projektAI/venv/Lib/site-packages/scipy/optimize/tests/test__dual_annealing.py
2021-06-06 22:13:05 +02:00

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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
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)
def test_visiting_stepping(self):
lu = list(zip(*self.ld_bounds))
lower = np.array(lu[0])
upper = np.array(lu[1])
dim = lower.size
vd = VisitingDistribution(lower, upper, self.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)
def test_visiting_dist_high_temperature(self):
lu = list(zip(*self.ld_bounds))
lower = np.array(lu[0])
upper = np.array(lu[1])
vd = VisitingDistribution(lower, upper, self.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)
@pytest.mark.skipif(Version(np.__version__) < Version('1.17'),
reason='Generator not available for numpy, < 1.17')
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_local_search_option_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,
local_search_options={"method": "SLSQP", "bounds": bounds})
with np.testing.suppress_warnings() as sup:
sup.record(RuntimeWarning, "Method CG cannot handle ")
dual_annealing(
func,
bounds=bounds,
local_search_options={"method": "CG", "bounds": bounds})
# Verify warning happened for Method cannot handle bounds.
assert sup.log
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,
local_search_options=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,
local_search_options=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)