diff --git a/docs/source/changelogs.rst b/docs/source/changelogs.rst
index 1dfc339..6b9aa61 100644
--- a/docs/source/changelogs.rst
+++ b/docs/source/changelogs.rst
@@ -1,7 +1,7 @@
 Changelogs
 ==============
 
-[2.0.1] - 2023-11-06
+[2.0.2] - 2023-11-06
 --------------------
 GADMA was updated and tested on Python3.10 with the latest versions of dependencies.
 
diff --git a/gadma/optimizers/__init__.py b/gadma/optimizers/__init__.py
index 5349406..604f527 100644
--- a/gadma/optimizers/__init__.py
+++ b/gadma/optimizers/__init__.py
@@ -8,10 +8,11 @@
 from .global_optimizer import register_global_optimizer, get_global_optimizer  # NOQA
 from .global_optimizer import all_global_optimizers, GlobalOptimizer  # NOQA
 from .genetic_algorithm import GeneticAlgorithm  # NOQA
-from .bayesian_optimization import GPyOptBayesianOptimizer  # NOQA
+# from .bayesian_optimization import GPyOptBayesianOptimizer  # NOQA
 from .bayesian_optimization import SMACBayesianOptimizer, SMACSquirrelOptimizer  # NOQA
 from .combinations import GlobalOptimizerAndLocalOptimizer  # NOQA
 from .linear_constrain import LinearConstrain  # NOQA
 from .optimizer_result import OptimizerResult  # NOQA
 from .gaussian_process import GaussianProcess  # NOQA
-from .gaussian_process import GPyGaussianProcess, SMACGaussianProcess  # NOQA
+# from .gaussian_process import GPyGaussianProcess
+from .gaussian_process import SMACGaussianProcess  # NOQA
diff --git a/gadma/optimizers/gaussian_process.py b/gadma/optimizers/gaussian_process.py
index fc671cd..5f0e20b 100644
--- a/gadma/optimizers/gaussian_process.py
+++ b/gadma/optimizers/gaussian_process.py
@@ -1,30 +1,30 @@
-# import numpy as np
-# import copy
-#
-#
-# class GaussianProcess(object):
-#     """
-#     Base class to keep Gaussian process for Bayesian optimization.
-#     """
-#     def __init__(self, gp_model):
-#         self.gp_model = gp_model
-#
-#     def train(self, X, Y, optimize=True):
-#         raise NotImplementedError
-#
-#     def get_noise(self):
-#         raise NotImplementedError
-#
-#     def predict(self, X):
-#         raise NotImplementedError
-#
-#     def get_K(self):
-#         raise NotImplementedError
-#
-#     def get_hypers(self):
-#         raise NotImplementedError
-#
-#
+import numpy as np
+import copy
+
+
+class GaussianProcess(object):
+    """
+    Base class to keep Gaussian process for Bayesian optimization.
+    """
+    def __init__(self, gp_model):
+        self.gp_model = gp_model
+
+    def train(self, X, Y, optimize=True):
+        raise NotImplementedError
+
+    def get_noise(self):
+        raise NotImplementedError
+
+    def predict(self, X):
+        raise NotImplementedError
+
+    def get_K(self):
+        raise NotImplementedError
+
+    def get_hypers(self):
+        raise NotImplementedError
+
+
 # class GPyGaussianProcess(GaussianProcess):
 #     def _convert(self, X, Y=None):
 #         X = np.array(X, dtype=float)
@@ -58,28 +58,28 @@
 #         theta.extend(self.gp_model.model.kern.lengthscale)
 #         theta.append(self.get_noise())
 #         return theta
-#
-#
-# class SMACGaussianProcess(GaussianProcess):
-#     def __init__(self, gp_model):
-#         super(SMACGaussianProcess, self).__init__(gp_model=gp_model)
-#         self.gp_model.normalize_y = True
-#
-#     def train(self, X, Y, optimize=True):
-#         self.gp_model._train(X, Y, do_optimize=optimize)
-#
-#     def get_noise(self):
-#         return 0
-#
-#     def predict(self, X):
-#         mu, var = self.gp_model.predict_marginalized_over_instances(
-#             np.array(X)
-#         )
-#         sigma = np.sqrt(var)
-#         return mu.reshape(mu.shape[0]), sigma.reshape(sigma.shape[0])
-#
-#     def get_K(self):
-#         return self.gp_model.kernel(self.gp_model.gp.X_train_)
-#
-#     def get_hypers(self):
-#         return np.exp(self.gp_model.hypers)
+
+
+class SMACGaussianProcess(GaussianProcess):
+    def __init__(self, gp_model):
+        super(SMACGaussianProcess, self).__init__(gp_model=gp_model)
+        self.gp_model.normalize_y = True
+
+    def train(self, X, Y, optimize=True):
+        self.gp_model._train(X, Y, do_optimize=optimize)
+
+    def get_noise(self):
+        return 0
+
+    def predict(self, X):
+        mu, var = self.gp_model.predict_marginalized_over_instances(
+            np.array(X)
+        )
+        sigma = np.sqrt(var)
+        return mu.reshape(mu.shape[0]), sigma.reshape(sigma.shape[0])
+
+    def get_K(self):
+        return self.gp_model.kernel(self.gp_model.gp.X_train_)
+
+    def get_hypers(self):
+        return np.exp(self.gp_model.hypers)
diff --git a/gadma/optimizers/optimizer_result.py b/gadma/optimizers/optimizer_result.py
index bec0299..613e494 100644
--- a/gadma/optimizers/optimizer_result.py
+++ b/gadma/optimizers/optimizer_result.py
@@ -83,52 +83,52 @@ def from_SciPy_OptimizeResult(
                                X_out=[scipy_result.x],
                                Y_out=[scipy_result.fun])
 
