[Tune] Added default values for utility kwargs (#8488)

python/ray/tune/suggest/bayesopt.py

@@ -32,8 +32,11 @@ class BayesOptSearch(Searcher):
         metric (str): The training result objective value attribute.
         mode (str): One of {min, max}. Determines whether objective is
             minimizing or maximizing the metric attribute.
-        utility_kwargs (dict): Parameters to define the utility function. Must
-            provide values for the keys `kind`, `kappa`, and `xi`.
+        utility_kwargs (dict): Parameters to define the utility function.
+            The default value is a dictionary with three keys:
+            - kind: ucb (Upper Confidence Bound)
+            - kappa: 2.576
+            - xi: 0.0
         random_state (int): Used to initialize BayesOpt.
         verbose (int): Sets verbosity level for BayesOpt packages.
         max_concurrent: Deprecated.
@@ -66,8 +69,6 @@ class BayesOptSearch(Searcher):
         assert byo is not None, (
             "BayesOpt must be installed!. You can install BayesOpt with"
             " the command: `pip install bayesian-optimization`.")
-        assert utility_kwargs is not None, (
-            "Must define arguments for the utility function!")
         assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!"
         self.max_concurrent = max_concurrent
         super(BayesOptSearch, self).__init__(
@@ -76,6 +77,15 @@ class BayesOptSearch(Searcher):
             max_concurrent=max_concurrent,
             use_early_stopped_trials=use_early_stopped_trials)
 
+        if utility_kwargs is None:
+            # The defaults arguments are the same
+            # as in the package BayesianOptimization
+            utility_kwargs = dict(
+                kind="ucb",
+                kappa=2.576,
+                xi=0.0,
+            )
+
         if mode == "max":
             self._metric_op = 1.
         elif mode == "min":

python/ray/tune/tests/test_tune_restore.py

@@ -212,11 +212,7 @@ class BayesoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
             space,
             metric="loss",
             mode="min",
-            utility_kwargs={
-                "kind": "ucb",
-                "kappa": 2.5,
-                "xi": 0.0
-            })
+        )
         return search_alg, cost