some new features

.venv/lib/python3.12/site-packages/statsmodels/base/tests/test_penalties.py

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+"""
+Created on Wed May 16 22:21:26 2018
+
+Author: Josef Perktold
+License: BSD-3
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+
+import statsmodels.base._penalties as smpen
+from statsmodels.tools.numdiff import approx_fprime, approx_hess
+
+
+class CheckPenalty:
+
+    def test_symmetry(self):
+        pen = self.pen
+        x = self.params
+
+        p = np.array([pen.func(np.atleast_1d(xi)) for xi in x])
+        assert_allclose(p, p[::-1], rtol=1e-10)
+        # func(0) should be 0
+        assert_allclose(pen.func(0 * np.atleast_1d(x[0])), 0, rtol=1e-10)
+
+    def test_derivatives(self):
+        pen = self.pen
+        x = self.params
+
+        ps = np.array([pen.deriv(np.atleast_1d(xi)) for xi in x])
+        psn = np.array([approx_fprime(np.atleast_1d(xi), pen.func) for xi in x])
+        assert_allclose(ps, psn, rtol=1e-7, atol=1e-8)
+
+        ph = np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
+        phn = np.array([approx_hess(np.atleast_1d(xi), pen.func) for xi in x])
+        if ph.ndim == 2:
+            # SmoothedSCAD returns only diagonal if hessian if independent
+            # TODO should ww allow this also in L@?
+            ph = np.array([np.diag(phi) for phi in ph])
+        assert_allclose(ph, phn, rtol=1e-7, atol=1e-8)
+
+
+class TestL2Constraints0(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.L2ConstraintsPenalty()
+
+    def test_equivalence(self):
+        # compare plain penalty with included weights or restriction
+        pen = self.pen
+        x = self.params
+        k = x.shape[1]
+
+        pen2 = smpen.L2ConstraintsPenalty(weights=np.ones(k))
+        pen3 = smpen.L2ConstraintsPenalty(restriction=np.eye(k))
+        f = pen.func(x.T)
+        d = pen.deriv(x.T)
+        d2 =  np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
+        for pen_ in [pen2, pen3]:
+            assert_allclose(pen_.func(x.T), f, rtol=1e-7, atol=1e-8)
+            assert_allclose(pen_.deriv(x.T), d, rtol=1e-7, atol=1e-8)
+            d2_ =  np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
+            assert_allclose(d2_, d2, rtol=1e-10, atol=1e-8)
+
+
+class TestL2Constraints1(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.L2ConstraintsPenalty(restriction=[[1,0], [1, 1]])
+
+    def test_values(self):
+        pen = self.pen
+        x = self.params
+        r = pen.restriction
+        f = (r.dot(x.T)**2).sum(0)
+        assert_allclose(pen.func(x.T), f, rtol=1e-7, atol=1e-8)
+
+
+class TestSmoothedSCAD(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.SCADSmoothed(tau=0.05, c0=0.05)
+
+
+class TestPseudoHuber(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.PseudoHuber(0.1)
+
+    def test_backward_compatibility(self):
+        wts = [0.5]
+        pen = smpen.PseudoHuber(0.1, weights=wts)
+        assert_equal(pen.weights, wts)
+
+    def test_deprecated_priority(self):
+        weights = [1.0]
+        pen = smpen.PseudoHuber(0.1, weights=weights)
+
+        assert_equal(pen.weights, weights)
+
+    def test_weights_assignment(self):
+        weights = [1.0, 2.0]
+        pen = smpen.PseudoHuber(0.1, weights=weights)
+        assert_equal(pen.weights, weights)
+
+
+class TestL2(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.L2()
+
+    def test_backward_compatibility(self):
+        wts = [0.5]
+        pen = smpen.L2(weights=wts)
+        assert_equal(pen.weights, wts)
+
+    def test_deprecated_priority(self):
+        weights = [1.0]
+        pen = smpen.L2(weights=weights)
+        assert_equal(pen.weights, weights)
+
+    def test_weights_assignment(self):
+        weights = [1.0, 2.0]
+        pen = smpen.L2(weights=weights)
+        assert_equal(pen.weights, weights)
+
+
+class TestNonePenalty(CheckPenalty):
+
+    @classmethod
+    def setup_class(cls):
+        x0 = np.linspace(-0.2, 0.2, 11)
+        cls.params = np.column_stack((x0, x0))
+        cls.pen = smpen.NonePenalty()