Add tests for monotonic constraint in ridge regression

Author: statmlben

tests/_test_monotonic.py

@@ -0,0 +1,50 @@
+import numpy as np
+from sklearn.datasets import make_regression
+
+from rehline import plq_Ridge_Regressor
+
+
+def test_monotonic_increasing():
+    """Test monotonic increasing constraint."""
+    # Generate synthetic data
+    X, y = make_regression(n_samples=100, n_features=10, noise=0.1, random_state=42)
+
+    # Define monotonic increasing constraint
+    constraint = [{"name": "monotonic", "decreasing": False}]
+
+    # Fit model
+    clf = plq_Ridge_Regressor(loss={"name": "huber"}, constraint=constraint, C=1.0)
+    clf.fit(X, y)
+
+    # Check if coefficients are non-decreasing
+    coef = clf.coef_
+    diffs = np.diff(coef)
+
+    # Allow for small numerical errors
+    assert np.all(diffs >= -1e-3), f"Coefficients are not monotonic increasing: {coef}"
+
+
+def test_monotonic_decreasing():
+    """Test monotonic decreasing constraint."""
+    # Generate synthetic data
+    X, y = make_regression(n_samples=100, n_features=10, noise=0.1, random_state=42)
+
+    # Define monotonic decreasing constraint
+    constraint = [{"name": "monotonic", "decreasing": True}]
+
+    # Fit model
+    clf = plq_Ridge_Regressor(loss={"name": "huber"}, constraint=constraint, C=1.0)
+    clf.fit(X, y)
+
+    # Check if coefficients are non-increasing
+    coef = clf.coef_
+    diffs = np.diff(coef)
+
+    # Allow for small numerical errors
+    assert np.all(diffs <= 1e-3), f"Coefficients are not monotonic decreasing: {coef}"
+
+
+if __name__ == "__main__":
+    test_monotonic_increasing()
+    test_monotonic_decreasing()
+    print("All monotonic constraint tests passed!")