Update mlos_core API to support with multi-factor optimization

mlos_bench/mlos_bench/optimizers/mlos_core_optimizer.py

@@ -46,11 +46,6 @@ def __init__(self,
                  service: Optional[Service] = None):
         super().__init__(tunables, config, global_config, service)
 
-        # TODO: Remove after implementing multi-target optimization in mlos_core
-        if len(self._opt_targets) != 1:
-            raise NotImplementedError(f"Multi-target optimization is not supported: {self}")
-        (self._opt_target, self._opt_sign) = list(self._opt_targets.items())[0]
-
         opt_type = getattr(OptimizerType, self._config.pop(
             'optimizer_type', DEFAULT_OPTIMIZER_TYPE.name))
 
@@ -79,6 +74,7 @@ def __init__(self,
 
         self._opt: BaseOptimizer = OptimizerFactory.create(
             parameter_space=self.config_space,
+            optimization_targets=list(self._opt_targets),
             optimizer_type=opt_type,
             optimizer_kwargs=self._config,
             space_adapter_type=space_adapter_type,
@@ -99,26 +95,41 @@ def bulk_register(self,
                       configs: Sequence[dict],
                       scores: Sequence[Optional[Dict[str, TunableValue]]],
                       status: Optional[Sequence[Status]] = None) -> bool:
+
         if not super().bulk_register(configs, scores, status):
             return False
+
         df_configs = self._to_df(configs)  # Impute missing values, if necessary
-        df_scores = pd.Series(
-            [self._extract_target(score) for score in scores],
-            dtype=float) * self._opt_sign
+
+        df_scores = self._adjust_signs_df(
+            pd.DataFrame([{} if score is None else score for score in scores]))
+
+        opt_targets = list(self._opt_targets)
         if status is not None:
+            # Select only the completed trials, set scores for failed trials to +inf.
             df_status = pd.Series(status)
-            df_scores[df_status != Status.SUCCEEDED] = float("inf")
+            df_scores.loc[df_status != Status.SUCCEEDED, opt_targets] = float("inf")
             df_status_completed = df_status.apply(Status.is_completed)
             df_configs = df_configs[df_status_completed]
             df_scores = df_scores[df_status_completed]
-        self._opt.register(df_configs, df_scores)
+
+        # TODO: Specify (in the config) which metrics to pass to the optimizer.
+        # Issue: https://github.com/microsoft/MLOS/issues/745
+        self._opt.register(df_configs, df_scores[opt_targets].astype(float))
+
         if _LOG.isEnabledFor(logging.DEBUG):
             (score, _) = self.get_best_observation()
             _LOG.debug("Warm-up END: %s :: %s", self, score)
+
         return True
 
-    def _extract_target(self, scores: Optional[Dict[str, TunableValue]]) -> Optional[TunableValue]:
-        return None if scores is None else scores[self._opt_target]
+    def _adjust_signs_df(self, df_scores: pd.DataFrame) -> pd.DataFrame:
+        """
+        In-place adjust the signs of the scores for MINIMIZATION problem.
+        """
+        for (opt_target, opt_dir) in self._opt_targets.items():
+            df_scores[opt_target] *= opt_dir
+        return df_scores
 
     def _to_df(self, configs: Sequence[Dict[str, TunableValue]]) -> pd.DataFrame:
         """
@@ -175,21 +186,21 @@ def suggest(self) -> TunableGroups:
 
     def register(self, tunables: TunableGroups, status: Status,
                  score: Optional[Dict[str, TunableValue]] = None) -> Optional[Dict[str, float]]:
-        registered_score = super().register(tunables, status, score)  # With _opt_sign applied
+        registered_score = super().register(tunables, status, score)  # Sign-adjusted for MINIMIZATION
         if status.is_completed():
             assert registered_score is not None
             df_config = self._to_df([tunables.get_param_values()])
             _LOG.debug("Score: %s Dataframe:\n%s", registered_score, df_config)
-            self._opt.register(df_config, pd.Series([registered_score[self._opt_target]], dtype=float))
+            # TODO: Specify (in the config) which metrics to pass to the optimizer.
+            # Issue: https://github.com/microsoft/MLOS/issues/745
+            self._opt.register(df_config, pd.DataFrame([registered_score], dtype=float))
         return registered_score
 
     def get_best_observation(self) -> Union[Tuple[Dict[str, float], TunableGroups], Tuple[None, None]]:
-        df_config = self._opt.get_best_observation()
+        (df_config, df_score, _df_context) = self._opt.get_best_observations()
         if len(df_config) == 0:
             return (None, None)
         params = configspace_data_to_tunable_values(df_config.iloc[0].to_dict())
-        _LOG.debug("Best observation: %s", params)
-        score = params.pop("score")
-        assert score is not None
-        score = float(score) * self._opt_sign  # mlos_core always uses the `score` column
-        return ({self._opt_target: score}, self._tunables.copy().assign(params))
+        scores = self._adjust_signs_df(df_score).iloc[0].to_dict()
+        _LOG.debug("Best observation: %s score: %s", params, scores)
+        return (scores, self._tunables.copy().assign(params))

