From e05ead4a56d8cd856f416a86e8393df66c45fa52 Mon Sep 17 00:00:00 2001
From: appukuttan-shailesh <appukuttan.shailesh@gmail.com>
Date: Fri, 19 Aug 2022 12:19:47 +0200
Subject: [PATCH] Remove duplicate `RelativeDifferenceScore`

There seems two be two similar copies of `RelativeDifferenceScore`.
Keeping the second instance which seems to be more consistent with other scores.
---
 sciunit/scores/complete.py | 75 --------------------------------------
 1 file changed, 75 deletions(-)

diff --git a/sciunit/scores/complete.py b/sciunit/scores/complete.py
index 8d6046c..f3faadf 100644
--- a/sciunit/scores/complete.py
+++ b/sciunit/scores/complete.py
@@ -244,81 +244,6 @@ def __str__(self):
         return 'Ratio = %.2f' % self.score
     
     
-class RelativeDifferenceScore(Score):
-    """A relative difference between prediction and observation.
-    
-    The absolute value of the difference between the prediction and the
-    observation is divided by a reference value with the same units. This
-    reference scale should be chosen for each test such that normalization
-    produces directly comparable scores across tests. For example, if 5 volts
-    represents a medium size difference for TestA, and 10 seconds represents a
-    medium size difference for TestB, then 5 volts and 10 seconds should be
-    used for this reference scale in TestA and TestB, respectively. The
-    attribute `scale` can be passed to the compute method or set for the whole
-    class in advance. Otherwise, a scale of 1 (in the units of the
-    observation and prediction) will be used.
-    """
-
-    _allowed_types = (float,)
-
-    _description = ('The relative difference between the prediction and the observation')
-
-    _best = 0.0  # A RelativeDifferenceScore of 0.0 is best
-
-    _worst = np.inf
-    
-    scale = None
-
-    def _check_score(self, score):
-        if score < 0.0:
-            raise errors.InvalidScoreError(("RelativeDifferenceScore was initialized with "
-                                            "a score of %f, but a RelativeDifferenceScore "
-                                            "must be non-negative.") % score)
-
-    @classmethod
-    def compute(cls, observation: Union[dict, float, int, pq.Quantity],
-                     prediction: Union[dict, float, int, pq.Quantity],
-                     key=None,
-                     scale: Union[float, int, pq.Quantity, None] = None) -> 'RelativeDifferenceScore':
-        """Compute the relative difference between the observation and a prediction.
-
-        Returns:
-            RelativeDifferenceScore: A relative difference between an observation and a prediction.
-        """
-        assert isinstance(observation, (dict, float, int, pq.Quantity))
-        assert isinstance(prediction, (dict, float, int, pq.Quantity))
-        
-        obs, pred = cls.extract_means_or_values(observation, prediction,
-                                                key=key)
-        
-        scale = scale or cls.scale or (obs/float(obs))
-        assert type(obs) is type(scale)
-        assert type(obs) is type(pred)
-        if isinstance(obs, pq.Quantity):
-            assert obs.units == pred.units, \
-                "Prediction must have the same units as the observation"
-            assert obs.units == scale.units, \
-                "RelativeDifferenceScore.Scale must have the same units as the observation"
-        assert scale > 0, \
-            "RelativeDifferenceScore.scale must be positive (not %g)" % scale
-        value = np.abs(pred - obs) / scale
-        value = utils.assert_dimensionless(value)
-        return RelativeDifferenceScore(value)
-
-    @property
-    def norm_score(self) -> float:
-        """Return 1.0 for a ratio of 0.0, falling to 0.0 for extremely large values.
-
-        Returns:
-            float: The value of the norm score.
-        """
-        x = self.score
-        return 1 / (1+x)
-
-    def __str__(self):
-        return 'Relative Difference = %.2f' % self.score
-      
-
 class RelativeDifferenceScore(Score):
     """A relative difference between prediction and observation.