Add warning

sdmetrics/single_column/statistical/kscomplement.py

@@ -1,5 +1,6 @@
 """Kolmogorov-Smirnov test based Metric."""
 
+import numpy as np
 import pandas as pd
 from scipy.stats import ks_2samp
 
@@ -56,7 +57,13 @@ def compute(real_data, synthetic_data):
             real_data = pd.to_numeric(real_data)
             synthetic_data = pd.to_numeric(synthetic_data)
 
-        statistic, _ = ks_2samp(real_data, synthetic_data)
+        try:
+            statistic, _ = ks_2samp(real_data, synthetic_data)
+        except ValueError as e:
+            if str(e) == 'Data passed to ks_2samp must not be empty':
+                return np.nan
+            else:
+                raise ValueError(e)
 
         return 1 - statistic
 

sdmetrics/timeseries/inter_row_msas.py

@@ -1,5 +1,7 @@
 """InterRowMSAS module."""
 
+import warnings
+
 import numpy as np
 import pandas as pd
 
@@ -77,17 +79,28 @@ def compute(real_data, synthetic_data, n_rows_diff=1, apply_log=False):
             real_values = np.log(real_values)
             synthetic_values = np.log(synthetic_values)
 
-        def calculate_differences(keys, values, n_rows_diff):
+        def calculate_differences(keys, values, n_rows_diff, data_name):
+            group_sizes = values.groupby(keys).size()
+            num_invalid_groups = group_sizes[group_sizes <= n_rows_diff].count()
+            if num_invalid_groups > 0:
+                warnings.warn(
+                    f"n_rows_diff '{n_rows_diff}' is greater than the "
+                    f'size of {num_invalid_groups} sequence keys in {data_name}.'
+                )
+
             differences = values.groupby(keys).apply(
                 lambda group: np.mean(
                     group.to_numpy()[n_rows_diff:] - group.to_numpy()[:-n_rows_diff]
                 )
                 if len(group) > n_rows_diff
                 else np.nan
             )
+
             return pd.Series(differences)
 
-        real_diff = calculate_differences(real_keys, real_values, n_rows_diff)
-        synthetic_diff = calculate_differences(synthetic_keys, synthetic_values, n_rows_diff)
+        real_diff = calculate_differences(real_keys, real_values, n_rows_diff, 'real_data')
+        synthetic_diff = calculate_differences(
+            synthetic_keys, synthetic_values, n_rows_diff, 'synthetic_data'
+        )
 
         return KSComplement.compute(real_diff, synthetic_diff)

tests/unit/timeseries/test_inter_row_msas.py

@@ -154,3 +154,23 @@ def test_compute_invalid_apply_log(self):
                 n_rows_diff=1,
                 apply_log='True',  # Should be a boolean, not a string
             )
+
+    def test_compute_warning(self):
+        """Test a warning is raised when n_rows_diff is greater than sequence values size."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        real_values = pd.Series([1, 2, 3, 4, 5, 6])
+        synthetic_keys = pd.Series(['id3', 'id3', 'id3', 'id4', 'id4', 'id4'])
+        synthetic_values = pd.Series([1, 10, 3, 7, 5, 1])
+
+        # Run and Assert
+        warn_msg = "n_rows_diff '10' is greater than the size of 2 sequence keys in real_data."
+        with pytest.warns(UserWarning, match=warn_msg):
+            score = InterRowMSAS.compute(
+                real_data=(real_keys, real_values),
+                synthetic_data=(synthetic_keys, synthetic_values),
+                n_rows_diff=10,
+            )
+
+        # Assert
+        assert pd.isna(score)