Combine experiment and gs fields into AnalysisBase (#3137)

Summary:

AnalysisBase will have optional `_experiment` and `_generation_strategy` fields, with getter and setter properties, as well as the `standard_generation_strategy` prop from `Scheduler`.  `Scheduler` and `AxClient` will inherit these from it.

The naming is less than optimal with AnalysisBase holding the experiment and GS.

This is otherwise a no-op change designed to reduce pyre errors.

Differential Revision: D66712036

ax/modelbridge/tests/test_prediction_utils.py

@@ -28,7 +28,7 @@ def test_predict_at_point(self) -> None:
 
         observation_features = ObservationFeatures(parameters={"x1": 0.3, "x2": 0.5})
         y_hat, se_hat = predict_at_point(
-            model=none_throws(ax_client.generation_strategy.model),
+            model=none_throws(ax_client.standard_generation_strategy.model),
             obsf=observation_features,
             metric_names={"test_metric1"},
         )
@@ -37,7 +37,7 @@ def test_predict_at_point(self) -> None:
         self.assertEqual(len(se_hat), 1)
 
         y_hat, se_hat = predict_at_point(
-            model=none_throws(ax_client.generation_strategy.model),
+            model=none_throws(ax_client.standard_generation_strategy.model),
             obsf=observation_features,
             metric_names={"test_metric1", "test_metric2", "test_metric:agg"},
             scalarized_metric_config=[
@@ -51,7 +51,7 @@ def test_predict_at_point(self) -> None:
         self.assertEqual(len(se_hat), 3)
 
         y_hat, se_hat = predict_at_point(
-            model=none_throws(ax_client.generation_strategy.model),
+            model=none_throws(ax_client.standard_generation_strategy.model),
             obsf=observation_features,
             metric_names={"test_metric1"},
             scalarized_metric_config=[
@@ -75,7 +75,7 @@ def test_predict_by_features(self) -> None:
             20: ObservationFeatures(parameters={"x1": 0.8, "x2": 0.5}),
         }
         predictions_map = predict_by_features(
-            model=none_throws(ax_client.generation_strategy.model),
+            model=none_throws(ax_client.standard_generation_strategy.model),
             label_to_feature_dict=observation_features_dict,
             metric_names={"test_metric1"},
         )

ax/service/ax_client.py

@@ -599,8 +599,8 @@ def get_next_trial(
         # TODO[T79183560]: Ensure correct handling of generator run when using
         # foreign keys.
         self._update_generation_strategy_in_db_if_possible(
-            generation_strategy=self.generation_strategy,
-            new_generator_runs=[self.generation_strategy._generator_runs[-1]],
+            generation_strategy=self.standard_generation_strategy,
+            new_generator_runs=[self.standard_generation_strategy._generator_runs[-1]],
         )
         return none_throws(trial.arm).parameters, trial.index
 
@@ -625,7 +625,7 @@ def get_current_trial_generation_limit(self) -> tuple[int, bool]:
         if self.generation_strategy._experiment is None:
             self.generation_strategy.experiment = self.experiment
 
-        return self.generation_strategy.current_generator_run_limit()
+        return self.standard_generation_strategy.current_generator_run_limit()
 
     def get_next_trials(
         self,
@@ -950,7 +950,7 @@ def get_max_parallelism(self) -> list[tuple[int, int]]:
             Mapping of form {num_trials -> max_parallelism_setting}.
         """
         parallelism_settings = []
-        for step in self.generation_strategy._steps:
+        for step in self.standard_generation_strategy._steps:
             parallelism_settings.append(
                 (step.num_trials, step.max_parallelism or step.num_trials)
             )
@@ -1071,15 +1071,15 @@ def get_contour_plot(
             raise ValueError(
                 f'Metric "{metric_name}" is not associated with this optimization.'
             )
-        if self.generation_strategy.model is not None:
+        if self.standard_generation_strategy.model is not None:
             try:
                 logger.info(
                     f"Retrieving contour plot with parameter '{param_x}' on X-axis "
                     f"and '{param_y}' on Y-axis, for metric '{metric_name}'. "
                     "Remaining parameters are affixed to the middle of their range."
                 )
                 return plot_contour(
-                    model=none_throws(self.generation_strategy.model),
+                    model=none_throws(self.standard_generation_strategy.model),
                     param_x=param_x,
                     param_y=param_y,
                     metric_name=metric_name,
@@ -1089,8 +1089,8 @@ def get_contour_plot(
                 # Some models don't implement '_predict', which is needed
                 # for the contour plots.
                 logger.info(
-                    f"Model {self.generation_strategy.model} does not implement "
-                    "`predict`, so it cannot be used to generate a response "
+                    f"Model {self.standard_generation_strategy.model} does not "
+                    "implement `predict`, so it cannot be used to generate a response "
                     "surface plot."
                 )
         raise UnsupportedPlotError(
@@ -1112,14 +1112,14 @@ def get_feature_importances(self, relative: bool = True) -> AxPlotConfig:
         """
         if not self.experiment.trials:
             raise ValueError("Cannot generate plot as there are no trials.")
-        cur_model = self.generation_strategy.model
+        cur_model = self.standard_generation_strategy.model
         if cur_model is not None:
             try:
                 return plot_feature_importance_by_feature(cur_model, relative=relative)
             except NotImplementedError:
                 logger.info(
-                    f"Model {self.generation_strategy.model} does not implement "
-                    "`feature_importances`, so it cannot be used to generate "
+                    f"Model {self.standard_generation_strategy.model} does not "
+                    "implement `feature_importances`, so it cannot be used to generate "
                     "this plot. Only certain models, implement feature importances."
                 )
 
