Merge branch 'main' into feature-branch-timeseries-metrics

.github/workflows/dependency_checker.yml

@@ -16,6 +16,7 @@ jobs:
       run: |
         python -m pip install .[dev]
         make check-deps OUTPUT_FILEPATH=latest_requirements.txt
+        make fix-lint
     - name: Create pull request
       id: cpr
       uses: peter-evans/create-pull-request@v4

latest_requirements.txt

@@ -4,4 +4,4 @@ pandas==2.2.3
 plotly==5.24.1
 scikit-learn==1.5.2
 scipy==1.13.1
-tqdm==4.66.5
+tqdm==4.67.0

pyproject.toml

@@ -76,7 +76,7 @@ dev = [
     'watchdog>=1.0.1,<5',
 
     # style check
-    'ruff>=0.3.2,<0.7.2',
+    'ruff>=0.3.2,<1',
 
     # distribute on PyPI
     'twine>=1.10.0,<6',

sdmetrics/column_pairs/statistical/contingency_similarity.py

@@ -44,7 +44,7 @@ def compute(cls, real_data, synthetic_data):
         contingency_synthetic = synthetic.groupby(list(columns), dropna=False).size() / len(
             synthetic
         )
-        combined_index = contingency_real.index.union(contingency_synthetic.index)
+        combined_index = contingency_real.index.union(contingency_synthetic.index, sort=False)
         contingency_synthetic = contingency_synthetic.reindex(combined_index, fill_value=0)
         contingency_real = contingency_real.reindex(combined_index, fill_value=0)
         diff = abs(contingency_real - contingency_synthetic).fillna(0)

tests/integration/reports/multi_table/test_quality_report.py

@@ -342,7 +342,7 @@ def test_quality_report_with_errors():
             None,
         ],
     })
-    assert score == 0.7249603174603174
+    assert score == 0.7249603174603175
     pd.testing.assert_frame_equal(properties, expected_properties)
     pd.testing.assert_frame_equal(details_column_shapes, expected_details)
 

tests/unit/column_pairs/statistical/test_contingency_similarity.py

@@ -1,6 +1,7 @@
 from unittest.mock import patch
 
 import pandas as pd
+import pytest
 
 from sdmetrics.column_pairs.statistical import ContingencySimilarity
 
@@ -53,3 +54,15 @@ def test_normalize(self, normalize_mock):
         # Assert
         normalize_mock.assert_called_once_with(raw_score)
         assert result == normalize_mock.return_value
+
+    @pytest.mark.filterwarnings('error:.*The values in the array are unorderable.*:RuntimeWarning')
+    def test_no_runtime_warning_raised(self):
+        """Test that no RuntimeWarning warning is raised when the metric is computed."""
+        # Setup
+        real_data = pd.DataFrame(data={'A': ['value'] * 4, 'B': ['1', '2', '3', pd.NA]})
+        synthetic_data = pd.DataFrame(data={'A': ['value'] * 3, 'B': ['1', '2', pd.NA]})
+
+        # Run and Assert
+        ContingencySimilarity.compute(
+            real_data=real_data[['A', 'B']], synthetic_data=synthetic_data[['A', 'B']]
+        )