Merge branch 'hyperparametertuning' of https://github.com/KernelTuner/kernel_tuner into hyperparametertuning

Author: fjwillemsen

kernel_tuner/backends/hypertuner.py

@@ -17,6 +17,8 @@
 
 
 class ScoreObserver(BenchmarkObserver):
+    """BenchmarkObserver subclass for registering the hyperparameter tuning score."""
+
     def __init__(self, dev):
         self.dev = dev
         self.scores = []

kernel_tuner/python.py

@@ -1017,5 +1017,4 @@ def to_ax_searchspace(self):
         raise NotImplementedError(
             "Conversion to Ax SearchSpace has not been fully implemented as Ax Searchspaces can't capture full complexity."
         )
-
-        return ax_searchspace
+        # return ax_searchspace

kernel_tuner/searchspace.py

@@ -860,10 +860,8 @@ def __optimize_multi_ultrafast(self, max_fevals, predict_eval_ratio=5):
         while self.fevals < max_fevals:
             aqfs = self.multi_afs
             # if we take the prediction only once, we want to go from most exploiting to most exploring, because the more exploiting an AF is, the more it relies on non-stale information from the model
-            if (
-                last_prediction_time * predict_eval_ratio <= last_eval_time
-                or last_prediction_counter >= predict_eval_ratio
-            ):
+            fit_observations = last_prediction_time * predict_eval_ratio <= last_eval_time or last_prediction_counter >= predict_eval_ratio
+            if fit_observations:
                 last_prediction_counter = 0
                 pred_start = time.perf_counter()
                 if last_eval_time > 0.0:

kernel_tuner/strategies/bayes_opt.py

@@ -41,6 +41,10 @@ classifiers = [
     "Topic :: Scientific/Engineering",
     "Topic :: Software Development",
     "Topic :: System :: Distributed Computing",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13"
 ]
 
 # ATTENTION: if anything is changed here, run `poetry update`

kernel_tuner/strategies/bayes_opt_old.py

@@ -41,8 +41,8 @@ def vector_add():
 # skip some strategies if their dependencies are not installed
 strategies = []
 for s in strategy_map.keys():
-    if 'gpytorch' in s.lower() or 'botorch_alt' in s.lower() or 'bayes_opt_old' in s.lower():
-        continue
+    if 'gpytorch' in s.lower() or 'botorch_alt' in s.lower():
+        continue    # TODO issue warning for uninstalled dependencies?
     if 'gpytorch' in s.lower():
         strategies.append(pytest.param(s, marks=skip_if_no_bayesopt_gpytorch))
     elif 'botorch' in s.lower():