Separating out get_map_values helper from MapUnitX transform (facebook#3313)

Summary:

This commit separates out a `get_map_values` helper function from the `MapUnitX` transform.

Differential Revision: D69213291

Author: SebastianAment

Diff for: ax/modelbridge/transforms/map_unit_x.py

@@ -41,14 +41,7 @@ def __init__(
     ) -> None:
         assert observations is not None, "MapUnitX requires observations"
         assert search_space is not None, "MapUnitX requires search space"
-        # Loop through observation features and identify parameters that
-        # are not part of the search space. Store all observed values to
-        # infer bounds
-        map_values = defaultdict(list)
-        for obs in observations:
-            for p in obs.features.parameters:
-                if p not in search_space.parameters:
-                    map_values[p].append(obs.features.parameters[p])
+        map_values = get_map_values(search_space, observations)
 
         # pyre-fixme[24]: Generic type `list` expects 1 type parameter, use
         #  `typing.List` to avoid runtime subscripting errors.
@@ -81,3 +74,30 @@ def untransform_observation_features(
                     scale_fac = (u - l) / self.target_range
                     obsf.parameters[p_name] = scale_fac * (param - self.target_lb) + l
         return observation_features
+
+
+def get_map_values(
+    search_space: SearchSpace,
+    observations: list[Observation],
+) -> dict[str, list[float]]:
+    """Computes a dictionary mapping the name of a map parameter to its associated
+    progression values, in the same order as they occur in the observations.
+
+    Args:
+        search_space: The search space.
+        observations: A list of observations associated with the search space.
+
+    Returns:
+        The dictionary mapping the name of a map metric to the associated values,
+        in the same order they occur in `observations`.
+    """
+    # Loop through observation features and identify parameters that
+    # are not part of the search space. Store all observed values to
+    # infer bounds
+    map_values = defaultdict(list)
+    for obs in observations:
+        # if we had access to the original data object, could loop over data.map_keys
+        for p in obs.features.parameters:
+            if p not in search_space.parameters:
+                map_values[p].append(obs.features.parameters[p])
+    return map_values