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cleanup and smoothed_values added.
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@ -160,5 +160,5 @@ class AUROCWithMWU(torchmetrics.Metric):
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# Compute auroc with the weight set to 1/2 when positive & negative have identical scores.
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auroc = auroc_le - (auroc_le - auroc_lt) / 2.0
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return auroc
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return auroc
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@ -21,7 +21,8 @@ def _smooth(
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label_smoothing: smoothing constant.
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Returns: Smoothed values.
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"""
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return value * (1.0 - label_smoothing) + 0.5 * label_smoothing
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smoothed_values = value * (1.0 - label_smoothing) + 0.5 * label_smoothing
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return smoothed_values
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def _binary_cross_entropy_with_clipping(
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@ -179,7 +180,6 @@ class RCE(torchmetrics.Metric):
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pred_ce = self.binary_cross_entropy.compute()
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rce = (1.0 - (pred_ce / baseline_ce)) * 100
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return rce
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def reset(self):
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