Make optimization faster by simplifying search

This commit makes the whole optimization process considerably faster by
simplifying the search steps. Basically less iterations when fitting the
gaussian process as well as when optimizing the acquisition function.

Author: fmfn

bayes_opt/bayesian_optimization.py

@@ -80,7 +80,7 @@ def __init__(self, f, pbounds, random_state=None, verbose=2):
             kernel=Matern(nu=2.5),
             alpha=1e-6,
             normalize_y=True,
-            n_restarts_optimizer=25,
+            n_restarts_optimizer=5,
             random_state=self._random_state,
         )
 

bayes_opt/util.py

@@ -4,7 +4,7 @@
 from scipy.optimize import minimize
 
 
-def acq_max(ac, gp, y_max, bounds, random_state, n_warmup=100000, n_iter=250):
+def acq_max(ac, gp, y_max, bounds, random_state, n_warmup=10000, n_iter=10):
     """
     A function to find the maximum of the acquisition function
 

tests/test_bayesian_optimization.py

@@ -217,11 +217,11 @@ def test_set_bounds():
 def test_set_gp_params():
     optimizer = BayesianOptimization(target_func, PBOUNDS, random_state=1)
     assert optimizer._gp.alpha == 1e-6
-    assert optimizer._gp.n_restarts_optimizer == 25
+    assert optimizer._gp.n_restarts_optimizer == 5
 
     optimizer.set_gp_params(alpha=1e-2)
     assert optimizer._gp.alpha == 1e-2
-    assert optimizer._gp.n_restarts_optimizer == 25
+    assert optimizer._gp.n_restarts_optimizer == 5
 
     optimizer.set_gp_params(n_restarts_optimizer=7)
     assert optimizer._gp.alpha == 1e-2