mlos_core/mlos_core/optimizers/__init__.py

@@ -7,7 +7,7 @@
 """
 
 from enum import Enum
-from typing import Optional, TypeVar
+from typing import List, Optional, TypeVar
 
 import ConfigSpace
 
@@ -62,6 +62,7 @@ class OptimizerFactory:
     @staticmethod
     def create(*,
                parameter_space: ConfigSpace.ConfigurationSpace,
+               optimization_targets: List[str],
                optimizer_type: OptimizerType = DEFAULT_OPTIMIZER_TYPE,
                optimizer_kwargs: Optional[dict] = None,
                space_adapter_type: SpaceAdapterType = SpaceAdapterType.IDENTITY,
@@ -74,6 +75,8 @@ def create(*,
         ----------
         parameter_space : ConfigSpace.ConfigurationSpace
             Input configuration space.
+        optimization_targets : List[str]
+            The names of the optimization targets to minimize.
         optimizer_type : OptimizerType
             Optimizer class as defined by Enum.
         optimizer_kwargs : Optional[dict]
@@ -102,6 +105,7 @@ def create(*,
 
         optimizer: ConcreteOptimizer = optimizer_type.value(
             parameter_space=parameter_space,
+            optimization_targets=optimization_targets,
             space_adapter=space_adapter,
             **optimizer_kwargs
         )

mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py

@@ -29,6 +29,7 @@ class SmacOptimizer(BaseBayesianOptimizer):
 
     def __init__(self, *,  # pylint: disable=too-many-locals
                  parameter_space: ConfigSpace.ConfigurationSpace,
+                 optimization_targets: List[str],
                  space_adapter: Optional[BaseSpaceAdapter] = None,
                  seed: Optional[int] = 0,
                  run_name: Optional[str] = None,
@@ -46,6 +47,9 @@ def __init__(self, *,  # pylint: disable=too-many-locals
         parameter_space : ConfigSpace.ConfigurationSpace
             The parameter space to optimize.
 
+        optimization_targets : List[str]
+            The names of the optimization targets to minimize.
+
         space_adapter : BaseSpaceAdapter
             The space adapter class to employ for parameter space transformations.
 
@@ -86,6 +90,7 @@ def __init__(self, *,  # pylint: disable=too-many-locals
         """
         super().__init__(
             parameter_space=parameter_space,
+            optimization_targets=optimization_targets,
             space_adapter=space_adapter,
         )
 
@@ -125,6 +130,7 @@ def __init__(self, *,  # pylint: disable=too-many-locals
 
         scenario: Scenario = Scenario(
             self.optimizer_parameter_space,
+            objectives=self._optimization_targets,
             name=run_name,
             output_directory=Path(output_directory),
             deterministic=True,
@@ -186,6 +192,10 @@ def __init__(self, *,  # pylint: disable=too-many-locals
             intensifier=intensifier,
             random_design=random_design,
             config_selector=config_selector,
+            multi_objective_algorithm=Optimizer_Smac.get_multi_objective_algorithm(
+                scenario,
+                # objective_weights=[1, 2],  # TODO: pass weights as constructor args
+            ),
             overwrite=True,
             logging_level=False,  # Use the existing logger
         )
@@ -228,15 +238,16 @@ def _dummy_target_func(config: ConfigSpace.Configuration, seed: int = 0) -> None
         # -- this planned to be fixed in some future release: https://github.com/automl/SMAC3/issues/946
         raise RuntimeError('This function should never be called.')
 
-    def _register(self, configurations: pd.DataFrame, scores: pd.Series, context: Optional[pd.DataFrame] = None) -> None:
+    def _register(self, configurations: pd.DataFrame,
+                  scores: pd.DataFrame, context: Optional[pd.DataFrame] = None) -> None:
         """Registers the given configurations and scores.
 
         Parameters
         ----------
         configurations : pd.DataFrame
             Dataframe of configurations / parameters. The columns are parameter names and the rows are the configurations.
 
-        scores : pd.Series
+        scores : pd.DataFrame
             Scores from running the configurations. The index is the same as the index of the configurations.
 
         context : pd.DataFrame
@@ -248,10 +259,11 @@ def _register(self, configurations: pd.DataFrame, scores: pd.Series, context: Op
             warn(f"Not Implemented: Ignoring context {list(context.columns)}", UserWarning)
 
         # Register each trial (one-by-one)
-        for config, score in zip(self._to_configspace_configs(configurations), scores.tolist()):
+        for (config, (_i, score)) in zip(self._to_configspace_configs(configurations), scores.iterrows()):
             # Retrieve previously generated TrialInfo (returned by .ask()) or create new TrialInfo instance
-            info: TrialInfo = self.trial_info_map.get(config, TrialInfo(config=config, seed=self.base_optimizer.scenario.seed))
-            value: TrialValue = TrialValue(cost=score, time=0.0, status=StatusType.SUCCESS)
+            info: TrialInfo = self.trial_info_map.get(
+                config, TrialInfo(config=config, seed=self.base_optimizer.scenario.seed))
+            value = TrialValue(cost=list(score.astype(float)), time=0.0, status=StatusType.SUCCESS)
             self.base_optimizer.tell(info, value, save=False)
 
         # Save optimizer once we register all configs
@@ -293,7 +305,7 @@ def surrogate_predict(self, configurations: pd.DataFrame, context: Optional[pd.D
         if context is not None:
             warn(f"Not Implemented: Ignoring context {list(context.columns)}", UserWarning)
         if self._space_adapter and not isinstance(self._space_adapter, IdentityAdapter):
-            raise NotImplementedError()
+            raise NotImplementedError("Space adapter not supported for surrogate_predict.")
 
