Deprecate FixedParameter in favor of single-element ChoiceParameter (#…

…3397)

Summary:

As titled. Deprecating `FixedParameter`


Doing a "soft" deprecation (i.e. instantiate`ChoiceParameter` if `FixedParameterConfig` is given) becuase there are partner integration code that relies on `FixedParameterConfig` (e.g. https://fburl.com/code/7nxmqhim)

Differential Revision: D68241762

ax/core/parameter.py

@@ -527,7 +527,7 @@ class ChoiceParameter(Parameter):
         name: Name of the parameter.
         parameter_type: Enum indicating the type of parameter
             value (e.g. string, int).
-        values: List of allowed values for the parameter.
+        values: List of allowed value(s) for the parameter.
         is_ordered: If False, the parameter is a categorical variable.
             Defaults to False if parameter_type is STRING and ``values``
             is longer than 2, else True.
@@ -566,9 +566,6 @@ def __init__(
         self._is_task = is_task
         self._is_fidelity = is_fidelity
         self._target_value: TParamValue = self.cast(target_value)
-        # A choice parameter with only one value is a FixedParameter.
-        if not len(values) > 1:
-            raise UserInputError(f"{self._name}({values}): {FIXED_CHOICE_PARAM_ERROR}")
         # Cap the number of possible values.
         if len(values) > MAX_VALUES_CHOICE_PARAM:
             raise UserInputError(
@@ -680,9 +677,6 @@ def set_values(self, values: list[TParamValue]) -> ChoiceParameter:
         Args:
             values: New list of allowed values.
         """
-        # A choice parameter with only one value is a FixedParameter.
-        if not len(values) > 1:
-            raise UserInputError(FIXED_CHOICE_PARAM_ERROR)
         self._values = self._cast_values(values)
         return self
 
@@ -757,7 +751,10 @@ def domain_repr(self) -> str:
 
 
 class FixedParameter(Parameter):
-    """Parameter object that specifies a single fixed value."""
+    """
+    *DEPRECATED*: Use ChoiceParameter with a single value instead.
+
+    Parameter object that specifies a single fixed value."""
 
     def __init__(
         self,
@@ -768,7 +765,10 @@ def __init__(
         target_value: TParamValue = None,
         dependents: dict[TParamValue, list[str]] | None = None,
     ) -> None:
-        """Initialize FixedParameter
+        """
+        *DEPRECATED*: Use ChoiceParameter with a single value instead.
+
+        Initialize FixedParameter
 
         Args:
             name: Name of the parameter.
@@ -780,6 +780,10 @@ def __init__(
             dependents: Optional mapping for parameters in hierarchical search
                 spaces; format is { value -> list of dependent parameter names }.
         """
+        warn(
+            "FixedParameter is deprecated. Use ChoiceParameter with a single value "
+            "instead.",
+        )
         if is_fidelity and (target_value is None):
             raise UserInputError(
                 "`target_value` should not be None for the fidelity parameter: "

ax/modelbridge/transforms/remove_fixed.py

@@ -6,7 +6,7 @@
 
 # pyre-strict
 
-from typing import Optional, TYPE_CHECKING
+from typing import Optional, TYPE_CHECKING, Union
 
 from ax.core.observation import Observation, ObservationFeatures
 from ax.core.parameter import ChoiceParameter, FixedParameter, RangeParameter
@@ -21,9 +21,9 @@
 
 
 class RemoveFixed(Transform):
-    """Remove fixed parameters.
+    """Remove fixed parameters and single-choice choice parameters from the search space.
 
-    Fixed parameters should not be included in the SearchSpace.
+    Fixed parameters and single-choice choice parameters should not be included in the SearchSpace.
     This transform removes these parameters, leaving only tunable parameters.
 
