Support MultiTypeExperiment in Instantiation (#2939)

Summary:
Pull Request resolved: #2939

1. **InstatntiationBase:** Add support returning MultiTypeExperiment in InstatntiationBase._make_experiment.
2. **MultiTypeExperiment:** Add add_tracking_metrics function in MultiTypeExperiment to support batch adding metrics when creating a MultiTypeExperiment.
3. **AxClient**: Add support for creating MultiTypeExperiment, add_trial_type and add_tracking_metrics.

Reviewed By: sdaulton

Differential Revision: D64612495

fbshipit-source-id: d162b8965cfdb516bd4d484d321cff42835b617f

Author: andycylmeta

ax/core/multi_type_experiment.py

@@ -49,6 +49,7 @@ def __init__(
         default_trial_type: str,
         default_runner: Runner,
         optimization_config: OptimizationConfig | None = None,
+        tracking_metrics: list[Metric] | None = None,
         status_quo: Arm | None = None,
         description: str | None = None,
         is_test: bool = False,
@@ -65,6 +66,7 @@ def __init__(
             default_runner: Default runner for trials of the default type.
             optimization_config: Optimization config of the experiment.
             tracking_metrics: Additional tracking metrics not used for optimization.
+                These are associated with the default trial type.
             runner: Default runner used for trials on this experiment.
             status_quo: Arm representing existing "control" arm.
             description: Description of the experiment.
@@ -101,6 +103,7 @@ def __init__(
             experiment_type=experiment_type,
             properties=properties,
             default_data_type=default_data_type,
+            tracking_metrics=tracking_metrics,
         )
 
     def add_trial_type(self, trial_type: str, runner: Runner) -> "MultiTypeExperiment":
@@ -163,6 +166,45 @@ def add_tracking_metric(
             self._metric_to_canonical_name[metric.name] = canonical_name
         return self
 
+    def add_tracking_metrics(
+        self,
+        metrics: list[Metric],
+        metrics_to_trial_types: dict[str, str] | None = None,
+        canonical_names: dict[str, str] | None = None,
+    ) -> Experiment:
+        """Add a list of new metrics to the experiment.
+
+        If any of the metrics are already defined on the experiment,
+        we raise an error and don't add any of them to the experiment
+
+        Args:
+            metrics: Metrics to be added.
+            metrics_to_trial_types: The mapping from metric names to corresponding
+                trial types for each metric. If provided, the metrics will be
+                added to their trial types. If not provided, then the default
+                trial type will be used.
+            canonical_names: A mapping of metric names to their
+                canonical names(The default metrics for which the metrics are
+                proxies.)
+
+        Returns:
+            The experiment with the added metrics.
+        """
+        metrics_to_trial_types = metrics_to_trial_types or {}
+        canonical_name = None
+        for metric in metrics:
+            if canonical_names is not None:
+                canonical_name = none_throws(canonical_names).get(metric.name, None)
+
+            self.add_tracking_metric(
+                metric=metric,
+                trial_type=metrics_to_trial_types.get(
+                    metric.name, self._default_trial_type
+                ),
+                canonical_name=canonical_name,
+            )
+        return self
+
     # pyre-fixme[14]: `update_tracking_metric` overrides method defined in
     #  `Experiment` inconsistently.
     def update_tracking_metric(

ax/core/tests/test_multi_type_experiment.py

@@ -171,6 +171,40 @@ def test_runner_for_trial_type(self) -> None:
         ):
             self.experiment.runner_for_trial_type(trial_type="invalid")
 
