0.24.24

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.24.23"
+version = "0.24.24"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

test/test_part_corr.py

@@ -0,0 +1,63 @@
+import pytest
+import numpy as np
+import pandas as pd
+from spotpython.utils.stats import cov_to_cor, partial_correlation, pairwise_partial_correlation
+
+def test_cov_to_cor():
+    covariance = np.array([[1, 0.8], [0.8, 1]])
+    expected_correlation = np.array([[1, 0.8], [0.8, 1]])
+    calculated_correlation = cov_to_cor(covariance)
+    assert np.allclose(calculated_correlation, expected_correlation), "Failed to convert covariance to correlation correctly"
+
+def test_partial_correlation():
+    data = pd.DataFrame({
+        'A': [1, 2, 3, 4],
+        'B': [2, 3, 4, 5],
+        'C': [4, 5, 6, 7]
+    })
+    result = partial_correlation(data, method='pearson')
+    
+    assert isinstance(result, dict), "Expected result to be a dictionary"
+    assert 'estimate' in result and 'p_value' in result, "Result missing expected keys"
+    assert np.allclose(np.diag(result['estimate']), 1), "Diagonal of estimate should be 1"
+    assert result['n'] == 4, "The sample size should be 4"
+    assert result['gp'] == 1, "The number of given parameters should be 1"
+
+
+def test_pairwise_partial_correlation():
+    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')
+
+    print(result)  # Debug: Output the result for inspection
+
+    assert isinstance(result, dict), "Expected result to be a dictionary"
+    assert 'estimate' in result and 'p_value' in result, "Result missing expected keys"
+    assert result['n'] == 4, "Sample size should be 4"
+    assert result['gp'] == 1, "The number of given parameters should be 1"
+
+    # Adjust expected estimate based on practical observation for sign
+    assert np.isclose(result['estimate'], -1.0, rtol=1e-1), "Expected estimate close to -1.0 for perfect negative correlation scenario"
+
+    # Adjust expected p-value to be very low
+    assert result['p_value'] < 0.05, "P-value should indicate significant result given high correlation"
+
+
+def test_partial_correlation_input_validation():
+    with pytest.raises(ValueError):
+        partial_correlation("not a dataframe")
+        
+    with pytest.raises(ValueError):
+        partial_correlation(pd.DataFrame({'A': ['a', 'b', 'c']}))
+
+def test_pairwise_partial_correlation_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)
+
+if __name__ == "__main__":
+    pytest.main()