rename n_dim to size, rename n_trials to budget, swap their order
This commit is contained in:
parent
6778965d70
commit
b0810cee8c
|
|
@ -3,14 +3,14 @@ from .random import another_random_cube
|
|||
from ..utilities import wrap_untrustworthy, final
|
||||
|
||||
|
||||
def dlib_cube(objective, n_trials, n_dim):
|
||||
if n_dim > 35:
|
||||
return another_random_cube(objective, n_trials, n_dim)
|
||||
def dlib_cube(objective, size, budget):
|
||||
if size > 35:
|
||||
return another_random_cube(objective, size, budget)
|
||||
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
|
||||
def __objective(*args):
|
||||
return _objective(list(args))
|
||||
|
||||
find_min_global(__objective, [0.0] * n_dim, [1.0] * n_dim, n_trials)
|
||||
find_min_global(__objective, [0.0] * size, [1.0] * size, budget)
|
||||
return _objective(final)
|
||||
|
|
|
|||
|
|
@ -43,18 +43,18 @@ def make_evolopy(optimizer_name, popsize=None):
|
|||
}
|
||||
optimizer = optimizers[optimizer_name.upper()]
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
# ps = n_dim if popsize is None else popsize
|
||||
def f(objective, size, budget):
|
||||
# ps = size if popsize is None else popsize
|
||||
# ps = max(4, ps) if optimizer_name.upper() == "DE" else ps
|
||||
ps = int(4 + 3 * np.log(n_dim)) if popsize is None else popsize
|
||||
ps = int(4 + 3 * np.log(size)) if popsize is None else popsize
|
||||
if optimizer_name.upper() == "GA" and ps & 1:
|
||||
ps += 1 # force popsize to be even
|
||||
|
||||
_objective = wrap_untrustworthy(
|
||||
objective, n_trials, bounding="sine", raising=True
|
||||
objective, budget, bounding="sine", raising=True
|
||||
)
|
||||
try:
|
||||
optimizer(_objective, 0.0, 1.0, n_dim, ps, n_trials)
|
||||
optimizer(_objective, 0.0, 1.0, size, ps, budget)
|
||||
except ExhaustedTrialsError:
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -21,10 +21,10 @@ def _fix_logging(original_function):
|
|||
def make_biteopt(depth=1):
|
||||
from fcmaes.optimizer import Bite_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = Bite_cpp(max_evaluations=n_trials, M=depth)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = Bite_cpp(max_evaluations=budget, M=depth)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
|
||||
return _objective(final)
|
||||
|
||||
|
|
@ -37,10 +37,10 @@ def make_biteopt(depth=1):
|
|||
def make_csma(isigma=3):
|
||||
from fcmaes.optimizer import Csma_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = Csma_cpp(max_evaluations=n_trials)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = Csma_cpp(max_evaluations=budget)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(
|
||||
_objective, bounds, sdevs=1 / isigma
|
||||
)
|
||||
|
|
@ -55,12 +55,12 @@ def make_csma(isigma=3):
|
|||
def make_fcmaes(popsize=None):
|
||||
from fcmaes.optimizer import Cma_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
ps = int(4 + 3 * np.log(n_dim)) if popsize is None else popsize
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
ps = int(4 + 3 * np.log(size)) if popsize is None else popsize
|
||||
optim = Cma_cpp(
|
||||
max_evaluations=n_trials,
|
||||
max_evaluations=budget,
|
||||
popsize=ps,
|
||||
stop_hist=0.0,
|
||||
workers=1,
|
||||
|
|
@ -79,11 +79,11 @@ def make_fcmaes(popsize=None):
|
|||
def make_crfmnes(popsize=None):
|
||||
from fcmaes.optimizer import Crfmnes_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
ps = int(4 + 3 * np.log(n_dim)) if popsize is None else popsize
|
||||
optim = Crfmnes_cpp(max_evaluations=n_trials, popsize=ps)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
ps = int(4 + 3 * np.log(size)) if popsize is None else popsize
|
||||
optim = Crfmnes_cpp(max_evaluations=budget, popsize=ps)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds, sdevs=1 / 3)
|
||||
return _objective(final)
|
||||
|
||||
|
|
@ -98,10 +98,10 @@ _broken = """
|
|||
def make_lde(popsize=None):
|
||||
