Update ask/tell to suggest/ingest

Author: shuds13

optimas/generators/ax/developer/multitask.py

@@ -260,7 +260,7 @@ def _check_inputs(
             "to the number of high-fidelity trials"
         )
 
-    def ask(self, trials: List[Trial]) -> List[Trial]:
+    def suggest(self, trials: List[Trial]) -> List[Trial]:
         """Fill in the parameter values of the requested trials."""
         for trial in trials:
             next_trial = self._get_next_trial_arm()
@@ -275,7 +275,7 @@ def ask(self, trials: List[Trial]) -> List[Trial]:
                 trial.trial_index = trial_index
         return trials
 
-    def tell(self, trials: List[Trial]) -> None:
+    def ingest(self, trials: List[Trial]) -> None:
         """Incorporate evaluated trials into experiment."""
         if self.gen_state == NOT_STARTED:
             self._incorporate_external_data(trials)

optimas/generators/ax/service/base.py

@@ -142,7 +142,7 @@ def model(self) -> AxModelManager:
         """Get access to the underlying model using an `AxModelManager`."""
         return self._model
 
-    def ask(self, num_points: Optional[int]) -> List[dict]:
+    def suggest(self, num_points: Optional[int]) -> List[dict]:
         """Request the next set of points to evaluate."""
         points = []
         for _ in range(num_points):
@@ -157,7 +157,7 @@ def ask(self, num_points: Optional[int]) -> List[dict]:
             points.append(point)
         return points
 
-    def tell(self, results: List[dict]) -> None:
+    def ingest(self, results: List[dict]) -> None:
         """Send the results of evaluations to the generator."""
         for result in results:
             trial = Trial.from_dict(

optimas/generators/base.py

@@ -205,7 +205,7 @@ def ask_trials(self, n_trials: int) -> List[Trial]:
         if n_trials > self.n_queued_trials:
             n_gen = n_trials - self.n_queued_trials
             # Ask generator for n_gen points, using the standardized API
-            gen_points = self.ask(n_gen)
+            gen_points = self.suggest(n_gen)
             # Convert the points to the Trial format
             gen_trials = []
             for point in gen_points:
@@ -252,7 +252,7 @@ def tell_trials(
 
         """
         # Feed data to the generator, using the standardized API
-        self.tell([trial.to_dict() for trial in trials])
+        self.ingest([trial.to_dict() for trial in trials])
 
         # Perform additional checks that rely on the trial format
         for trial in trials:
@@ -583,7 +583,7 @@ def get_libe_specs(self) -> Dict:
         libE_specs = {}
         return libE_specs
 
-    def ask(self, num_points: Optional[int]) -> List[dict]:
+    def suggest(self, num_points: Optional[int]) -> List[dict]:
         """Request the next set of points to evaluate.
 
         Parameters
@@ -594,7 +594,7 @@ def ask(self, num_points: Optional[int]) -> List[dict]:
         """
         return []
 
-    def tell(self, results: List[dict]) -> None:
+    def ingest(self, results: List[dict]) -> None:
         """
         Send the results of evaluations to the generator.
 

optimas/generators/external.py

@@ -16,10 +16,11 @@ def __init__(
         )
         self.gen = ext_gen
 
-    def ask(self, n_trials):
+    def suggest(self, n_trials):
         """Request the next set of points to evaluate."""
-        return self.gen.ask(n_trials)
+        return self.gen.suggest(n_trials)
 
-    def tell(self, trials):
+    def ingest(self, trials):
         """Send the results of evaluations to the generator."""
-        self.gen.tell(trials)
+        self.gen.ingest(trials)
+        

optimas/generators/grid_sampling.py

@@ -67,7 +67,7 @@ def _create_configurations(self) -> None:
             all_configs.append(config)
         self._all_configs = all_configs
 
-    def ask(self, num_points: Optional[int]) -> List[dict]:
+    def suggest(self, num_points: Optional[int]) -> List[dict]:
         """Request the next set of points to evaluate."""
         points = []
         for _ in range(num_points):

optimas/generators/line_sampling.py

@@ -103,7 +103,7 @@ def _create_configurations(self) -> None:
         # Store configurations.
         self._all_configs = all_configs
 
-    def ask(self, num_points: Optional[int]) -> List[dict]:
+    def suggest(self, num_points: Optional[int]) -> List[dict]:
         """Request the next set of points to evaluate."""
         points = []
         for _ in range(num_points):

optimas/generators/random_sampling.py

@@ -57,7 +57,7 @@ def __init__(
         self._rng = np.random.default_rng(seed)
         self._define_generator_parameters()
 
-    def ask(self, num_points: Optional[int]) -> List[dict]:
+    def suggest(self, num_points: Optional[int]) -> List[dict]:
         """Request the next set of points to evaluate."""
         configs = self._generate_sampling[self._distribution](num_points)
         points = []