fix merge conflicts

bloptools/bayesian/acquisition/__init__.py

@@ -18,35 +18,35 @@
     config[acq_func_name]["identifiers"].append(acq_func_name)
 
 
-def parse_acq_func(acq_func_identifier):
+def parse_acq_func_identifier(identifier):
     acq_func_name = None
     for _acq_func_name in config.keys():
-        if acq_func_identifier.lower() in config[_acq_func_name]["identifiers"]:
+        if identifier.lower() in config[_acq_func_name]["identifiers"]:
             acq_func_name = _acq_func_name
 
     if acq_func_name is None:
-        raise ValueError(f'Unrecognized acquisition function identifier "{acq_func_identifier}".')
+        raise ValueError(f'Unrecognized acquisition function identifier "{identifier}".')
 
     return acq_func_name
 
 
-def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=True, verbose=False, **acq_func_kwargs):
-    """
-    Generates an acquisition function from a supplied identifier.
+def get_acquisition_function(agent, identifier="qei", return_metadata=True, verbose=False, **acq_func_kwargs):
+    """Generates an acquisition function from a supplied identifier. A list of acquisition functions and
+    their identifiers can be found at `agent.all_acq_funcs`.
     """
 
-    acq_func_name = parse_acq_func(acq_func_identifier)
-    acq_func_config = agent.acq_func_config["upper_confidence_bound"]
+    acq_func_name = parse_acq_func_identifier(identifier)
+    acq_func_config = config["upper_confidence_bound"]
 
-    if agent.acq_func_config[acq_func_name]["multitask_only"] and (agent.num_tasks == 1):
+    if config[acq_func_name]["multitask_only"] and (agent.num_tasks == 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
     if acq_func_name == "expected_improvement":
         acq_func = analytic.ConstrainedLogExpectedImprovement(
             constraint=agent.constraint,
             model=agent.model,
-            best_f=agent.best_scalarized_objective,
+            best_f=agent.max_scalarized_objective,
             posterior_transform=ScalarizedPosteriorTransform(weights=agent.objective_weights_torch, offset=0),
         )
         acq_func_meta = {"name": acq_func_name, "args": {}}
@@ -55,7 +55,7 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func = monte_carlo.qConstrainedExpectedImprovement(
             constraint=agent.constraint,
             model=agent.model,
-            best_f=agent.best_scalarized_objective,
+            best_f=agent.max_scalarized_objective,
             posterior_transform=ScalarizedPosteriorTransform(weights=agent.objective_weights_torch, offset=0),
         )
         acq_func_meta = {"name": acq_func_name, "args": {}}
@@ -64,7 +64,7 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func = analytic.ConstrainedLogProbabilityOfImprovement(
             constraint=agent.constraint,
             model=agent.model,
-            best_f=agent.best_scalarized_objective,
+            best_f=agent.max_scalarized_objective,
             posterior_transform=ScalarizedPosteriorTransform(weights=agent.objective_weights_torch, offset=0),
         )
         acq_func_meta = {"name": acq_func_name, "args": {}}
@@ -73,7 +73,7 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func = monte_carlo.qConstrainedProbabilityOfImprovement(
             constraint=agent.constraint,
             model=agent.model,
-            best_f=agent.best_scalarized_objective,
+            best_f=agent.max_scalarized_objective,
             posterior_transform=ScalarizedPosteriorTransform(weights=agent.objective_weights_torch, offset=0),
         )
         acq_func_meta = {"name": acq_func_name, "args": {}}
@@ -119,11 +119,11 @@ def get_acquisition_function(agent, acq_func_identifier="qei", return_metadata=T
         acq_func_meta = {"name": acq_func_name, "args": {"beta": beta}}
 
     elif acq_func_name == "expected_mean":
-        acq_func = get_acquisition_function(agent, acq_func_identifier="ucb", beta=0, return_metadata=False)
+        acq_func = get_acquisition_function(agent, identifier="ucb", beta=0, return_metadata=False)
         acq_func_meta = {"name": acq_func_name, "args": {}}
 
