[tune] Add scikit-optimize to Tune (ray-project#3924)

doc/source/tune-searchalg.rst

@@ -103,6 +103,30 @@ An example of this can be found in `sigopt_example.py <https://github.com/ray-pr
     :show-inheritance:
     :noindex:
 
+Scikit-Optimize Search
+----------------------
+
+The ``SkOptSearch`` is a SearchAlgorithm that is backed by `Scikit-Optimize <https://scikit-optimize.github.io>`__ 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 SkOptSearch.
+
+In order to use this search algorithm, you will need to install Scikit-Optimize via the following command:
+
+.. code-block:: bash
+
+    $ pip install scikit-optimize
+
+This algorithm requires using the `Scikit-Optimize ask and tell interface <https://scikit-optimize.github.io/notebooks/ask-and-tell.html>`__. This interface requires using the `Optimizer <https://scikit-optimize.github.io/#skopt.Optimizer>`__ provided by Scikit-Optimize. You can use SkOptSearch like follows:
+
+.. code-block:: python
+
+    optimizer = Optimizer(dimension, ...)
+    run_experiments(experiment_config, search_alg=SkOptSearch(optimizer, parameter_names, ... ))
+
+An example of this can be found in `skopt_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/skopt_example.py>`__.
+
+.. autoclass:: ray.tune.suggest.SkOptSearch
+    :show-inheritance:
+    :noindex:
+
 Contributing a New Algorithm
 ----------------------------
 

docker/examples/Dockerfile

@@ -10,4 +10,5 @@ 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 pip install --upgrade sigopt
+RUN pip install --upgrade scikit-optimize
 RUN conda install pytorch-cpu torchvision-cpu -c pytorch

python/ray/tune/examples/skopt_example.py

@@ -0,0 +1,56 @@
+"""This test checks that Skopt 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 SkOptSearch
+
+
+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
+    from skopt import Optimizer
+
+    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)
+
+    config = {
+        "skopt_exp": {
+            "run": "exp",
+            "num_samples": 10 if args.smoke_test else 50,
+            "config": {
+                "iterations": 100,
+            },
+            "stop": {
+                "timesteps_total": 100
+            },
+        }
+    }
+    optimizer = Optimizer([(0, 20), (-100, 100)])
+    algo = SkOptSearch(
+        optimizer, ["width", "height"],
+        max_concurrent=4,
+        reward_attr="neg_mean_loss")
+    scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
+    run_experiments(config, search_alg=algo, scheduler=scheduler)

python/ray/tune/suggest/__init__.py

@@ -3,6 +3,7 @@
 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.skopt import SkOptSearch
 from ray.tune.suggest.sigopt import SigOptSearch
 from ray.tune.suggest.variant_generator import grid_search, function, \
     sample_from
@@ -12,6 +13,7 @@
     "BasicVariantGenerator",
     "BayesOptSearch",
     "HyperOptSearch",
+    "SkOptSearch",
     "SigOptSearch",
     "SuggestionAlgorithm",
     "grid_search",

python/ray/tune/suggest/skopt.py

@@ -0,0 +1,88 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+try:
+    import skopt
+except Exception:
+    skopt = None
+
+from ray.tune.suggest.suggestion import SuggestionAlgorithm
+
+
+class SkOptSearch(SuggestionAlgorithm):
+    """A wrapper around skopt to provide trial suggestions.
+
+    Requires skopt to be installed.
+
+    Parameters:
+        optimizer (skopt.optimizer.Optimizer): Optimizer provided
+            from skopt.
+        parameter_names (list): List of parameter names. Should match
+            the dimension of the optimizer output.
+        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.
+
+    Example:
+        >>> from skopt import Optimizer
+        >>> optimizer = Optimizer([(0,20),(-100,100)])
+        >>> config = {
+        >>>     "my_exp": {
+        >>>         "run": "exp",
+        >>>         "num_samples": 10,
+        >>>         "stop": {
+        >>>             "training_iteration": 100
+        >>>         },
+        >>>     }
+        >>> }
+        >>> algo = SkOptSearch(optimizer,
+        >>>     ["width", "height"], max_concurrent=4,
+        >>>     reward_attr="neg_mean_loss")
+    """
+
+    def __init__(self,
+                 optimizer,
+                 parameter_names,
+                 max_concurrent=10,
+                 reward_attr="episode_reward_mean",
+                 **kwargs):
+        assert skopt is not None, """skopt must be installed!
+            You can install Skopt with the command:
+            `pip install scikit-optimize`."""
+        assert type(max_concurrent) is int and max_concurrent > 0
+        self._max_concurrent = max_concurrent
+        self._parameters = parameter_names
+        self._reward_attr = reward_attr
+        self._skopt_opt = optimizer
+        self._live_trial_mapping = {}
+        super(SkOptSearch, self).__init__(**kwargs)
+
+    def _suggest(self, trial_id):
+        if self._num_live_trials() >= self._max_concurrent:
+            return None
+        suggested_config = self._skopt_opt.ask()
+        self._live_trial_mapping[trial_id] = suggested_config
+        return dict(zip(self._parameters, suggested_config))
+
+    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 skopt unless early terminated or errored.
+
+        The result is internally negated when interacting with Skopt
+        so that Skopt Optimizers can "maximize" this value,
+        as it minimizes on default.
+        """
+        skopt_trial_info = self._live_trial_mapping.pop(trial_id)
+        if result:
+            self._skopt_opt.tell(skopt_trial_info, -result[self._reward_attr])
+
+    def _num_live_trials(self):
+        return len(self._live_trial_mapping)

test/jenkins_tests/run_multi_node_tests.sh

@@ -377,6 +377,10 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/genetic_example.py \
     --smoke-test
 
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    python /ray/python/ray/tune/examples/skopt_example.py \
+    --smoke-test
+
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2