logger + nans

sdmetrics/single_column/statistical/key_uniqueness.py

@@ -1,9 +1,11 @@
 """Key Uniqueness Metric."""
+import logging
 
-from sdmetrics.errors import InvalidDataError
 from sdmetrics.goal import Goal
 from sdmetrics.single_column.base import SingleColumnMetric
 
+LOGGER = logging.getLogger(__name__)
+
 
 class KeyUniqueness(SingleColumnMetric):
     """Key uniqueness metric.
@@ -41,11 +43,12 @@ def compute_breakdown(cls, real_data, synthetic_data):
                 The score breakdown of the key uniqueness metric.
         """
         has_duplicates = real_data.duplicated().any()
-        if has_duplicates:
-            raise InvalidDataError('The real data contains NA or duplicate values.')
+        has_nans = real_data.isna().any()
+        if has_duplicates or has_nans:
+            LOGGER.info('The real data contains NA or duplicate values.')
 
-        duplicates_synthetic = synthetic_data.duplicated()
-        score = 1 - duplicates_synthetic.sum() / len(synthetic_data)
+        nans_or_duplicates_synthetic = synthetic_data.duplicated() | synthetic_data.isna()
+        score = 1 - nans_or_duplicates_synthetic.sum() / len(synthetic_data)
 
         return {'score': score}
 

tests/unit/single_column/statistical/test_key_uniqueness.py

@@ -2,9 +2,7 @@
 
 import numpy as np
 import pandas as pd
-import pytest
 
-from sdmetrics.errors import InvalidDataError
 from sdmetrics.single_column.statistical import KeyUniqueness
 
 
@@ -22,19 +20,22 @@ def test_compute_breakdown(self):
         result = metric.compute_breakdown(real_data, synthetic_data)
 
         # Assert
-        assert result == {'score': 0.6}
+        assert result == {'score': 0.5}
 
-    def test_compute_breakdown_with_duplicates_in_real_data(self):
+    @patch('sdmetrics.single_column.statistical.key_uniqueness.LOGGER')
+    def test_compute_breakdown_with_duplicates_in_real_data(self, logger_mock):
         """Test the ``compute_breakdown`` method with duplicates in the real data."""
         # Setup
         real_data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10] * 2)
         synthetic_data = pd.Series([1, 2, np.nan, 3, np.nan, 5, 2, np.nan, 6, None])
         metric = KeyUniqueness()
 
-        # Run and Assert
+        # Run
+        metric.compute_breakdown(real_data, synthetic_data)
+
+        # 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)
+        logger_mock.info.assert_called_once_with(expected_message)
 
     @patch('sdmetrics.single_column.statistical.key_uniqueness.KeyUniqueness.compute_breakdown')
     def test_compute(self, compute_breakdown_mock):