@@ -1247,7 +1247,8 @@ def get_model_predictions(
             else set(none_throws(self.experiment.metrics).keys())
         )
         model = none_throws(
-            self.generation_strategy.model, "No model has been instantiated yet."
+            self.standard_generation_strategy.model,
+            "No model has been instantiated yet.",
         )
 
         # Construct a dictionary that maps from a label to an
@@ -1306,8 +1307,8 @@ def fit_model(self) -> None:
                 "At least one trial must be completed with data to fit a model."
             )
         # Check if we should transition before generating the next candidate.
-        self.generation_strategy._maybe_transition_to_next_node()
-        self.generation_strategy._fit_current_model(data=None)
+        self.standard_generation_strategy._maybe_transition_to_next_node()
+        self.standard_generation_strategy._fit_current_model(data=None)
 
     def verify_trial_parameterization(
         self, trial_index: int, parameterization: TParameterization
@@ -1496,29 +1497,10 @@ def from_json_snapshot(
 
     # ---------------------- Private helper methods. ---------------------
 
-    @property
-    def experiment(self) -> Experiment:
-        """Returns the experiment set on this Ax client."""
-        return none_throws(
-            self._experiment,
-            (
-                "Experiment not set on Ax client. Must first "
-                "call load_experiment or create_experiment to use handler functions."
-            ),
-        )
-
     def get_trial(self, trial_index: int) -> Trial:
         """Return a trial on experiment cast as Trial"""
         return checked_cast(Trial, self.experiment.trials[trial_index])
 
-    @property
-    def generation_strategy(self) -> GenerationStrategy:
-        """Returns the generation strategy, set on this experiment."""
-        return none_throws(
-            self._generation_strategy,
-            "No generation strategy has been set on this optimization yet.",
-        )
-
     @property
     def objective(self) -> Objective:
         return none_throws(self.experiment.optimization_config).objective
@@ -1586,7 +1568,7 @@ def get_best_trial(
     ) -> tuple[int, TParameterization, TModelPredictArm | None] | None:
         return self._get_best_trial(
             experiment=self.experiment,
-            generation_strategy=self.generation_strategy,
+            generation_strategy=self.standard_generation_strategy,
             trial_indices=trial_indices,
             use_model_predictions=use_model_predictions,
         )
@@ -1600,7 +1582,7 @@ def get_pareto_optimal_parameters(
     ) -> dict[int, tuple[TParameterization, TModelPredictArm]]:
         return self._get_pareto_optimal_parameters(
             experiment=self.experiment,
-            generation_strategy=self.generation_strategy,
+            generation_strategy=self.standard_generation_strategy,
             trial_indices=trial_indices,
             use_model_predictions=use_model_predictions,
         )
@@ -1614,7 +1596,7 @@ def get_hypervolume(
     ) -> float:
         return BestPointMixin._get_hypervolume(
             experiment=self.experiment,
-            generation_strategy=self.generation_strategy,
+            generation_strategy=self.standard_generation_strategy,
             optimization_config=optimization_config,
             trial_indices=trial_indices,
             use_model_predictions=use_model_predictions,
@@ -1817,7 +1799,7 @@ def _gen_new_generator_run(
             else None
         )
         with with_rng_seed(seed=self._random_seed):
-            return none_throws(self.generation_strategy).gen(
+            return none_throws(self.standard_generation_strategy).gen(
                 experiment=self.experiment,
                 n=n,
                 pending_observations=self._get_pending_observation_features(

ax/service/scheduler.py

@@ -162,8 +162,6 @@ class Scheduler(AnalysisBase, BestPointMixin):
             been saved, as otherwise experiment state could get corrupted.**
     """
 
