work at xpd

bloptools/bayesian/agent.py

@@ -95,12 +95,17 @@ def __init__(
 
         self.table = pd.DataFrame()
 
-        self.initialized = False
         self._train_models = True
         self.a_priori_hypers = None
 
         self.plots = {"objectives": {}}
 
+
+    @property
+    def has_models(self):
+        return all([hasattr(obj, "model") for obj in self.objectives])
+
+
     def tell(self, new_table=None, append=True, train=True, **kwargs):
         """
         Inform the agent about new inputs and targets for the model.
@@ -112,9 +117,12 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
         self.table = pd.concat([self.table, new_table]) if append else new_table
         self.table.index = np.arange(len(self.table))
 
+        if len(self.table) < 2:
+            return
+
         skew_dims = self.latent_dim_tuples
 
-        if self.initialized:
+        if self.has_models:
             cached_hypers = self.hypers
 
         inputs = self.table.loc[:, self.dofs.subset(active=True).device_names].values.astype(float)
@@ -173,7 +181,7 @@ def tell(self, new_table=None, append=True, train=True, **kwargs):
             try:
                 self.train_models()
             except botorch.exceptions.errors.ModelFittingError:
-                if self.initialized:
+                if self.has_models:
                     self._set_hypers(cached_hypers)
                 else:
                     raise RuntimeError("Could not fit model on initialization!")
@@ -197,7 +205,7 @@ def ask(self, acq_func_identifier="qei", n=1, route=True, sequential=True, **acq
             print(f'finding points with acquisition function "{acq_func_name}" ...')
 
         if acq_func_type in ["analytic", "monte_carlo"]:
-            if not self.initialized:
+            if not self.has_models:
                 raise RuntimeError(
                     f'Can\'t construct non-trivial acquisition function "{acq_func_identifier}"'
                     f" (the agent is not initialized!)"
@@ -330,7 +338,7 @@ def learn(
             else:
                 self.tell(new_table=data)
 
-        if self.sample_center_on_init and not self.initialized:
+        if self.sample_center_on_init and not self.has_models:
             new_table = yield from self.acquire(self.dofs.subset(active=True, read_only=False).limits.mean(axis=1))
             new_table.loc[:, "acq_func"] = "sample_center_on_init"
             self.tell(new_table=new_table, train=False)
@@ -344,8 +352,6 @@ def learn(
                     new_table.loc[:, "acq_func"] = acq_func_meta["name"]
                     self.tell(new_table=new_table, train=train)
 
-        self.initialized = True
-
     def get_acquisition_function(self, acq_func_identifier, return_metadata=False):
         return acquisition.get_acquisition_function(
             self, acq_func_identifier=acq_func_identifier, return_metadata=return_metadata
@@ -356,7 +362,8 @@ def reset(self):
         Reset the agent.
         """
         self.table = pd.DataFrame()
-        self.initialized = False
+        for obj in self.objectives:
+            del obj.model
 
     def benchmark(
         self, output_dir="./", runs=16, n_init=64, learning_kwargs_list=[{"acq_func": "qei", "n": 4, "iterations": 16}]

bloptools/bayesian/objectives.py

@@ -28,7 +28,7 @@ def _validate_objectives(objectives):
 class Objective:
     key: str
     name: str = None
-    target: float | str = "max"
+    target: Union[float, str] = "max"
     log: bool = False
     weight: numeric = 1.0
     limits: Tuple[numeric, numeric] = None