docstring

sdmetrics/single_column/statistical/key_uniqueness.py

@@ -8,7 +8,7 @@
 class KeyUniqueness(SingleColumnMetric):
     """Key uniqueness metric.
 
-    Compute the fraction of rows in the synthetic data that are unique.
+    The proportion of data points in the synthetic data that are unique.
 
     Attributes:
         name (str):
@@ -51,7 +51,7 @@ def compute_breakdown(cls, real_data, synthetic_data):
 
     @classmethod
     def compute(cls, real_data, synthetic_data):
-        """Compute the key uniqueness of two columns.
+        """Compute the key uniqueness metric.
 
         Args:
             real_data (pandas.Series):
@@ -61,7 +61,7 @@ def compute(cls, real_data, synthetic_data):
 
         Returns:
             float:
-                The key uniqueness of the two columns.
+                The proportion of data points in the synthetic data that are unique.
         """
         return cls.compute_breakdown(real_data, synthetic_data)['score']
 

tests/unit/single_column/statistical/test_key_uniqueness.py

@@ -31,7 +31,7 @@ def test_compute_breakdown_with_duplicates_in_real_data(self):
         synthetic_data = pd.Series([1, 2, np.nan, 3, np.nan, 5, 2, np.nan, 6, None])
         metric = KeyUniqueness()
 
-        # Run
+        # Run and Assert
         expected_message = 'The real data contains NA or duplicate values.'
         with pytest.raises(InvalidDataError, match=expected_message):
             metric.compute_breakdown(real_data, synthetic_data)
@@ -49,6 +49,7 @@ def test_compute(self, compute_breakdown_mock):
         result = metric.compute(real_data, synthetic_data)
 
         # Assert
+        compute_breakdown_mock.assert_called_once_with(real_data, synthetic_data)
         assert result == 0.6
 
     @patch('sdmetrics.single_column.statistical.key_uniqueness.SingleColumnMetric.normalize')