Revert back to allowing inducing point method to be optional

aepsych/acquisition/lookahead.py

@@ -16,6 +16,7 @@
 from botorch.models.gpytorch import GPyTorchModel
 from botorch.utils.transforms import t_batch_mode_transform
 from scipy.stats import norm
+from torch import Tensor
 
 from .lookahead_utils import (
     approximate_lookahead_levelset_at_xstar,
@@ -245,6 +246,8 @@ def construct_inputs_local_lookahead(
 class GlobalLookaheadAcquisitionFunction(LookaheadAcquisitionFunction):
     def __init__(
         self,
+        lb: Tensor,
+        ub: Tensor,
         model: GPyTorchModel,
         lookahead_type: Literal["levelset", "posterior"] = "levelset",
         target: Optional[float] = None,
@@ -256,14 +259,16 @@ def __init__(
         A global look-ahead acquisition function.
 
         Args:
-            model (GPyTorchModel): The gpytorch model to use.
+            lb (Tensor): Lower bounds of the input space, used to generate the query set (Xq).
+            ub (Tensor): Upper bounds of the input space, used to generate the query set (Xq).
+            model (GPyTorchModel): The gpytorch model.
             lookahead_type (Literal["levelset", "posterior"]): The type of look-ahead to perform (default is "levelset").
                 - If the lookahead_type is "levelset", the acqf will consider the posterior probability that a point is above or below the target level set.
                 - If the lookahead_type is "posterior", the acqf will consider the posterior probability that a point will be detected or not.
             target (float, optional): Threshold value to target in p-space.
-            posterior_transform (PosteriorTransform, optional): Optional transformation to apply to the posterior.
-            query_set_size (int, optional): Number of points in the query set.
-            Xq (torch.Tensor, optional): (m x d) global reference set.
+            posterior_transform (PosteriorTransform, optional): Posterior transform to use. Defaults to None.
+            query_set_size (int, optional): Size of the query set. Defaults to 256.
+            Xq (Tensor, optional): (m x d) global reference set. Defaults to None.
         """
         super().__init__(model=model, target=target, lookahead_type=lookahead_type)
         self.posterior_transform = posterior_transform
@@ -282,7 +287,7 @@ def __init__(
             assert int(query_set_size) == query_set_size  # make sure casting is safe
             # if the asserts above pass and Xq is None, query_set_size is not None so this is safe
             query_set_size = int(query_set_size)  # cast
-            Xq = make_scaled_sobol(model.lb, model.ub, query_set_size)
+            Xq = make_scaled_sobol(lb, ub, query_set_size)
         self.register_buffer("Xq", Xq)
 
     @t_batch_mode_transform(expected_q=1)
@@ -335,8 +340,10 @@ def _compute_acqf(
 class ApproxGlobalSUR(GlobalSUR):
     def __init__(
         self,
+        lb: Tensor,
+        ub: Tensor,
         model: GPyTorchModel,
-        lookahead_type="levelset",
+        lookahead_type: Literal["levelset", "poserior"] = "levelset",
         target: Optional[float] = None,
         query_set_size: Optional[int] = 256,
         Xq: Optional[torch.Tensor] = None,
@@ -346,7 +353,9 @@ def __init__(
         Args:
 
             model (GPyTorchModel): The gpytorch model to use.
-            lookahed_type (str): The type of look-ahead to perform (default is "levelset").
+            lookahead_type (Literal["levelset", "posterior"]): The type of look-ahead to perform (default is "levelset").
+                - If the lookahead_type is "levelset", the acqf will consider the posterior probability that a point is above or below the target level set.
+                - If the lookahead_type is "posterior", the acqf will consider the posterior probability that a point will be detected or not.
             target (float, optional): Threshold value to target in p-space.
             query_set_size (int, optional): Number of points in the query set.
             Xq (torch.Tensor, optional): (m x d) global reference set.
@@ -355,6 +364,8 @@ def __init__(
             lookahead_type == "levelset"
         ), f"ApproxGlobalSUR only supports lookahead on level set, got {lookahead_type}!"
         super().__init__(
+            lb=lb,
+            ub=ub,
             model=model,
             target=target,
             lookahead_type=lookahead_type,
@@ -431,23 +442,29 @@ class SMOCU(GlobalLookaheadAcquisitionFunction):
 
