0.35.1

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.35.0"
+version = "0.35.1"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/spot/spot.py

@@ -212,6 +212,13 @@ def __init__(
         self.optimizer_control = optimizer_control
         self.surrogate_control = surrogate_control
 
+        # Kernel selection from fun_control (NEW)
+        self.kernel = None
+        self.kernel_params = None
+        if fun_control is not None:
+            self.kernel = fun_control.get("kernel", "gauss")
+            self.kernel_params = fun_control.get("kernel_params", {})
+
         self.counter = 0
         self.success_rate = 0.0
         self.success_counter = 0
@@ -710,6 +717,8 @@ def surrogate_setup(self, surrogate) -> None:
                 use_nystrom=self.surrogate_control["use_nystrom"],
                 nystrom_m=self.surrogate_control["nystrom_m"],
                 nystrom_seed=self.surrogate_control["nystrom_seed"],
+                kernel=self.kernel,
+                kernel_params=self.kernel_params,
             )
 
     def get_spot_attributes_as_df(self) -> pd.DataFrame:
@@ -1695,6 +1704,10 @@ def fit_surrogate(self) -> None:
         logger.debug("In fit_surrogate(): self.y: %s", self.y)
         logger.debug("In fit_surrogate(): self.X.shape: %s", self.X.shape)
         logger.debug("In fit_surrogate(): self.y.shape: %s", self.y.shape)
+        # Pass kernel options to surrogate if Kriging is used
+        if hasattr(self.surrogate, "kernel"):
+            self.surrogate.kernel = self.kernel
+            self.surrogate.kernel_params = self.kernel_params
         X_points = self.X.shape[0]
         y_points = self.y.shape[0]
         if X_points == y_points:

src/spotpython/utils/init.py

@@ -57,6 +57,8 @@ def fun_control_init(
     horizon=None,
     hyperdict=None,
     infill_criterion="y",
+    kernel="gauss",
+    kernel_params={},
     log_every_n_steps=50,
     log_level=50,
     lower=None,
@@ -208,6 +210,11 @@ def fun_control_init(
             For example: `spotriver.hyperdict.river_hyper_dict import RiverHyperDict`
         infill_criterion (str):
             Can be `"y"`, `"s"`, `"ei"` (negative expected improvement), or `"all"`. Default is "y".
+        kernel (str):
+            The kernel to be used by the Kriging surrogate model.
+            Can be either "gauss", "matern32", "matern52", or "exponential". Default is "gauss".
+        kernel_params (dict):
+            The parameters for the kernel function. Default is an empty dictionary.
         log_every_n_steps (int):
             Lightning: How often to log within steps. Default: 50.
         log_level (int):
@@ -392,6 +399,8 @@ def fun_control_init(
                 'horizon': 7,
                 'infill_criterion': 'y',
                 'k_folds': None,
+                'kernel': 'gauss',
+                'kernel_params': {},
                 'loss_function': None,
                 'lower': None,
                 'max_surrogate_points': 100,
@@ -483,6 +492,8 @@ def fun_control_init(
         "hyperdict": hyperdict,
         "infill_criterion": infill_criterion,
         "k_folds": 3,
+        "kernel": kernel,
+        "kernel_params": kernel_params,
         "log_every_n_steps": log_every_n_steps,
         "log_graph": False,
         "log_level": log_level,

test/test_get_spot_attributes_as_df.py

@@ -51,6 +51,8 @@ def test_get_spot_attributes_as_df():
                             'ident',
                             'infill_criterion',
                             'k',
+                            'kernel',
+                            'kernel_params',
                             'log_level',
                             'lower',
                             'max_surrogate_points',