+    def test_add_tracking_metrics(self) -> None:
+        type1_metrics = [
+            BraninMetric("m3_type1", ["x1", "x2"]),
+            BraninMetric("m4_type1", ["x1", "x2"]),
+        ]
+        type2_metrics = [
+            BraninMetric("m3_type2", ["x1", "x2"]),
+            BraninMetric("m4_type2", ["x1", "x2"]),
+        ]
+        default_type_metrics = [
+            BraninMetric("m5_default_type", ["x1", "x2"]),
+        ]
+        self.experiment.add_tracking_metrics(
+            metrics=type1_metrics + type2_metrics + default_type_metrics,
+            metrics_to_trial_types={
+                "m3_type1": "type1",
+                "m4_type1": "type1",
+                "m3_type2": "type2",
+                "m4_type2": "type2",
+            },
+        )
+        self.assertDictEqual(
+            self.experiment._metric_to_trial_type,
+            {
+                "m1": "type1",
+                "m2": "type2",
+                "m3_type1": "type1",
+                "m4_type1": "type1",
+                "m3_type2": "type2",
+                "m4_type2": "type2",
+                "m5_default_type": "type1",
+            },
+        )
+
 
 class MultiTypeExperimentUtilsTest(TestCase):
     def setUp(self) -> None:

ax/service/ax_client.py

@@ -27,19 +27,22 @@
 from ax.core.generator_run import GeneratorRun
 from ax.core.map_data import MapData
 from ax.core.map_metric import MapMetric
+from ax.core.multi_type_experiment import MultiTypeExperiment
 from ax.core.objective import MultiObjective, Objective
 from ax.core.observation import ObservationFeatures
 from ax.core.optimization_config import (
     MultiObjectiveOptimizationConfig,
     OptimizationConfig,
 )
+from ax.core.runner import Runner
 from ax.core.trial import Trial
 from ax.core.types import (
     TEvaluationOutcome,
     TModelPredictArm,
     TParameterization,
     TParamValue,
 )
+
 from ax.core.utils import get_pending_observation_features_based_on_trial_status
 from ax.early_stopping.strategies import BaseEarlyStoppingStrategy
 from ax.early_stopping.utils import estimate_early_stopping_savings
@@ -90,6 +93,7 @@
 from ax.utils.common.typeutils import checked_cast
 from pyre_extensions import assert_is_instance, none_throws
 
+
 logger: Logger = get_logger(__name__)
 
 
@@ -251,6 +255,8 @@ def create_experiment(
         immutable_search_space_and_opt_config: bool = True,
         is_test: bool = False,
         metric_definitions: dict[str, dict[str, Any]] | None = None,
+        default_trial_type: str | None = None,
+        default_runner: Runner | None = None,
     ) -> None:
         """Create a new experiment and save it if DBSettings available.
 
@@ -316,6 +322,15 @@ def create_experiment(
                 to that metric. Note these are modified in-place. Each
                 Metric must have its own dictionary (metrics cannot share a
                 single dictionary object).
+            default_trial_type: The default trial type if multiple
+                trial types are intended to be used in the experiment.  If specified,
+                a MultiTypeExperiment will be created. Otherwise, a single-type
+                Experiment will be created.
+            default_runner: The default runner in this experiment.
+                This applies to MultiTypeExperiment (when default_trial_type
+                is specified) and needs to be specified together with
+                default_trial_type. This will be ignored for single-type Experiment
+                (when default_trial_type is not specified).
         """
         self._validate_early_stopping_strategy(support_intermediate_data)
 
@@ -344,6 +359,8 @@ def create_experiment(
             support_intermediate_data=support_intermediate_data,
             immutable_search_space_and_opt_config=immutable_search_space_and_opt_config,
             is_test=is_test,
+            default_trial_type=default_trial_type,
+            default_runner=default_runner,
             **objective_kwargs,
         )
         self._set_runner(experiment=experiment)
@@ -416,6 +433,8 @@ def add_tracking_metrics(
         self,
         metric_names: list[str],
         metric_definitions: dict[str, dict[str, Any]] | None = None,
+        metrics_to_trial_types: dict[str, str] | None = None,
+        canonical_names: dict[str, str] | None = None,
     ) -> None:
         """Add a list of new metrics to the experiment.
 
