some new features

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

@@ -0,0 +1,194 @@
+from statsmodels.compat.scipy import SP_LT_15, SP_LT_17
+import pytest
+from numpy.testing import assert_
+from numpy.testing import assert_almost_equal
+
+from statsmodels.base.optimizer import (
+    _fit_newton,
+    _fit_nm,
+    _fit_bfgs,
+    _fit_cg,
+    _fit_ncg,
+    _fit_powell,
+    _fit_lbfgs,
+    _fit_basinhopping,
+    _fit_minimize,
+)
+
+fit_funcs = {
+    "newton": _fit_newton,
+    "nm": _fit_nm,  # Nelder-Mead
+    "bfgs": _fit_bfgs,
+    "cg": _fit_cg,
+    "ncg": _fit_ncg,
+    "powell": _fit_powell,
+    "lbfgs": _fit_lbfgs,
+    "basinhopping": _fit_basinhopping,
+    "minimize": _fit_minimize,
+}
+
+
+def dummy_func(x):
+    return x ** 2
+
+
+def dummy_score(x):
+    return 2.0 * x
+
+
+def dummy_hess(x):
+    return [[2.0]]
+
+
+def dummy_bounds_constraint_func(x):
+    return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2
+
+
+def dummy_bounds():
+    return ((0, None), (0, None))
+
+
+def dummy_bounds_tight():
+    return ((2, None), (3.5, None))
+
+
+def dummy_constraints():
+    cons = (
+        {"type": "ineq", "fun": lambda x: x[0] - 2 * x[1] + 2},
+        {"type": "ineq", "fun": lambda x: -x[0] - 2 * x[1] + 6},
+        {"type": "ineq", "fun": lambda x: -x[0] + 2 * x[1] + 2},
+    )
+    return cons
+
+
+@pytest.mark.smoke
+def test_full_output_false(reset_randomstate):
+    # newton needs f, score, start, fargs, kwargs
+    # bfgs needs f, score start, fargs, kwargs
+    # nm needs ""
+    # cg ""
+    # ncg ""
+    # powell ""
+    for method in fit_funcs:
+        func = fit_funcs[method]
+        if method == "newton":
+            xopt, retvals = func(
+                dummy_func,
+                dummy_score,
+                [1.0],
+                (),
+                {},
+                hess=dummy_hess,
+                full_output=False,
+                disp=0,
+            )
+
+        else:
+            xopt, retvals = func(
+                dummy_func,
+                dummy_score,
+                [1.0],
+                (),
+                {},
+                full_output=False,
+                disp=0
+            )
+        assert_(retvals is None)
+        if method == "powell" and SP_LT_15:
+            # Fixed in SP 1.5
+            assert_(xopt.shape == () and xopt.size == 1)
+        else:
+            assert_(len(xopt) == 1)
+
+
+def test_full_output(reset_randomstate):
+    for method in fit_funcs:
+        func = fit_funcs[method]
+        if method == "newton":
+            xopt, retvals = func(
+                dummy_func,
+                dummy_score,
+                [1.0],
+                (),
+                {},
+                hess=dummy_hess,
+                full_output=True,
+                disp=0,
+            )
+
+        else:
+            xopt, retvals = func(
+                dummy_func,
+                dummy_score,
+                [1.0],
+                (),
+                {},
+                full_output=True,
+                disp=0
+            )
+
+        assert_(retvals is not None)
+        assert_("converged" in retvals)
+
+        if method == "powell" and SP_LT_15:
+            # Fixed in SP 1.5
+            assert_(xopt.shape == () and xopt.size == 1)
+        else:
+            assert_(len(xopt) == 1)
+
+
+def test_minimize_scipy_slsqp():
+    func = fit_funcs["minimize"]
+    xopt, _ = func(
+        dummy_bounds_constraint_func,
+        None,
+        (2.0, 0.0),
+        (),
+        {
+            "min_method": "SLSQP",
+            "bounds": dummy_bounds(),
+            "constraints": dummy_constraints(),
+        },
+        hess=None,
+        full_output=False,
+        disp=0,
+    )
+    assert_almost_equal(xopt, [1.4, 1.7], 4)
+
+
+@pytest.mark.skipif(SP_LT_15, reason="Powell bounds support added in SP 1.5")
+def test_minimize_scipy_powell():
+    func = fit_funcs["minimize"]
+    xopt, _ = func(
+        dummy_bounds_constraint_func,
+        None,
+        (3, 4.5),
+        (),
+        {
+            "min_method": "Powell",
+            "bounds": dummy_bounds_tight(),
+        },
+        hess=None,
+        full_output=False,
+        disp=0,
+    )
+    assert_almost_equal(xopt, [2, 3.5], 4)
+
+
+@pytest.mark.skipif(SP_LT_17, reason="NM bounds support added in SP 1.7")
+def test_minimize_scipy_nm():
+    func = fit_funcs["minimize"]
+    xopt, _ = func(
+        dummy_bounds_constraint_func,
+        None,
+        (3, 4.5),
+        (),
+        {
+            "min_method": "Nelder-Mead",
+            "bounds": dummy_bounds_tight(),
+        },
+        hess=None,
+        full_output=False,
+        disp=0,
+    )
+    assert_almost_equal(xopt, [2, 3.5], 4)