361 lines
14 KiB
Python
361 lines
14 KiB
Python
# Dual annealing unit tests implementation.
|
|
# Copyright (c) 2018 Sylvain Gubian <sylvain.gubian@pmi.com>,
|
|
# Yang Xiang <yang.xiang@pmi.com>
|
|
# Author: Sylvain Gubian, PMP S.A.
|
|
"""
|
|
Unit tests for the dual annealing global optimizer
|
|
"""
|
|
from scipy.optimize import dual_annealing, Bounds
|
|
|
|
from scipy.optimize._dual_annealing import EnergyState
|
|
from scipy.optimize._dual_annealing import LocalSearchWrapper
|
|
from scipy.optimize._dual_annealing import ObjectiveFunWrapper
|
|
from scipy.optimize._dual_annealing import StrategyChain
|
|
from scipy.optimize._dual_annealing import VisitingDistribution
|
|
from scipy.optimize import rosen, rosen_der
|
|
import pytest
|
|
import numpy as np
|
|
from numpy.testing import assert_equal, assert_allclose, assert_array_less
|
|
from pytest import raises as assert_raises
|
|
from scipy._lib._util import check_random_state
|
|
from scipy._lib._pep440 import Version
|
|
|
|
|
|
class TestDualAnnealing:
|
|
|
|
def setup_method(self):
|
|
# A function that returns always infinity for initialization tests
|
|
self.weirdfunc = lambda x: np.inf
|
|
# 2-D bounds for testing function
|
|
self.ld_bounds = [(-5.12, 5.12)] * 2
|
|
# 4-D bounds for testing function
|
|
self.hd_bounds = self.ld_bounds * 4
|
|
# Number of values to be generated for testing visit function
|
|
self.nbtestvalues = 5000
|
|
self.high_temperature = 5230
|
|
self.low_temperature = 0.1
|
|
self.qv = 2.62
|
|
self.seed = 1234
|
|
self.rs = check_random_state(self.seed)
|
|
self.nb_fun_call = 0
|
|
self.ngev = 0
|
|
|
|
def callback(self, x, f, context):
|
|
# For testing callback mechanism. Should stop for e <= 1 as
|
|
# the callback function returns True
|
|
if f <= 1.0:
|
|
return True
|
|
|
|
def func(self, x, args=()):
|
|
# Using Rastrigin function for performing tests
|
|
if args:
|
|
shift = args
|
|
else:
|
|
shift = 0
|
|
y = np.sum((x - shift) ** 2 - 10 * np.cos(2 * np.pi * (
|
|
x - shift))) + 10 * np.size(x) + shift
|
|
self.nb_fun_call += 1
|
|
return y
|
|
|
|
def rosen_der_wrapper(self, x, args=()):
|
|
self.ngev += 1
|
|
return rosen_der(x, *args)
|
|
|
|
# FIXME: there are some discontinuities in behaviour as a function of `qv`,
|
|
# this needs investigating - see gh-12384
|
|
@pytest.mark.parametrize('qv', [1.1, 1.41, 2, 2.62, 2.9])
|
|
def test_visiting_stepping(self, qv):
|
|
lu = list(zip(*self.ld_bounds))
|
|
lower = np.array(lu[0])
|
|
upper = np.array(lu[1])
|
|
dim = lower.size
|
|
vd = VisitingDistribution(lower, upper, qv, self.rs)
|
|
values = np.zeros(dim)
|
|
x_step_low = vd.visiting(values, 0, self.high_temperature)
|
|
# Make sure that only the first component is changed
|
|
assert_equal(np.not_equal(x_step_low, 0), True)
|
|
values = np.zeros(dim)
|
|
x_step_high = vd.visiting(values, dim, self.high_temperature)
|
|
# Make sure that component other than at dim has changed
|
|
assert_equal(np.not_equal(x_step_high[0], 0), True)
|
|
|
|
@pytest.mark.parametrize('qv', [2.25, 2.62, 2.9])
|
|
def test_visiting_dist_high_temperature(self, qv):
|
|
lu = list(zip(*self.ld_bounds))
|
|
lower = np.array(lu[0])
|
|
upper = np.array(lu[1])
|
|
vd = VisitingDistribution(lower, upper, qv, self.rs)
|
|
# values = np.zeros(self.nbtestvalues)
|
|
# for i in np.arange(self.nbtestvalues):
|
|
# values[i] = vd.visit_fn(self.high_temperature)
|
|
values = vd.visit_fn(self.high_temperature, self.nbtestvalues)
