Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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183 lines
6.6 KiB
183 lines
6.6 KiB
import pytest
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import numpy as np
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from numpy.testing import TestCase, assert_array_equal
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import scipy.sparse as sps
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from scipy.optimize._constraints import (
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Bounds, LinearConstraint, NonlinearConstraint, PreparedConstraint,
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new_bounds_to_old, old_bound_to_new, strict_bounds)
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class TestStrictBounds(TestCase):
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def test_scalarvalue_unique_enforce_feasibility(self):
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m = 3
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lb = 2
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ub = 4
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enforce_feasibility = False
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [-np.inf, -np.inf, -np.inf])
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assert_array_equal(strict_ub, [np.inf, np.inf, np.inf])
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enforce_feasibility = True
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [2, 2, 2])
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assert_array_equal(strict_ub, [4, 4, 4])
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def test_vectorvalue_unique_enforce_feasibility(self):
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m = 3
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lb = [1, 2, 3]
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ub = [4, 5, 6]
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enforce_feasibility = False
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [-np.inf, -np.inf, -np.inf])
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assert_array_equal(strict_ub, [np.inf, np.inf, np.inf])
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enforce_feasibility = True
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [1, 2, 3])
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assert_array_equal(strict_ub, [4, 5, 6])
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def test_scalarvalue_vector_enforce_feasibility(self):
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m = 3
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lb = 2
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ub = 4
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enforce_feasibility = [False, True, False]
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [-np.inf, 2, -np.inf])
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assert_array_equal(strict_ub, [np.inf, 4, np.inf])
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def test_vectorvalue_vector_enforce_feasibility(self):
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m = 3
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lb = [1, 2, 3]
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ub = [4, 6, np.inf]
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enforce_feasibility = [True, False, True]
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strict_lb, strict_ub = strict_bounds(lb, ub,
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enforce_feasibility,
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m)
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assert_array_equal(strict_lb, [1, -np.inf, 3])
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assert_array_equal(strict_ub, [4, np.inf, np.inf])
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def test_prepare_constraint_infeasible_x0():
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lb = np.array([0, 20, 30])
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ub = np.array([0.5, np.inf, 70])
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x0 = np.array([1, 2, 3])
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enforce_feasibility = np.array([False, True, True], dtype=bool)
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bounds = Bounds(lb, ub, enforce_feasibility)
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pytest.raises(ValueError, PreparedConstraint, bounds, x0)
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pc = PreparedConstraint(Bounds(lb, ub), [1, 2, 3])
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assert (pc.violation([1, 2, 3]) > 0).any()
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assert (pc.violation([0.25, 21, 31]) == 0).all()
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x0 = np.array([1, 2, 3, 4])
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A = np.array([[1, 2, 3, 4], [5, 0, 0, 6], [7, 0, 8, 0]])
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enforce_feasibility = np.array([True, True, True], dtype=bool)
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linear = LinearConstraint(A, -np.inf, 0, enforce_feasibility)
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pytest.raises(ValueError, PreparedConstraint, linear, x0)
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pc = PreparedConstraint(LinearConstraint(A, -np.inf, 0),
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[1, 2, 3, 4])
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assert (pc.violation([1, 2, 3, 4]) > 0).any()
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assert (pc.violation([-10, 2, -10, 4]) == 0).all()
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def fun(x):
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return A.dot(x)
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def jac(x):
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return A
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def hess(x, v):
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return sps.csr_matrix((4, 4))
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nonlinear = NonlinearConstraint(fun, -np.inf, 0, jac, hess,
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enforce_feasibility)
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pytest.raises(ValueError, PreparedConstraint, nonlinear, x0)
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pc = PreparedConstraint(nonlinear, [-10, 2, -10, 4])
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assert (pc.violation([1, 2, 3, 4]) > 0).any()
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assert (pc.violation([-10, 2, -10, 4]) == 0).all()
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def test_violation():
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def cons_f(x):
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return np.array([x[0] ** 2 + x[1], x[0] ** 2 - x[1]])
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nlc = NonlinearConstraint(cons_f, [-1, -0.8500], [2, 2])
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pc = PreparedConstraint(nlc, [0.5, 1])
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assert_array_equal(pc.violation([0.5, 1]), [0., 0.])
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np.testing.assert_almost_equal(pc.violation([0.5, 1.2]), [0., 0.1])
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np.testing.assert_almost_equal(pc.violation([1.2, 1.2]), [0.64, 0])
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np.testing.assert_almost_equal(pc.violation([0.1, -1.2]), [0.19, 0])
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np.testing.assert_almost_equal(pc.violation([0.1, 2]), [0.01, 1.14])
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def test_new_bounds_to_old():
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lb = np.array([-np.inf, 2, 3])
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ub = np.array([3, np.inf, 10])
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bounds = [(None, 3), (2, None), (3, 10)]
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assert_array_equal(new_bounds_to_old(lb, ub, 3), bounds)
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bounds_single_lb = [(-1, 3), (-1, None), (-1, 10)]
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assert_array_equal(new_bounds_to_old(-1, ub, 3), bounds_single_lb)
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bounds_no_lb = [(None, 3), (None, None), (None, 10)]
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assert_array_equal(new_bounds_to_old(-np.inf, ub, 3), bounds_no_lb)
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bounds_single_ub = [(None, 20), (2, 20), (3, 20)]
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assert_array_equal(new_bounds_to_old(lb, 20, 3), bounds_single_ub)
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bounds_no_ub = [(None, None), (2, None), (3, None)]
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assert_array_equal(new_bounds_to_old(lb, np.inf, 3), bounds_no_ub)
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bounds_single_both = [(1, 2), (1, 2), (1, 2)]
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assert_array_equal(new_bounds_to_old(1, 2, 3), bounds_single_both)
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bounds_no_both = [(None, None), (None, None), (None, None)]
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assert_array_equal(new_bounds_to_old(-np.inf, np.inf, 3), bounds_no_both)
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def test_old_bounds_to_new():
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bounds = ([1, 2], (None, 3), (-1, None))
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lb_true = np.array([1, -np.inf, -1])
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ub_true = np.array([2, 3, np.inf])
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lb, ub = old_bound_to_new(bounds)
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assert_array_equal(lb, lb_true)
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assert_array_equal(ub, ub_true)
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bounds = [(-np.inf, np.inf), (np.array([1]), np.array([1]))]
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lb, ub = old_bound_to_new(bounds)
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assert_array_equal(lb, [-np.inf, 1])
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assert_array_equal(ub, [np.inf, 1])
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def test_bounds_repr():
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from numpy import array, inf # so that eval works
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for args in (
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(-1.0, 5.0),
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(-1.0, np.inf, True),
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(np.array([1.0, -np.inf]), np.array([2.0, np.inf])),
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(np.array([1.0, -np.inf]), np.array([2.0, np.inf]), np.array([True, False])),
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):
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bounds = Bounds(*args)
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bounds2 = eval(repr(Bounds(*args)))
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assert_array_equal(bounds.lb, bounds2.lb)
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assert_array_equal(bounds.ub, bounds2.ub)
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assert_array_equal(bounds.keep_feasible, bounds2.keep_feasible)
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