     Transform is done in-place for observation features.
@@ -38,24 +38,25 @@ def __init__(
     ) -> None:
         assert search_space is not None, "RemoveFixed requires search space"
         # Identify parameters that should be transformed
-        self.fixed_parameters: dict[str, FixedParameter] = {
+        self.single_choice_params: dict[str, Union[FixedParameter, ChoiceParameter]] = {
             p_name: p
             for p_name, p in search_space.parameters.items()
             if isinstance(p, FixedParameter)
+            or (isinstance(p, ChoiceParameter) and len(p.values) == 1)
         }
 
     def transform_observation_features(
         self, observation_features: list[ObservationFeatures]
     ) -> list[ObservationFeatures]:
         for obsf in observation_features:
-            for p_name in self.fixed_parameters:
+            for p_name in self.single_choice_params:
                 obsf.parameters.pop(p_name, None)
         return observation_features
 
     def _transform_search_space(self, search_space: SearchSpace) -> SearchSpace:
         tunable_parameters: list[ChoiceParameter | RangeParameter] = []
         for p in search_space.parameters.values():
-            if p.name not in self.fixed_parameters:
+            if p.name not in self.single_choice_params:
                 # If it's not in fixed_parameters, it must be a tunable param.
                 # pyre: p_ is declared to have type `Union[ChoiceParameter,
                 # pyre: RangeParameter]` but is used as type `ax.core.
@@ -75,6 +76,9 @@ def untransform_observation_features(
         self, observation_features: list[ObservationFeatures]
     ) -> list[ObservationFeatures]:
         for obsf in observation_features:
-            for p_name, p in self.fixed_parameters.items():
-                obsf.parameters[p_name] = p.value
+            for p_name, p in self.single_choice_params.items():
+                if isinstance(p, FixedParameter):
+                    obsf.parameters[p_name] = p.value
+                else:
+                    obsf.parameters[p_name] = p.values[0]
         return observation_features

ax/modelbridge/transforms/tests/test_remove_fixed_transform.py

@@ -33,6 +33,7 @@ def setUp(self) -> None:
                     "b", parameter_type=ParameterType.STRING, values=["a", "b", "c"]
                 ),
                 FixedParameter("c", parameter_type=ParameterType.STRING, value="a"),
+                ChoiceParameter("d", parameter_type=ParameterType.INT, values=[1]),
             ]
         )
         self.t = RemoveFixed(
@@ -41,11 +42,11 @@ def setUp(self) -> None:
         )
 
     def test_Init(self) -> None:
-        self.assertEqual(list(self.t.fixed_parameters.keys()), ["c"])
+        self.assertEqual(list(self.t.single_choice_params.keys()), ["c", "d"])
 
     def test_TransformObservationFeatures(self) -> None:
         observation_features = [
-            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "a"})
+            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "a", "d": 1})
         ]
         obs_ft2 = deepcopy(observation_features)
         obs_ft2 = self.t.transform_observation_features(obs_ft2)
@@ -56,10 +57,10 @@ def test_TransformObservationFeatures(self) -> None:
         self.assertEqual(obs_ft2, observation_features)
 
         observation_features = [
-            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "a"})
+            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "a", "d": 1})
         ]
         observation_features_different = [
-            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "b"})
+            ObservationFeatures(parameters={"a": 2.2, "b": "b", "c": "b", "d": 10})
         ]
         # Fixed parameter is out of design. It will still get removed.
         t_obs = self.t.transform_observation_features(observation_features)
@@ -72,12 +73,16 @@ def test_TransformSearchSpace(self) -> None:
         ss2 = self.search_space.clone()
         ss2 = self.t.transform_search_space(ss2)
         self.assertEqual(ss2.parameters.get("c"), None)
+        self.assertEqual(ss2.parameters.get("d"), None)
 
