Include progression information as metadata when transforming (Map)Da…

…ta to Observations (facebook#3001)

Summary:

This updates `observations_from_data` to include progression info as observation feature metadata by default. More specifically:

- Updates `observations_from_data` to subsume behavior of `observations_from_map_data` as special case.
- Updates calls to `observations_from_map_data` to instead call `observations_from_data`
- Removes `observations_from_map_data` which is used exclusively by `MapTorchModelBridge`

Reviewed By: saitcakmak

Differential Revision: D65255312

ax/core/observation.py

@@ -426,7 +426,7 @@ def get_feature_cols(data: Data, is_map_data: bool = False) -> list[str]:
     feature_cols = OBS_COLS.intersection(data.df.columns)
     # note we use this check, rather than isinstance, since
     # only some Modelbridges (e.g. MapTorchModelBridge)
-    # use observations_from_map_data, which is required
+    # use observations_from_data, which is required
     # to properly handle MapData features (e.g. fidelity).
     if is_map_data:
         data = checked_cast(MapData, data)
@@ -448,174 +448,103 @@ def get_feature_cols(data: Data, is_map_data: bool = False) -> list[str]:
 
 def observations_from_data(
     experiment: experiment.Experiment,
-    data: Data,
-    statuses_to_include: set[TrialStatus] | None = None,
-    statuses_to_include_map_metric: set[TrialStatus] | None = None,
-) -> list[Observation]:
-    """Convert Data to observations.
-
-    Converts a Data object to a list of Observation objects. Pulls arm parameters from
-    from experiment. Overrides fidelity parameters in the arm with those found in the
-    Data object.
-
-    Uses a diagonal covariance matrix across metric_names.
-
-    Args:
-        experiment: Experiment with arm parameters.
-        data: Data of observations.
-        statuses_to_include: data from non-MapMetrics will only be included for trials
-            with statuses in this set. Defaults to all statuses except abandoned.
-        statuses_to_include_map_metric: data from MapMetrics will only be included for
-            trials with statuses in this set. Defaults to completed status only.
-
-    Returns:
-        List of Observation objects.
-    """
-    if statuses_to_include is None:
-        statuses_to_include = NON_ABANDONED_STATUSES
-    if statuses_to_include_map_metric is None:
-        statuses_to_include_map_metric = {TrialStatus.COMPLETED}
-    feature_cols = get_feature_cols(data)
-    observations = []
-    arm_name_only = len(feature_cols) == 1  # there will always be an arm name
-    # One DataFrame where all rows have all features.
-    isnull = data.df[feature_cols].isnull()
-    isnull_any = isnull.any(axis=1)
-    incomplete_df_cols = isnull[isnull_any].any()
-
-    # Get the incomplete_df columns that are complete, and usable as groupby keys.
-    complete_feature_cols = list(
-        OBS_COLS.intersection(incomplete_df_cols.index[~incomplete_df_cols])
-    )
-
-    if set(feature_cols) == set(complete_feature_cols):
-        complete_df = data.df
-        incomplete_df = None
-    else:
-        # The groupby and filter is expensive, so do it only if we have to.
-        grouped = data.df.groupby(by=complete_feature_cols)
-        complete_df = grouped.filter(lambda r: ~r[feature_cols].isnull().any().any())
-        incomplete_df = grouped.filter(lambda r: r[feature_cols].isnull().any().any())
-
-    # Get Observations from complete_df
-    observations.extend(
-        _observations_from_dataframe(
-            experiment=experiment,
-            df=complete_df,
-            cols=feature_cols,
-            arm_name_only=arm_name_only,
-            statuses_to_include=statuses_to_include,
-            statuses_to_include_map_metric=statuses_to_include_map_metric,
-            map_keys=[],
-        )
-    )
-    if incomplete_df is not None:
-        # Get Observations from incomplete_df
-        observations.extend(
-            _observations_from_dataframe(
-                experiment=experiment,
-                df=incomplete_df,
-                cols=complete_feature_cols,
-                arm_name_only=arm_name_only,
-                statuses_to_include=statuses_to_include,
-                statuses_to_include_map_metric=statuses_to_include_map_metric,
-                map_keys=[],
-            )
-        )
-    return observations
-
-
-def observations_from_map_data(
-    experiment: experiment.Experiment,
-    map_data: MapData,
+    data: Data | MapData,
     statuses_to_include: set[TrialStatus] | None = None,
     statuses_to_include_map_metric: set[TrialStatus] | None = None,
     map_keys_as_parameters: bool = False,
     limit_rows_per_metric: int | None = None,
     limit_rows_per_group: int | None = None,
 ) -> list[Observation]:
-    """Convert MapData to observations.
+    """Convert Data (or MapData) to observations.
 
