import pytest import numpy as np from numpy.testing import TestCase, assert_array_equal import scipy.sparse as sps from scipy.optimize._constraints import ( Bounds, LinearConstraint, NonlinearConstraint, PreparedConstraint, new_bounds_to_old, old_bound_to_new, strict_bounds) class TestStrictBounds(TestCase): def test_scalarvalue_unique_enforce_feasibility(self): m = 3 lb = 2 ub = 4 enforce_feasibility = False strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [-np.inf, -np.inf, -np.inf]) assert_array_equal(strict_ub, [np.inf, np.inf, np.inf]) enforce_feasibility = True strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [2, 2, 2]) assert_array_equal(strict_ub, [4, 4, 4]) def test_vectorvalue_unique_enforce_feasibility(self): m = 3 lb = [1, 2, 3] ub = [4, 5, 6] enforce_feasibility = False strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [-np.inf, -np.inf, -np.inf]) assert_array_equal(strict_ub, [np.inf, np.inf, np.inf]) enforce_feasibility = True strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [1, 2, 3]) assert_array_equal(strict_ub, [4, 5, 6]) def test_scalarvalue_vector_enforce_feasibility(self): m = 3 lb = 2 ub = 4 enforce_feasibility = [False, True, False] strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [-np.inf, 2, -np.inf]) assert_array_equal(strict_ub, [np.inf, 4, np.inf]) def test_vectorvalue_vector_enforce_feasibility(self): m = 3 lb = [1, 2, 3] ub = [4, 6, np.inf] enforce_feasibility = [True, False, True] strict_lb, strict_ub = strict_bounds(lb, ub, enforce_feasibility, m) assert_array_equal(strict_lb, [1, -np.inf, 3]) assert_array_equal(strict_ub, [4, np.inf, np.inf]) def test_prepare_constraint_infeasible_x0(): lb = np.array([0, 20, 30]) ub = np.array([0.5, np.inf, 70]) x0 = np.array([1, 2, 3]) enforce_feasibility = np.array([False, True, True], dtype=bool) bounds = Bounds(lb, ub, enforce_feasibility) pytest.raises(ValueError, PreparedConstraint, bounds, x0) pc = PreparedConstraint(Bounds(lb, ub), [1, 2, 3]) assert (pc.violation([1, 2, 3]) > 0).any() assert (pc.violation([0.25, 21, 31]) == 0).all() x0 = np.array([1, 2, 3, 4]) A = np.array([[1, 2, 3, 4], [5, 0, 0, 6], [7, 0, 8, 0]]) enforce_feasibility = np.array([True, True, True], dtype=bool) linear = LinearConstraint(A, -np.inf, 0, enforce_feasibility) pytest.raises(ValueError, PreparedConstraint, linear, x0) pc = PreparedConstraint(LinearConstraint(A, -np.inf, 0), [1, 2, 3, 4]) assert (pc.violation([1, 2, 3, 4]) > 0).any() assert (pc.violation([-10, 2, -10, 4]) == 0).all() def fun(x): return A.dot(x) def jac(x): return A def hess(x, v): return sps.csr_matrix((4, 4)) nonlinear = NonlinearConstraint(fun, -np.inf, 0, jac, hess, enforce_feasibility) pytest.raises(ValueError, PreparedConstraint, nonlinear, x0) pc = PreparedConstraint(nonlinear, [-10, 2, -10, 4]) assert (pc.violation([1, 2, 3, 4]) > 0).any() assert (pc.violation([-10, 2, -10, 4]) == 0).all() def test_violation(): def cons_f(x): return np.array([x[0] ** 2 + x[1], x[0] ** 2 - x[1]]) nlc = NonlinearConstraint(cons_f, [-1, -0.8500], [2, 2]) pc = PreparedConstraint(nlc, [0.5, 1]) assert_array_equal(pc.violation([0.5, 1]), [0., 0.]) np.testing.assert_almost_equal(pc.violation([0.5, 1.2]), [0., 0.1]) np.testing.assert_almost_equal(pc.violation([1.2, 