     def __init__(
         self,
+        lb: Tensor,
+        ub: Tensor,
         model: GPyTorchModel,
-        lookahead_type="posterior",
+        lookahead_type: Literal["levelset", "posterior"] = "posterior",
         target: Optional[float] = None,
         query_set_size: Optional[int] = 256,
         Xq: Optional[torch.Tensor] = None,
         k: Optional[float] = 20.0,
     ) -> None:
         """
         model (GPyTorchModel): The gpytorch model to use.
-        lookahead_type (str): The type of look-ahead to perform (default is "posterior").
+        lookahead_type (Literal["levelset", "posterior"]): The type of look-ahead to perform (default is "posterior").
+                - If the lookahead_type is "levelset", the acqf will consider the posterior probability that a point is above or below the target level set.
+                - If the lookahead_type is "posterior", the acqf will consider the posterior probability that a point will be detected or not.
         target (float, optional): Threshold value to target in p-space. Default is None.
         query_set_size (int, optional): Number of points in the query set. Default is 256.
         Xq (torch.Tensor, optional): (m x d) global reference set. Default is None.
         k (float, optional): Scaling factor for the softmax approximation, controlling the "softness" of the maximum operation. Default is 20.0.
         """
 
         super().__init__(
+            lb=lb,
+            ub=ub,
             model=model,
             target=target,
             lookahead_type=lookahead_type,
@@ -530,7 +547,7 @@ def _compute_acqf(
 def construct_inputs_global_lookahead(
     model: GPyTorchModel,
     training_data: None,
-    lookahead_type="levelset",
+    lookahead_type: Literal["levelset", "posterior"] = "levelset",
     target: Optional[float] = None,
     posterior_transform: Optional[PosteriorTransform] = None,
     query_set_size: Optional[int] = 256,
@@ -543,7 +560,9 @@ def construct_inputs_global_lookahead(
     Args:
         model (GPyTorchModel): The gpytorch model to use.
         training_data (None): Placeholder for compatibility; not used in this function.
-        lookahead_type (str): Type of look-ahead to perform. Default is "levelset".
+        lookahead_type (Literal["levelset", "posterior"]): The type of look-ahead to perform (default is "levelset").
+                - If the lookahead_type is "levelset", the acqf will consider the posterior probability that a point is above or below the target level set.
+                - If the lookahead_type is "posterior", the acqf will consider the posterior probability that a point will be detected or not.
         target (float, optional): Target threshold value in probability space. Default is None.
         posterior_transform (PosteriorTransform, optional): Optional transformation to apply to the posterior. Default is None.
         query_set_size (int, optional): Number of points in the query set. Default is 256.

aepsych/benchmark/benchmark.py

@@ -155,6 +155,7 @@ def run_experiment(
         np.random.seed(seed)
         config_dict["common"]["lb"] = str(problem.lb.tolist())
         config_dict["common"]["ub"] = str(problem.ub.tolist())
+        config_dict["common"]["dim"] = str(problem.lb.shape[0])
         config_dict["common"]["parnames"] = str(
             [f"par{i}" for i in range(len(problem.ub.tolist()))]
         )

aepsych/benchmark/example_problems.py

@@ -1,6 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.
 import os
-from typing import List, Optional, Union
+from typing import List, Union
 
 import numpy as np
 import torch
@@ -11,7 +11,7 @@
     novel_discrimination_testfun,
 )
 from aepsych.models import GPClassificationModel
-from aepsych.models.inducing_point_allocators import KMeansAllocator
+from aepsych.models.inducing_points import KMeansAllocator
 