from fcmaes.optimizer import LDe_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = LDe_cpp(max_evaluations=n_trials, popsize=popsize)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = LDe_cpp(max_evaluations=budget, popsize=popsize)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
|
||||
return _objective(final)
|
||||
|
||||
|
|
@ -116,10 +116,10 @@ def make_lde(popsize=None):
|
|||
def make_da(local=True):
|
||||
from fcmaes.optimizer import Da_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = Da_cpp(max_evaluations=n_trials, use_local_search=local)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = Da_cpp(max_evaluations=budget, use_local_search=local)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
|
||||
return _objective(final)
|
||||
|
||||
|
|
@ -134,10 +134,10 @@ def make_da(local=True):
|
|||
def make_gclde(popsize=None):
|
||||
from fcmaes.optimizer import GCLDE_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = GCLDE_cpp(max_evaluations=n_trials, popsize=popsize)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = GCLDE_cpp(max_evaluations=budget, popsize=popsize)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
|
||||
return _objective(final)
|
||||
|
||||
|
|
@ -151,10 +151,10 @@ def make_gclde(popsize=None):
|
|||
def make_lclde(popsize=None):
|
||||
from fcmaes.optimizer import LCLDE_cpp
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
optim = LCLDE_cpp(max_evaluations=n_trials, popsize=popsize)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
optim = LCLDE_cpp(max_evaluations=budget, popsize=popsize)
|
||||
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
|
||||
return _objective(final)
|
||||
|
||||
|
|
|
|||
|
|
@ -303,8 +303,8 @@ assert ol.RSLSQP is ol.RSQP, "weirdness is gone, please adjust accordingly"
|
|||
assert ol.SLSQP is ol.SQP, "weirdness is gone, please adjust accordingly"
|
||||
|
||||
|
||||
def nevergrad_cube_factory(optimizer, objective, n_trials, n_dim):
|
||||
instrument = ng.p.Array(lower=0, upper=1, shape=(n_dim,)) # better sigma
|
||||
def nevergrad_cube_factory(optimizer, objective, size, budget):
|
||||
instrument = ng.p.Array(lower=0, upper=1, shape=(size,)) # better sigma
|
||||
# ev.RBO, ev.QRBO, ev.MidQRBO, and ev.LBO still complain, though:
|
||||
# /home/py/.local/lib/python3.10/site-packages/nevergrad/parametrization/_datalayers.py:107: NevergradRuntimeWarning: Bounds are 1.0 sigma away from each other at the closest, you should aim for at least 3 for better quality.
|
||||
if optimizer in (ev.RBO, ev.QRBO, ev.MidQRBO, ev.LBO):
|
||||
|
|
@ -313,7 +313,7 @@ def nevergrad_cube_factory(optimizer, objective, n_trials, n_dim):
|
|||
opt = optimizer
|
||||
assert opt is not None, optimizer
|
||||
|
||||
optimizer = opt(parametrization=instrument, budget=n_trials, num_workers=1)
|
||||
optimizer = opt(parametrization=instrument, budget=budget, num_workers=1)
|
||||
|
||||
feval_count = 0
|
||||
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ NLOPTIMIZERS = {
|
|||
}
|
||||
|
||||
|
||||
def nlopt_cube_factory(objective, n_trials, n_dim, method):
|
||||
def nlopt_cube_factory(objective, size, budget, method):
|
||||
optim = NLOPTIMIZERS[method]
|
||||
|
||||
feval_count = 0
|
||||
|
|
@ -57,19 +57,19 @@ def nlopt_cube_factory(objective, n_trials, n_dim, method):
|
|||
nonlocal feval_count, best_so_far
|
||||
fx = objective(x)
|
||||
feval_count += 1
|
||||
if feval_count <= n_trials:
|
||||
if feval_count <= budget:
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x)
|
||||
return fx
|
||||
|
||||
opt = nlopt.opt(optim, n_dim)
|
||||
opt.set_lower_bounds([0.0] * n_dim)
|
||||
opt.set_upper_bounds([1.0] * n_dim)
|
||||
opt = nlopt.opt(optim, size)
|
||||
opt.set_lower_bounds([0.0] * size)
|
||||
opt.set_upper_bounds([1.0] * size)
|
||||
opt.set_min_objective(_objective)
|
||||
opt.set_maxeval(n_trials)
|
||||
opt.set_maxeval(budget)
|
||||
|
||||
try:
|
||||
opt.optimize([0.5] * n_dim)
|
||||
opt.optimize([0.5] * size)
|
||||
except nlopt.RoundoffLimited as e:
|
||||
print( # FIXME: de-uglify this!
|
||||
"\033[33m",
|
||||
|
|
@ -86,106 +86,106 @@ def nlopt_cube_factory(objective, n_trials, n_dim, method):
|
|||
return fopt, xopt, feval_count
|
||||
|
||||
|
||||
def nlopt_ags_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_ags")
|
||||
def nlopt_ags_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_ags")
|
||||
|
||||
|
||||
def nlopt_crs2_lm_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_crs2_lm")
|
||||
def nlopt_crs2_lm_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_crs2_lm")
|
||||
|
||||
|
||||
def nlopt_direct_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_direct")
|
||||
def nlopt_direct_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_direct")
|
||||
|
||||
|
||||
def nlopt_direct_l_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_direct_l")
|
||||
def nlopt_direct_l_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_direct_l")
|
||||
|
||||
|
||||
def nlopt_direct_l_noscal_cube(objective, n_trials, n_dim):
|
||||
def nlopt_direct_l_noscal_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, "gn_direct_l_noscal"
|
||||
objective, size, budget, "gn_direct_l_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_lr_cube(objective, n_trials, n_dim):
|
||||
def nlopt_direct_lr_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, "gn_direct_l_rand"
|
||||
objective, size, budget, "gn_direct_l_rand"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_lr_noscal_cube(objective, n_trials, n_dim):
|
||||
def nlopt_direct_lr_noscal_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, "gn_direct_l_rand_noscal"
|
||||
objective, size, budget, "gn_direct_l_rand_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_noscal_cube(objective, n_trials, n_dim):
|
||||
def nlopt_direct_noscal_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, "gn_direct_noscal"
|
||||
objective, size, budget, "gn_direct_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_esch_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_esch")
|
||||
def nlopt_esch_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_esch")
|
||||
|
||||
|
||||
def nlopt_isres_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_isres")
|
||||
def nlopt_isres_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_isres")
|
||||
|
||||
|
||||
def nlopt_mlsl_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_mlsl")
|
||||
def nlopt_mlsl_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_mlsl")
|
||||
|
||||
|
||||
def nlopt_mlsl_lds_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_mlsl_lds")
|
||||
def nlopt_mlsl_lds_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_mlsl_lds")
|
||||
|
||||
|
||||
def nlopt_orig_direct_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "gn_orig_direct")
|
||||
def nlopt_orig_direct_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "gn_orig_direct")
|
||||
|
||||
|
||||
def nlopt_orig_direct_l_cube(objective, n_trials, n_dim):
|
||||
def nlopt_orig_direct_l_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, "gn_orig_direct_l"
|
||||
objective, size, budget, "gn_orig_direct_l"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_auglag_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_auglag")
|
||||
def nlopt_auglag_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_auglag")
|
||||
|
||||
|
||||
def nlopt_auglag_eq_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_auglag_eq")
|
||||
def nlopt_auglag_eq_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_auglag_eq")
|
||||
|
||||
|
||||
def nlopt_bobyqa_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_bobyqa")
|
||||
def nlopt_bobyqa_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_bobyqa")
|
||||
|
||||
|
||||
def nlopt_cobyla_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_cobyla")
|
||||
def nlopt_cobyla_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_cobyla")
|
||||
|
||||
|
||||
def nlopt_neldermead_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_neldermead")
|
||||
def nlopt_neldermead_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_neldermead")
|
||||
|
||||
|
||||
def nlopt_newuoa_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_newuoa")
|
||||
def nlopt_newuoa_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_newuoa")
|
||||
|
||||
|
||||
def nlopt_newuoa_bound_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_newuoa_bound")
|
||||
def nlopt_newuoa_bound_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_newuoa_bound")
|
||||
|
||||
|
||||
def nlopt_praxis_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_praxis")
|
||||
def nlopt_praxis_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_praxis")
|
||||
|
||||
|
||||
def nlopt_sbplx_cube(objective, n_trials, n_dim):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, "ln_sbplx")
|
||||
def nlopt_sbplx_cube(objective, size, budget):
|
||||
return nlopt_cube_factory(objective, size, budget, "ln_sbplx")
|
||||
|
||||
|
||||
NLOPT_OPTIMIZERS = [
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import numpy as np
|
|||
def make_birect(deepness=23, *, longest=False, pruning=False):
|
||||
from ..internal.birect import birect
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
def f(objective, size, budget):
|
||||
feval_count = 0
|
||||
|
||||
def _objective(x):
|
||||
|
|
@ -16,10 +16,10 @@ def make_birect(deepness=23, *, longest=False, pruning=False):
|
|||
|
||||
xopt, fopt = birect(
|
||||
_objective,
|
||||
(0,) * n_dim,
|
||||
(1,) * n_dim,
|
||||
(0,) * size,
|
||||
(1,) * size,
|
||||
min_diag=2.0 ** -float(deepness),
|
||||
max_evals=n_trials,
|
||||
max_evals=budget,
|
||||
by_longest=longest,
|
||||
pruning=pruning,
|
||||
F=np.float64,
|
||||
|
|
@ -39,7 +39,7 @@ def make_soo(deepness=None, *, K=3):
|
|||
assert K >= 2
|
||||
from ..internal.soo import soo
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
def f(objective, size, budget):
|
||||
feval_count = 0
|
||||
|
||||
def _objective(x):
|
||||
|
|
@ -48,7 +48,7 @@ def make_soo(deepness=None, *, K=3):
|
|||
return objective(x)
|
||||
|
||||
xopt, history = soo(
|
||||
_objective, np.full(n_dim, 0.5), 0.5, n_trials, K=K, h_max=deepness
|
||||
_objective, np.full(size, 0.5), 0.5, budget, K=K, h_max=deepness
|
||||
)
|
||||
fopt = history[-1]
|
||||
return fopt, xopt, feval_count
|
||||
|
|
@ -63,11 +63,11 @@ def make_mercury(
|
|||
):
|
||||
from ..internal.hg import minimize as hg
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
_objective = wrap_untrustworthy(objective, n_trials, bounding=bounding)
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(objective, budget, bounding=bounding)
|
||||
|
||||
init = (0.5,) * n_dim
|
||||
iterations = (n_trials - 1 + popsize * 2) // (popsize * 2 + 1)
|
||||
init = (0.5,) * size
|
||||
iterations = (budget - 1 + popsize * 2) // (popsize * 2 + 1)
