0.10.28

Fixes an error in get_tuned_architecture. If spot uses noise, then min_mean_X is returned.

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -2398,6 +2398,14 @@
             "         1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1]])\n",
             "targets: tensor([2, 5, 7, 6, 1, 3])\n"
           ]
+        },
+        {
+          "ename": "",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+          ]
         }
       ],
       "source": [

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.10.27"
+version = "0.10.28"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/hyperparameters/values.py

@@ -936,21 +936,29 @@ def get_default_hyperparameters_for_core_model(fun_control) -> dict:
     return values
 
 
-def get_tuned_architecture(spot_tuner, fun_control) -> dict:
+def get_tuned_architecture(spot_tuner, fun_control, force_minX=False) -> dict:
     """
-    Returns the tuned architecture.
+    Returns the tuned architecture. If the spot tuner has noise,
+    it returns the architecture with the lowest mean (.min_mean_X),
+    otherwise it returns the architecture with the lowest value (.min_X).
 
     Args:
         spot_tuner (object):
             spot tuner object.
         fun_control (dict):
             dictionary containing control parameters for the hyperparameter tuning.
+        force_minX (bool):
+            If True, return the architecture with the lowest value (.min_X).
 
     Returns:
         (dict):
             dictionary containing the tuned architecture.
     """
-    X = spot_tuner.to_all_dim(spot_tuner.min_X.reshape(1, -1))
+    if not spot_tuner.noise or force_minX:
+        X = spot_tuner.to_all_dim(spot_tuner.min_X.reshape(1, -1))
+    else:
+        # noise or force_minX is False:
+        X = spot_tuner.to_all_dim(spot_tuner.min_mean_X.reshape(1, -1))
     config = get_one_config_from_X(X, fun_control)
     return config