add some optimizers from fcmaes (AKA fast-cma-es)

fcmaescube2.py

@@ -0,0 +1,172 @@
+from scipy.optimize import Bounds
+from unittest.mock import patch
+from utils 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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _objective = wrap_untrustworthy(objective, n_trials)
+        bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
+        optim = Csma_cpp(max_evaluations=n_trials)
+        _xopt, _fopt, _feval_count = optim.minimize(
+            _objective, bounds, sdevs=1 / isigma
+        )
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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 = Cma_cpp(
+            max_evaluations=n_trials,
+            popsize=ps,
+            stop_hist=0.0,
+            workers=1,
+            delayed_update=False,
+        )
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds, sdevs=1 / 3)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds, sdevs=1 / 3)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    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, n_trials, n_dim, with_count):
+        _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)
+        _xopt, _fopt, _feval_count = optim.minimize(_objective, bounds)
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    name = "fcmaes_lclde"
+    name += f"_auto" if popsize is None else f"_ps{popsize}"
+    f.__name__ = name + "_cube"
+    return f

notwacube.py

@@ -2,6 +2,16 @@ from dlibcube2 import dlib_cube
 from evolopycube2 import make_evolopy
 from nevergradcube2 import NEVERGRAD2_OPTIMIZERS
 from nloptcube2 import nlopt_neldermead_cube
+from fcmaescube2 import (
+    make_biteopt,
+    make_csma,
+    make_fcmaes,
+    make_crfmnes,
+    # make_lde,
+    make_da,
+    make_gclde,
+    make_lclde,
+)
 from notwacube2 import make_birect, make_mercury, make_soo
 from randomcube2 import another_random_cube, quasirandom_cube
 from scipycube2 import (
@@ -62,6 +72,17 @@ EVOLOPY_OPTIMIZERS = [
     for popsize in (None, 5, 10, 15)
 ]
 
+FCMAES_OPTIMIZERS = [
+    make_biteopt(1),
+    make_biteopt(2),
+    make_csma(3),
+    make_da(False),  # TODO: fix local=True case.
+] + [
+    maker(popsize)
+    for maker in (make_fcmaes, make_crfmnes, make_gclde, make_lclde)
+    for popsize in (None, 5, 10, 15)
+]
+
 
 def collect_everything():
     G = globals().values()