Remove best point properties from GSInterface (#1990)

Summary:
Pull Request resolved: #1990

Probably to be reimplemented at a later time

Reviewed By: saitcakmak

Differential Revision: D51307864

fbshipit-source-id: 7730d004364d03a06deadbee83b1ac3af6f514db

ax/core/generation_strategy_interface.py

@@ -114,102 +114,3 @@ def last_generator_run(self) -> Optional[GeneratorRun]:
         """
         # Used to restore current model when decoding a serialized GS.
         return self._generator_runs[-1] if self._generator_runs else None
-
-    @abstractmethod
-    def get_pareto_optimal_parameters(
-        self,
-        experiment: Experiment,
-        optimization_config: Optional[OptimizationConfig] = None,
-        trial_indices: Optional[Iterable[int]] = None,
-        use_model_predictions: bool = True,
-    ) -> Dict[int, Tuple[TParameterization, TModelPredictArm]]:
-        """Identifies the best parameterizations tried in the experiment so far,
-        using model predictions if ``use_model_predictions`` is true and using
-        observed values from the experiment otherwise. By default, uses model
-        predictions to account for observation noise.
-
-        NOTE: The format of this method's output is as follows:
-        { trial_index --> (parameterization, (means, covariances) }, where means
-        are a dictionary of form { metric_name --> metric_mean } and covariances
-        are a nested dictionary of form
-        { one_metric_name --> { another_metric_name: covariance } }.
-
-        Args:
-            experiment: Experiment, from which to find Pareto-optimal arms.
-            optimization_config: Optimization config to use in place of the one stored
-                on the experiment.
-            trial_indices: Indices of trials for which to retrieve data. If None will
-                retrieve data from all available trials.
-            use_model_predictions: Whether to extract the Pareto frontier using
-                model predictions or directly observed values. If ``True``,
-                the metric means and covariances in this method's output will
-                also be based on model predictions and may differ from the
-                observed values.
-
-        Returns:
-            A mapping from trial index to the tuple of:
-            - the parameterization of the arm in that trial,
-            - two-item tuple of metric means dictionary and covariance matrix
-                (model-predicted if ``use_model_predictions=True`` and observed
-                otherwise).
-        """
-        pass
-
-    @abstractmethod
-    def get_best_trial(
-        self,
-        experiment: Experiment,
-        optimization_config: Optional[OptimizationConfig] = None,
-        trial_indices: Optional[Iterable[int]] = None,
-    ) -> Optional[Tuple[int, TParameterization, Optional[TModelPredictArm]]]:
-        """Given an experiment, returns the best predicted parameterization and
-        corresponding prediction based on the most recent Trial with predictions.
-        If no trials have predictions returns None.
-
-        Only some models return predictions. For instance GPEI does
-        while Sobol does not.
-
-        TModelPredictArm is of the form:
-            ({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
-
-        Args:
-            experiment: Experiment, on which to identify best raw objective arm.
-            optimization_config: Optimization config to use in place of the one stored
-                on the experiment.
-            trial_indices: Indices of trials for which to retrieve data. If None will
-                retrieve data from all available trials.
-
-        Returns:
-            Tuple of trial index, parameterization, and model predictions for it."""
-        pass
-
-    @abstractmethod
-    def get_hypervolume(
-        self,
-        experiment: Experiment,
-        optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
-        trial_indices: Optional[Iterable[int]] = None,
-    ) -> float:
-        """Calculate hypervolume of a pareto frontier based on the posterior means of
-        given observation features.
-
-        Given a model and features to evaluate calculate the hypervolume of the pareto
-        frontier formed from their predicted outcomes.
-
-        Args:
-            modelbridge: Modelbridge used to predict metrics outcomes.
-            optimization_config: Optimization config
-            selected_metrics: If specified, hypervolume will only be evaluated on
-                the specified subset of metrics. Otherwise, all metrics will be used.
-            trial_indices: Indices of trials for which to retrieve data. If None will
-                retrieve data from all available trials.
-
-        Returns:
-            calculated hypervolume.
-        """
-        pass
-
-    @abstractproperty
-    def num_baseline_trials(self) -> int:
-        """Number of trials generated by the first step in this generation strategy."""
-        pass