|
||||
center, history = hg(
|
||||
_objective,
|
||||
init,
|
||||
|
|
@ -81,12 +81,12 @@ def make_mercury(
|
|||
|
||||
# use our remaining evals on something, anything.
|
||||
feval_count = _objective(check)
|
||||
wasted = max(0, -(feval_count - n_trials))
|
||||
wasted = max(0, -(feval_count - budget))
|
||||
if wasted:
|
||||
print(f"wasting {wasted} of {n_trials} evaluations")
|
||||
print(f"wasting {wasted} of {budget} evaluations")
|
||||
for _ in range(wasted):
|
||||
prng = np.random.default_rng()
|
||||
x = prng.uniform(size=n_dim)
|
||||
x = prng.uniform(size=size)
|
||||
fx = _objective(x)
|
||||
|
||||
return _objective(final)
|
||||
|
|
|
|||
|
|
@ -2,34 +2,34 @@ from ..utilities import phi
|
|||
import numpy as np
|
||||
|
||||
|
||||
def another_random_cube(objective, n_trials, n_dim, seed=None):
|
||||
def another_random_cube(objective, size, budget, seed=None):
|
||||
prng = np.random.default_rng(seed)
|
||||
fopt = None
|
||||
xopt = None
|
||||
for i in range(n_trials):
|
||||
x = prng.uniform(size=n_dim)
|
||||
for i in range(budget):
|
||||
x = prng.uniform(size=size)
|
||||
fx = objective(x)
|
||||
if fopt is None or xopt is None or fx < fopt:
|
||||
fopt = fx
|
||||
xopt = x
|
||||
return fopt, xopt, n_trials
|
||||
return fopt, xopt, budget
|
||||
|
||||
|
||||
def quasirandom_cube(objective, n_trials, n_dim):
|
||||
def quasirandom_cube(objective, size, budget):
|
||||
# http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/
|
||||
magic = phi(n_dim)
|
||||
alpha = np.zeros(n_dim)
|
||||
for i in range(n_dim):
|
||||
magic = phi(size)
|
||||
alpha = np.zeros(size)
|
||||
for i in range(size):
|
||||
alpha[i] = pow(1 / magic, i + 1) % 1
|
||||
xs = np.zeros((n_trials, n_dim))
|
||||
xs = np.zeros((size, budget))
|
||||
xs[0, :] = 0.5 # first point is always dead center
|
||||
for i in range(1, n_trials):
|
||||
for i in range(1, budget):
|
||||
xs[i] = (xs[i - 1] + alpha) % 1
|
||||
|
||||
best_so_far = None
|
||||
for i, x in zip(range(n_trials), xs):
|
||||
for i, x in zip(range(budget), xs):
|
||||
fx = objective(x)
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x)
|
||||
fopt, xopt = best_so_far
|
||||
return fopt, xopt, n_trials
|
||||
return fopt, xopt, budget
|
||||
|
|
|
|||
|
|
@ -23,13 +23,13 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
# "trust-krylov",
|
||||
), method
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
def f(objective, size, budget):
|
||||
prng = np.random.default_rng()
|
||||
_objective = wrap_untrustworthy(
|
||||
objective, n_trials, raising=True, bounding="sine"
|
||||
objective, budget, raising=True, bounding="sine"
|
||||
)
|
||||
x0 = np.full(n_dim, 0.5)
|
||||
bounds = scopt.Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
x0 = np.full(size, 0.5)
|
||||
bounds = scopt.Bounds([0.0] * size, [1.0] * size)
