[tune] Add BayesOpt (#3864)

Adds BayesOpt as a Tune suggestion algorithm.

doc/source/tune-searchalg.rst

@@ -27,6 +27,29 @@ By default, Tune uses the `default search space and variant generation process <
 
 Note that other search algorithms will not necessarily extend this class and may require a different search space declaration than the default Tune format.
 
+BayesOpt Search
+---------------
+
+The ``BayesOptSearch`` is a SearchAlgorithm that is backed by the `bayesian-optimization <https://github.com/fmfn/BayesianOptimization>`__ package to perform sequential model-based hyperparameter optimization. Note that this class does not extend ``ray.tune.suggest.BasicVariantGenerator``, so you will not be able to use Tune's default variant generation/search space declaration when using BayesOptSearch.
+
+In order to use this search algorithm, you will need to install Bayesian Optimization via the following command:
+
+.. code-block:: bash
+
+    $ pip install bayesian-optimization
+
+This algorithm requires `setting a search space and defining a utility function <https://github.com/fmfn/BayesianOptimization/blob/master/examples/advanced-tour.ipynb>`__. You can use BayesOptSearch like follows:
+
+.. code-block:: python
+
+    run_experiments(experiment_config, search_alg=BayesOptSearch(bayesopt_space, utility_kwargs=utility_params, ... ))
+
+An example of this can be found in `bayesopt_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/bayesopt_example.py>`__.
+
+.. autoclass:: ray.tune.suggest.BayesOptSearch
+    :show-inheritance:
+    :noindex:
+
 HyperOpt Search (Tree-structured Parzen Estimators)
 ---------------------------------------------------
 

docker/examples/Dockerfile

@@ -7,5 +7,6 @@ RUN conda install -y numpy
 RUN apt-get install -y zlib1g-dev
 RUN pip install gym[atari] opencv-python==3.2.0.8 tensorflow lz4 keras pytest-timeout smart_open
 RUN pip install -U h5py  # Mutes FutureWarnings
+RUN pip install --upgrade bayesian-optimization
 RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git
 RUN conda install pytorch-cpu torchvision-cpu -c pytorch

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)

test/jenkins_tests/run_multi_node_tests.sh

@@ -354,6 +354,10 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/hyperopt_example.py \
     --smoke-test
 
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/tune/examples/bayesopt_example.py \
+    --smoke-test
+
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/tune_mnist_keras.py \
     --smoke-test