thursday/thursday/candidates/fcmaes.py

165 lines
4.9 KiB
Python

from scipy.optimize import Bounds
from unittest.mock import patch
from ..utilities import wrap_untrustworthy, final
import numpy as np
def _fix_logging(original_function):
from functools import wraps
@wraps(original_function)
def wrapped_function(*args, **kwargs):
with patch("logging.FileHandler"): # do not create a file on import
import fcmaes.optimizer
fcmaes.optimizer._logger = None
return original_function(*args, **kwargs)
return wrapped_function
@_fix_logging
def make_biteopt(depth=1):
from fcmaes.optimizer import Bite_cpp
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)
name = f"fcmaes_biteopt_d{depth}"
f.__name__ = name + "_cube"
return f
@_fix_logging
def make_csma(isigma=3):
from fcmaes.optimizer import Csma_cpp
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
)
return _objective(final)
name = f"fcmaes_csma_is{isigma:02}"
f.__name__ = name + "_cube"
return f
@_fix_logging
def make_fcmaes(popsize=None):
from fcmaes.optimizer import Cma_cpp
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=budget,
popsize=ps,
stop_hist=0.0,
workers=1,
delayed_update=False,
)
_xopt, _fopt, _feval_count = optim.minimize(_objective, bounds, sdevs=1 / 3)
return _objective(final)
name = "fcmaes_cma"
name += f"_auto" if popsize is None else f"_ps{popsize}"
f.__name__ = name + "_cube"
return f
@_fix_logging
def make_crfmnes(popsize=None):
from fcmaes.optimizer import Crfmnes_cpp
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)
name = "fcmaes_crfmnes"
name += f"_auto" if popsize is None else f"_ps{popsize}"
f.__name__ = name + "_cube"
return f
_broken = """
@_fix_logging
def make_lde(popsize=None):
from fcmaes.optimizer import LDe_cpp
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)
name = "fcmaes_lde"
name += f"_auto" if popsize is None else f"_ps{popsize}"
f.__name__ = name + "_cube"
return f
"""
@_fix_logging
def make_da(local=True):
from fcmaes.optimizer import Da_cpp
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)
name = "fcmaes_da"
if local:
name += "_local"
f.__name__ = name + "_cube"
return f
@_fix_logging
def make_gclde(popsize=None):
from fcmaes.optimizer import GCLDE_cpp
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)
name = "fcmaes_gclde"
name += f"_auto" if popsize is None else f"_ps{popsize}"
f.__name__ = name + "_cube"
return f
@_fix_logging
def make_lclde(popsize=None):
from fcmaes.optimizer import LCLDE_cpp
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)
name = "fcmaes_lclde"
name += f"_auto" if popsize is None else f"_ps{popsize}"
f.__name__ = name + "_cube"
return f