275 lines
12 KiB
Python
275 lines
12 KiB
Python
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"""
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Unit test for constraint conversion
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"""
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import numpy as np
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from numpy.testing import (assert_array_almost_equal,
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assert_allclose, assert_warns, suppress_warnings)
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import pytest
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from scipy.optimize import (NonlinearConstraint, LinearConstraint,
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OptimizeWarning, minimize, BFGS)
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from .test_minimize_constrained import (Maratos, HyperbolicIneq, Rosenbrock,
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IneqRosenbrock, EqIneqRosenbrock,
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BoundedRosenbrock, Elec)
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class TestOldToNew:
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x0 = (2, 0)
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bnds = ((0, None), (0, None))
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method = "trust-constr"
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def test_constraint_dictionary_1(self):
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2
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cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
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{'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
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{'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
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with suppress_warnings() as sup:
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sup.filter(UserWarning, "delta_grad == 0.0")
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res = minimize(fun, self.x0, method=self.method,
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bounds=self.bnds, constraints=cons)
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assert_allclose(res.x, [1.4, 1.7], rtol=1e-4)
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assert_allclose(res.fun, 0.8, rtol=1e-4)
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def test_constraint_dictionary_2(self):
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2
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cons = {'type': 'eq',
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'fun': lambda x, p1, p2: p1*x[0] - p2*x[1],
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'args': (1, 1.1),
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'jac': lambda x, p1, p2: np.array([[p1, -p2]])}
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with suppress_warnings() as sup:
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sup.filter(UserWarning, "delta_grad == 0.0")
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res = minimize(fun, self.x0, method=self.method,
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bounds=self.bnds, constraints=cons)
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assert_allclose(res.x, [1.7918552, 1.62895927])
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assert_allclose(res.fun, 1.3857466063348418)
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def test_constraint_dictionary_3(self):
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2
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cons = [{'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
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NonlinearConstraint(lambda x: x[0] - x[1], 0, 0)]
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with suppress_warnings() as sup:
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sup.filter(UserWarning, "delta_grad == 0.0")
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res = minimize(fun, self.x0, method=self.method,
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bounds=self.bnds, constraints=cons)
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assert_allclose(res.x, [1.75, 1.75], rtol=1e-4)
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assert_allclose(res.fun, 1.125, rtol=1e-4)
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class TestNewToOld:
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def test_multiple_constraint_objects(self):
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2 + (x[2] - 0.75) ** 2
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x0 = [2, 0, 1]
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coni = [] # only inequality constraints (can use cobyla)
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methods = ["slsqp", "cobyla", "trust-constr"]
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# mixed old and new
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coni.append([{'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
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NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
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coni.append([LinearConstraint([1, -2, 0], -2, np.inf),
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NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
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coni.append([NonlinearConstraint(lambda x: x[0] - 2 * x[1] + 2, 0, np.inf),
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NonlinearConstraint(lambda x: x[0] - x[1], -1, 1)])
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for con in coni:
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funs = {}
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for method in methods:
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with suppress_warnings() as sup:
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sup.filter(UserWarning)
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result = minimize(fun, x0, method=method, constraints=con)
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funs[method] = result.fun
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assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-4)
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assert_allclose(funs['cobyla'], funs['trust-constr'], rtol=1e-4)
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def test_individual_constraint_objects(self):
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2 + (x[2] - 0.75) ** 2
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x0 = [2, 0, 1]
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cone = [] # with equality constraints (can't use cobyla)
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coni = [] # only inequality constraints (can use cobyla)
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methods = ["slsqp", "cobyla", "trust-constr"]
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# nonstandard data types for constraint equality bounds
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cone.append(NonlinearConstraint(lambda x: x[0] - x[1], 1, 1))
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cone.append(NonlinearConstraint(lambda x: x[0] - x[1], [1.21], [1.21]))
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cone.append(NonlinearConstraint(lambda x: x[0] - x[1],
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1.21, np.array([1.21])))
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# multiple equalities
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cone.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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1.21, 1.21)) # two same equalities
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cone.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[1.21, 1.4], [1.21, 1.4])) # two different equalities
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cone.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[1.21, 1.21], 1.21)) # equality specified two ways
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cone.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[1.21, -np.inf], [1.21, np.inf])) # equality + unbounded
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# nonstandard data types for constraint inequality bounds
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coni.append(NonlinearConstraint(lambda x: x[0] - x[1], 1.21, np.inf))
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coni.append(NonlinearConstraint(lambda x: x[0] - x[1], [1.21], np.inf))
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coni.append(NonlinearConstraint(lambda x: x[0] - x[1],
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1.21, np.array([np.inf])))
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coni.append(NonlinearConstraint(lambda x: x[0] - x[1], -np.inf, -3))
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coni.append(NonlinearConstraint(lambda x: x[0] - x[1],
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np.array(-np.inf), -3))
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# multiple inequalities/equalities
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coni.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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1.21, np.inf)) # two same inequalities
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cone.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[1.21, -np.inf], [1.21, 1.4])) # mixed equality/inequality
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coni.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[1.1, .8], [1.2, 1.4])) # bounded above and below
