Adding notes about the initial example

libensemble/gen_funcs/persistent_ax_multitask.py

@@ -102,9 +102,7 @@ def get_MTGP(
     """
 
     if isinstance(experiment, MultiTypeExperiment):
-        trial_index_to_type = {
-            t.index: t.trial_type for t in experiment.trials.values()
-        }
+        trial_index_to_type = {t.index: t.trial_type for t in experiment.trials.values()}
         transforms = MT_MTGP_trans
         transform_configs = {
             "TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
@@ -275,9 +273,7 @@ def persistent_gp_mt_ax_gen_f(H, persis_info, gen_specs, libE_info):
             if not os.path.exists("model_history"):
                 os.mkdir("model_history")
             # Register metric and runner in order to be able to save to json.
-            _, encoder_registry, decoder_registry = register_metrics(
-                {AxMetric: None}
-            )
+            _, encoder_registry, decoder_registry = register_metrics({AxMetric: None})
             _, encoder_registry, decoder_registry = register_runner(
                 AxRunner,
                 encoder_registry=encoder_registry,

libensemble/tests/regression_tests/test_persistent_aposmm_ibcdfo_pounders.py

@@ -52,10 +52,6 @@
     sys.exit("Ensure https://github.com/POptUS/minq has been cloned and that minq/py/minq5/ is on the PYTHONPATH")
 
 
-def sum_squared(x):
-    return np.sum(np.power(x, 2))
-
-
 def synthetic_beamline_mapping(H, _, sim_specs):
     x = H["x"][0]
     assert len(x) == 4, "Assuming 4 inputs to this function"
@@ -76,7 +72,7 @@ def synthetic_beamline_mapping(H, _, sim_specs):
 
     nworkers, is_manager, libE_specs, _ = parse_args()
 
-    assert nworkers == 2, "This test is just for two workers"
+    assert nworkers == 2, "This test is just for two workers, as only one localopt run is being performed"
 
     for inst in range(2):
         if inst == 0:
@@ -109,9 +105,9 @@ def synthetic_beamline_mapping(H, _, sim_specs):
             "persis_in": ["f", "fvec"] + [n[0] for n in gen_out],
             "out": gen_out,
             "user": {
-                "initial_sample_size": 1,
-                "stop_after_k_runs": 1,
-                "max_active_runs": 1,
+                "initial_sample_size": 1,  # The initial sampled point will be the starting point
+                "stop_after_k_runs": 1,  # Only one local optimization run will be performed
+                "max_active_runs": 1,  # Only one local optimization run will be performed,
                 "sample_points": np.atleast_2d(0.1 * (np.arange(n) + 1)),
                 "localopt_method": "ibcdfo_pounders",
                 "run_max_eval": 100 * (n + 1),

libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py

@@ -50,7 +50,7 @@ def run_simulation(H, persis_info, sim_specs, libE_info):
         z = 8
     elif task == "cheap_model":
         z = 1
-    print('in sim', task)
+    print("in sim", task)
 
     libE_output = np.zeros(1, dtype=sim_specs["out"])
     calc_status = WORKER_DONE