@@ -428,20 +447,34 @@ def add_tracking_metrics(
                 to that metric. Note these are modified in-place. Each
                 Metric must have its is own dictionary (metrics cannot share a
                 single dictionary object).
+            metrics_to_trial_types: Only applicable to MultiTypeExperiment.
+                The mapping from metric names to corresponding
+                trial types for each metric. If provided, the metrics will be
+                added with their respective trial types. If not provided, then the
+                default trial type will be used.
+            canonical_names: A mapping from metric name (of a particular trial type)
+                to the metric name of the default trial type. Only applicable to
+                MultiTypeExperiment.
         """
         metric_definitions = (
             self.metric_definitions
             if metric_definitions is None
             else metric_definitions
         )
-        self.experiment.add_tracking_metrics(
-            metrics=[
-                self._make_metric(
-                    name=metric_name, metric_definitions=metric_definitions
-                )
-                for metric_name in metric_names
-            ]
-        )
+        metric_objects = [
+            self._make_metric(name=metric_name, metric_definitions=metric_definitions)
+            for metric_name in metric_names
+        ]
+
+        if isinstance(self.experiment, MultiTypeExperiment):
+            experiment = assert_is_instance(self.experiment, MultiTypeExperiment)
+            experiment.add_tracking_metrics(
+                metrics=metric_objects,
+                metrics_to_trial_types=metrics_to_trial_types,
+                canonical_names=canonical_names,
+            )
+        else:
+            self.experiment.add_tracking_metrics(metrics=metric_objects)
 
     @copy_doc(Experiment.remove_tracking_metric)
     def remove_tracking_metric(self, metric_name: str) -> None:

ax/service/tests/test_ax_client.py

@@ -21,6 +21,7 @@
 from ax.core.arm import Arm
 from ax.core.generator_run import GeneratorRun
 from ax.core.metric import Metric
+from ax.core.multi_type_experiment import MultiTypeExperiment
 from ax.core.optimization_config import MultiObjectiveOptimizationConfig
 from ax.core.outcome_constraint import ObjectiveThreshold, OutcomeConstraint
 from ax.core.parameter import (
@@ -57,6 +58,7 @@
 from ax.modelbridge.model_spec import ModelSpec
 from ax.modelbridge.random import RandomModelBridge
 from ax.modelbridge.registry import Models
+from ax.runners.synthetic import SyntheticRunner
 
 from ax.service.ax_client import AxClient, ObjectiveProperties
 from ax.service.utils.best_point import (
@@ -83,7 +85,7 @@
 from ax.utils.testing.mock import mock_botorch_optimize
 from ax.utils.testing.modeling_stubs import get_observation1, get_observation1trans
 from botorch.test_functions.multi_objective import BraninCurrin
-from pyre_extensions import none_throws
+from pyre_extensions import assert_is_instance, none_throws
 
 if TYPE_CHECKING:
     from ax.core.types import TTrialEvaluation
@@ -821,6 +823,7 @@ def test_create_experiment(self) -> None:
             is_test=True,
         )
         assert ax_client._experiment is not None
+        self.assertEqual(ax_client.experiment.__class__.__name__, "Experiment")
         self.assertEqual(ax_client._experiment, ax_client.experiment)
         self.assertEqual(
             # pyre-fixme[16]: `Optional` has no attribute `search_space`.
@@ -903,6 +906,107 @@ def test_create_experiment(self) -> None:
                 {"test_objective", "some_metric", "test_tracking_metric"},
             )
 
