add collect and load search-data methods for test-functions

surfaces/test_functions/_base_objective_function.py

@@ -11,13 +11,17 @@
 from hyperactive import Hyperactive
 from hyperactive.optimizers import GridSearchOptimizer
 
+from ..machine_learning.data_collector import SurfacesDataCollector
+
 
 class ObjectiveFunction:
     def __init__(self, metric="score", input_type="dictionary", sleep=0):
         self.metric = metric
         self.input_type = input_type
         self.sleep = sleep
 
+        self.sql_data = SurfacesDataCollector()
+
     def search_space(self, min=-5, max=5, step=0.1, value_typ="array"):
         search_space_ = {}
 
@@ -31,8 +35,7 @@ def search_space(self, min=-5, max=5, step=0.1, value_typ="array"):
 
         return search_space_
 
-    @property
-    def search_data(self):
+    def collect_data(self, if_exists="append"):
         para_names = list(self.search_space().keys())
         search_data_cols = para_names + ["score"]
         search_data = pd.DataFrame([], columns=search_data_cols)
@@ -44,7 +47,7 @@ def search_data(self):
         while search_data_length < search_space_size:
             hyper = Hyperactive(verbosity=["progress_bar"])
             hyper.add_search(
-                self,
+                self.objective_function_dict,
                 self.search_space(value_typ="list"),
                 initialize={},
                 n_iter=search_space_size,
@@ -54,12 +57,21 @@ def search_data(self):
             hyper.run()
 
             search_data = pd.concat(
-                [search_data, hyper.search_data(self)], ignore_index=True
+                [search_data, hyper.search_data(self.objective_function_dict)],
+                ignore_index=True,
             )
+
             search_data = search_data.drop_duplicates(subset=para_names)
             search_data_length = len(search_data)
 
-        return search_data
+        self.sql_data.save(self.__name__, search_data, if_exists)
+
+    def load_search_data(self):
+        try:
+            dataframe = self.sql_data.load(self.__name__)
+        except:
+            print("Path 2 database: ", self.sql_data.path)
+        return dataframe
 
     def return_metric(self, loss):
         if self.metric == "score":