1.2]), [0.64, 0]) np.testing.assert_almost_equal(pc.violation([0.1, -1.2]), [0.19, 0]) np.testing.assert_almost_equal(pc.violation([0.1, 2]), [0.01, 1.14]) def test_new_bounds_to_old(): lb = np.array([-np.inf, 2, 3]) ub = np.array([3, np.inf, 10]) bounds = [(None, 3), (2, None), (3, 10)] assert_array_equal(new_bounds_to_old(lb, ub, 3), bounds) bounds_single_lb = [(-1, 3), (-1, None), (-1, 10)] assert_array_equal(new_bounds_to_old(-1, ub, 3), bounds_single_lb) bounds_no_lb = [(None, 3), (None, None), (None, 10)] assert_array_equal(new_bounds_to_old(-np.inf, ub, 3), bounds_no_lb) bounds_single_ub = [(None, 20), (2, 20), (3, 20)] assert_array_equal(new_bounds_to_old(lb, 20, 3), bounds_single_ub) bounds_no_ub = [(None, None), (2, None), (3, None)] assert_array_equal(new_bounds_to_old(lb, np.inf, 3), bounds_no_ub) bounds_single_both = [(1, 2), (1, 2), (1, 2)] assert_array_equal(new_bounds_to_old(1, 2, 3), bounds_single_both) bounds_no_both = [(None, None), (None, None), (None, None)] assert_array_equal(new_bounds_to_old(-np.inf, np.inf, 3), bounds_no_both) def test_old_bounds_to_new(): bounds = ([1, 2], (None, 3), (-1, None)) lb_true = np.array([1, -np.inf, -1]) ub_true = np.array([2, 3, np.inf]) lb, ub = old_bound_to_new(bounds) assert_array_equal(lb, lb_true) assert_array_equal(ub, ub_true) bounds = [(-np.inf, np.inf), (np.array([1]), np.array([1]))] lb, ub = old_bound_to_new(bounds) assert_array_equal(lb, [-np.inf, 1]) assert_array_equal(ub, [np.inf, 1]) class TestBounds: def test_repr(self): # so that eval works from numpy import array, inf # noqa for args in ( (-1.0, 5.0), (-1.0, np.inf, True), (np.array([1.0, -np.inf]), np.array([2.0, np.inf])), (np.array([1.0, -np.inf]), np.array([2.0, np.inf]), np.array([True, False])), ): bounds = Bounds(*args) bounds2 = eval(repr(Bounds(*args))) assert_array_equal(bounds.lb, bounds2.lb) assert_array_equal(bounds.ub, bounds2.ub) assert_array_equal(bounds.keep_feasible, bounds2.keep_feasible) def test_array(self): # gh13501 b = Bounds(lb=[0.0, 0.0], ub=[1.0, 1.0]) assert isinstance(b.lb, np.ndarray) assert isinstance(b.ub, np.ndarray) def test_defaults(self): b1 = Bounds() b2 = Bounds(np.asarray(-np.inf), np.asarray(np.inf)) assert b1.lb == b2.lb assert b1.ub == b2.ub def test_input_validation(self): message = "`lb`, `ub`, and `keep_feasible` must be broadcastable." with pytest.raises(ValueError, match=message): Bounds([1, 2], [1, 2, 3]) def test_residual(self): bounds = Bounds(-2, 4) x0 = [-1, 2] np.testing.assert_allclose(bounds.residual(x0), ([1, 4], [5, 2])) class TestLinearConstraint: def test_defaults(self): A = np.eye(4) lc = LinearConstraint(A) lc2 = LinearConstraint(A, -np.inf, np.inf) assert_array_equal(lc.lb, lc2.lb) assert_array_equal(lc.ub, lc2.ub) def test_input_validation(self): A = np.eye(4) message = "`lb`, `ub`, and `keep_feasible` must be broadcastable" with pytest.raises(ValueError, match=message): LinearConstraint(A, [1, 2], [1, 2, 3]) A = np.empty((4, 3, 5)) message = "`A` must have exactly two dimensions." with pytest.raises(ValueError, match=message): LinearConstraint(A) def test_residual(self): A = np.eye(2) lc = LinearConstraint(A, -2, 4) x0 = [-1, 2] np.testing.assert_allclose(lc.residual(x0), ([1, 4], [5, 2]))