-    Converts a MapData object to a list of Observation objects. Pulls arm parameters
-    from experiment. Overrides fidelity parameters in the arm with those found in the
-    Data object.
+    Converts a Data (or MapData) object to a list of Observation objects.
+    Pulls arm parameters from from experiment. Overrides fidelity parameters
+    in the arm with those found in the Data object.
 
     Uses a diagonal covariance matrix across metric_names.
 
     Args:
         experiment: Experiment with arm parameters.
-        map_data: MapData of observations.
+        data: Data (or MapData) of observations.
         statuses_to_include: data from non-MapMetrics will only be included for trials
             with statuses in this set. Defaults to all statuses except abandoned.
         statuses_to_include_map_metric: data from MapMetrics will only be included for
             trials with statuses in this set. Defaults to all statuses except abandoned.
         map_keys_as_parameters: Whether map_keys should be returned as part of
             the parameters of the Observation objects.
-        limit_rows_per_metric: If specified, uses MapData.subsample() with
+        limit_rows_per_metric: If specified, and if data is an instance of MapData,
+            uses MapData.subsample() with
             `limit_rows_per_metric` equal to the specified value on the first
             map_key (map_data.map_keys[0]) to subsample the MapData. This is
             useful in, e.g., cases where learning curves are frequently
             updated, leading to an intractable number of Observation objects
             created.
-        limit_rows_per_group: If specified, uses MapData.subsample() with
+        limit_rows_per_group: If specified, and if data is an instance of MapData,
+            uses MapData.subsample() with
             `limit_rows_per_group` equal to the specified value on the first
             map_key (map_data.map_keys[0]) to subsample the MapData.
 
     Returns:
         List of Observation objects.
     """
+    is_map_data = isinstance(data, MapData)
+
     if statuses_to_include is None:
         statuses_to_include = NON_ABANDONED_STATUSES
     if statuses_to_include_map_metric is None:
         statuses_to_include_map_metric = NON_ABANDONED_STATUSES
-    if limit_rows_per_metric is not None or limit_rows_per_group is not None:
-        map_data = map_data.subsample(
-            map_key=map_data.map_keys[0],
-            limit_rows_per_metric=limit_rows_per_metric,
-            limit_rows_per_group=limit_rows_per_group,
-            include_first_last=True,
-        )
-    feature_cols = get_feature_cols(map_data, is_map_data=True)
-    observations = []
+
+    map_keys = []
+    obs_cols = OBS_COLS
+    if is_map_data:
+        data = checked_cast(MapData, data)
+
+        if limit_rows_per_metric is not None or limit_rows_per_group is not None:
+            data = data.subsample(
+                map_key=data.map_keys[0],
+                limit_rows_per_metric=limit_rows_per_metric,
+                limit_rows_per_group=limit_rows_per_group,
+                include_first_last=True,
+            )
+
+        map_keys.extend(data.map_keys)
+        obs_cols = obs_cols.union(data.map_keys)
+        df = data.map_df
+    else:
+        df = data.df
+
+    feature_cols = get_feature_cols(data, is_map_data=is_map_data)
+
     arm_name_only = len(feature_cols) == 1  # there will always be an arm name
     # One DataFrame where all rows have all features.
-    isnull = map_data.map_df[feature_cols].isnull()
+    isnull = df[feature_cols].isnull()
     isnull_any = isnull.any(axis=1)
     incomplete_df_cols = isnull[isnull_any].any()
 