|
||||
# jac = "cs" if method == "dogleg" else None # doesn't work
|
||||
jac = "2-point" if jacobian is None else jacobian
|
||||
hess = "2-point" if hessian is None else hessian
|
||||
|
|
@ -37,16 +37,16 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
# can be used to approximate the Hessian. Available quasi-Newton methods
|
||||
# implementing this interface are: BFGS; SR1.
|
||||
tol = None # 0.0
|
||||
# options = dict(maxfun=n_trials) if method == "TNC" else dict(maxiter=n_trials)
|
||||
# options = dict(maxfun=budget) if method == "TNC" else dict(maxiter=budget)
|
||||
|
||||
if method in ("BFGS", "CG", "COBYLA", "SLSQP", "trust-constr"):
|
||||
options = dict(maxiter=n_trials)
|
||||
options = dict(maxiter=budget)
|
||||
elif method in ("Nelder-Mead", "Powell"):
|
||||
options = dict(maxfev=n_trials, maxiter=n_trials)
|
||||
options = dict(maxfev=budget, maxiter=budget)
|
||||
elif method in ("L-BFGS-B", "TNC"):
|
||||
options = dict(maxfun=n_trials, maxiter=n_trials)
|
||||
options = dict(maxfun=budget, maxiter=budget)
|
||||
else:
|
||||
options = dict(maxfun=n_trials, maxfev=n_trials, maxiter=n_trials)
|
||||
options = dict(maxfun=budget, maxfev=budget, maxiter=budget)
|
||||
|
||||
# silence some warnings:
|
||||
if method in ("Nelder-Mead", "Powell"):
|
||||
|
|
@ -67,12 +67,12 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
def check_evals():
|
||||
evals = _objective(check)
|
||||
checks.append(evals)
|
||||
return evals < n_trials
|
||||
return evals < budget
|
||||
|
||||
first_try = True
|
||||
while check_evals():
|
||||
if not first_try:
|
||||
x0 = prng.uniform(size=n_dim)
|
||||
x0 = prng.uniform(size=size)
|
||||
|
||||
try:
|
||||
res = scopt.minimize(
|
||||
|
|
@ -91,7 +91,7 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
# well, this is pointless.
|
||||
fopt, xopt, feval_count = res.fun, res.x, res.nfev
|
||||
# print("success:", res.success)
|
||||
# if not res.success and res.nfev < n_trials // 2:
|
||||
# if not res.success and res.nfev < budget // 2:
|
||||
shut_up = (
|
||||
method == "SLSQP"
|
||||
and res.message == "Inequality constraints incompatible"
|
||||
|
|
@ -99,7 +99,7 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
res.message
|
||||
== "The maximum number of function evaluations is exceeded."
|
||||
)
|
||||
if not shut_up and not res.success and res.nfev < n_trials // 3:
|
||||
if not shut_up and not res.success and res.nfev < budget // 3:
|
||||
print("", method, res, "", sep="\n")
|
||||
|
||||
first_try = False
|
||||
|
|
@ -126,7 +126,7 @@ def make_scipy(method, *, jacobian=None, hessian=None):
|
|||
return f
|
||||
|
||||
|
||||
def scipy_basinhopping_cube(objective, n_trials, n_dim):
|
||||
def scipy_basinhopping_cube(objective, size, budget):
|
||||
progress = 1e-2 # TODO: make configurable?
|
||||
# NOTE: could also callbacks to extract solutions instead of wrapping objective functions?
|
||||
|
||||
|
|
@ -136,14 +136,14 @@ def scipy_basinhopping_cube(objective, n_trials, n_dim):
|
|||
def dummy_minimizer(fun, x0, args, **options):
|
||||
return scopt.OptimizeResult(x=x0, fun=fun(x0), success=True, nfev=1)
|
||||
|
||||
x0 = np.full(n_dim, 0.5)
|
||||
x0 = np.full(size, 0.5)
|
||||
res = scopt.basinhopping(
|
||||
objective,
|
||||
x0,
|
||||
minimizer_kwargs=dict(method=dummy_minimizer),
|
||||
accept_test=accept_bounded,
|
||||
disp=False,
|
||||
niter=n_trials,
|
||||
niter=budget,
|
||||
# TODO: try without any progress vars at all.
|
||||
T=progress,
|
||||
stepsize=progress / 2,
|
||||
|
|
@ -153,13 +153,13 @@ def scipy_basinhopping_cube(objective, n_trials, n_dim):
|
|||
return fopt, xopt, feval_count
|
||||
|
||||
|
||||
def scipy_direct_cube(objective, n_trials, n_dim):
|
||||
bounds = scopt.Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
def scipy_direct_cube(objective, size, budget):
|
||||
bounds = scopt.Bounds([0.0] * size, [1.0] * size)
|
||||
# TODO: try different values of eps. default 0.0001
|
||||
res = scopt.direct(
|
||||
objective,
|
||||
bounds=bounds,
|
||||
maxfun=n_trials,
|
||||
maxfun=budget,
|
||||
maxiter=1_000_000,
|
||||
vol_tol=0,
|
||||
)
|
||||
|
|
@ -168,13 +168,13 @@ def scipy_direct_cube(objective, n_trials, n_dim):
|
|||
return fopt, xopt, feval_count
|
||||
|
||||
|
||||
def scipy_direct_l_cube(objective, n_trials, n_dim):
|
||||
bounds = scopt.Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
def scipy_direct_l_cube(objective, size, budget):
|
||||
bounds = scopt.Bounds([0.0] * size, [1.0] * size)
|
||||
# TODO: try different values of eps. default 0.0001
|
||||
res = scopt.direct(
|
||||
objective,
|
||||
bounds=bounds,
|
||||
maxfun=n_trials,
|
||||
maxfun=budget,
|
||||
maxiter=1_000_000,
|
||||
vol_tol=0,
|
||||
locally_biased=True,
|
||||
|
|
@ -198,13 +198,13 @@ def make_shgo(method="cobyla", init="large", it=1, mei=False, li=False, ftol=12)
|
|||
# https://docs.scipy.org/doc/scipy/reference/optimize.minimize-cobyla.html
|
||||
pass
|
||||
|
||||
def f(objective, n_trials, n_dim):
|
||||
def f(objective, size, budget):
|
||||
_objective = wrap_untrustworthy(
|
||||
objective, n_trials, bounding="clip", raising=True
|
||||
objective, budget, bounding="clip", raising=True
|
||||
)
|
||||
bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
|
||||
bounds = Bounds([0.0] * size, [1.0] * size)
|
||||
npt = dict(
|
||||
large=(n_dim + 1) * (n_dim + 2) // 2, medium=2 * n_dim + 1, small=n_dim + 1
|
||||
large=(size + 1) * (size + 2) // 2, medium=2 * size + 1, small=size + 1
|
||||
)[init]
|
||||
_method = method if method != "cobyqa" else method # TODO: handle cobyqa case.