|
|
|
|
# Visiting distribution is a distorted version of Cauchy-Lorentz
|
|
# distribution, and as no 1st and higher moments (no mean defined,
|
|
# no variance defined).
|
|
# Check that big tails values are generated
|
|
assert_array_less(np.min(values), 1e-10)
|
|
assert_array_less(1e+10, np.max(values))
|
|
|
|
def test_reset(self):
|
|
owf = ObjectiveFunWrapper(self.weirdfunc)
|
|
lu = list(zip(*self.ld_bounds))
|
|
lower = np.array(lu[0])
|
|
upper = np.array(lu[1])
|
|
es = EnergyState(lower, upper)
|
|
assert_raises(ValueError, es.reset, owf, check_random_state(None))
|
|
|
|
def test_low_dim(self):
|
|
ret = dual_annealing(
|
|
self.func, self.ld_bounds, seed=self.seed)
|
|
assert_allclose(ret.fun, 0., atol=1e-12)
|
|
assert ret.success
|
|
|
|
def test_high_dim(self):
|
|
ret = dual_annealing(self.func, self.hd_bounds, seed=self.seed)
|
|
assert_allclose(ret.fun, 0., atol=1e-12)
|
|
assert ret.success
|
|
|
|
def test_low_dim_no_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
no_local_search=True, seed=self.seed)
|
|
assert_allclose(ret.fun, 0., atol=1e-4)
|
|
|
|
def test_high_dim_no_ls(self):
|
|
ret = dual_annealing(self.func, self.hd_bounds,
|
|
no_local_search=True, seed=self.seed)
|
|
assert_allclose(ret.fun, 0., atol=1e-4)
|
|
|
|
def test_nb_fun_call(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds, seed=self.seed)
|
|
assert_equal(self.nb_fun_call, ret.nfev)
|
|
|
|
def test_nb_fun_call_no_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
no_local_search=True, seed=self.seed)
|
|
assert_equal(self.nb_fun_call, ret.nfev)
|
|
|
|
def test_max_reinit(self):
|
|
assert_raises(ValueError, dual_annealing, self.weirdfunc,
|
|
self.ld_bounds)
|
|
|
|
def test_reproduce(self):
|
|
res1 = dual_annealing(self.func, self.ld_bounds, seed=self.seed)
|
|
res2 = dual_annealing(self.func, self.ld_bounds, seed=self.seed)
|
|
res3 = dual_annealing(self.func, self.ld_bounds, seed=self.seed)
|
|
# If we have reproducible results, x components found has to
|
|
# be exactly the same, which is not the case with no seeding
|
|
assert_equal(res1.x, res2.x)
|
|
assert_equal(res1.x, res3.x)
|
|
|
|
def test_rand_gen(self):
|
|
# check that np.random.Generator can be used (numpy >= 1.17)
|
|
# obtain a np.random.Generator object
|
|
rng = np.random.default_rng(1)
|
|
|
|
res1 = dual_annealing(self.func, self.ld_bounds, seed=rng)
|
|
# seed again
|
|
rng = np.random.default_rng(1)
|
|
res2 = dual_annealing(self.func, self.ld_bounds, seed=rng)
|
|
# If we have reproducible results, x components found has to
|
|
# be exactly the same, which is not the case with no seeding
|
|
assert_equal(res1.x, res2.x)
|
|
|
|
def test_bounds_integrity(self):
|
|
wrong_bounds = [(-5.12, 5.12), (1, 0), (5.12, 5.12)]
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
wrong_bounds)
|
|
|
|
def test_bound_validity(self):
|
|
invalid_bounds = [(-5, 5), (-np.inf, 0), (-5, 5)]
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
invalid_bounds)
|
|
invalid_bounds = [(-5, 5), (0, np.inf), (-5, 5)]
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
invalid_bounds)
|
|
invalid_bounds = [(-5, 5), (0, np.nan), (-5, 5)]