-    experiment: Experiment
-    generation_strategy: GenerationStrategyInterface
     # pyre-fixme[24]: Generic type `LoggerAdapter` expects 1 type parameter.
     logger: LoggerAdapter
     # Mapping of form {short string identifier -> message to show in reported
@@ -491,21 +489,6 @@ def runner(self) -> Runner:
             )
         return runner
 
-    @property
-    def standard_generation_strategy(self) -> GenerationStrategy:
-        """Used for operations in the scheduler that can only be done with
-        and instance of ``GenerationStrategy``.
-        """
-        gs = self.generation_strategy
-        if not isinstance(gs, GenerationStrategy):
-            raise NotImplementedError(
-                "This functionality is only supported with instances of "
-                "`GenerationStrategy` (one that uses `GenerationStrategy` "
-                "class) and not yet with other types of "
-                "`GenerationStrategyInterface`."
-            )
-        return gs
-
     def __repr__(self) -> str:
         """Short user-friendly string representation."""
         if not hasattr(self, "experiment"):

ax/service/tests/test_ax_client.py

@@ -504,7 +504,10 @@ def test_default_generation_strategy_continuous(self, _a, _b, _c, _d) -> None:
         """
         ax_client = get_branin_optimization()
         self.assertEqual(
-            [s.model for s in none_throws(ax_client.generation_strategy)._steps],
+            [
+                s.model
+                for s in none_throws(ax_client.standard_generation_strategy)._steps
+            ],
             [Models.SOBOL, Models.BOTORCH_MODULAR],
         )
         with self.assertRaisesRegex(ValueError, ".* no trials"):
@@ -719,7 +722,7 @@ def test_default_generation_strategy_continuous_for_moo(
             },
         )
         self.assertEqual(
-            [s.model for s in none_throws(ax_client.generation_strategy)._steps],
+            [s.model for s in ax_client.standard_generation_strategy._steps],
             [Models.SOBOL, Models.BOTORCH_MODULAR],
         )
         with self.assertRaisesRegex(ValueError, ".* no trials"):
@@ -782,7 +785,7 @@ def test_create_experiment(self) -> None:
                 steps=[GenerationStep(model=Models.SOBOL, num_trials=30)]
             )
         )
-        with self.assertRaisesRegex(AssertionError, "Experiment not set on Ax client"):
+        with self.assertRaisesRegex(AssertionError, "Experiment not set on AxClient"):
             ax_client.experiment
         ax_client.create_experiment(
             name="test_experiment",
@@ -1019,7 +1022,7 @@ def test_create_single_objective_experiment_with_objectives_dict(self) -> None:
                 steps=[GenerationStep(model=Models.SOBOL, num_trials=30)]
             )
         )
-        with self.assertRaisesRegex(AssertionError, "Experiment not set on Ax client"):
+        with self.assertRaisesRegex(AssertionError, "Experiment not set on AxClient"):
             ax_client.experiment
         ax_client.create_experiment(
             name="test_experiment",
@@ -1080,7 +1083,7 @@ def test_create_single_objective_experiment_with_objectives_dict(self) -> None:
     def test_create_experiment_with_metric_definitions(self) -> None:
         """Test basic experiment creation."""
         ax_client = AxClient()
-        with self.assertRaisesRegex(AssertionError, "Experiment not set on Ax client"):
+        with self.assertRaisesRegex(AssertionError, "Experiment not set on AxClient"):
             ax_client.experiment
 
         metric_definitions = {
@@ -1347,7 +1350,7 @@ def test_create_moo_experiment(self) -> None:
                 steps=[GenerationStep(model=Models.SOBOL, num_trials=30)]
             )
         )
-        with self.assertRaisesRegex(AssertionError, "Experiment not set on Ax client"):
+        with self.assertRaisesRegex(AssertionError, "Experiment not set on AxClient"):
             ax_client.experiment
         ax_client.create_experiment(
             name="test_experiment",
@@ -1581,10 +1584,9 @@ def test_keep_generating_without_data(self) -> None:
                 {"name": "y", "type": "range", "bounds": [0.0, 15.0]},
             ],
         )
-        self.assertFalse(
-            ax_client.generation_strategy._steps[0].enforce_num_trials, False
-        )
-        self.assertFalse(ax_client.generation_strategy._steps[1].max_parallelism, None)
+        gs = ax_client.standard_generation_strategy
+        self.assertFalse(gs._steps[0].enforce_num_trials, False)
+        self.assertFalse(gs._steps[1].max_parallelism, None)
         for _ in range(10):
             parameterization, trial_index = ax_client.get_next_trial()
 