     elif acq_func_name == "monte_carlo_expected_mean":
-        acq_func = get_acquisition_function(agent, acq_func_identifier="qucb", beta=0, return_metadata=False)
+        acq_func = get_acquisition_function(agent, identifier="qucb", beta=0, return_metadata=False)
         acq_func_meta = {"name": acq_func_name, "args": {}}
 
     return (acq_func, acq_func_meta) if return_metadata else acq_func

bloptools/bayesian/acquisition/analytic.py

@@ -6,8 +6,19 @@
 
 
 class ConstrainedUpperConfidenceBound(UpperConfidenceBound):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Upper confidence bound, but scaled by some constraint.
+    NOTE: Because the UCB can be negative, we constrain it by adjusting the Gaussian quantile.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):
@@ -26,17 +37,37 @@ def forward(self, x):
 
 
 class ConstrainedLogExpectedImprovement(LogExpectedImprovement):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Log expected improvement, but scaled by some constraint.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):
         return (super().forward(x) + self.constraint(x).log().squeeze(-1)).exp()
 
 
 class ConstrainedLogProbabilityOfImprovement(LogProbabilityOfImprovement):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Log probability of improvement acquisition function, but scaled by some constraint.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):

bloptools/bayesian/acquisition/config.yml

@@ -65,7 +65,7 @@ monte_carlo_probability_of_improvement:
   type: monte_carlo
 
 random:
-  description: A uniform random sampel of the parameters.
+  description: Uniformly-sampled random points.
   identifiers:
   - r
   multitask_only: false

bloptools/bayesian/acquisition/monte_carlo.py

@@ -8,8 +8,19 @@
 
 
 class qConstrainedUpperConfidenceBound(qUpperConfidenceBound):
-    def __init__(self, constraint, beta=4, *args, **kwargs):
-        super().__init__(beta=beta, *args, **kwargs)
+    """Monte Carlo expected improvement, but scaled by some constraint.
+    NOTE: Because the UCB can be negative, we constrain it by adjusting the Gaussian quantile.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, constraint, beta=4, **kwargs):
+        super().__init__(beta=beta, **kwargs)
         self.constraint = constraint
         self.beta = torch.tensor(beta)
 
@@ -30,35 +41,76 @@ def forward(self, x):
 
 
 class qConstrainedExpectedImprovement(qExpectedImprovement):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Monte Carlo expected improvement, but scaled by some constraint.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):
         return super().forward(x) * self.constraint(x).squeeze(-1)
 
 
 class qConstrainedProbabilityOfImprovement(qProbabilityOfImprovement):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Monte Carlo probability of improvement, but scaled by some constraint.
+
+    Parameters
+    ----------
+    model:
+        A BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):
         return super().forward(x) * self.constraint(x).squeeze(-1)
 
 
 class qConstrainedNoisyExpectedHypervolumeImprovement(qNoisyExpectedHypervolumeImprovement):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """Monte Carlo noisy expected hypervolume improvement, but scaled by some constraint.
+    Only works with multi-objective models.
+
+    Parameters
+    ----------
+    model:
+        A multi-objective BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):
         return super().forward(x) * self.constraint(x).squeeze(-1)
 
 
 class qConstrainedLowerBoundMaxValueEntropy(qLowerBoundMaxValueEntropy):
-    def __init__(self, constraint, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    """GIBBON (General-purpose Information-Based Bayesian OptimisatioN), but scaled by some constraint.
+
+    Parameters
+    ----------
+    model:
+        A multi-objective BoTorch model over which to compute the acquisition function.
+    constraint:
+        A callable which when evaluated on inputs returns the probability of feasibility.
+    """
+
+    def __init__(self, model, constraint, **kwargs):
+        super().__init__(model=model, **kwargs)
         self.constraint = constraint
 
     def forward(self, x):