v0.3.0

hyperlight replaces lightning, add_core_model*, modify_hyper_parameter do not return fun_control

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.2.55"
+version = "0.3.0"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/data/lightning2_hyper_dict.json

@@ -261,7 +261,6 @@ def modify_hyper_parameter_levels(fun_control, hyperparameter, levels) -> dict:
     fun_control["core_model_hyper_dict"][hyperparameter].update({"levels": levels})
     fun_control["core_model_hyper_dict"][hyperparameter].update({"lower": 0})
     fun_control["core_model_hyper_dict"][hyperparameter].update({"upper": len(levels) - 1})
-    return fun_control
 
 
 def modify_hyper_parameter_bounds(fun_control, hyperparameter, bounds) -> dict:
@@ -285,7 +284,6 @@ def modify_hyper_parameter_bounds(fun_control, hyperparameter, bounds) -> dict:
     """
     fun_control["core_model_hyper_dict"][hyperparameter].update({"lower": bounds[0]})
     fun_control["core_model_hyper_dict"][hyperparameter].update({"upper": bounds[1]})
-    return fun_control
 
 
 def get_default_values(fun_control) -> dict:
@@ -606,7 +604,6 @@ def add_core_model_to_fun_control(core_model, fun_control, hyper_dict, filename=
     var_type = get_var_type(fun_control)
     var_name = get_var_name(fun_control)
     fun_control.update({"var_type": var_type, "var_name": var_name})
-    return fun_control
 
 
 def get_one_core_model_from_X(X, fun_control=None):

src/spotPython/data/lightning2_hyper_dict.py

@@ -46,7 +46,9 @@ def setup(self, stage=None):
             train_indexes, val_indexes = all_splits[self.hparams.k]
             train_indexes, val_indexes = train_indexes.tolist(), val_indexes.tolist()
             self.data_train = Subset(dataset_full, train_indexes)
+            print(f"Train Dataset Size: {len(self.data_train)}")
             self.data_val = Subset(dataset_full, val_indexes)
+            print(f"Val Dataset Size: {len(self.data_val)}")
 
     def train_dataloader(self):
         return DataLoader(