|
||||
try:
|
||||
|
|
@ -214,7 +214,7 @@ def make_shgo(method="cobyla", init="large", it=1, mei=False, li=False, ftol=12)
|
|||
n=npt,
|
||||
iters=it,
|
||||
minimizer_kwargs=dict(method=_method, ftol=10**-ftol),
|
||||
options=dict(maxfev=n_trials, minimize_every_iter=mei, local_iters=li),
|
||||
options=dict(maxfev=budget, minimize_every_iter=mei, local_iters=li),
|
||||
sampling_method="simplicial",
|
||||
)
|
||||
except ExhaustedTrialsError:
|
||||
|
|
|
|||
|
|
@ -28,8 +28,8 @@ def flipit(transformations, flippy):
|
|||
transformations[flip_dim] = (mul, add)
|
||||
|
||||
|
||||
def make_objective(problem, n_dim, *, fix_stuff=0):
|
||||
obj = problem(n_dim)
|
||||
def make_objective(problem, size, *, fix_stuff=0):
|
||||
obj = problem(size)
|
||||
name = problem.__name__
|
||||
flippy = 0 # when positive, helps removes positive correlations in solutions
|
||||
trippy = None # when not None, moves solution away from center: (dim, dir)
|
||||
|
|
@ -41,7 +41,7 @@ def make_objective(problem, n_dim, *, fix_stuff=0):
|
|||
elif name == "Csendes" or name == "Infinity":
|
||||
# this problem is weird... let's avoid division by zero, okay?
|
||||
# these problems are duplicates of each other. weird.
|
||||
replacement = n_dim * (2 + np.sin(1))
|
||||
replacement = size * (2 + np.sin(1))
|
||||
_fun = obj.fun
|
||||
obj.fun = lambda x: replacement if np.any(x == 0.0) else _fun(x)
|
||||
elif name == "Keane":
|
||||
|
|
@ -73,18 +73,18 @@ def make_objective(problem, n_dim, *, fix_stuff=0):
|
|||
obj.fun = lambda x: _fun(x + adjust)
|
||||
|
||||
if fix_stuff >= 1:
|
||||
stuff = {2: too_positive_2, 3: too_positive_3, 4: too_positive_4}[n_dim]
|
||||
stuff = {2: too_positive_2, 3: too_positive_3, 4: too_positive_4}[size]
|
||||
if name.lower() in stuff:
|
||||
# too positively correlated, do some evil.
|
||||
ind = stuff.index(name.lower())
|
||||
flippy = ind % n_dim + 1 # uniformly select a dimension to "flip"
|
||||
flippy = ind % size + 1 # uniformly select a dimension to "flip"
|
||||
|
||||
if fix_stuff >= 2:
|
||||
stuff = {2: too_centered_2, 3: too_centered_3, 4: too_centered_4}[n_dim]
|
||||
stuff = {2: too_centered_2, 3: too_centered_3, 4: too_centered_4}[size]
|
||||
if name.lower() in stuff:
