0.24.26

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.24.25"
+version = "0.24.26"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/utils/stats.py

@@ -44,7 +44,7 @@ def partial_correlation(x, method="pearson") -> dict:
         Commun Stat Appl Methods 22, 6 (Nov 2015), 665–674.
 
     Examples:
-        >>> from spotpython.utils.stats import cov_to_cor
+        >>> from spotpython.utils.stats import partial_correlation
         >>> import numpy as np
         >>> import pandas as pd
         >>> data = pd.DataFrame({
@@ -61,6 +61,7 @@ def partial_correlation(x, method="pearson") -> dict:
                           [0. , 0. , 0. ]]), ...
         }
     """
+    eps = 1e-6
     if isinstance(x, pd.DataFrame):
         x = x.to_numpy()
     if not isinstance(x, np.ndarray):
@@ -83,13 +84,12 @@ def partial_correlation(x, method="pearson") -> dict:
     p_cor = -cov_to_cor(icvx)
     np.fill_diagonal(p_cor, 1)
 
-    epsilon = 1e-10  # small value to prevent division by zero
     if method == "kendall":
         denominator = np.sqrt(2 * (2 * (n - gp) + 5) / (9 * (n - gp) * (n - 1 - gp)))
         statistic = p_cor / denominator
         p_value = 2 * norm.cdf(-np.abs(statistic))
     else:
-        factor = np.sqrt((n - 2 - gp) / (1 - p_cor**2 + epsilon))
+        factor = np.sqrt((n - 2 - gp) / (1 + eps - p_cor**2))
         statistic = p_cor * factor
         p_value = 2 * t.cdf(-np.abs(statistic), df=n - 2 - gp)
 
@@ -99,8 +99,9 @@ def partial_correlation(x, method="pearson") -> dict:
     return {"estimate": p_cor, "p_value": p_value, "statistic": statistic, "n": n, "gp": gp, "method": method}
 
 
-def pairwise_partial_correlation(x, y, z, method="pearson") -> dict:
-    """Calculate the pairwise partial correlation between two variables given others.
+def partial_correlation_test(x, y, z, method="pearson") -> dict:
+    """The partial correlation coefficient between x and y given z.
+        x and y should be arrays (vectors) of the same length, and z should be a data frame (matrix).
 
     Args:
         x (array-like): The first variable as a 1-dimensional array or list.

test/test_part_corr.py

@@ -1,7 +1,7 @@
 import pytest
 import numpy as np
 import pandas as pd
-from spotpython.utils.stats import cov_to_cor, partial_correlation, pairwise_partial_correlation
+from spotpython.utils.stats import cov_to_cor, partial_correlation, partial_correlation_test
 
 def test_cov_to_cor():
     covariance = np.array([[1, 0.8], [0.8, 1]])
@@ -24,11 +24,11 @@ def test_partial_correlation():
     assert result['gp'] == 1, "The number of given parameters should be 1"
 
 
-def test_pairwise_partial_correlation():
+def test_partial_correlation_test():
     x = [1, 2, 3, 4]
     y = [2, 3, 4, 5]
     z = pd.DataFrame({'C': [4, 5, 6, 7]})
-    result = pairwise_partial_correlation(x, y, z, method='pearson')
+    result = partial_correlation_test(x, y, z, method='pearson')
 
     print(result)  # Debug: Output the result for inspection
 
@@ -51,13 +51,13 @@ def test_partial_correlation_input_validation():
     with pytest.raises(ValueError):
         partial_correlation(pd.DataFrame({'A': ['a', 'b', 'c']}))
 
-def test_pairwise_partial_correlation_input_validation():
+def test_partial_correlation_test_input_validation():
     x = [1, 2, 3, 4]
     y = [2, 3, 4, 5]
     z_invalid = "not a dataframe"
 
     with pytest.raises(ValueError):
-        pairwise_partial_correlation(x, y, z_invalid)
+        partial_correlation_test(x, y, z_invalid)
 
 if __name__ == "__main__":
     pytest.main()