     def test_w_parameter_distributions(self) -> None:
         rss = get_robust_search_space()
         rss.add_parameter(
             FixedParameter("d", parameter_type=ParameterType.STRING, value="a"),
         )
+        rss.add_parameter(
+            ChoiceParameter("e", parameter_type=ParameterType.INT, values=[1]),
+        )
         # Transform a non-distributional parameter.
         t = RemoveFixed(
             search_space=rss,
@@ -90,6 +95,7 @@ def test_w_parameter_distributions(self) -> None:
         # pyre-fixme[16]: `SearchSpace` has no attribute `parameter_distributions`.
         self.assertEqual(len(rss.parameter_distributions), 2)
         self.assertNotIn("d", rss.parameters)
+        self.assertNotIn("e", rss.parameters)
         # Test with environmental variables.
         all_params = list(rss.parameters.values())
         rss = RobustSearchSpace(
@@ -102,6 +108,9 @@ def test_w_parameter_distributions(self) -> None:
         rss.add_parameter(
             FixedParameter("d", parameter_type=ParameterType.STRING, value="a"),
         )
+        rss.add_parameter(
+            ChoiceParameter("e", parameter_type=ParameterType.INT, values=[1]),
+        )
         t = RemoveFixed(
             search_space=rss,
             observations=[],

ax/service/utils/instantiation.py

@@ -7,6 +7,7 @@
 # pyre-strict
 
 import enum
+import warnings
 from collections.abc import Sequence
 from copy import deepcopy
 from dataclasses import dataclass
@@ -251,10 +252,6 @@ def _make_choice_param(
         parameter_type: str | None,
     ) -> ChoiceParameter:
         values = representation["values"]
-        assert isinstance(values, list) and len(values) > 1, (
-            f"Cannot parse parameter {name}: for choice parameters, json representation"
-            " should include a list of two or more values."
-        )
         return ChoiceParameter(
             name=name,
             parameter_type=cls._to_parameter_type(
@@ -283,27 +280,19 @@ def _make_fixed_param(
         name: str,
         representation: TParameterRepresentation,
         parameter_type: str | None,
-    ) -> FixedParameter:
-        assert "value" in representation, "Value is required for fixed parameters."
-        value = representation["value"]
-        assert type(value) in PARAM_TYPES.values(), (
-            f"Cannot parse fixed parameter {name}: for fixed parameters, json "
-            "representation should include a single value."
+    ) -> ChoiceParameter:
+        warnings.warn(
+            "`fixed` parameters are deprecated. Please use `ChoiceParameter` with a "
+            "single-value instead. This config will instantiate a `ChoiceParameter`",
+            DeprecationWarning,
+            stacklevel=2,
         )
-        return FixedParameter(
+        # Convert TParameterRepresentation to a ChoiceParameter representation.
+        representation["values"] = [representation["value"]]  # pyre-ignore[6]
+        return cls._make_choice_param(
             name=name,
-            parameter_type=(
-                cls._get_parameter_type(type(value))  # pyre-ignore[6]
-                if parameter_type is None
-                # pyre-ignore[6]
-                else cls._get_parameter_type(PARAM_TYPES[parameter_type])
-            ),
-            value=value,  # pyre-ignore[6]
-            is_fidelity=assert_is_instance(
-                representation.get("is_fidelity", False), bool
-            ),
-            target_value=representation.get("target_value", None),  # pyre-ignore[6]
-            dependents=representation.get("dependents", None),  # pyre-ignore[6]
+            representation=representation,
+            parameter_type=parameter_type,
         )
 
     @classmethod
@@ -358,22 +347,11 @@ def parameter_from_json(
                 "values" in representation
             ), "Values are required for choice parameters."
             values = representation["values"]
-            if isinstance(values, list) and len(values) == 1:
-                logger.info(
-                    f"Choice parameter {name} contains only one value, converting to a"
-                    + " fixed parameter instead."
-                )
-                # update the representation to a fixed parameter class
-                parameter_class = "fixed"
-                representation["type"] = parameter_class
-                representation["value"] = values[0]
-                del representation["values"]
-            else:
-                return cls._make_choice_param(
-                    name=name,
-                    representation=representation,
-                    parameter_type=parameter_type,
-                )
+            return cls._make_choice_param(
+                name=name,
+                representation=representation,
+                parameter_type=parameter_type,
+            )
 
         if parameter_class == "fixed":
             assert not any(isinstance(val, list) for val in representation.values())