separate search_data and collect_data methods

surfaces/mathematical_functions/_base_objective_function.py

@@ -8,8 +8,7 @@
 import pandas as pd
 from functools import reduce
 
-from hyperactive import Hyperactive
-from hyperactive.optimizers import GridSearchOptimizer
+from gradient_free_optimizers import GridSearchOptimizer
 
 from .._base_test_function import BaseTestFunction
 
@@ -41,7 +40,7 @@ def search_space(self, min=-5, max=5, step=0.1, value_types="array"):
 
         return search_space_
 
-    def collect_data(self, if_exists="append"):
+    def search_data(self):
         self.search_space = self.search_space(value_types="list")
 
         para_names = list(self.search_space.keys())
@@ -53,26 +52,22 @@ def collect_data(self, if_exists="append"):
         search_space_size = reduce((lambda x, y: x * y), dim_sizes_list)
 
         while search_data_length < search_space_size:
-            hyper = Hyperactive(verbosity=["progress_bar"])
-            hyper.add_search(
-                self.objective_function_dict,
-                self.search_space,
-                initialize={},
-                n_iter=search_space_size,
-                optimizer=GridSearchOptimizer(direction="orthogonal"),
-                memory_warm_start=search_data,
-            )
-            hyper.run()
+            print("\n search_space_size", search_space_size)
+            opt = GridSearchOptimizer(self.search_space, direction="orthogonal")
+            opt.search(self.objective_function_dict, n_iter=search_space_size)
 
             search_data = pd.concat(
-                [search_data, hyper.search_data(self.objective_function_dict)],
+                [search_data, opt.search_data],
                 ignore_index=True,
             )
 
             search_data = search_data.drop_duplicates(subset=para_names)
             search_data_length = len(search_data)
+            print("\n search_data_length", search_data_length, "\n")
+        return search_data
 
-        self.sql_data.save(self.__name__, search_data, if_exists)
+    def collect_data(self, if_exists="append"):
+        self.sql_data.save(self.__name__, self.search_data(), if_exists)
 
     def return_metric(self, loss):
         if self.metric == "score":