Added KSD

src/insight/metrics/__init__.py

@@ -7,6 +7,7 @@
     HellingerDistance,
     JensenShannonDivergence,
     KendallTauCorrelation,
+    KolmogorovSmirnovDistance,
     KullbackLeiblerDivergence,
     Mean,
     Norm,
@@ -16,23 +17,24 @@
 from .metrics_usage import CorrMatrix, DiffCorrMatrix, OneColumnMap, TwoColumnMap
 
 __all__ = [
-    "OneColumnMetric",
-    "TwoColumnMetric",
-    "OneColumnMap",
-    "TwoColumnMap",
+    "BhattacharyyaCoefficient",
     "CorrMatrix",
-    "DiffCorrMatrix",
     "CramersV",
+    "DiffCorrMatrix",
     "EarthMoversDistance",
-    "Mean",
-    "StandardDeviation",
-    "KendallTauCorrelation",
-    "Norm",
-    "TwoDataFrameMetric",
     "EarthMoversDistanceBinned",
+    "HellingerDistance",
     "JensenShannonDivergence",
+    "KendallTauCorrelation",
+    "KolmogorovSmirnovDistance",
     "KullbackLeiblerDivergence",
-    "HellingerDistance",
-    "BhattacharyyaCoefficient",
+    "Mean",
+    "Norm",
+    "OneColumnMap",
+    "OneColumnMetric",
+    "StandardDeviation",
     "TotalVariationDistance",
+    "TwoColumnMap",
+    "TwoColumnMetric",
+    "TwoDataFrameMetric",
 ]

src/insight/metrics/metrics.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 from scipy.spatial.distance import jensenshannon
-from scipy.stats import entropy, wasserstein_distance
+from scipy.stats import entropy, ks_2samp, wasserstein_distance
 
 from ..check import Check, ColumnCheck
 from .base import OneColumnMetric, TwoColumnMetric
@@ -489,3 +489,35 @@ def check_column_types(
     def _compute_metric(self, sr_a: pd.Series, sr_b: pd.Series):
         (p, q) = zipped_hist((sr_a, sr_b), check=self.check)
         return np.linalg.norm(ty.cast(pd.Series, p) - ty.cast(pd.Series, q), ord=1) / 2
+
+
+class KolmogorovSmirnovDistance(TwoColumnMetric):
+    """Kolmogorov-Smirnov Distance between two probability distributions.
+
+    The statistic ranges from 0 to 1, where a value of 0 indicates the two variables follow identical distributions,
+    and a value of 1 indicates they follow completely different distributions.
+    """
+
+    name = "kolmogorov_smirnov_distance"
+
+    @classmethod
+    def check_column_types(
+        cls, sr_a: pd.Series, sr_b: pd.Series, check: Check = ColumnCheck()
+    ) -> 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:
+        """Calculate the metric.
+        Args:
+            sr_a (pd.Series): values of a variable.
+            sr_b (pd.Series): values of another variable to compare.
+        Returns:
+            The Kolmogorov-Smirnov distance between sr_a and sr_b.
+        """
+        if sr_a.empty or sr_b.empty:
+            return 1.0
+        return ks_2samp(sr_a, sr_b)[0]  # The first element is the KS statistic

tests/test_metrics/test_metrics.py

@@ -13,6 +13,7 @@
     HellingerDistance,
     JensenShannonDivergence,
     KendallTauCorrelation,
+    KolmogorovSmirnovDistance,
     KullbackLeiblerDivergence,
     Mean,
     Norm,
@@ -27,6 +28,7 @@
 emd = EarthMoversDistance()
 hellinger_distance = HellingerDistance()
 kl_divergence = KullbackLeiblerDivergence()
+kolmogorov_smirnov_distance = KolmogorovSmirnovDistance()
 js_divergence = JensenShannonDivergence()
 norm = Norm()
 norm_ord1 = Norm(ord=1)
@@ -286,6 +288,41 @@ def infer_dtype(self, sr: pd.Series) -> pd.Series:
     assert abs(kl_divergence_with_custom_check(sr_g, sr_h) - 0.3) < 0.01
 
 
+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
+
+    # Test with distributions that are completely different
+    assert kolmogorov_smirnov_distance(pd.Series([1, 1, 1]), pd.Series([2, 2, 2])) == 1
+
+    # Test with distributions that are slightly different
+    assert 0 < kolmogorov_smirnov_distance(pd.Series([1, 2, 3]), pd.Series([1, 2, 4])) < 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
+
+    # 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
+
+    # 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
+
+    # Test with series containing NaN values
+    assert 0 <= kolmogorov_smirnov_distance(pd.Series([1, np.nan, 3]), pd.Series([1, 2, 3])) <= 1
+
+
 def test_js_divergence(group1, group2, group3):
     assert js_divergence(pd.Series([1, 0]), pd.Series([1, 0])) == 0
 

tests/test_metrics/test_metrics_usage.py

@@ -3,7 +3,14 @@
 import pytest
 
 from insight.check import ColumnCheck
-from insight.metrics import CorrMatrix, CramersV, DiffCorrMatrix, EarthMoversDistance, TwoColumnMap
+from insight.metrics import (
+    CorrMatrix,
+    CramersV,
+    DiffCorrMatrix,
+    EarthMoversDistance,
+    KolmogorovSmirnovDistance,
+    TwoColumnMap,
+)
 
 
 @pytest.fixture(scope="module")
@@ -44,6 +51,22 @@ def test_two_column_map(data):
     assert all(np.isnan(emd_map_df["metric_val"][cont]) for cont in continuous_cols)
 
 
+def test_two_column_map_with_ksd(data):
+    df, categorical_cols, continuous_cols = data[0], data[1], data[2]
+    df1 = df.sample(1000).reset_index(drop=True)
+    df2 = df.sample(1000).reset_index(drop=True)
+
+    ksd = KolmogorovSmirnovDistance()
+
+    col_map = TwoColumnMap(ksd)
+    ksd_map_df = col_map(df1, df2)
+    assert col_map.name == f"{str(ksd)}_map"
+
+    assert set(ksd_map_df.columns.to_list()) == set(["metric_val"])
+    assert all(not np.isnan(ksd_map_df["metric_val"][cat]) for cat in categorical_cols)
+    assert all(not np.isnan(ksd_map_df["metric_val"][cont]) for cont in continuous_cols)
+
+
 def test_metric_matrix(data):
     df, categorical_cols, continuous_cols = data[0], data[1], data[2]
     df1 = df.sample(1000).reset_index(drop=True)