make objectives toggleable

bloptools/bayesian/acquisition/__init__.py

@@ -37,7 +37,7 @@ def get_acquisition_function(agent, identifier="qei", return_metadata=True, verb
     acq_func_name = parse_acq_func_identifier(identifier)
     acq_func_config = config["upper_confidence_bound"]
 
-    if config[acq_func_name]["multitask_only"] and (agent.num_tasks == 1):
+    if config[acq_func_name]["multitask_only"] and (len(agent.objectives) == 1):
         raise ValueError(f'Acquisition function "{acq_func_name}" is only for multi-task optimization problems!')
 
     # there is probably a better way to structure this

bloptools/bayesian/agent.py

@@ -117,6 +117,8 @@ def __init__(
         self.initialized = False
         self.a_priori_hypers = None
 
+        self.n_last_trained = 0
+
     def tell(self, x: Mapping, y: Mapping, metadata=None, append=True, train_models=True, hypers=None):
         """
         Inform the agent about new inputs and targets for the model.
@@ -147,10 +149,10 @@ def tell(self, x: Mapping, y: Mapping, metadata=None, append=True, train_models=
 
         # TODO: should be a check per model
         if len(self.table) > 2:
-            # if n_before_tell % self.train_every != n_after_tell % self.train_every:
-            self._update_models(train=train_models, a_priori_hypers=hypers)
+            if int(self.n_last_trained / self.train_every) != int(len(self.table) / self.train_every):
+                self._construct_models(train=train_models, a_priori_hypers=hypers)
 
-    def _update_models(self, train=True, skew_dims=None, a_priori_hypers=None):
+    def _construct_models(self, train=True, skew_dims=None, a_priori_hypers=None):
         skew_dims = skew_dims if skew_dims is not None else self.latent_dim_tuples
 
         inputs = self.table.loc[:, self.dofs.subset(active=True).names].values.astype(float)
@@ -173,7 +175,7 @@ def _update_models(self, train=True, skew_dims=None, a_priori_hypers=None):
 
             likelihood = gpytorch.likelihoods.GaussianLikelihood(
                 noise_constraint=gpytorch.constraints.Interval(
-                    torch.tensor(1e-2).square(),
+                    torch.tensor(1e-4).square(),
                     torch.tensor(1 / obj.min_snr).square(),
                 ),
                 # noise_prior=gpytorch.priors.torch_priors.LogNormalPrior(loc=loc, scale=scale),
@@ -293,14 +295,12 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True):
                 raise ValueError()
 
             # define dummy acqf objective
-            acqf_obj = None
+            acqf_obj = 0
 
         acq_func_meta["duration"] = duration = ttime.monotonic() - start_time
 
         if self.verbose:
-            print(
-                f"found points {acq_points} with acqf {acq_func_meta['name']} in {duration:.01f} seconds (obj = {acqf_obj})"
-            )
+            print(f"found points {acq_points} in {1e3*duration:.01f} ms (obj = {acqf_obj})")
 
         if route and n > 1:
             routing_index = utils.route(self.dofs.subset(active=True, read_only=False).readback, acq_points)
@@ -619,7 +619,7 @@ def forget(self, index, train=True):
         Make the agent forget some index of the data table.
         """
         self.table.drop(index=index, inplace=True)
-        self._update_models(train=train)
+        self._construct_models(train=train)
 
     def forget_last_n(self, n, train=True):
         """
@@ -687,6 +687,8 @@ def _train_models(self, **kwargs):
         if self.verbose:
             print(f"trained models in {ttime.monotonic() - t0:.01f} seconds")
 
+        self.n_last_trained = len(self.table)
+
     @property
     def all_acq_funcs(self):
         """Description and identifiers for all supported acquisition functions."""

bloptools/bayesian/dofs.py

@@ -9,7 +9,7 @@
 from ophyd import Signal, SignalRO
 
 DEFAULT_BOUNDS = (-5.0, +5.0)
-DOF_FIELDS = ["name", "description", "readback", "lower_limit", "upper_limit", "units", "active", "read_only", "tags"]
+DOF_FIELDS = ["description", "readback", "lower_limit", "upper_limit", "units", "active", "read_only", "tags"]
 
 numeric = Union[float, int]
 
@@ -110,9 +110,9 @@ def __repr__(self):
     @property
     def summary(self) -> pd.DataFrame:
         table = pd.DataFrame(columns=DOF_FIELDS)
-        for i, dof in enumerate(self.dofs):
+        for dof in self.dofs:
             for attr in table.columns:
-                table.loc[i, attr] = getattr(dof, attr)
+                table.loc[dof.name, attr] = getattr(dof, attr)
 
         # convert dtypes
         for attr in ["readback", "lower_limit", "upper_limit"]:

bloptools/bayesian/objectives.py

@@ -7,8 +7,8 @@
 
 numeric = Union[float, int]
 
-DEFAULT_MINIMUM_SNR = 1e1
-OBJ_FIELDS = ["description", "target", "limits", "weight", "log", "n", "snr", "min_snr"]
+DEFAULT_MINIMUM_SNR = 1e2
+OBJ_FIELDS = ["description", "target", "active", "limits", "weight", "log", "n", "snr", "min_snr"]
 
 
 class DuplicateNameError(ValueError):
@@ -30,6 +30,7 @@ class Objective:
     target: Union[float, str] = "max"
     log: bool = False
     weight: numeric = 1.0
+    active: bool = True
     limits: Tuple[numeric, numeric] = None
     min_snr: numeric = DEFAULT_MINIMUM_SNR
     units: str = None

bloptools/bayesian/plotting.py

@@ -233,13 +233,14 @@ def _plot_acqf_many_dofs(
 
     # test_inputs has shape (..., 1, n_active_dofs)
     test_inputs = agent.test_inputs_grid() if gridded else agent.test_inputs(n=1024)
+    *test_dim, input_dim = test_inputs.shape
     test_x = test_inputs[..., 0, axes[0]].detach().squeeze().numpy()
     test_y = test_inputs[..., 0, axes[1]].detach().squeeze().numpy()
 
     for iacq_func, acq_func_identifier in enumerate(acq_funcs):
         acq_func, acq_func_meta = acquisition.get_acquisition_function(agent, acq_func_identifier)
 
-        test_acqf = acq_func(test_inputs).detach().squeeze().numpy()
+        test_acqf = acq_func(test_inputs.reshape(-1, 1, input_dim)).detach().reshape(test_dim).squeeze().numpy()
 
         if gridded:
             agent.acq_axes[iacq_func].set_title(acq_func_meta["name"])

bloptools/tests/conftest.py

@@ -71,7 +71,7 @@ def agent(db):
 @pytest.fixture(scope="function")
 def multi_agent(db):
     """
-    A simple agent minimizing two Styblinski-Tang functions
+    A simple agent minimizing two Himmelblau's functions
     """
 
     def digestion(db, uid):