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coni.append(NonlinearConstraint(
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lambda x: [x[0] - x[1], x[1] - x[2]],
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[-1.2, -1.4], [-1.1, -.8])) # - bounded above and below
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# quick check of LinearConstraint class (very little new code to test)
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cone.append(LinearConstraint([1, -1, 0], 1.21, 1.21))
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cone.append(LinearConstraint([[1, -1, 0], [0, 1, -1]], 1.21, 1.21))
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cone.append(LinearConstraint([[1, -1, 0], [0, 1, -1]],
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[1.21, -np.inf], [1.21, 1.4]))
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for con in coni:
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funs = {}
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for method in methods:
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with suppress_warnings() as sup:
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sup.filter(UserWarning)
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result = minimize(fun, x0, method=method, constraints=con)
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funs[method] = result.fun
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assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-3)
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assert_allclose(funs['cobyla'], funs['trust-constr'], rtol=1e-3)
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for con in cone:
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funs = {}
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for method in methods[::2]: # skip cobyla
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with suppress_warnings() as sup:
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sup.filter(UserWarning)
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result = minimize(fun, x0, method=method, constraints=con)
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funs[method] = result.fun
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assert_allclose(funs['slsqp'], funs['trust-constr'], rtol=1e-3)
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class TestNewToOldSLSQP:
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method = 'slsqp'
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elec = Elec(n_electrons=2)
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elec.x_opt = np.array([-0.58438468, 0.58438466, 0.73597047,
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-0.73597044, 0.34180668, -0.34180667])
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brock = BoundedRosenbrock()
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brock.x_opt = [0, 0]
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list_of_problems = [Maratos(),
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HyperbolicIneq(),
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Rosenbrock(),
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IneqRosenbrock(),
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EqIneqRosenbrock(),
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elec,
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brock
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]
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def test_list_of_problems(self):
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for prob in self.list_of_problems:
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with suppress_warnings() as sup:
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sup.filter(UserWarning)
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result = minimize(prob.fun, prob.x0,
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method=self.method,
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bounds=prob.bounds,
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constraints=prob.constr)
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assert_array_almost_equal(result.x, prob.x_opt, decimal=3)
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def test_warn_mixed_constraints(self):
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# warns about inefficiency of mixed equality/inequality constraints
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2 + (x[2] - 0.75) ** 2
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cons = NonlinearConstraint(lambda x: [x[0]**2 - x[1], x[1] - x[2]],
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[1.1, .8], [1.1, 1.4])
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bnds = ((0, None), (0, None), (0, None))
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with suppress_warnings() as sup:
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sup.filter(UserWarning, "delta_grad == 0.0")
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assert_warns(OptimizeWarning, minimize, fun, (2, 0, 1),
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method=self.method, bounds=bnds, constraints=cons)
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def test_warn_ignored_options(self):
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# warns about constraint options being ignored
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def fun(x):
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return (x[0] - 1) ** 2 + (x[1] - 2.5) ** 2 + (x[2] - 0.75) ** 2
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x0 = (2, 0, 1)
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if self.method == "slsqp":
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bnds = ((0, None), (0, None), (0, None))
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else:
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bnds = None
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cons = NonlinearConstraint(lambda x: x[0], 2, np.inf)
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res = minimize(fun, x0, method=self.method,
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bounds=bnds, constraints=cons)
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# no warnings without constraint options
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assert_allclose(res.fun, 1)
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cons = LinearConstraint([1, 0, 0], 2, np.inf)
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res = minimize(fun, x0, method=self.method,
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bounds=bnds, constraints=cons)
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# no warnings without constraint options
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assert_allclose(res.fun, 1)
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cons = []
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cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
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keep_feasible=True))
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cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
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hess=BFGS()))
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cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
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finite_diff_jac_sparsity=42))
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cons.append(NonlinearConstraint(lambda x: x[0]**2, 2, np.inf,
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finite_diff_rel_step=42))
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cons.append(LinearConstraint([1, 0, 0], 2, np.inf,
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keep_feasible=True))
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for con in cons:
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assert_warns(OptimizeWarning, minimize, fun, x0,
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method=self.method, bounds=bnds, constraints=cons)
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class TestNewToOldCobyla:
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method = 'cobyla'
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list_of_problems = [
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Elec(n_electrons=2),
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Elec(n_electrons=4),
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]
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@pytest.mark.slow
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def test_list_of_problems(self):
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for prob in self.list_of_problems:
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with suppress_warnings() as sup:
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sup.filter(UserWarning)
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truth = minimize(prob.fun, prob.x0,
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method='trust-constr',
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bounds=prob.bounds,
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constraints=prob.constr)
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result = minimize(prob.fun, prob.x0,
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method=self.method,
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bounds=prob.bounds,
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constraints=prob.constr)
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assert_allclose(result.fun, truth.fun, rtol=1e-3)
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