[tune] Save and Restore for bayesopt (#5623)

python/ray/tune/suggest/bayesopt.py

@@ -4,6 +4,7 @@ from __future__ import print_function
 
 import copy
 import logging
+import pickle
 try:  # Python 3 only -- needed for lint test.
     import bayes_opt as byo
 except ImportError:
@@ -111,3 +112,13 @@ class BayesOptSearch(SuggestionAlgorithm):
 
     def _num_live_trials(self):
         return len(self._live_trial_mapping)
+
+    def save(self, checkpoint_dir):
+        trials_object = self.optimizer
+        with open(checkpoint_dir, "wb") as output:
+            pickle.dump(trials_object, output)
+
+    def restore(self, checkpoint_dir):
+        with open(checkpoint_dir, "rb") as input:
+            trials_object = pickle.load(input)
+        self.optimizer = trials_object

python/ray/tune/suggest/skopt.py

@@ -3,6 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import logging
+import pickle
 try:
     import skopt as sko
 except ImportError:
@@ -157,3 +158,13 @@ class SkOptSearch(SuggestionAlgorithm):
 
     def _num_live_trials(self):
         return len(self._live_trial_mapping)
+
+    def save(self, checkpoint_dir):
+        trials_object = self._skopt_opt
+        with open(checkpoint_dir, "wb") as output:
+            pickle.dump(trials_object, output)
+
+    def restore(self, checkpoint_dir):
+        with open(checkpoint_dir, "rb") as input:
+            trials_object = pickle.load(input)
+        self._skopt_opt = trials_object

python/ray/tune/tests/test_tune_restore.py

@@ -15,6 +15,7 @@ from ray.tests.utils import recursive_fnmatch
 from ray.tune.util import validate_save_restore
 from ray.rllib import _register_all
 from ray.tune.suggest.hyperopt import HyperOptSearch
+from ray.tune.suggest.bayesopt import BayesOptSearch
 
 
 class TuneRestoreTest(unittest.TestCase):
@@ -146,7 +147,7 @@ class HyperoptWarmStartTest(unittest.TestCase):
     def run_exp_1(self):
         search_alg, cost = self.set_basic_conf()
         results_exp_1 = tune.run(cost, num_samples=15, search_alg=search_alg)
-        self.log_dir = os.path.join(self.tmpdir, "trials_algo1.pkl")
+        self.log_dir = os.path.join(self.tmpdir, "trials_algo_hyo.pkl")
         search_alg.save(self.log_dir)
         return results_exp_1
 
@@ -169,5 +170,60 @@ class HyperoptWarmStartTest(unittest.TestCase):
         self.assertEqual(trials_1_config + trials_2_config, trials_3_config)
 
 
+class BayesoptWarmStartTest(unittest.TestCase):
+    def setUp(self):
+        ray.init(local_mode=True)
+        self.tmpdir = tempfile.mkdtemp()
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdir)
+        ray.shutdown()
+        _register_all()
+
+    def set_basic_conf(self):
+        space = {"width": (0, 20), "height": (-100, 100)}
+
+        def cost(space, reporter):
+            loss = space["width"]**2 + space["height"]**2
+            reporter(loss=loss)
+
+        search_alg = BayesOptSearch(
+            space,
+            max_concurrent=1,
+            metric="loss",
+            mode="min",
+            utility_kwargs={
+                "kind": "ucb",
+                "kappa": 2.5,
+                "xi": 0.0
+            })
+        return search_alg, cost
+
+    def run_exp_1(self):
+        search_alg, cost = self.set_basic_conf()
+        results_exp_1 = tune.run(cost, num_samples=15, search_alg=search_alg)
+        self.log_dir = os.path.join(self.tmpdir, "trials_algo_byo.pkl")
+        search_alg.save(self.log_dir)
+        return results_exp_1
+
+    def run_exp_2(self):
+        search_alg2, cost = self.set_basic_conf()
+        search_alg2.restore(self.log_dir)
+        return tune.run(cost, num_samples=15, search_alg=search_alg2)
+
+    def run_exp_3(self):
+        search_alg3, cost = self.set_basic_conf()
+        return tune.run(cost, num_samples=30, search_alg=search_alg3)
+
+    def testBayesoptWarmStart(self):
+        results_exp_1 = self.run_exp_1()
+        results_exp_2 = self.run_exp_2()
+        results_exp_3 = self.run_exp_3()
+        trials_1_config = [trial.config for trial in results_exp_1.trials]
+        trials_2_config = [trial.config for trial in results_exp_2.trials]
+        trials_3_config = [trial.config for trial in results_exp_3.trials]
+        self.assertEqual(trials_1_config + trials_2_config, trials_3_config)
+
+
 if __name__ == "__main__":
     unittest.main(verbosity=2)