@@ -2100,14 +2102,14 @@ def test_sqa_storage(self) -> None:
                 # pyre-fixme[6]: For 2nd param expected `Union[List[Tuple[Dict[str, U...
                 raw_data=branin(*parameters.values()),
             )
-        gs = ax_client.generation_strategy
+        gs = ax_client.standard_generation_strategy
         ax_client = AxClient(db_settings=db_settings)
         ax_client.load_experiment_from_database("test_experiment")
         # Some fields of the reloaded GS are not expected to be set (both will be
         # set during next model fitting call), so we unset them on the original GS as
         # well.
         gs._unset_non_persistent_state_fields()
-        ax_client.generation_strategy._unset_non_persistent_state_fields()
+        ax_client.standard_generation_strategy._unset_non_persistent_state_fields()
         self.assertEqual(gs, ax_client.generation_strategy)
         with self.assertRaises(ValueError):
             # Overwriting existing experiment.
@@ -2461,8 +2463,9 @@ def helper_test_get_pareto_optimal_points(
             num_trials=20, outcome_constraints=outcome_constraints
         )
         ax_client.fit_model()
+        gs = ax_client.standard_generation_strategy
         self.assertEqual(
-            ax_client.generation_strategy._curr.model_spec_to_gen_from.model_key,
+            gs._curr.model_spec_to_gen_from.model_key,
             "BoTorch",
         )
 
@@ -2487,7 +2490,7 @@ def helper_test_get_pareto_optimal_points(
         # This overwrites the `predict` call to return the original observations,
         # while testing the rest of the code as if we're using predictions.
         # pyre-fixme[16]: `Optional` has no attribute `model`.
-        model = ax_client.generation_strategy.model.model
+        model = ax_client.standard_generation_strategy.model.model
         ys = model.surrogate.training_data[0].Y
         with patch.object(
             model, "predict", return_value=(ys, torch.zeros(*ys.shape, ys.shape[-1]))
@@ -2531,8 +2534,9 @@ def helper_test_get_pareto_optimal_points_from_sobol_step(
         ax_client, _ = get_branin_currin_optimization_with_N_sobol_trials(
             num_trials=20, minimize=minimize, outcome_constraints=outcome_constraints
         )
+        gs = ax_client.standard_generation_strategy
         self.assertEqual(
-            ax_client.generation_strategy._curr.model_spec_to_gen_from.model_key,
+            gs._curr.model_spec_to_gen_from.model_key,
             "Sobol",
         )
 
@@ -2643,8 +2647,8 @@ def test_get_pareto_optimal_points_objective_threshold_inference(
         ax_client, _ = get_branin_currin_optimization_with_N_sobol_trials(
             num_trials=20, include_objective_thresholds=False
         )
-        ax_client.generation_strategy._maybe_transition_to_next_node()
-        ax_client.generation_strategy._fit_current_model(
+        ax_client.standard_generation_strategy._maybe_transition_to_next_node()
+        ax_client.standard_generation_strategy._fit_current_model(
             data=ax_client.experiment.lookup_data()
         )
 
@@ -2855,7 +2859,8 @@ def test_with_hss(self) -> None:
         # Make sure we actually tried a Botorch iteration and all the transforms it
         # applies.
         self.assertEqual(
-            ax_client.generation_strategy._generator_runs[-1]._model_key, "BoTorch"
+            ax_client.standard_generation_strategy._generator_runs[-1]._model_key,
+            "BoTorch",
         )
         self.assertEqual(len(ax_client.experiment.trials), 6)
         ax_client.attach_trial(
@@ -2970,7 +2975,7 @@ def test_torch_device(self) -> None:
                 torch_device=device,
             )
         ax_client = get_branin_optimization(torch_device=device)
-        gpei_step_kwargs = ax_client.generation_strategy._steps[1].model_kwargs
+        gpei_step_kwargs = ax_client.standard_generation_strategy._steps[1].model_kwargs
         self.assertEqual(gpei_step_kwargs["torch_device"], device)
 
     def test_repr_function(
@@ -2999,7 +3004,7 @@ def test_gen_fixed_features(self) -> None:
             name="fixed_features",
         )
         with mock.patch.object(
-            GenerationStrategy, "gen", wraps=ax_client.generation_strategy.gen
+            GenerationStrategy, "gen", wraps=ax_client.standard_generation_strategy.gen
         ) as mock_gen:
             with self.subTest("fixed_features is None"):
                 params, idx = ax_client.get_next_trial()