Rename ask->ask_trials / _ask->ask

doc/source/examples/ps_line_sampling.rst

@@ -46,7 +46,7 @@ where :math:`x_0` and :math:`x_1` have a default values of :math:`5` and
 
    all_trials = []
    while True:
-       trial = gen.ask(1)
+       trial = gen.ask_trials(1)
        if trial:
            all_trials.append(trial[0])
        else:

doc/source/examples/ps_random_sampling.rst

@@ -45,7 +45,7 @@ distribution such as:
 
    all_trials = []
    while len(all_trials) <= 100:
-       trial = gen.ask(1)
+       trial = gen.ask_trials(1)
        if trial:
            all_trials.append(trial[0])
        else:

optimas/gen_functions.py

@@ -69,7 +69,7 @@ def persistent_generator(H, persis_info, gen_specs, libE_info):
         # Store this information in the format expected by libE
         H_o = np.zeros(number_of_gen_points, dtype=gen_specs["out"])
         for i in range(number_of_gen_points):
-            generated_trials = generator.ask(1)
+            generated_trials = generator.ask_trials(1)
             if generated_trials:
                 trial = generated_trials[0]
                 for var, val in zip(
@@ -110,7 +110,7 @@ def persistent_generator(H, persis_info, gen_specs, libE_info):
                         ev = Evaluation(parameter=par, value=y)
                         trial.complete_evaluation(ev)
                 # Register trial with unknown SEM
-                generator.tell([trial])
+                generator.tell_trials([trial])
             # Set the number of points to generate to that number:
             number_of_gen_points = min(n + n_failed_gens, max_evals - n_gens)
             n_failed_gens = 0

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, trials: List[Trial]) -> List[Trial]:
+    def ask(self, trials: List[Trial]) -> List[Trial]:
         """Fill in the parameter values of the requested trials."""
         for trial in trials:
             parameters, trial_id = self._ax_client.get_next_trial(
@@ -154,7 +154,7 @@ def _ask(self, trials: List[Trial]) -> List[Trial]:
             trial.ax_trial_id = trial_id
         return trials
 
-    def _tell(self, trials: List[Trial]) -> None:
+    def tell(self, trials: List[Trial]) -> None:
         """Incorporate evaluated trials into Ax client."""
         for trial in trials:
             try:

optimas/generators/base.py

@@ -187,7 +187,7 @@ def n_evaluated_trials(self) -> int:
                 n_evaluated += 1
         return n_evaluated
 
-    def ask(self, n_trials: int) -> List[Trial]:
+    def ask_trials(self, n_trials: int) -> List[Trial]:
         """Ask the generator to suggest the next ``n_trials`` to evaluate.
 
         Parameters
@@ -215,7 +215,7 @@ def ask(self, n_trials: int) -> List[Trial]:
                     )
                 )
             # Ask the generator to fill them.
-            gen_trials = self._ask(gen_trials)
+            gen_trials = self.ask(gen_trials)
             # Keep only trials that have been given data.
             for trial in gen_trials:
                 if len(trial.parameter_values) > 0:
@@ -236,7 +236,7 @@ def ask(self, n_trials: int) -> List[Trial]:
                 trials.append(trial)
         return trials
 
-    def tell(
+    def tell_trials(
         self, trials: List[Trial], allow_saving_model: Optional[bool] = True
     ) -> None:
         """Give trials back to generator once they have been evaluated.
@@ -250,7 +250,7 @@ def tell(
             incorporating the evaluated trials. By default ``True``.
 
         """
-        self._tell(trials)
+        self.tell(trials)
         for trial in trials:
             if trial not in self._given_trials:
                 self._add_external_evaluated_trial(trial)
@@ -290,7 +290,7 @@ def incorporate_history(self, history: np.ndarray) -> None:
         trials = self._create_trials_from_external_data(
             history_ended, ignore_unrecognized_parameters=True
         )
-        self.tell(trials, allow_saving_model=False)
+        self.tell_trials(trials, allow_saving_model=False)
         # Communicate to history array whether the trial has been ignored.
         for trial in trials:
             i = np.where(history["trial_index"] == trial.index)[0][0]
@@ -578,7 +578,7 @@ def get_libe_specs(self) -> Dict:
         libE_specs = {}
         return libE_specs
 
-    def _ask(self, trials: List[Trial]) -> List[Trial]:
+    def ask(self, trials: List[Trial]) -> List[Trial]:
         """Ask method to be implemented by the Generator subclasses.
 
         Parameters