|
||||
# uniformly select offsets to "trip".
|
||||
ind = stuff.index(name.lower())
|
||||
trippy = (ind % n_dim, ind // n_dim % n_dim) # (dim, dir)
|
||||
trippy = (ind % size, ind // size % size) # (dim, dir)
|
||||
|
||||
transformations = [make_transform(lo, hi) for lo, hi in obj.bounds]
|
||||
|
||||
|
|
@ -108,15 +108,15 @@ def make_objective(problem, n_dim, *, fix_stuff=0):
|
|||
return objective
|
||||
|
||||
|
||||
def make_objectives(n_dim, n_trials=None, fix_stuff=0):
|
||||
problems = all_problems[n_dim]
|
||||
return [make_objective(problem, n_dim, fix_stuff=fix_stuff) for problem in problems]
|
||||
def make_objectives(size, budget=None, fix_stuff=0):
|
||||
problems = all_problems[size]
|
||||
return [make_objective(problem, size, fix_stuff=fix_stuff) for problem in problems]
|
||||
|
||||
|
||||
def find_objective(query, n_dim=None):
|
||||
def find_objective(query, size=None):
|
||||
results = []
|
||||
for p_dim, problems in all_problems.items():
|
||||
if n_dim is not None and p_dim != n_dim:
|
||||
if size is not None and p_dim != size:
|
||||
continue
|
||||
for problem in problems:
|
||||
if problem.__name__.lower() == query.lower():
|
||||
|
|
@ -311,14 +311,14 @@ def main(argv, display=True):
|
|||
percents = dict(frugal_percent=1.0, greedy_percent=2.0)
|
||||
|
||||
which = parties[argv[1]] if len(argv) > 1 else parties["standard"]
|
||||
n_dim = int(argv[2]) if len(argv) > 2 else -2
|
||||
n_trials = int(argv[3]) if len(argv) > 3 else fib(abs(n_dim) + 4) * 10
|
||||
size = int(argv[2]) if len(argv) > 2 else -2
|
||||
budget = int(argv[3]) if len(argv) > 3 else fib(abs(size) + 4) * 10
|
||||
|
||||
place_names = ("1st", "2nd", "3rd", "4th")
|
||||
assert n_dim < 0, "unsupported in this version"
|
||||
n_dim = abs(n_dim)
|
||||
assert size < 0, "unsupported in this version"
|
||||
size = abs(size)
|
||||
place_scores = (5, 3, 2, 1)
|
||||
objectives = GO_BENCHMARKS[n_dim] # * multiple
|
||||
objectives = GO_BENCHMARKS[size] # * multiple
|
||||
|
||||
optimizers = list(which) # copy
|
||||
before = len(optimizers)
|
||||
|
|
@ -349,8 +349,8 @@ def main(argv, display=True):
|
|||
wrapped = COWrap(
|
||||
objective,
|
||||
optimizer=optimizer,
|
||||
n_trials=n_trials,
|
||||
n_dim=n_dim,
|
||||
budget=budget,
|
||||
size=size,
|
||||
**percents,
|
||||
)
|
||||
else:
|
||||
|
|
@ -380,7 +380,7 @@ def main(argv, display=True):
|
|||
note(
|
||||
f"Using {opt_name} to optimize {obj_realname} ({obj_name}) [{run}] ..."
|
||||
)
|
||||
_ = optimizer(wrapped, n_trials=n_trials, n_dim=n_dim)
|
||||
_ = optimizer(wrapped, size=size, budget=budget)
|
||||
fopt, xopt = wrapped.finish()
|
||||
result = (fopt, opt_name, wrapped.history)
|
||||
results.setdefault(obj_name, []).append(result)
|
||||
|
|
@ -436,7 +436,7 @@ def main(argv, display=True):
|
|||
f"\n\033[1m{blah} scoring optimizers:\033[m"
|
||||
f" (awards={place_scores})"
|
||||
f" (obj={len(objectives)}, opt={len(optimizers)})"
|
||||
f" (dims={n_dim}, evals={n_trials})"
|
||||
f" (dims={size}, evals={budget})"
|
||||
)
|
||||
for opt_name, opt_point in sorted(points.items(), key=lambda t: -t[1]):
|
||||
# place = place_names[i] if i < len(place_names) else " "
|
||||
|
|
@ -464,7 +464,7 @@ def main(argv, display=True):
|
|||
f"\n\033[1malternatively scored optimizers:\033[m"
|
||||
f" (awards={place_scores})"
|
||||
f" (obj={len(objectives)}, opt={len(optimizers)})"
|
||||
f" (dims={n_dim}, evals={n_trials})"
|
||||
f" (dims={size}, evals={budget})"
|
||||
)
|
||||
for opt_name, opt_score in sorted(more_scores.items(), key=lambda t: -t[1]):
|
||||
# if opt_score < 1: continue
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ class ExhaustedTrialsError(Exception):
|
|||
|
||||
|
||||
def wrap_untrustworthy(
|
||||
objective, n_trials, *, raising=False, bounding=None, softplus=False, eps=0.0
|
||||
objective, budget, *, raising=False, bounding=None, softplus=False, eps=0.0
|
||||
):
|
||||
# also handles bounding now, so it may be used for other purposes as well. whoops.
|
||||
feval_count = 0
|
||||
|
|
@ -24,13 +24,13 @@ def wrap_untrustworthy(
|
|||
assert best_so_far is not None
|
||||
fopt, xopt = best_so_far
|
||||
return fopt, xopt, feval_count
|
||||
if raising and feval_count >= n_trials:
|
||||
if raising and feval_count >= budget:
|
||||
raise ExhaustedTrialsError()
|
||||
if bounding is not None:
|
||||
x = do_bounding(x, bounding)
|
||||
fx = objective(x)
|
||||
feval_count += 1
|
||||
if n_trials is None or feval_count <= n_trials:
|
||||
if budget is None or feval_count <= budget:
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x.copy())
|
||||
return scalar_softplus(fx) + eps if softplus else fx
|
||||
|
|
|
|||
|
|
@ -105,7 +105,7 @@ def needs_rerun(key, value):
|
|||
if not value["history"]: # not sure what happened here
|
||||
return True
|
||||
|
||||
n_dim = len(value["xopt"])
|
||||
size = len(value["xopt"])
|
||||
ng = []
|
||||
|
||||
kd = decode_key(key)
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ def do_bounding(x, method="clip"):
