Fix acq min bug

bayes_opt/acquisition.py

@@ -362,7 +362,7 @@ def _smart_minimize(
                     continue
 
                 # Store it if better than previous minimum(maximum).
-                if min_acq is None or np.squeeze(res.fun) >= min_acq:
+                if min_acq is None or np.squeeze(res.fun) < min_acq:
                     x_try = res.x
                     x_min = x_try
                     min_acq = np.squeeze(res.fun)

bayes_opt/bayesian_optimization.py

@@ -422,14 +422,14 @@ def load_state(self, path: str | PathLike[str]) -> None:
             self._space.set_bounds(new_bounds)
             self._bounds_transformer.initialize(self._space)
 
-        self._gp.set_params(**state["gp_params"])
-        if isinstance(self._gp.kernel, dict):
-            kernel_params = self._gp.kernel
-            self._gp.kernel = Matern(
-                length_scale=kernel_params["length_scale"],
-                length_scale_bounds=tuple(kernel_params["length_scale_bounds"]),
-                nu=kernel_params["nu"],
-            )
+        # Construct the GP kernel
+        kernel = Matern(**state["gp_params"]["kernel"])
+        # Re-construct the GP parameters
+        gp_params = {k: v for k, v in state["gp_params"].items() if k != "kernel"}
+        gp_params["kernel"] = kernel
+
+        # Set the GP parameters
+        self.set_gp_params(**gp_params)
 
         self._gp.fit(self._space.params, self._space.target)
 

tests/test_acquisition.py

@@ -407,7 +407,10 @@ def verify_optimizers_match(optimizer1, optimizer2):
     rng = np.random.default_rng()
     assert rng.bit_generator.state["state"]["state"] == rng.bit_generator.state["state"]["state"]
 
-    assert optimizer1._gp.kernel.get_params() == optimizer2._gp.kernel.get_params()
+    kernel_params1 = optimizer1._gp.kernel.get_params()
+    kernel_params2 = optimizer2._gp.kernel.get_params()
+    for k in kernel_params1:
+        assert (np.array(kernel_params1[k]) == np.array(kernel_params2[k])).all()
 
     suggestion1 = optimizer1.suggest()
     suggestion2 = optimizer2.suggest()

tests/test_bayesian_optimization.py

@@ -582,4 +582,4 @@ def area_of_triangle(sides):
     for _ in range(5):
         suggestion1 = optimizer.suggest()
         suggestion2 = new_optimizer.suggest()
-        np.testing.assert_array_almost_equal(suggestion1["sides"], suggestion2["sides"], decimal=10)
+        np.testing.assert_array_almost_equal(suggestion1["sides"], suggestion2["sides"], decimal=7)