Add count_only_trials_with_data to trial-based TCs (facebook#3012)

Summary:

For some GenerationStrategies we only want to progress if the trials in the expected statuses also actually have data, this extends trial based criterion to consider if we have data or not

Differential Revision: D64617987

ax/modelbridge/tests/test_generation_node.py

@@ -314,7 +314,8 @@ def test_node_string_representation(self) -> None:
                 "'not_in_statuses': None, 'transition_to': None, "
                 "'block_transition_if_unmet': True, 'block_gen_if_met': False, "
                 "'use_all_trials_in_exp': False, "
-                "'continue_trial_generation': False})])"
+                "'continue_trial_generation': False, "
+                "'count_only_trials_with_data': False})])"
             ),
         )
 

ax/modelbridge/tests/test_generation_node_input_constructors.py

@@ -113,7 +113,6 @@ def test_all_constructors_have_expected_signature_for_purpose(self) -> None:
                     untested_constructors.remove(constructor)
 
         # There should be no untested constructors left.
-        print(untested_constructors)
         self.assertEqual(len(untested_constructors), 0)
 
     def test_consume_all_n_constructor(self) -> None:

ax/modelbridge/tests/test_transition_criterion.py

@@ -580,7 +580,8 @@ def test_repr(self) -> None:
             + "'block_transition_if_unmet': False, "
             + "'block_gen_if_met': True, "
             + "'use_all_trials_in_exp': False, "
-            + "'continue_trial_generation': False})",
+            + "'continue_trial_generation': False, "
+            + "'count_only_trials_with_data': False})",
         )
         minimum_trials_in_status_criterion = MinTrials(
             only_in_statuses=[TrialStatus.COMPLETED, TrialStatus.EARLY_STOPPED],
@@ -599,7 +600,8 @@ def test_repr(self) -> None:
             + "'block_transition_if_unmet': False, "
             + "'block_gen_if_met': True, "
             + "'use_all_trials_in_exp': False, "
-            + "'continue_trial_generation': False})",
+            + "'continue_trial_generation': False, "
+            + "'count_only_trials_with_data': False})",
         )
         minimum_preference_occurrences_criterion = MinimumPreferenceOccurances(
             metric_name="m1", threshold=3

ax/modelbridge/transition_criterion.py

@@ -31,7 +31,6 @@
 
 
 class TransitionCriterion(SortableBase, SerializationMixin):
-    # TODO: @mgarrard rename to ActionCriterion
     """
     Simple class to describe a condition which must be met for this GenerationNode to
     take an action such as generation, transition, etc.
@@ -262,6 +261,8 @@ class TrialBasedCriterion(TransitionCriterion):
             batch from different ``GenerationNodes``. This flag should be set to
             True for the last node in a set of ``GenerationNodes`` expected to
             create a given ``BatchTrial``.
+        count_only_trials_with_data: If set to True, only trials with data will be
+            counted towards the ``threshold``. Defaults to False.
     """
 
     def __init__(
@@ -274,11 +275,13 @@ def __init__(
         transition_to: str | None = None,
         use_all_trials_in_exp: bool | None = False,
         continue_trial_generation: bool | None = False,
+        count_only_trials_with_data: bool = False,
     ) -> None:
         self.threshold = threshold
         self.only_in_statuses = only_in_statuses
         self.not_in_statuses = not_in_statuses
         self.use_all_trials_in_exp = use_all_trials_in_exp
+        self.count_only_trials_with_data = count_only_trials_with_data
         super().__init__(
             transition_to=transition_to,
             block_transition_if_unmet=block_transition_if_unmet,
@@ -334,6 +337,14 @@ def num_contributing_to_threshold(
             trials_from_node: The set of trials generated by this GenerationNode.
         """
         all_trials_to_check = self.all_trials_to_check(experiment=experiment)
+        if self.count_only_trials_with_data:
+            all_trials_to_check = {
+                trial_index
+                for trial_index in all_trials_to_check
+                # TODO[@mgarrard]: determine if we need to actually check data with
+                # more granularity, e.g. number of days of data, etc.
+                if trial_index in experiment.data_by_trial
+            }
         # Some criteria may rely on experiment level data, instead of only trials
         # generated from the node associated with the criterion.
         if self.use_all_trials_in_exp:
@@ -415,6 +426,8 @@ class MaxGenerationParallelism(TrialBasedCriterion):
             create a given ``BatchTrial``. Defaults to False for
             MaxGenerationParallelism since this criterion isn't currently used for
             node -> node or trial -> trial transition.
+        count_only_trials_with_data: If set to True, only trials with data will be
+            counted towards the ``threshold``. Defaults to False.
     """
 
     def __init__(
@@ -497,6 +510,8 @@ class MaxTrials(TrialBasedCriterion):
             batch from different ``GenerationNodes``. This flag should be set to
             True for the last node in a set of ``GenerationNodes`` expected to
             create a given ``BatchTrial``.
+        count_only_trials_with_data: If set to True, only trials with data will be
+            counted towards the ``threshold``. Defaults to False.
     """
 
     def __init__(
@@ -509,6 +524,7 @@ def __init__(
         block_gen_if_met: bool | None = False,
         use_all_trials_in_exp: bool | None = False,
         continue_trial_generation: bool | None = False,
+        count_only_trials_with_data: bool = False,
     ) -> None:
         super().__init__(
             threshold=threshold,
@@ -519,6 +535,7 @@ def __init__(
             block_transition_if_unmet=block_transition_if_unmet,
             use_all_trials_in_exp=use_all_trials_in_exp,
             continue_trial_generation=continue_trial_generation,
+            count_only_trials_with_data=count_only_trials_with_data,
         )
 
     def block_continued_generation_error(
@@ -573,6 +590,8 @@ class MinTrials(TrialBasedCriterion):
             batch from different ``GenerationNodes``. This flag should be set to
             True for the last node in a set of ``GenerationNodes`` expected to
             create a given ``BatchTrial``.
+        count_only_trials_with_data: If set to True, only trials with data will be
+            counted towards the ``threshold``. Defaults to False.
     """
 
     def __init__(
@@ -585,6 +604,7 @@ def __init__(
         block_gen_if_met: bool | None = False,
         use_all_trials_in_exp: bool | None = False,
         continue_trial_generation: bool | None = False,
+        count_only_trials_with_data: bool = False,
     ) -> None:
         super().__init__(
             threshold=threshold,
@@ -595,6 +615,7 @@ def __init__(
             block_transition_if_unmet=block_transition_if_unmet,
             use_all_trials_in_exp=use_all_trials_in_exp,
             continue_trial_generation=continue_trial_generation,
+            count_only_trials_with_data=count_only_trials_with_data,
         )
 
     def block_continued_generation_error(