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
invalid_bounds)
|
|
|
|
def test_deprecated_local_search_options_bounds(self):
|
|
func = lambda x: np.sum((x-5) * (x-1))
|
|
bounds = list(zip([-6, -5], [6, 5]))
|
|
# Test bounds can be passed (see gh-10831)
|
|
|
|
with pytest.warns(RuntimeWarning, match=r"Method CG cannot handle "):
|
|
dual_annealing(
|
|
func,
|
|
bounds=bounds,
|
|
minimizer_kwargs={"method": "CG", "bounds": bounds})
|
|
|
|
def test_minimizer_kwargs_bounds(self):
|
|
func = lambda x: np.sum((x-5) * (x-1))
|
|
bounds = list(zip([-6, -5], [6, 5]))
|
|
# Test bounds can be passed (see gh-10831)
|
|
dual_annealing(
|
|
func,
|
|
bounds=bounds,
|
|
minimizer_kwargs={"method": "SLSQP", "bounds": bounds})
|
|
|
|
with pytest.warns(RuntimeWarning, match=r"Method CG cannot handle "):
|
|
dual_annealing(
|
|
func,
|
|
bounds=bounds,
|
|
minimizer_kwargs={"method": "CG", "bounds": bounds})
|
|
|
|
def test_max_fun_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds, maxfun=100,
|
|
seed=self.seed)
|
|
|
|
ls_max_iter = min(max(
|
|
len(self.ld_bounds) * LocalSearchWrapper.LS_MAXITER_RATIO,
|
|
LocalSearchWrapper.LS_MAXITER_MIN),
|
|
LocalSearchWrapper.LS_MAXITER_MAX)
|
|
assert ret.nfev <= 100 + ls_max_iter
|
|
assert not ret.success
|
|
|
|
def test_max_fun_no_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
no_local_search=True, maxfun=500, seed=self.seed)
|
|
assert ret.nfev <= 500
|
|
assert not ret.success
|
|
|
|
def test_maxiter(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds, maxiter=700,
|
|
seed=self.seed)
|
|
assert ret.nit <= 700
|
|
|
|
# Testing that args are passed correctly for dual_annealing
|
|
def test_fun_args_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
args=((3.14159,)), seed=self.seed)
|
|
assert_allclose(ret.fun, 3.14159, atol=1e-6)
|
|
|
|
# Testing that args are passed correctly for pure simulated annealing
|
|
def test_fun_args_no_ls(self):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
args=((3.14159, )), no_local_search=True,
|
|
seed=self.seed)
|
|
assert_allclose(ret.fun, 3.14159, atol=1e-4)
|
|
|
|
def test_callback_stop(self):
|
|
# Testing that callback make the algorithm stop for
|
|
# fun value <= 1.0 (see callback method)
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
callback=self.callback, seed=self.seed)
|
|
assert ret.fun <= 1.0
|
|
assert 'stop early' in ret.message[0]
|
|
assert not ret.success
|
|
|
|
@pytest.mark.parametrize('method, atol', [
|
|
('Nelder-Mead', 2e-5),
|
|
('COBYLA', 1e-5),
|
|
('Powell', 1e-8),
|
|
('CG', 1e-8),
|
|
('BFGS', 1e-8),
|
|
('TNC', 1e-8),
|
|
('SLSQP', 2e-7),
|
|
])
|
|
def test_multi_ls_minimizer(self, method, atol):
|
|
ret = dual_annealing(self.func, self.ld_bounds,
|
|
minimizer_kwargs=dict(method=method),
|
|
seed=self.seed)
|
|
assert_allclose(ret.fun, 0., atol=atol)
|
|
|
|
def test_wrong_restart_temp(self):
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
self.ld_bounds, restart_temp_ratio=1)
|
|
assert_raises(ValueError, dual_annealing, self.func,
|
|
self.ld_bounds, restart_temp_ratio=0)
|
|
|
|
def test_gradient_gnev(self):
