[tune] Add BayesOpt (#3864)

Adds BayesOpt as a Tune suggestion algorithm.

python/ray/tune/examples/bayesopt_example.py

@@ -0,0 +1,61 @@
+"""This test checks that BayesOpt is functional.
+
+It also checks that it is usable with a separate scheduler.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import ray
+from ray.tune import run_experiments, register_trainable
+from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.suggest import BayesOptSearch
+
+
+def easy_objective(config, reporter):
+    import time
+    time.sleep(0.2)
+    for i in range(config["iterations"]):
+        reporter(
+            timesteps_total=i,
+            neg_mean_loss=-(config["height"] - 14)**2 +
+            abs(config["width"] - 3))
+        time.sleep(0.02)
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    args, _ = parser.parse_known_args()
+    ray.init(redirect_output=True)
+
+    register_trainable("exp", easy_objective)
+
+    space = {'width': (0, 20), 'height': (-100, 100)}
+
+    config = {
+        "my_exp": {
+            "run": "exp",
+            "num_samples": 10 if args.smoke_test else 1000,
+            "config": {
+                "iterations": 100,
+            },
+            "stop": {
+                "timesteps_total": 100
+            },
+        }
+    }
+    algo = BayesOptSearch(
+        space,
+        max_concurrent=4,
+        reward_attr="neg_mean_loss",
+        utility_kwargs={
+            "kind": "ucb",
+            "kappa": 2.5,
+            "xi": 0.0
+        })
+    scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
+    run_experiments(config, search_alg=algo, scheduler=scheduler)

python/ray/tune/suggest/__init__.py

@@ -1,6 +1,7 @@
 from ray.tune.suggest.search import SearchAlgorithm
 from ray.tune.suggest.basic_variant import BasicVariantGenerator
 from ray.tune.suggest.suggestion import SuggestionAlgorithm
+from ray.tune.suggest.bayesopt import BayesOptSearch
 from ray.tune.suggest.hyperopt import HyperOptSearch
 from ray.tune.suggest.variant_generator import grid_search, function, \
     sample_from
@@ -8,6 +9,7 @@ from ray.tune.suggest.variant_generator import grid_search, function, \
 __all__ = [
     "SearchAlgorithm",
     "BasicVariantGenerator",
+    "BayesOptSearch",
     "HyperOptSearch",
     "SuggestionAlgorithm",
     "grid_search",

python/ray/tune/suggest/bayesopt.py

@@ -0,0 +1,102 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+try:
+    import bayes_opt as byo
+except Exception:
+    byo = None
+
+from ray.tune.suggest.suggestion import SuggestionAlgorithm
+
+
+class BayesOptSearch(SuggestionAlgorithm):
+    """A wrapper around BayesOpt to provide trial suggestions.
+
+    Requires BayesOpt to be installed. You can install BayesOpt with the
+    command: `pip install bayesian-optimization`.
+
+    Parameters:
+        space (dict): Continuous search space. Parameters will be sampled from
+            this space which will be used to run trials.
+        max_concurrent (int): Number of maximum concurrent trials. Defaults
+            to 10.
+        reward_attr (str): The training result objective value attribute.
+            This refers to an increasing value.
+        utility_kwargs (dict): Parameters to define the utility function. Must
+            provide values for the keys `kind`, `kappa`, and `xi`.
+        random_state (int): Used to initialize BayesOpt.
+        verbose (int): Sets verbosity level for BayesOpt packages.
+
+    Example:
+        >>> space = {
+        >>>     'width': (0, 20),
+        >>>     'height': (-100, 100),
+        >>> }
+        >>> config = {
+        >>>     "my_exp": {
+        >>>         "run": "exp",
+        >>>         "num_samples": 10 if args.smoke_test else 1000,
+        >>>         "stop": {
+        >>>             "training_iteration": 100
+        >>>         },
+        >>>     }
+        >>> }
+        >>> algo = BayesOptSearch(
+        >>>     space, max_concurrent=4, reward_attr="neg_mean_loss")
+    """
+
+    def __init__(self,
+                 space,
+                 max_concurrent=10,
+                 reward_attr="episode_reward_mean",
+                 utility_kwargs=None,
+                 random_state=1,
+                 verbose=0,
+                 **kwargs):
+        assert byo is not None, (
+            "BayesOpt must be installed!. You can install BayesOpt with"
+            " the command: `pip install bayesian-optimization`.")
+        assert type(max_concurrent) is int and max_concurrent > 0
+        assert utility_kwargs is not None, (
+            "Must define arguments for the utiliy function!")
+        self._max_concurrent = max_concurrent
+        self._reward_attr = reward_attr
+        self._live_trial_mapping = {}
+
+        self.optimizer = byo.BayesianOptimization(
+            f=None, pbounds=space, verbose=verbose, random_state=random_state)
+
+        self.utility = byo.UtilityFunction(**utility_kwargs)
+
+        super(BayesOptSearch, self).__init__(**kwargs)
+
+    def _suggest(self, trial_id):
+        if self._num_live_trials() >= self._max_concurrent:
+            return None
+
+        new_trial = self.optimizer.suggest(self.utility)
+
+        self._live_trial_mapping[trial_id] = new_trial
+
+        return copy.deepcopy(new_trial)
+
+    def on_trial_result(self, trial_id, result):
+        pass
+
+    def on_trial_complete(self,
+                          trial_id,
+                          result=None,
+                          error=False,
+                          early_terminated=False):
+        """Passes the result to BayesOpt unless early terminated or errored"""
+        if result:
+            self.optimizer.register(
+                params=self._live_trial_mapping[trial_id],
+                target=result[self._reward_attr])
+            del self._live_trial_mapping[trial_id]
+
+    def _num_live_trials(self):
+        return len(self._live_trial_mapping)