[tune] Add hyperopt warm start feature (#5372)

python/ray/tune/suggest/hyperopt.py

@@ -5,6 +5,8 @@ from __future__ import print_function
 import numpy as np
 import copy
 import logging
+from functools import partial
+import pickle
 try:
     hyperopt_logger = logging.getLogger("hyperopt")
     hyperopt_logger.setLevel(logging.WARNING)
@@ -24,7 +26,9 @@ class HyperOptSearch(SuggestionAlgorithm):
     Requires HyperOpt to be installed from source.
     Uses the Tree-structured Parzen Estimators algorithm, although can be
     trivially extended to support any algorithm HyperOpt uses. Externally
-    added trials will not be tracked by HyperOpt.
+    added trials will not be tracked by HyperOpt. Trials of the current run
+    can be saved using save method, trials of a previous run can be loaded
+    using restore method, thus enabling a warm start feature.
 
     Parameters:
         space (dict): HyperOpt configuration. Parameters will be sampled
@@ -42,6 +46,13 @@ class HyperOptSearch(SuggestionAlgorithm):
             a list of dict of hyperopt-named variables.
             Choice variables should be indicated by their index in the
             list (see example)
+        n_initial_points (int): number of random evaluations of the
+            objective function before starting to aproximate it with
+            tree parzen estimators. Defaults to 20.
+        random_state_seed (int, array_like, None): seed for reproducible
+            results. Defaults to None.
+        gamma (float in range (0,1)): parameter governing the tree parzen
+            estimators suggestion algorithm. Defaults to 0.25.
 
     Example:
         >>> space = {
@@ -66,6 +77,9 @@ class HyperOptSearch(SuggestionAlgorithm):
                  metric="episode_reward_mean",
                  mode="max",
                  points_to_evaluate=None,
+                 n_initial_points=20,
+                 random_state_seed=None,
+                 gamma=0.25,
                  **kwargs):
         assert hpo is not None, "HyperOpt must be installed!"
         from hyperopt.fmin import generate_trials_to_calculate
@@ -87,7 +101,13 @@ class HyperOptSearch(SuggestionAlgorithm):
             self._metric_op = -1.
         elif mode == "min":
             self._metric_op = 1.
-        self.algo = hpo.tpe.suggest
+        if n_initial_points is None:
+            self.algo = hpo.tpe.suggest
+        else:
+            self.algo = partial(
+                hpo.tpe.suggest, n_startup_jobs=n_initial_points)
+        if gamma is not None:
+            self.algo = partial(self.algo, gamma=gamma)
         self.domain = hpo.Domain(lambda spc: spc, space)
         if points_to_evaluate is None:
             self._hpopt_trials = hpo.Trials()
@@ -99,7 +119,10 @@ class HyperOptSearch(SuggestionAlgorithm):
             self._hpopt_trials.refresh()
             self._points_to_evaluate = len(points_to_evaluate)
         self._live_trial_mapping = {}
-        self.rstate = np.random.RandomState()
+        if random_state_seed is None:
+            self.rstate = np.random.RandomState()
+        else:
+            self.rstate = np.random.RandomState(random_state_seed)
 
         super(HyperOptSearch, self).__init__(**kwargs)
 
@@ -183,3 +206,14 @@ class HyperOptSearch(SuggestionAlgorithm):
 
     def _num_live_trials(self):
         return len(self._live_trial_mapping)
+
+    def save(self, checkpoint_dir):
+        trials_object = (self._hpopt_trials, self.rstate.get_state())
+        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._hpopt_trials = trials_object[0]
+        self.rstate.set_state(trials_object[1])

python/ray/tune/tests/test_tune_restore.py

@@ -3,6 +3,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from hyperopt import hp
 import os
 import shutil
 import tempfile
@@ -13,6 +14,7 @@ from ray import tune
 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
 
 
 class TuneRestoreTest(unittest.TestCase):
@@ -112,5 +114,60 @@ class AutoInitTest(unittest.TestCase):
         _register_all()
 
 
+class HyperoptWarmStartTest(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 = {
+            "x": hp.uniform("x", 0, 10),
+            "y": hp.uniform("y", -10, 10),
+            "z": hp.uniform("z", -10, 0)
+        }
+
+        def cost(space, reporter):
+            loss = space["x"]**2 + space["y"]**2 + space["z"]**2
+            reporter(loss=loss)
+
+        search_alg = HyperOptSearch(
+            space,
+            max_concurrent=1,
+            metric="loss",
+            mode="min",
+            random_state_seed=5)
+        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_algo1.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 testHyperoptWarmStart(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)