|
|||
# FIXME: we need a way to determine the previous (or center) x somehow?
|
||||
from bitten_snes import _project_with
|
||||
|
||||
x = _project_with(x, old, np.array([[0.0, 1.0] * n_dim]), clipping=0.5)
|
||||
x = _project_with(x, old, np.array([[0.0, 1.0] * size]), clipping=0.5)
|
||||
elif method == "tria":
|
||||
hp = np.pi / 2
|
||||
x = np.abs(np.arcsin(np.sin(x * hp)) / hp)
|
||||
|
|
@ -66,7 +66,7 @@ class OWrap:
|
|||
def __init__(
|
||||
self,
|
||||
objective,
|
||||
n_trials,
|
||||
budget,
|
||||
frugal_percent=1.0,
|
||||
greedy_percent=2.0,
|
||||
history_frequency=10,
|
||||
|
|
@ -75,7 +75,7 @@ class OWrap:
|
|||
self.best_so_far = None
|
||||
self.warning = None
|
||||
self.objective = objective
|
||||
self.n_trials = n_trials
|
||||
self.budget = budget
|
||||
self.__name__ = objective.__name__ # for evolopy
|
||||
self.frugal_percent = float(frugal_percent)
|
||||
self.greedy_percent = float(greedy_percent)
|
||||
|
|
@ -114,7 +114,7 @@ class OWrap:
|
|||
assert np.isfinite(fx), "f(x) is not finite (NaN or Inf or -Inf)"
|
||||
self.feval_count += 1
|
||||
|
||||
if self.feval_count <= self.n_trials:
|
||||
if self.feval_count <= self.budget:
|
||||
if self.best_so_far is None or fx < self.best_so_far[0]:
|
||||
self.best_so_far = (fx, x)
|
||||
|
||||
|
|
@ -129,11 +129,11 @@ class OWrap:
|
|||
m33(f"{optimizer_name} did not abide to bounds")
|
||||
if self.warning == "finite":
|
||||
m33(f"{optimizer_name} passed a non-finite value")
|
||||
if self.feval_count >= self.n_trials * self.greedy_percent:
|
||||
m33(f"{optimizer_name} got greedy ({self.feval_count}>{self.n_trials})")
|
||||
# if self.feval_count <= self.n_trials * 0.95:
|
||||
if self.feval_count < self.n_trials * self.frugal_percent:
|
||||
m34(f"{optimizer_name} was frugal ({self.feval_count}<{self.n_trials})")
|
||||
if self.feval_count >= self.budget * self.greedy_percent:
|
||||
m33(f"{optimizer_name} got greedy ({self.feval_count}>{self.budget})")
|
||||
# if self.feval_count <= self.budget * 0.95:
|
||||
if self.feval_count < self.budget * self.frugal_percent:
|
||||
m34(f"{optimizer_name} was frugal ({self.feval_count}<{self.budget})")
|
||||
return self.best_so_far
|
||||
|
||||
@property
|
||||
|
|
@ -146,11 +146,11 @@ class OWrap:
|
|||
|
||||
|
||||
class COWrap:
|
||||
def __init__(self, objective, *, optimizer, n_trials, n_dim, **kwargs):
|
||||
def __init__(self, objective, *, optimizer, size, budget, **kwargs):
|
||||
self._objective = objective
|
||||
self.optimizer = optimizer
|
||||
self.n_trials = n_trials
|
||||
self.n_dim = n_dim
|
||||
self.budget = budget
|
||||
self.size = size
|
||||
self.kwargs = kwargs
|
||||
self._dirty = False
|
||||
self._history = None
|
||||
|
|
@ -193,7 +193,7 @@ class COWrap:
|
|||
@property
|
||||
def cache_name(self):
|
||||
opt_name = self.optimizer.__name__
|
||||
return f"COWrap_d{self.n_dim:02}_n{self.n_trials:03}_{opt_name}"
|
||||
return f"COWrap_d{self.size:02}_n{self.budget:03}_{opt_name}"
|
||||
|
||||
@property
|
||||
def history(self):
|
||||
|
|
@ -242,7 +242,7 @@ class COWrap:
|
|||
|
||||
def reset_objective(self):
|
||||
self._dirty = False
|
||||
self.ow = OWrap(self._objective, self.n_trials, **self.kwargs)
|
||||
self.ow = OWrap(self._objective, self.budget, **self.kwargs)
|
||||
self._check_cache()
|
||||
|
||||
def _check_cache(self):
|
||||
|
|
@ -282,7 +282,7 @@ class COWrap:
|
|||
# fopt, xopt = self.ow.best_so_far
|
||||
fopt, xopt = self.ow.finish(self.optimizer.__name__)
|
||||
|
||||
expected_length = self.n_trials // self.ow.history_frequency
|
||||
expected_length = self.budget // self.ow.history_frequency
|
||||
history = [float(fval) for fval in self.ow.history]
|
||||
history += [fopt] * (expected_length - len(history))
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user