add parameter constraints while adding new parameters

Summary:
AxClient and Experiment each have a method for adding parameters to the experiment (added in D93766951). In settings where we are dealing with constrained parameters, we need a similarly convenient interface for adding the constraints on those parameters. I think the cleanest approach is to add constraints as a kwarg to the existing method, which is what is done here.

I also fix a bug in the AxClient add parameters interface where the docstring says "status_quo_values: Optional parameter values for the new parameters to use in the status quo (baseline) arm, if one is defined. If None, the backfill values will be used for the status quo." but didn't use the backfill. The Experiment method this subsequently calls requiers status_quo_values, so this was actually producing an error if status_quo_values were not provided.

Differential Revision: D96522400

Author: bletham

ax/core/experiment.py

@@ -38,6 +38,7 @@
 from ax.core.objective import MultiObjective
 from ax.core.optimization_config import ObjectiveThreshold, OptimizationConfig
 from ax.core.parameter import DerivedParameter, Parameter
+from ax.core.parameter_constraint import ParameterConstraint
 from ax.core.runner import Runner
 from ax.core.search_space import SearchSpace
 from ax.core.trial import Trial
@@ -341,6 +342,7 @@ def add_parameters_to_search_space(
         self,
         parameters: Sequence[Parameter],
         status_quo_values: TParameterization | None = None,
+        parameter_constraints: Sequence[ParameterConstraint] | None = None,
     ) -> None:
         """
         Add new parameters to the experiment's search space. This allows extending
@@ -355,6 +357,8 @@ def add_parameters_to_search_space(
                 space.
             status_quo_values: Optional parameter values for the new parameters to
                 use in the status quo (baseline) arm, if one is defined.
+            parameter_constraints: Optional sequence of typed ParameterConstraint
+                objects to add to the search space after the parameters are added.
         """
         status_quo_values = status_quo_values or {}
 
@@ -408,6 +412,10 @@ def add_parameters_to_search_space(
         # Add parameters to search space
         self._search_space.add_parameters(parameters)
 
+        # Add parameter constraints to search space
+        if parameter_constraints:
+            self._search_space.add_parameter_constraints(list(parameter_constraints))
+
     def disable_parameters_in_search_space(
         self, default_parameter_values: TParameterization
     ) -> None:

ax/core/tests/test_experiment.py

@@ -37,6 +37,7 @@
     ParameterType,
     RangeParameter,
 )
+from ax.core.parameter_constraint import ParameterConstraint
 from ax.core.search_space import SearchSpace
 from ax.core.types import ComparisonOp
 from ax.exceptions.core import (
@@ -412,6 +413,31 @@ def test_add_search_space_parameters(self) -> None:
             self.assertIn("new_param", experiment.status_quo.parameters)
             self.assertEqual(experiment.status_quo.parameters["new_param"], 0.0)
 
+        with self.subTest("Add parameter with parameter constraints"):
+            experiment = self.experiment.clone_with(trial_indices=[])
+            num_existing_constraints = len(
+                experiment.search_space.parameter_constraints
+            )
+            constraint = ParameterConstraint(
+                inequality="new_param + w <= 5.0",
+            )
+            experiment.add_parameters_to_search_space(
+                parameters=[new_param],
+                status_quo_values={new_param.name: 0.0},
+                parameter_constraints=[constraint],
+            )
+            # Verify parameter was added
+            self.assertIn("new_param", experiment.search_space.parameters)
+            # Verify constraint was added
+            self.assertEqual(
+                len(experiment.search_space.parameter_constraints),
+                num_existing_constraints + 1,
+            )
+            added_constraint = experiment.search_space.parameter_constraints[-1]
+            self.assertIn("new_param", added_constraint.constraint_dict)
+            self.assertIn("w", added_constraint.constraint_dict)
+            self.assertEqual(added_constraint.bound, 5.0)
+
     def test_add_derived_parameter_to_search_space_with_trials(self) -> None:
         """Test adding DerivedParameters to an experiment that has existing trials.
 

ax/service/ax_client.py

@@ -30,6 +30,7 @@
 from ax.core.objective import MultiObjective, Objective
 from ax.core.observation import ObservationFeatures
 from ax.core.parameter import RangeParameter
+from ax.core.parameter_constraint import ParameterConstraint
 from ax.core.runner import Runner
 from ax.core.trial import Trial
 from ax.core.trial_status import TrialStatus
@@ -524,6 +525,7 @@ def add_parameters(
         parameters: Sequence[RangeParameterConfig | ChoiceParameterConfig],
         backfill_values: TParameterization,
         status_quo_values: TParameterization | None = None,
+        parameter_constraints: list[str] | None = None,
     ) -> None:
         """
         Add new parameters to the experiment's search space. This allows extending
@@ -543,6 +545,10 @@ def add_parameters(
             status_quo_values: Optional parameter values for the new parameters to
                 use in the status quo (baseline) arm, if one is defined. If None,
                 the backfill values will be used for the status quo.
+            parameter_constraints: Optional list of string representations of
+                parameter constraints to add (e.g., ``"x1 + x2 <= 5.0"``
+                or ``"x1 <= x2"``). May reference both existing and new
+                parameters.
         """
         parameters_to_add = [
             parameter_from_config(parameter_config) for parameter_config in parameters
@@ -563,9 +569,25 @@ def add_parameters(
         for parameter in parameters_to_add:
             if parameter.name in backfill_values:
                 parameter._backfill_value = backfill_values[parameter.name]
+
+        # Convert string constraints to typed ParameterConstraint objects.
+        typed_parameter_constraints: list[ParameterConstraint] = []
+        if parameter_constraints:
+            # Build a parameter map with both existing and new parameters so
+            # constraints can reference either.
+            parameter_map = {
+                **self.experiment.search_space.parameters,
+                **{p.name: p for p in parameters_to_add},
+            }
+            typed_parameter_constraints = [
+                InstantiationBase.constraint_from_str(c, parameter_map)
+                for c in parameter_constraints
+            ]
+
         self.experiment.add_parameters_to_search_space(
             parameters=parameters_to_add,
-            status_quo_values=status_quo_values,
+            status_quo_values=status_quo_values or backfill_values,
+            parameter_constraints=typed_parameter_constraints or None,
         )
         self._save_experiment_to_db_if_possible(experiment=self.experiment)
 

