[Tune] Added method to integrate previous analysis in BO (#8486)

python/ray/tune/suggest/bayesopt.py

@@ -38,6 +38,8 @@ class BayesOptSearch(Searcher):
             - kappa: 2.576
             - xi: 0.0
         random_state (int): Used to initialize BayesOpt.
+        analysis (ExperimentAnalysis): Optionally, the previous analysis
+            to integrate.
         verbose (int): Sets verbosity level for BayesOpt packages.
         max_concurrent: Deprecated.
         use_early_stopped_trials: Deprecated.
@@ -64,8 +66,27 @@ class BayesOptSearch(Searcher):
                  utility_kwargs=None,
                  random_state=1,
                  verbose=0,
+                 analysis=None,
                  max_concurrent=None,
                  use_early_stopped_trials=None):
+        """Instantiate new BayesOptSearch object.
+
+        Parameters:
+            space (dict): Continuous search space.
+                Parameters will be sampled from
+                this space which will be used to run trials.
+            metric (str): The training result objective value attribute.
+            mode (str): One of {min, max}. Determines whether objective is
+                minimizing or maximizing the metric attribute.
+            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.
+            analysis (ExperimentAnalysis): Optionally, the previous analysis
+                to integrate.
+            verbose (int): Sets verbosity level for BayesOpt packages.
+            max_concurrent: Deprecated.
+            use_early_stopped_trials: Deprecated.
+        """
         assert byo is not None, (
             "BayesOpt must be installed!. You can install BayesOpt with"
             " the command: `pip install bayesian-optimization`.")
@@ -96,6 +117,8 @@ class BayesOptSearch(Searcher):
             f=None, pbounds=space, verbose=verbose, random_state=random_state)
 
         self.utility = byo.UtilityFunction(**utility_kwargs)
+        if analysis is not None:
+            self.register_analysis(analysis)
 
     def suggest(self, trial_id):
         if self.max_concurrent:
@@ -107,6 +130,21 @@ class BayesOptSearch(Searcher):
 
         return copy.deepcopy(new_trial)
 
+    def register_analysis(self, analysis):
+        """Integrate the given analysis into the gaussian process.
+
+        Parameters
+        ------------------
+        analysis (ExperimentAnalysis): Optionally, the previous analysis
+            to integrate.
+        """
+        for (_, report), params in zip(analysis.dataframe().iterrows(),
+                                       analysis.get_all_configs().values()):
+            # We add the obtained results to the
+            # gaussian process optimizer
+            self.optimizer.register(
+                params=params, target=self._metric_op * report[self._metric])
+
     def on_trial_complete(self, trial_id, result=None, error=False):
         """Notification for the completion of trial."""
         if result: