Only load completed map metrics by default (#3409)

Summary:
Pull Request resolved: #3409

This commit ensures that the `Adapter`only loads completed map metrics by default. This makes sure that any method (not-necessarily map-data aware) can be applied by default.

Reviewed By: dme65, saitcakmak

Differential Revision: D70012386

fbshipit-source-id: 519df369fefc11aa3f8abd234827092db7d511c4

ax/core/observation.py

@@ -468,6 +468,7 @@ def observations_from_data(
     latest_rows_per_group: int | None = None,
     limit_rows_per_metric: int | None = None,
     limit_rows_per_group: int | None = None,
+    load_only_completed_map_metrics: bool = True,
 ) -> list[Observation]:
     """Convert Data (or MapData) to observations.
 
@@ -501,6 +502,8 @@ def observations_from_data(
             uses MapData.subsample() with `limit_rows_per_group` on the first
             map_key (map_data.map_keys[0]) to subsample the MapData. Ignored if
             `latest_rows_per_group` is specified.
+        load_only_completed_map_metrics: If True, only loads the last observation
+            for each completed MapMetric.
 
     Returns:
         List of Observation objects.
@@ -511,7 +514,8 @@ def observations_from_data(
         statuses_to_include_map_metric = NON_ABANDONED_STATUSES
     is_map_data = isinstance(data, MapData)
     map_keys = []
-    if is_map_data:
+    take_map_branch = is_map_data and not load_only_completed_map_metrics
+    if take_map_branch:
         data = assert_is_instance(data, MapData)
         map_keys.extend(data.map_keys)
         if latest_rows_per_group is not None:
@@ -526,7 +530,7 @@ def observations_from_data(
         df = data.map_df
     else:
         df = data.df
-    feature_cols = get_feature_cols(data, is_map_data=is_map_data)
+    feature_cols = get_feature_cols(data, is_map_data=take_map_branch)
     return _observations_from_dataframe(
         experiment=experiment,
         df=df,

ax/core/tests/test_observation.py

@@ -474,7 +474,9 @@ def test_ObservationsFromMapData(self) -> None:
                 MapKeyInfo(key="timestamp", default_value=0.0),
             ],
         )
-        observations = observations_from_data(experiment, data)
+        observations = observations_from_data(
+            experiment, data, load_only_completed_map_metrics=False
+        )
 
         self.assertEqual(len(observations), 3)
 

ax/modelbridge/base.py

@@ -306,6 +306,7 @@ def _prepare_observations(
             statuses_to_include=self.statuses_to_fit,
             statuses_to_include_map_metric=self.statuses_to_fit_map_metric,
             map_keys_as_parameters=map_keys_as_parameters,
+            load_only_completed_map_metrics=self._fit_only_completed_map_metrics,
         )
 
     def _transform_data(

ax/modelbridge/map_torch.py

@@ -201,8 +201,10 @@ def _fit(
         `self.parameters_with_map_keys` instead of `self.parameters`.
         """
         self.parameters = list(search_space.parameters.keys())
+        print("MAPTORCH parameters", parameters)
         if parameters is None:
             parameters = self.parameters_with_map_keys
+        print("MAPTORCH parameters", parameters)
         super()._fit(
             model=model,
             search_space=search_space,
@@ -265,6 +267,7 @@ def _prepare_observations(
             statuses_to_include=self.statuses_to_fit,
             statuses_to_include_map_metric=self.statuses_to_fit_map_metric,
             map_keys_as_parameters=True,
+            load_only_completed_map_metrics=False,
         )
 
     def _compute_in_design(