ax/service/tests/test_ax_client.py

@@ -1493,6 +1493,144 @@ def test_add_parameters(self) -> None:
         assert isinstance(param_x3, ChoiceParameter)
         self.assertEqual(param_x3.values, ["a", "b", "c"])
 
+    def test_add_parameters_backfill_values_used_for_status_quo(self) -> None:
+        """Test that backfill_values are used for the status quo arm when
+        status_quo_values is not provided.
+        """
+        ax_client = AxClient()
+        ax_client.create_experiment(
+            name="test_experiment",
+            parameters=[
+                {
+                    "name": "x1",
+                    "type": "range",
+                    "bounds": [0.0, 1.0],
+                    "value_type": "float",
+                },
+            ],
+            status_quo={"x1": 0.5},
+            is_test=True,
+            immutable_search_space_and_opt_config=False,
+        )
+
+        ax_client.add_parameters(
+            parameters=[
+                RangeParameterConfig(
+                    name="x2",
+                    bounds=(0.0, 10.0),
+                    parameter_type="float",
+                ),
+                ChoiceParameterConfig(
+                    name="x3",
+                    values=["a", "b", "c"],
+                    parameter_type="str",
+                ),
+            ],
+            backfill_values={"x2": 5.0, "x3": "a"},
+        )
+
+        # Verify the status quo arm was updated with backfill_values
+        status_quo = ax_client.experiment.status_quo
+        self.assertIsNotNone(status_quo)
+        assert status_quo is not None
+        self.assertEqual(status_quo.parameters["x1"], 0.5)
+        self.assertEqual(status_quo.parameters["x2"], 5.0)
+        self.assertEqual(status_quo.parameters["x3"], "a")
+
+    def test_add_parameters_with_constraints(self) -> None:
+        """Test that add_parameters correctly adds parameter constraints."""
+        ax_client = AxClient()
+        ax_client.create_experiment(
+            name="test_experiment",
+            parameters=[
+                {
+                    "name": "x1",
+                    "type": "range",
+                    "bounds": [0.0, 10.0],
+                    "value_type": "float",
+                },
+            ],
+            is_test=True,
+            immutable_search_space_and_opt_config=False,
+        )
+
+        with self.subTest("Sum constraint on new parameters"):
+            ax_client.add_parameters(
+                parameters=[
+                    RangeParameterConfig(
+                        name="x2",
+                        bounds=(0.0, 10.0),
+                        parameter_type="float",
+                    ),
+                ],
+                backfill_values={"x2": 5.0},
+                parameter_constraints=["x1 + x2 <= 5.0"],
+            )
+            search_space = ax_client.experiment.search_space
+            self.assertIn("x2", search_space.parameters)
+            self.assertEqual(len(search_space.parameter_constraints), 1)
+            constraint = search_space.parameter_constraints[0]
+            self.assertIn("x1", constraint.constraint_dict)
+            self.assertIn("x2", constraint.constraint_dict)
+            self.assertEqual(constraint.bound, 5.0)
+
+        with self.subTest("Order constraint referencing existing and new parameter"):
+            ax_client_2 = AxClient()
+            ax_client_2.create_experiment(
+                name="test_experiment_2",
+                parameters=[
+                    {
+                        "name": "x1",
+                        "type": "range",
+                        "bounds": [0.0, 10.0],
+                        "value_type": "float",
+                    },
+                ],
+                is_test=True,
+                immutable_search_space_and_opt_config=False,
+            )
+            ax_client_2.add_parameters(
+                parameters=[
+                    RangeParameterConfig(
+                        name="x2",
+                        bounds=(0.0, 10.0),
+                        parameter_type="float",
+                    ),
+                ],
+                backfill_values={"x2": 5.0},
+                parameter_constraints=["x1 <= x2"],
+            )
+            search_space = ax_client_2.experiment.search_space
+            self.assertEqual(len(search_space.parameter_constraints), 1)
+
+        with self.subTest("Constraint referencing non-existent parameter"):
+            ax_client_3 = AxClient()
+            ax_client_3.create_experiment(
+                name="test_experiment_3",
+                parameters=[
+                    {
+                        "name": "x1",
+                        "type": "range",
+                        "bounds": [0.0, 10.0],
+                        "value_type": "float",
+                    },
+                ],
+                is_test=True,
+                immutable_search_space_and_opt_config=False,
+            )
+            with self.assertRaises(ValueError):
+                ax_client_3.add_parameters(
+                    parameters=[
+                        RangeParameterConfig(
+                            name="x2",
+                            bounds=(0.0, 10.0),
+                            parameter_type="float",
+                        ),
+                    ],
+                    backfill_values={"x2": 5.0},
+                    parameter_constraints=["x1 + nonexistent <= 5.0"],
+                )
+
     def test_disable_parameters(self) -> None:
         """Test that disable_parameters correctly disables parameters in the search
         space."""