         # pylint: disable=protected-access
         if len(self._observations) <= self.base_optimizer._initial_design._n_configs:

mlos_core/mlos_core/optimizers/flaml_optimizer.py

@@ -6,7 +6,7 @@
 Contains the FlamlOptimizer class.
 """
 
-from typing import Dict, NamedTuple, Optional, Union
+from typing import Dict, List, NamedTuple, Optional, Union
 from warnings import warn
 
 import ConfigSpace
@@ -32,17 +32,22 @@ class FlamlOptimizer(BaseOptimizer):
 
     def __init__(self, *,
                  parameter_space: ConfigSpace.ConfigurationSpace,
+                 optimization_targets: List[str],
                  space_adapter: Optional[BaseSpaceAdapter] = None,
                  low_cost_partial_config: Optional[dict] = None,
                  seed: Optional[int] = None):
         """
-        Create an MLOS wrapper class for FLAML.
+        Create an MLOS wrapper for FLAML.
 
         Parameters
         ----------
         parameter_space : ConfigSpace.ConfigurationSpace
             The parameter space to optimize.
 
+        optimization_targets : List[str]
+            The names of the optimization targets to minimize.
+            For FLAML it must be a list with a single element, e.g., `["score"]`.
+
         space_adapter : BaseSpaceAdapter
             The space adapter class to employ for parameter space transformations.
 
@@ -55,9 +60,14 @@ def __init__(self, *,
         """
         super().__init__(
             parameter_space=parameter_space,
+            optimization_targets=optimization_targets,
             space_adapter=space_adapter,
         )
 
+        if len(self._optimization_targets) != 1:
+            raise ValueError("FLAML does not support multi-target optimization")
+        self._flaml_optimization_target = self._optimization_targets[0]
+
         # Per upstream documentation, it is recommended to set the seed for
         # flaml at the start of its operation globally.
         if seed is not None:
@@ -72,7 +82,7 @@ def __init__(self, *,
         self.evaluated_samples: Dict[ConfigSpace.Configuration, EvaluatedSample] = {}
         self._suggested_config: Optional[dict]
 
-    def _register(self, configurations: pd.DataFrame, scores: pd.Series,
+    def _register(self, configurations: pd.DataFrame, scores: pd.DataFrame,
                   context: Optional[pd.DataFrame] = None) -> None:
         """Registers the given configurations and scores.
 
@@ -81,15 +91,16 @@ def _register(self, configurations: pd.DataFrame, scores: pd.Series,
         configurations : pd.DataFrame
             Dataframe of configurations / parameters. The columns are parameter names and the rows are the configurations.
 
-        scores : pd.Series
+        scores : pd.DataFrame
             Scores from running the configurations. The index is the same as the index of the configurations.
 
         context : None
             Not Yet Implemented.
         """
         if context is not None:
             warn(f"Not Implemented: Ignoring context {list(context.columns)}", UserWarning)
-        for (_, config), score in zip(configurations.astype('O').iterrows(), scores):
+        for (_, config), score in zip(configurations.astype('O').iterrows(),
+                                      scores[self._flaml_optimization_target]):
             cs_config: ConfigSpace.Configuration = ConfigSpace.Configuration(
                 self.optimizer_parameter_space, values=config.to_dict())
             if cs_config in self.evaluated_samples:
@@ -140,7 +151,7 @@ def _target_function(self, config: dict) -> Union[dict, None]:
         """
         cs_config = normalize_config(self.optimizer_parameter_space, config)
         if cs_config in self.evaluated_samples:
-            return {'score': self.evaluated_samples[cs_config].score}
+            return {self._flaml_optimization_target: self.evaluated_samples[cs_config].score}
 
         self._suggested_config = dict(cs_config)  # Cleaned-up version of the config
         return None  # Returning None stops the process
@@ -156,7 +167,8 @@ def _get_next_config(self) -> dict:
         Returns
         -------
         result: dict
-            Dictionary with a single key, `score`, if config already evaluated; `None` otherwise.
+            A dictionary with a single key that is equal to the name of the optimization target,
+            if config already evaluated; `None` otherwise.
 
         Raises
         ------
@@ -182,7 +194,7 @@ def _get_next_config(self) -> dict:
             self._target_function,
             config=self.flaml_parameter_space,
             mode='min',
-            metric='score',
+            metric=self._flaml_optimization_target,
             points_to_evaluate=points_to_evaluate,
             evaluated_rewards=evaluated_rewards,
             num_samples=len(points_to_evaluate) + 1,