167 lines
4.8 KiB
Python
167 lines
4.8 KiB
Python
from .dlibcube2 import dlib_cube
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from .evolopycube2 import make_evolopy
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from .nevergradcube2 import NEVERGRAD2_OPTIMIZERS
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from .nloptcube2 import nlopt_neldermead_cube
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from .fcmaescube2 import (
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make_biteopt,
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make_csma,
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make_fcmaes,
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make_crfmnes,
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# make_lde,
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make_da,
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make_gclde,
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make_lclde,
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)
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from .notwacube2 import make_birect, make_mercury, make_soo
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from .randomcube2 import another_random_cube, quasirandom_cube
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from .scipycube2 import (
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make_shgo,
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scipy_basinhopping_cube,
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scipy_bfgs_2j_cube,
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scipy_bfgs_3j_cube,
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scipy_cg_2j_cube,
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scipy_cg_3j_cube,
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scipy_cobyla_cube,
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scipy_direct_cube,
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scipy_direct_l_cube,
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# scipy_dogleg_cube,
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scipy_lbfgsb_2j_cube,
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scipy_lbfgsb_3j_cube,
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scipy_neldermead_cube,
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# scipy_newtoncg_cube,
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scipy_powell_cube,
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scipy_slsqp_2j_cube,
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scipy_slsqp_3j_cube,
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scipy_tnc_2j_cube,
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scipy_tnc_3j_cube,
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scipy_trustconstr_2j_cube,
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scipy_trustconstr_3j_cube,
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# scipy_trustexact_2j_cube,
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# scipy_trustexact_3j_cube,
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# scipy_trustkrylov_cube,
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# scipy_trustncg_cube,
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)
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from . import tinytweaks as tt
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BASELINE_OPTIMIZERS = [
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another_random_cube,
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nlopt_neldermead_cube,
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quasirandom_cube,
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]
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SHGO_OPTIMIZERS = [
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make_shgo(method="cobyla"),
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make_shgo(method="slsqp"),
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make_shgo(method="cobyla", mei=True),
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make_shgo(method="slsqp", mei=True),
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]
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NOTWA_OPTIMIZERS = [
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make_birect(deepness=38, longest=True, pruning=False),
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make_mercury(13, isigma=tt.S3),
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make_mercury(29, isigma=tt.S3),
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make_soo(deepness=23, K=4),
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make_soo(deepness=35, K=3),
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make_soo(deepness=53, K=2),
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]
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EVOLOPY_OPTIMIZERS = [
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make_evolopy(name, popsize)
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for name in "BAT CS DE FFA GA GWO HHO JAYA MFO MVO PSO SCA SSA WOA".split()
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for popsize in (None, 5, 10, 15)
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]
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FCMAES_OPTIMIZERS = [
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make_biteopt(1),
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make_biteopt(2),
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make_csma(3),
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make_da(False), # TODO: fix local=True case.
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] + [
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maker(popsize)
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for maker in (make_fcmaes, make_crfmnes, make_gclde, make_lclde)
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for popsize in (None, 5, 10, 15)
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]
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def collect_everything():
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G = globals().values()
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λ = lambda G: [
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g for g in G if callable(g) and getattr(g, "__name__", "").endswith("_cube")
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]
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_seen = {}
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for thing in sum((λ(g) for g in G if type(g) is list), λ(G)):
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key = thing.__name__
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if key in _seen and _seen[key] is not thing:
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pass # print(f"Ignored: seen twice: {key}", file=sys.stderr)
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_seen[key] = thing
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# return list(_seen.values())
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return [v for k, v in sorted(_seen.items(), key=lambda t: t[0])]
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FUCKING_EVERYTHING = collect_everything()
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try:
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from previous import PREVIOUSLY_POSITIVE, PREVIOUSLY_NEGATIVE
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except ModuleNotFoundError:
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PREVIOUSLY_POSITIVE, PREVIOUSLY_NEGATIVE = [], []
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else:
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g = {v.__name__: v for v in FUCKING_EVERYTHING} # globals()
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def refind(stuff):
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_temp = []
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for opt in stuff:
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_old_len = len(_temp)
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if opt in g:
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_temp.append(g[opt])
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elif opt.startswith("ng_") or opt.startswith("ngx_"):
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if opt.startswith("ng_"):
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ngx = "ngx_" + opt.removeprefix("ng_")
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else:
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ngx = "ng_" + opt.removeprefix("ngx_") # whatever
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if ngx in g:
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_temp.append(g[ngx])
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else:
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for ng_opt in NEVERGRAD2_OPTIMIZERS:
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if ng_opt.__name__ == opt or ng_opt.__name__ == ngx:
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_temp.append(ng_opt)
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if len(_temp) == _old_len:
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# FIXME: this text is not necessarily for PREVIOUSLY_POSITIVE!
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print(
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f"Ignored: failed to find {opt} for PREVIOUSLY_POSITIVE",
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file=sys.stderr,
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)
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return _temp
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PREVIOUSLY_POSITIVE = refind(PREVIOUSLY_POSITIVE)
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PREVIOUSLY_NEGATIVE = refind(PREVIOUSLY_NEGATIVE)
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del g
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for opt in (another_random_cube, quasirandom_cube):
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if opt not in PREVIOUSLY_POSITIVE:
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PREVIOUSLY_POSITIVE.append(opt)
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for opt in (another_random_cube, quasirandom_cube):
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if opt not in PREVIOUSLY_NEGATIVE:
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PREVIOUSLY_NEGATIVE.append(opt)
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try:
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from elo_blacklist import blacklisted
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except ModuleNotFoundError:
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WHITELISTED_OPTIMIZERS = FUCKING_EVERYTHING.copy()
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else:
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WHITELISTED_OPTIMIZERS = [
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opt
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for opt in FUCKING_EVERYTHING
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if opt.__name__.removesuffix("_cube") not in blacklisted
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]
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book_of_optimizers = dict(
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baseline=BASELINE_OPTIMIZERS,
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everything=FUCKING_EVERYTHING,
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evolopy=EVOLOPY_OPTIMIZERS,
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negative=PREVIOUSLY_NEGATIVE,
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positive=PREVIOUSLY_POSITIVE,
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shgo=SHGO_OPTIMIZERS,
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whitelisted=WHITELISTED_OPTIMIZERS,
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)
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