KS Distance for continuous values only (#172)

* KS distance fixes

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update test_metrics_usage.py

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mark <[email protected]>

src/insight/metrics/metrics.py

@@ -520,8 +520,6 @@ def check_column_types(
     ) -> bool:
         if check.continuous(sr_a) and check.continuous(sr_b):
             return True
-        if check.categorical(sr_a) and check.categorical(sr_b):
-            return True
         return False
 
     def _compute_metric(self, sr_a: pd.Series, sr_b: pd.Series) -> float:

tests/test_metrics/test_metrics.py

@@ -292,37 +292,50 @@ def infer_dtype(self, sr: pd.Series) -> pd.Series:
 
 def test_kolmogorov_smirnov_distance(group1):
     # Test with identical distributions
-    assert kolmogorov_smirnov_distance(pd.Series([1, 2, 3]), pd.Series([1, 2, 3])) == 0
-    assert kolmogorov_smirnov_distance(group1, group1) == 0
+    cat_a = pd.Series(["a", "b", "c", "b"], dtype="string")
+    cat_b = pd.Series(["a", "b", "c", "a"], dtype="string")
+    cont_a = pd.Series([1, 2, 3, 3], dtype="int64")
+    cont_b = pd.Series([1, 1, 2, 3], dtype="int64")
 
-    # Test with distributions that are completely different
-    assert kolmogorov_smirnov_distance(pd.Series([1, 1, 1]), pd.Series([2, 2, 2])) == 1
+    class SimpleCheck(ColumnCheck):
+        def infer_dtype(self, sr: pd.Series) -> pd.Series:
+            return sr
+
+        def continuous(self, sr: pd.Series) -> bool:
+            return sr.dtype.kind in ("i", "f")
+
+        def categorical(self, sr: pd.Series) -> bool:
+            return sr.dtype == "string"
+
+    ksd = KolmogorovSmirnovDistance(check=SimpleCheck())
 
-    # Test with distributions that are slightly different
-    assert 0 < kolmogorov_smirnov_distance(pd.Series([1, 2, 3]), pd.Series([1, 2, 4])) < 1
+    assert ksd(cat_a, cat_b) is None
+    assert ksd(cat_a, cont_b) is None
+    assert ksd(cont_a, cat_b) is None
+    assert ksd(cont_a, cont_a) == 0
+
+    assert ksd(cont_a, cont_b) == 0.25
+
+    # Test with distributions that are completely different
+    assert ksd(pd.Series([1, 1, 1]), pd.Series([2, 2, 2])) == 1
 
     # Test with random distributions
     np.random.seed(0)
     group2 = pd.Series(np.random.normal(0, 1, 1000))
     group3 = pd.Series(np.random.normal(0.5, 1, 1000))
-    assert 0 < kolmogorov_smirnov_distance(group2, group3) < 1
+    assert 0 < ksd(group2, group3) < 1
 
     # Test with distributions of different lengths
-    assert 0 < kolmogorov_smirnov_distance(pd.Series([1, 2, 3]), pd.Series([1, 2, 3, 4])) < 1
-
-    # Test with categorical data
-    cat1 = pd.Series(["a", "b", "c", "a"])
-    cat2 = pd.Series(["b", "c", "d"])
-    assert 0 < kolmogorov_smirnov_distance(cat1, cat2) < 1
+    assert 0 < ksd(pd.Series([1, 2, 3]), pd.Series([1, 2, 3, 4])) < 1
 
     # Edge cases
     # Test with one or both series empty
-    assert kolmogorov_smirnov_distance(pd.Series([]), pd.Series([1, 2, 3])) == 1
-    assert kolmogorov_smirnov_distance(pd.Series([1, 2, 3]), pd.Series([])) == 1
-    assert kolmogorov_smirnov_distance(pd.Series([]), pd.Series([])) == 1
+    assert ksd(pd.Series([]), pd.Series([1, 2, 3])) == 1
+    assert ksd(pd.Series([1, 2, 3]), pd.Series([])) == 1
+    assert ksd(pd.Series([]), pd.Series([])) == 1
 
     # Test with series containing NaN values
-    assert 0 <= kolmogorov_smirnov_distance(pd.Series([1, np.nan, 3]), pd.Series([1, 2, 3])) <= 1
+    assert 0 <= ksd(pd.Series([1, np.nan, 3]), pd.Series([1, 2, 3])) <= 1
 
 
 def test_js_divergence(group1, group2, group3):

tests/test_metrics/test_metrics_usage.py

@@ -65,7 +65,7 @@ def test_two_column_map_with_ksd(data):
 
     assert col_map.name == expected_column_name
     assert set(ksd_map_df.columns.to_list()) == set([expected_column_name])
-    assert all(not np.isnan(ksd_map_df[expected_column_name][cat]) for cat in categorical_cols)
+    assert all(np.isnan(ksd_map_df[expected_column_name][cat]) for cat in categorical_cols)
     assert all(not np.isnan(ksd_map_df[expected_column_name][cont]) for cont in continuous_cols)