values
     self._bin_dim = bin_dim
+    self._unique_name_suffix = statistic_suffix
 
   @property
   def statistics(self) -> Mapping[str, base.Statistic]:
     binned_prediction_wrapper = wrappers.ContinuousToBins(
         which='predictions',
         bin_values=self._bin_values,
         bin_dim=self._bin_dim,
+        unique_name_suffix=self._unique_name_suffix,
     )
     return {
         'TruePositives': wrappers.WrappedStatistic(

weatherbenchX/metrics/wrappers_test.py

@@ -27,7 +27,10 @@ class ContinuousToBinaryTest(parameterized.TestCase):
   def test_constant_threshold(self):
     target = test_utils.mock_target_data(random=True)
     ctb = wrappers.ContinuousToBinary(
-        which='both', threshold_value=0.5, threshold_dim='threshold'
+        which='both',
+        threshold_value=0.5,
+        threshold_dim='threshold',
+        unique_name_suffix='test',
     )
 
     x = target.geopotential