     # Get the incomplete_df columns that are complete, and usable as groupby keys.
-    obs_cols_and_map = OBS_COLS.union(map_data.map_keys)
     complete_feature_cols = list(
-        obs_cols_and_map.intersection(incomplete_df_cols.index[~incomplete_df_cols])
+        obs_cols.intersection(incomplete_df_cols.index[~incomplete_df_cols])
     )
 
     if set(feature_cols) == set(complete_feature_cols):
-        complete_df = map_data.map_df
+        complete_df = df
         incomplete_df = None
     else:
         # The groupby and filter is expensive, so do it only if we have to.
-        grouped = map_data.map_df.groupby(
-            by=(
-                complete_feature_cols
-                if len(complete_feature_cols) > 1
-                else complete_feature_cols[0]
-            )
-        )
+        grouped = df.groupby(by=complete_feature_cols)
         complete_df = grouped.filter(lambda r: ~r[feature_cols].isnull().any().any())
         incomplete_df = grouped.filter(lambda r: r[feature_cols].isnull().any().any())
 
     # Get Observations from complete_df
-    observations.extend(
-        _observations_from_dataframe(
-            experiment=experiment,
-            df=complete_df,
-            cols=feature_cols,
-            arm_name_only=arm_name_only,
-            map_keys=map_data.map_keys,
-            statuses_to_include=statuses_to_include,
-            statuses_to_include_map_metric=statuses_to_include_map_metric,
-            map_keys_as_parameters=map_keys_as_parameters,
-        )
+    observations = _observations_from_dataframe(
+        experiment=experiment,
+        df=complete_df,
+        cols=feature_cols,
+        arm_name_only=arm_name_only,
+        map_keys=map_keys,
+        statuses_to_include=statuses_to_include,
+        statuses_to_include_map_metric=statuses_to_include_map_metric,
+        map_keys_as_parameters=map_keys_as_parameters,
     )
     if incomplete_df is not None:
         # Get Observations from incomplete_df
@@ -625,7 +554,7 @@ def observations_from_map_data(
                 df=incomplete_df,
                 cols=complete_feature_cols,
                 arm_name_only=arm_name_only,
-                map_keys=map_data.map_keys,
+                map_keys=map_keys,
                 statuses_to_include=statuses_to_include,
                 statuses_to_include_map_metric=statuses_to_include_map_metric,
                 map_keys_as_parameters=map_keys_as_parameters,

ax/core/tests/test_observation.py

@@ -24,7 +24,6 @@
     ObservationData,
     ObservationFeatures,
     observations_from_data,
-    observations_from_map_data,
     recombine_observations,
     separate_observations,
 )
@@ -475,7 +474,7 @@ def test_ObservationsFromMapData(self) -> None:
                 MapKeyInfo(key="timestamp", default_value=0.0),
             ],
         )
-        observations = observations_from_map_data(experiment, data)
+        observations = observations_from_data(experiment, data)
 
         self.assertEqual(len(observations), 3)
 

ax/modelbridge/base.py

@@ -297,6 +297,7 @@ def _prepare_observations(
             data=data,
             statuses_to_include=self.statuses_to_fit,
             statuses_to_include_map_metric=self.statuses_to_fit_map_metric,
+            map_keys_as_parameters=False,
         )
 
     def _transform_data(

ax/modelbridge/map_torch.py

@@ -20,7 +20,7 @@
     Observation,
     ObservationData,
     ObservationFeatures,
-    observations_from_map_data,
+    observations_from_data,
     separate_observations,
 )
 from ax.core.optimization_config import OptimizationConfig
@@ -252,19 +252,16 @@ def _array_to_observation_features(
     def _prepare_observations(
         self, experiment: Experiment | None, data: Data | None
     ) -> list[Observation]:
-        """The difference b/t this method and ModelBridge._prepare_observations(...)
-        is that this one uses `observations_from_map_data`.
-        """
         if experiment is None or data is None:
             return []
-        return observations_from_map_data(
+        return observations_from_data(
             experiment=experiment,
-            map_data=data,  # pyre-ignore[6]: Checked in __init__.
-            map_keys_as_parameters=True,
+            data=data,
             limit_rows_per_metric=self._map_data_limit_rows_per_metric,
             limit_rows_per_group=self._map_data_limit_rows_per_group,
             statuses_to_include=self.statuses_to_fit,
             statuses_to_include_map_metric=self.statuses_to_fit_map_metric,
+            map_keys_as_parameters=True,
         )
 
     def _compute_in_design(