Compatibility with optional dependencies

kernel_tuner/strategies/bayes_opt.py

@@ -451,7 +451,7 @@ def predict_list(self, lst: list) -> Tuple[list, list, list]:
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             mu, std = self.__model.predict(lst, return_std=True)
-            return mu, std
+            return list(zip(mu, std)), mu, std
 
     def fit_observations_to_model(self):
         """Update the model based on the current list of observations."""
@@ -540,7 +540,7 @@ def initial_sample(self):
             if self.is_valid(observation):
                 collected_samples += 1
         self.fit_observations_to_model()
-        _, std = self.predict_list(self.unvisited_cache)
+        _, _, std = self.predict_list(self.unvisited_cache)
         self.initial_sample_mean = np.mean(self.__valid_observations)
         # Alternatively:
         # self.initial_sample_std = np.std(self.__valid_observations)
@@ -736,11 +736,11 @@ def __optimize_multi_advanced(self, max_fevals, increase_precision=False):
                 if self.__visited_num >= self.searchspace_size or self.fevals >= max_fevals:
                     break
                 if increase_precision is True:
-                    predictions, _, std = self.predict_list(self.unvisited_cache)
+                    predictions = self.predict_list(self.unvisited_cache)
                     hyperparam = self.contextual_variance(std)
                 list_of_acquisition_values = af(predictions, hyperparam)
                 best_af = self.argopt(list_of_acquisition_values)
-                del predictions[best_af]  # to avoid going out of bounds
+                # del predictions[best_af]  # to avoid going out of bounds
                 candidate_params = self.unvisited_cache[best_af]
                 candidate_index = self.find_param_config_index(candidate_params)
                 observation = self.evaluate_objective_function(candidate_params)
@@ -855,13 +855,12 @@ def af_random(self, predictions=None, hyperparam=None) -> list:
     def af_probability_of_improvement(self, predictions=None, hyperparam=None) -> list:
         """Acquisition function Probability of Improvement (PI)."""
         # prefetch required data
-        x_mu, x_std = predictions
         if hyperparam is None:
             hyperparam = self.af_params["explorationfactor"]
         fplus = self.current_optimum - hyperparam
 
         # precompute difference of improvement
-        list_diff_improvement = list(-((fplus - x_mu) / (x_std + 1e-9)) for (x_mu, x_std) in predictions)
+        list_diff_improvement = list(-((fplus - x_mu) / (x_std + 1e-9)) for x_mu, x_std in predictions[0])
 
         # compute probability of improvement with CDF in bulk
         list_prob_improvement = norm.cdf(list_diff_improvement)
@@ -870,10 +869,15 @@ def af_probability_of_improvement(self, predictions=None, hyperparam=None) -> li
     def af_expected_improvement(self, predictions=None, hyperparam=None) -> list:
         """Acquisition function Expected Improvement (EI)."""
         # prefetch required data
-        x_mu, x_std = predictions
         if hyperparam is None:
             hyperparam = self.af_params["explorationfactor"]
         fplus = self.current_optimum - hyperparam
+        if len(predictions) == 3:
+            predictions, x_mu, x_std = predictions
+        elif len(predictions) == 2:
+            x_mu, x_std = predictions
+        else:
+            raise ValueError(f"Invalid predictions size {len(predictions)}")
 
         # precompute difference of improvement, CDF and PDF in bulk
         list_diff_improvement = list((fplus - x_mu) / (x_std + 1e-9) for (x_mu, x_std) in predictions)
@@ -892,6 +896,7 @@ def af_lower_confidence_bound(self, predictions=None, hyperparam=None) -> list:
         if hyperparam is None:
             hyperparam = self.af_params["explorationfactor"]
         beta = hyperparam
+        _, x_mu, x_std = predictions
 
         # compute LCB in bulk
         list_lower_confidence_bound = (x_mu - beta * x_std)
@@ -900,7 +905,7 @@ def af_lower_confidence_bound(self, predictions=None, hyperparam=None) -> list:
     def af_lower_confidence_bound_srinivas(self, predictions=None, hyperparam=None) -> list:
         """Acquisition function Lower Confidence Bound (UCB-S) after Srinivas, 2010 / Brochu, 2010."""
         # prefetch required data
-        x_mu, x_std = predictions
+        _, x_mu, x_std = predictions
         if hyperparam is None:
             hyperparam = self.af_params["explorationfactor"]