Extra check to make sure non-x0 supporting strategies raise an exception, implemented unsupported options check in Bayesian Optimization

Author: fjwillemsen

kernel_tuner/strategies/bayes_opt.py

@@ -13,7 +13,7 @@
 
 # BO imports
 from kernel_tuner.searchspace import Searchspace
-from kernel_tuner.strategies.common import CostFunc
+from kernel_tuner.strategies.common import CostFunc, get_options
 from kernel_tuner.util import StopCriterionReached
 
 try:
@@ -26,6 +26,24 @@
 
 supported_methods = ["poi", "ei", "lcb", "lcb-srinivas", "multi", "multi-advanced", "multi-fast", "multi-ultrafast"]
 
+# _options dict is used for generating documentation, but is not used to check for unsupported strategy_options in bayes_opt
+_options = dict(
+    covariancekernel=(
+        'The Covariance kernel to use, choose any from "constantrbf", "rbf", "matern32", "matern52"',
+        "matern32",
+    ),
+    covariancelengthscale=("The covariance length scale", 1.5),
+    method=(
+        "The Bayesian Optimization method to use, choose any from " + ", ".join(supported_methods),
+        "multi-ultrafast",
+    ),
+    samplingmethod=(
+        "Method used for initial sampling the parameter space, either random or Latin Hypercube Sampling (LHS)",
+        "lhs",
+    ),
+    popsize=("Number of initial samples", 20),
+)
+
 
 def generate_normalized_param_dicts(tune_params: dict, eps: float) -> Tuple[dict, dict]:
     """Generates normalization and denormalization dictionaries."""
@@ -92,6 +110,9 @@ def tune(searchspace: Searchspace, runner, tuning_options):
     :rtype: list(dict()), dict()
 
     """
+    # we don't actually use this for Bayesian Optimization, but it is used to check for unsupported options  
+    get_options(tuning_options.strategy_options, _options, unsupported=["x0"])
+
     max_fevals = tuning_options.strategy_options.get("max_fevals", 100)
     prune_parameterspace = tuning_options.strategy_options.get("pruneparameterspace", True)
     if not bayes_opt_present:
@@ -143,25 +164,6 @@ def tune(searchspace: Searchspace, runner, tuning_options):
     return cost_func.results
 
 
-# _options dict is used for generating documentation, but is not used to check for unsupported strategy_options in bayes_opt
-_options = dict(
-    covariancekernel=(
-        'The Covariance kernel to use, choose any from "constantrbf", "rbf", "matern32", "matern52"',
-        "matern32",
-    ),
-    covariancelengthscale=("The covariance length scale", 1.5),
-    method=(
-        "The Bayesian Optimization method to use, choose any from " + ", ".join(supported_methods),
-        "multi-ultrafast",
-    ),
-    samplingmethod=(
-        "Method used for initial sampling the parameter space, either random or Latin Hypercube Sampling (LHS)",
-        "lhs",
-    ),
-    popsize=("Number of initial samples", 20),
-)
-
-
 class BayesianOptimization:
     def __init__(
         self,

test/strategies/test_strategies.py

@@ -82,14 +82,17 @@ def test_strategies(vector_add, strategy):
             assert isinstance(res[expected_key], expected_type)
 
     # check if strategy respects user-specified starting point (x0)
+    x0 = [256]
+    filter_options["x0"] = x0
     if not strategy in ["brute_force", "random_sample", "bayes_opt"]:
-        x0 = [256]
-        filter_options["x0"] = x0
-
         results, _ = kernel_tuner.tune_kernel(*vector_add, strategy=strategy, strategy_options=filter_options,
-                                         verbose=False, cache=cache_filename, simulation_mode=True)
-
+                                            verbose=False, cache=cache_filename, simulation_mode=True)
         assert results[0]["block_size_x"] == x0[0]
+    else:
+        with pytest.raises(ValueError):
+            results, _ = kernel_tuner.tune_kernel(*vector_add, strategy=strategy, strategy_options=filter_options,
+                                            verbose=False, cache=cache_filename, simulation_mode=True)
+