|
|
minimizer_opts = {
|
|
'jac': self.rosen_der_wrapper,
|
|
}
|
|
ret = dual_annealing(rosen, self.ld_bounds,
|
|
minimizer_kwargs=minimizer_opts,
|
|
seed=self.seed)
|
|
assert ret.njev == self.ngev
|
|
|
|
def test_from_docstring(self):
|
|
func = lambda x: np.sum(x * x - 10 * np.cos(2 * np.pi * x)) + 10 * np.size(x)
|
|
lw = [-5.12] * 10
|
|
up = [5.12] * 10
|
|
ret = dual_annealing(func, bounds=list(zip(lw, up)), seed=1234)
|
|
assert_allclose(ret.x,
|
|
[-4.26437714e-09, -3.91699361e-09, -1.86149218e-09,
|
|
-3.97165720e-09, -6.29151648e-09, -6.53145322e-09,
|
|
-3.93616815e-09, -6.55623025e-09, -6.05775280e-09,
|
|
-5.00668935e-09], atol=4e-8)
|
|
assert_allclose(ret.fun, 0.000000, atol=5e-13)
|
|
|
|
@pytest.mark.parametrize('new_e, temp_step, accepted, accept_rate', [
|
|
(0, 100, 1000, 1.0097587941791923),
|
|
(0, 2, 1000, 1.2599210498948732),
|
|
(10, 100, 878, 0.8786035869128718),
|
|
(10, 60, 695, 0.6812920690579612),
|
|
(2, 100, 990, 0.9897404249173424),
|
|
])
|
|
def test_accept_reject_probabilistic(
|
|
self, new_e, temp_step, accepted, accept_rate):
|
|
# Test accepts unconditionally with e < current_energy and
|
|
# probabilistically with e > current_energy
|
|
|
|
rs = check_random_state(123)
|
|
|
|
count_accepted = 0
|
|
iterations = 1000
|
|
|
|
accept_param = -5
|
|
current_energy = 1
|
|
for _ in range(iterations):
|
|
energy_state = EnergyState(lower=None, upper=None)
|
|
# Set energy state with current_energy, any location.
|
|
energy_state.update_current(current_energy, [0])
|
|
|
|
chain = StrategyChain(
|
|
accept_param, None, None, None, rs, energy_state)
|
|
# Normally this is set in run()
|
|
chain.temperature_step = temp_step
|
|
|
|
# Check if update is accepted.
|
|
chain.accept_reject(j=1, e=new_e, x_visit=[2])
|
|
if energy_state.current_energy == new_e:
|
|
count_accepted += 1
|
|
|
|
assert count_accepted == accepted
|
|
|
|
# Check accept rate
|
|
pqv = 1 - (1 - accept_param) * (new_e - current_energy) / temp_step
|
|
rate = 0 if pqv <= 0 else np.exp(np.log(pqv) / (1 - accept_param))
|
|
|
|
assert_allclose(rate, accept_rate)
|
|
|
|
def test_bounds_class(self):
|
|
# test that result does not depend on the bounds type
|
|
def func(x):
|
|
f = np.sum(x * x - 10 * np.cos(2 * np.pi * x)) + 10 * np.size(x)
|
|
return f
|
|
lw = [-5.12] * 5
|
|
up = [5.12] * 5
|
|
|
|
# Unbounded global minimum is all zeros. Most bounds below will force
|
|
# a DV away from unbounded minimum and be active at solution.
|
|
up[0] = -2.0
|
|
up[1] = -1.0
|
|
lw[3] = 1.0
|
|
lw[4] = 2.0
|
|
|
|
# run optimizations
|
|
bounds = Bounds(lw, up)
|
|
ret_bounds_class = dual_annealing(func, bounds=bounds, seed=1234)
|
|
|
|
bounds_old = list(zip(lw, up))
|
|
ret_bounds_list = dual_annealing(func, bounds=bounds_old, seed=1234)
|
|
|
|
# test that found minima, function evaluations and iterations match
|
|
assert_allclose(ret_bounds_class.x, ret_bounds_list.x, atol=1e-8)
|
|
assert_allclose(ret_bounds_class.x, np.arange(-2, 3), atol=1e-7)
|
|
assert_allclose(ret_bounds_list.fun, ret_bounds_class.fun, atol=1e-9)
|
|
assert ret_bounds_list.nfev == ret_bounds_class.nfev
|