remove prints

SimonBlanke · May 26, 2024 · f10d867 · f10d867
1 parent 1059993
commit f10d867
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Showing 2 changed files with 0 additions and 9 deletions.
diff --git a/src/surfaces/data_collector/surfaces_data_collector.py b/src/surfaces/data_collector/surfaces_data_collector.py
@@ -33,7 +33,6 @@ def _init_search_data(self, objective_function, search_space):
 
     def _array_search_space(self, objective_function, search_space):
         while self.search_data_length < self.search_space_size:
-            print("\n ------------ search_space_size", self.search_space_size)
             opt = GridSearchOptimizer(
                 search_space,
                 direction="orthogonal",
@@ -52,13 +51,9 @@ def _array_search_space(self, objective_function, search_space):
 
             self.search_data = self.search_data.drop_duplicates(subset=self.para_names)
             self.search_data_length = len(self.search_data)
-            print(
-                "\n ------------ self.search_data_length", self.search_data_length, "\n"
-            )
 
     def _list_search_space(self, objective_function, search_space):
         while self.search_data_length < self.search_space_size:
-            print("\n ------------ search_space_size", self.search_space_size)
 
             hyper = Hyperactive(verbosity=["progress_bar"])
             hyper.add_search(
@@ -78,9 +73,6 @@ def _list_search_space(self, objective_function, search_space):
 
             self.search_data = self.search_data.drop_duplicates(subset=self.para_names)
             self.search_data_length = len(self.search_data)
-            print(
-                "\n ------------ self.search_data_length", self.search_data_length, "\n"
-            )
 
     def collect(
         self,

diff --git a/src/surfaces/test_functions/machine_learning/tabular_classifiers.py b/src/surfaces/test_functions/machine_learning/tabular_classifiers.py
@@ -56,7 +56,6 @@ def k_neighbors_classifier(params):
             )
             X, y = params["dataset"]()
             scores = cross_val_score(knc, X, y, cv=params["cv"], scoring=self.metric)
-            print("\n score ", scores.mean(), "\n")
             return scores.mean()
 
         self.pure_objective_function = k_neighbors_classifier