-    @staticmethod
-    def from_GPyOpt_OptimizerResult(gpyopt_obj):
-        """
-        Create OptimizerResult from instance of bayesian optimization.
-
-        :param gpyopt_obj: Object of GPyOpt optimizer
-        :type gpyopt_obj: GPyOpt.methods.BayesianOptimization
-        """
-        gpyopt_obj._compute_results()
-        if (gpyopt_obj.num_acquisitions == gpyopt_obj.max_iter and
-                not gpyopt_obj.initial_iter):
-            message = '   ** Maximum number of iterations reached **'
-            success = True
-            status = 1
-        elif (gpyopt_obj._distance_last_evaluations() < gpyopt_obj.eps and
-                not gpyopt_obj.initial_iter):
-            message = '   ** Two equal location selected **'
-            success = True
-            status = 1
-        elif (gpyopt_obj.max_time < gpyopt_obj.cum_time and
-                not gpyopt_obj.initial_iter):
-            message = '   ** Evaluation time reached **'
-            success = True
-            status = 0
-        else:
-            message = '** GPyOpt Bayesian Optimization class initialized '\
-                      'successfully **'
-            success = False
-            status = 2
-
-        if hasattr(gpyopt_obj.f, 'cache_info'):
-            n_eval = gpyopt_obj.f.cache_info.misses
-        else:
-            n_eval = len(gpyopt_obj.Y)
-
-        return OptimizerResult(x=gpyopt_obj.x_opt,
-                               y=gpyopt_obj.fx_opt,
-                               success=success,
-                               status=status,
-                               message=message,
-                               X=gpyopt_obj.X,
-                               Y=gpyopt_obj.Y,
-                               n_eval=n_eval,
-                               n_iter=gpyopt_obj.num_acquisitions,
-                               X_out=gpyopt_obj.X,
-                               Y_out=gpyopt_obj.Y)
+#     @staticmethod
+#     def from_GPyOpt_OptimizerResult(gpyopt_obj):
+#         """
+#         Create OptimizerResult from instance of bayesian optimization.
+# 
+#         :param gpyopt_obj: Object of GPyOpt optimizer
+#         :type gpyopt_obj: GPyOpt.methods.BayesianOptimization
+#         """
+#         gpyopt_obj._compute_results()
+#         if (gpyopt_obj.num_acquisitions == gpyopt_obj.max_iter and
+#                 not gpyopt_obj.initial_iter):
+#             message = '   ** Maximum number of iterations reached **'
+#             success = True
+#             status = 1
+#         elif (gpyopt_obj._distance_last_evaluations() < gpyopt_obj.eps and
+#                 not gpyopt_obj.initial_iter):
+#             message = '   ** Two equal location selected **'
+#             success = True
+#             status = 1
+#         elif (gpyopt_obj.max_time < gpyopt_obj.cum_time and
+#                 not gpyopt_obj.initial_iter):
+#             message = '   ** Evaluation time reached **'
+#             success = True
+#             status = 0
+#         else:
+#             message = '** GPyOpt Bayesian Optimization class initialized '\
+#                       'successfully **'
+#             success = False
+#             status = 2
+# 
+#         if hasattr(gpyopt_obj.f, 'cache_info'):
+#             n_eval = gpyopt_obj.f.cache_info.misses
+#         else:
+#             n_eval = len(gpyopt_obj.Y)
+# 
+#         return OptimizerResult(x=gpyopt_obj.x_opt,
+#                                y=gpyopt_obj.fx_opt,
+#                                success=success,
+#                                status=status,
+#                                message=message,
+#                                X=gpyopt_obj.X,
+#                                Y=gpyopt_obj.Y,
+#                                n_eval=n_eval,
+#                                n_iter=gpyopt_obj.num_acquisitions,
+#                                X_out=gpyopt_obj.X,
+#                                Y_out=gpyopt_obj.Y)
 
     def __repr__(self):
         string = f"  status: {self.status}\n"\