+    def test_create_multitype_experiment(self) -> None:
+        """
+        Test create multitype experiment, add trial type, and add metrics to
+        different trial types
+        """
+        ax_client = AxClient(
+            GenerationStrategy(
+                steps=[GenerationStep(model=Models.SOBOL, num_trials=30)]
+            )
+        )
+        ax_client.create_experiment(
+            name="test_experiment",
+            parameters=[
+                {
+                    "name": "x",
+                    "type": "range",
+                    "bounds": [0.001, 0.1],
+                    "value_type": "float",
+                    "log_scale": True,
+                    "digits": 6,
+                },
+                {
+                    "name": "y",
+                    "type": "choice",
+                    "values": [1, 2, 3],
+                    "value_type": "int",
+                    "is_ordered": True,
+                },
+                {"name": "x3", "type": "fixed", "value": 2, "value_type": "int"},
+                {
+                    "name": "x4",
+                    "type": "range",
+                    "bounds": [1.0, 3.0],
+                    "value_type": "int",
+                },
+                {
+                    "name": "x5",
+                    "type": "choice",
+                    "values": ["one", "two", "three"],
+                    "value_type": "str",
+                },
+                {
+                    "name": "x6",
+                    "type": "range",
+                    "bounds": [1.0, 3.0],
+                    "value_type": "int",
+                },
+            ],
+            objectives={"test_objective": ObjectiveProperties(minimize=True)},
+            outcome_constraints=["some_metric >= 3", "some_metric <= 4.0"],
+            parameter_constraints=["x4 <= x6"],
+            tracking_metric_names=["test_tracking_metric"],
+            is_test=True,
+            default_trial_type="test_trial_type",
+            default_runner=SyntheticRunner(),
+        )
+
+        self.assertEqual(ax_client.experiment.__class__.__name__, "MultiTypeExperiment")
+        experiment = assert_is_instance(ax_client.experiment, MultiTypeExperiment)
+        self.assertEqual(
+            experiment._trial_type_to_runner["test_trial_type"].__class__.__name__,
+            "SyntheticRunner",
+        )
+        self.assertEqual(
+            experiment._metric_to_trial_type,
+            {
+                "test_tracking_metric": "test_trial_type",
+                "test_objective": "test_trial_type",
+                "some_metric": "test_trial_type",
+            },
+        )
+        experiment.add_trial_type(
+            trial_type="test_trial_type_2",
+            runner=SyntheticRunner(),
+        )
+        ax_client.add_tracking_metrics(
+            metric_names=[
+                "some_metric2_type1",
+                "some_metric3_type1",
+                "some_metric4_type2",
+                "some_metric5_type2",
+            ],
+            metrics_to_trial_types={
+                "some_metric2_type1": "test_trial_type",
+                "some_metric4_type2": "test_trial_type_2",
+                "some_metric5_type2": "test_trial_type_2",
+            },
+        )
+        self.assertEqual(
+            experiment._metric_to_trial_type,
+            {
+                "test_tracking_metric": "test_trial_type",
+                "test_objective": "test_trial_type",
+                "some_metric": "test_trial_type",
+                "some_metric2_type1": "test_trial_type",
+                "some_metric3_type1": "test_trial_type",
+                "some_metric4_type2": "test_trial_type_2",
+                "some_metric5_type2": "test_trial_type_2",
+            },
+        )
+
     def test_create_single_objective_experiment_with_objectives_dict(self) -> None:
         ax_client = AxClient(
             GenerationStrategy(

ax/service/tests/test_instantiation_utils.py

@@ -17,6 +17,7 @@
     RangeParameter,
 )
 from ax.core.search_space import HierarchicalSearchSpace
+from ax.runners.synthetic import SyntheticRunner
 from ax.service.utils.instantiation import InstantiationBase
 from ax.utils.common.testutils import TestCase
 from ax.utils.common.typeutils import checked_cast
@@ -431,3 +432,21 @@ def test_hss(self) -> None:
         self.assertIsInstance(search_space, HierarchicalSearchSpace)
         # pyre-fixme[16]: `SearchSpace` has no attribute `_root`.
         self.assertEqual(search_space._root.name, "root")
+
+    def test_make_multitype_experiment_with_default_trial_type(self) -> None:
+        experiment = InstantiationBase.make_experiment(
+            name="test_make_experiment",
+            parameters=[{"name": "x", "type": "range", "bounds": [0, 1]}],
+            tracking_metric_names=None,
+            default_trial_type="test_trial_type",
+            default_runner=SyntheticRunner(),
+        )
+        self.assertEqual(experiment.__class__.__name__, "MultiTypeExperiment")
+
+    def test_make_single_type_experiment_with_no_default_trial_type(self) -> None:
+        experiment = InstantiationBase.make_experiment(
+            name="test_make_experiment",
+            parameters=[{"name": "x", "type": "range", "bounds": [0, 1]}],
+            tracking_metric_names=None,
+        )
+        self.assertEqual(experiment.__class__.__name__, "Experiment")