better verbose output for agent.ask()

bloptools/bayesian/agent.py

@@ -105,7 +105,7 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
         """
         Inform the agent about new inputs and targets for the model.
 
-        If run with no arguments, it will just reconstruct all the models. 
+        If run with no arguments, it will just reconstruct all the models.
         """
 
         new_table = pd.DataFrame() if new_table is None else new_table
@@ -193,6 +193,9 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
 
         start_time = ttime.monotonic()
 
+        if self.verbose:
+            print(f'finding points with acquisition function "{acq_func_name}" ...')
+
         if acq_func_type in ["analytic", "monte_carlo"]:
             if not self.initialized:
                 raise RuntimeError(
@@ -228,9 +231,6 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             acq_func_meta["read_only_values"] = read_only_X
 
         else:
-
-            acqf_objective = None
-
             if acq_func_name == "random":
                 acquisition_X = torch.rand()
                 acq_func_meta = {"name": "random", "args": {}}
@@ -247,10 +247,16 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             else:
                 raise ValueError()
 
+            # define dummy acqf objective
+            acqf_objective = None
+
         acq_func_meta["duration"] = duration = ttime.monotonic() - start_time
 
         if self.verbose:
-            print(f"found points {acquisition_X} with acqf {acq_func_meta['name']} in {duration:.01f} seconds (obj = {acqf_objective})")
+            summary = pd.DataFrame(acquisition_X, columns=self.dofs.subset(active=True, read_only=False).names)
+            summary.insert(0, "acqf", acqf_objective)
+
+            print(f"found points in {duration:.03f} seconds:\n" + summary.__repr__())
 
         if route and n > 1:
             routing_index = utils.route(self.dofs.subset(active=True, read_only=False).readback, acquisition_X)
@@ -266,7 +272,7 @@ def acquire(self, acquisition_inputs):
         """
         try:
             acquisition_devices = self.dofs.subset(active=True, read_only=False).devices
-            #read_only_devices = self.dofs.subset(active=True, read_only=True).devices
+            # read_only_devices = self.dofs.subset(active=True, read_only=True).devices
 
             # the acquisition plan always takes as arguments:
             # (things to move, where to move them, things to trigger once you get there)
@@ -350,12 +356,12 @@ def reset(self):
     def benchmark(
         self, output_dir="./", runs=16, n_init=64, learning_kwargs_list=[{"acq_func": "qei", "n": 4, "iterations": 16}]
     ):
-        cache_limits = {dof.name:dof.limits for dof in self.dofs}
+        cache_limits = {dof.name: dof.limits for dof in self.dofs}
 
         for run in range(runs):
             for dof in self.dofs:
                 offset = 0.25 * np.ptp(dof.limits) * np.random.uniform(low=-1, high=1)
-                dof.limits = (dof.limits[0] + offset, dof.limits[1] + offset)
+                dof.limits = (cache_limits[dof.name][0] + offset, cache_limits[dof.name][1] + offset)
 
             self.reset()