fix non unqiue data point warning

Author: brendan

bayes_opt/target_space.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 from .util import ensure_rng, NotUniqueError
+from .util import Colours
 
 
 def _hashable(x):
@@ -183,10 +184,11 @@ def register(self, params, target, constraint_value=None):
         if x in self:
             if self._allow_duplicate_points:
                 self.n_duplicate_points = self.n_duplicate_points + 1
-                warnings.warn(f'Data point {x} is not unique. {self.n_duplicate_points} duplicates registered.'
-                              f' Continuing ...')
+                print(f'{Colours.RED}Data point {x} is not unique. {self.n_duplicate_points} duplicates registered.'
+                              f' Continuing ...{Colours.END}')
             else:
-                raise NotUniqueError('Data point {} is not unique'.format(x))
+                raise NotUniqueError(f'Data point {x} is not unique. You can set "allow_duplicate_points=True" to '
+                                     f'avoid this error')
 
         self._params = np.concatenate([self._params, x.reshape(1, -1)])
         self._target = np.concatenate([self._target, [target]])

examples/test.py

@@ -0,0 +1,19 @@
+import numpy as np
+from bayes_opt import BayesianOptimization
+from bayes_opt import UtilityFunction
+
+
+def f(x):
+    return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)
+
+
+if __name__ == '__main__':
+    optimizer = BayesianOptimization(f=None, pbounds={'x': (-2, 2)}, verbose=2, random_state=1, allow_duplicate_points=True)
+    optimizer.set_gp_params(normalize_y=True, alpha=2.5e-3, n_restarts_optimizer=20)  # tuning of the gaussian parameters...
+    utility = UtilityFunction(kind="ucb", kappa=5, xi=1)  # kappa determines explore/Exploitation ratio
+    for point in range(20):
+        next_point_to_probe = optimizer.suggest(utility)
+        NextPointValues = np.array(list(next_point_to_probe.values()))
+        mean,std = optimizer._gp.predict(NextPointValues.reshape(1, -1),return_std=True)
+        target = f(**next_point_to_probe)
+        optimizer.register(params=next_point_to_probe, target=target)