add scipy shgo optimizers (with cobyla and slsqp)

notwacube.py

@@ -2,6 +2,7 @@ from dlibcube2 import dlib_cube
 from nloptcube2 import nlopt_neldermead_cube
 from randomcube2 import another_random_cube, quasirandom_cube
 from scipycube2 import (
+    make_shgo,
     scipy_basinhopping_cube,
     scipy_bfgs_2j_cube,
     scipy_bfgs_3j_cube,
@@ -34,6 +35,13 @@ BASELINE_OPTIMIZERS = [
     quasirandom_cube,
 ]
 
+SHGO_OPTIMIZERS = [
+    make_shgo(method="cobyla"),
+    make_shgo(method="slsqp"),
+    make_shgo(method="cobyla", mei=True),
+    make_shgo(method="slsqp", mei=True),
+]
+
 
 def collect_everything():
     G = globals().values()
@@ -109,6 +117,7 @@ else:
 book_of_optimizers = dict(
     baseline=BASELINE_OPTIMIZERS,
     everything=FUCKING_EVERYTHING,
+    shgo=SHGO_OPTIMIZERS,
     negative=PREVIOUSLY_NEGATIVE,
     positive=PREVIOUSLY_POSITIVE,
     whitelisted=WHITELISTED_OPTIMIZERS,

scipycube2.py

@@ -186,6 +186,58 @@ def scipy_direct_l_cube(objective, n_trials, n_dim, with_count):
     return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
 
 
+def make_shgo(method="cobyla", init="large", it=1, mei=False, li=False, ftol=12):
+    from scipy.optimize import shgo, Bounds
+
+    if method == "cobyqa":
+        from cobyqa import minimize as cobyqa
+        from cobyqa.optimize import EXIT_RHOEND_SUCCESS, EXIT_MAXFEV_WARNING
+
+        raise NotImplementedError("TODO")
+    else:
+        # from scipy.optimize import minimize
+        # https://docs.scipy.org/doc/scipy/reference/optimize.minimize-cobyla.html
+        pass
+
+    def f(objective, n_trials, n_dim, with_count):
+        _objective = wrap_untrustworthy(
+            objective, n_trials, bounding="clip", raising=True
+        )
+        bounds = Bounds([0.0] * n_dim, [1.0] * n_dim)
+        npt = dict(
+            large=(n_dim + 1) * (n_dim + 2) // 2, medium=2 * n_dim + 1, small=n_dim + 1
+        )[init]
+        _method = method if method != "cobyqa" else method  # TODO: handle cobyqa case.
+        try:
+            shgo(
+                _objective,
+                bounds=bounds,
+                n=npt,
+                iters=it,
+                minimizer_kwargs=dict(method=_method, ftol=10**-ftol),
+                options=dict(maxfev=n_trials, minimize_every_iter=mei, local_iters=li),
+                sampling_method="simplicial",
+            )
+        except ExhaustedTrialsError:
+            pass
+        fopt, xopt, feval_count = _objective(final)
+        return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
+
+    name = f"shgo_{method}"
+    if it != 1:
+        name += f"_it{it}"
+    if li:
+        name += f"_li{li}"
+    if mei:
+        name += "_mei"
+    if init != "large":
+        name += f"_{init}"
+    if ftol != 12:
+        name += f"_ft{ftol:02}"
+    f.__name__ = name + "_cube"
+    return f
+
+
 scipy_bfgs_2j_cube = make_scipy("BFGS", jacobian="2-point")
 scipy_bfgs_3j_cube = make_scipy("BFGS", jacobian="3-point")
 scipy_cg_2j_cube = make_scipy("CG", jacobian="2-point")