 """The DiscrimLowDim, DiscrimHighDim, ContrastSensitivity6d, and Hartmann6Binary classes
 are copied from bernoulli_lse github repository (https://github.com/facebookresearch/bernoulli_lse)
@@ -104,13 +104,13 @@ def __init__(
         )
         y = torch.LongTensor(self.data[:, 0])
         x = torch.Tensor(self.data[:, 1:])
+        inducing_size = 100
 
         # Fit a model, with a large number of inducing points
         self.m = GPClassificationModel(
-            lb=self.bounds[0],
-            ub=self.bounds[1],
-            inducing_size=100,
-            inducing_point_method=KMeansAllocator(bounds=self.bounds),
+            dim=6,
+            inducing_size=inducing_size,
+            inducing_point_method=KMeansAllocator(dim=6),
         )
 
         self.m.fit(

aepsych/generators/acqf_thompson_sampler_generator.py

@@ -41,13 +41,17 @@ class AcqfThompsonSamplerGenerator(AEPsychGenerator):
 
     def __init__(
         self,
+        lb: torch.Tensor,
+        ub: torch.Tensor,
         acqf: AcquisitionFunction,
         acqf_kwargs: Optional[Dict[str, Any]] = None,
         samps: int = 1000,
         stimuli_per_trial: int = 1,
     ) -> None:
         """Initialize OptimizeAcqfGenerator.
         Args:
+            lb (torch.Tensor): Lower bounds for the optimization.
+            ub (torch.Tensor): Upper bounds for the optimization.
             acqf (AcquisitionFunction): Acquisition function to use.
             acqf_kwargs (Dict[str, object], optional): Extra arguments to
                 pass to acquisition function. Defaults to no arguments.
@@ -61,6 +65,8 @@ def __init__(
         self.acqf_kwargs = acqf_kwargs
         self.samps = samps
         self.stimuli_per_trial = stimuli_per_trial
+        self.lb = lb
+        self.ub = ub
 
     def _instantiate_acquisition_fn(self, model: ModelProtocol) -> AcquisitionFunction:
         """Instantiate the acquisition function with the model and any extra arguments.
@@ -124,7 +130,7 @@ def _gen(
         starttime = time.time()
 
         seed = gen_options.get("seed")
-        bounds = torch.tensor(np.c_[model.lb, model.ub]).T.cpu()
+        bounds = torch.tensor(np.c_[self.lb, self.ub]).T.cpu()
         bounds_cpu = bounds.cpu()
         effective_dim = bounds.shape[-1] * num_points
         if effective_dim <= SobolEngine.MAXDIM:
@@ -160,12 +166,16 @@ def from_config(cls, config: Config) -> AcqfThompsonSamplerGenerator:
             AcqfThompsonSamplerGenerator: The initialized generator.
         """
         classname = cls.__name__
+        lb = config.gettensor(classname, "lb")
+        ub = config.gettensor(classname, "ub")
         acqf = config.getobj(classname, "acqf", fallback=None)
         extra_acqf_args = cls._get_acqf_options(acqf, config)
         stimuli_per_trial = config.getint(classname, "stimuli_per_trial")
         samps = config.getint(classname, "samps", fallback=1000)
 
         return cls(
+            lb=lb,
+            ub=ub,
             acqf=acqf,
             acqf_kwargs=extra_acqf_args,
             samps=samps,

aepsych/generators/epsilon_greedy_generator.py

@@ -15,15 +15,25 @@
 
 
 class EpsilonGreedyGenerator(AEPsychGenerator):
-    def __init__(self, subgenerator: AEPsychGenerator, epsilon: float = 0.1) -> None:
+    def __init__(
+        self,
+        lb: torch.Tensor,
+        ub: torch.Tensor,
+        subgenerator: AEPsychGenerator,
+        epsilon: float = 0.1,
+    ) -> None:
         """Initialize EpsilonGreedyGenerator.
 
         Args:
+            lb (torch.Tensor): Lower bounds for the optimization.
+            ub (torch.Tensor): Upper bounds for the optimization.
             subgenerator (AEPsychGenerator): The generator to use when not exploiting.
             epsilon (float): The probability of exploration. Defaults to 0.1.
         """
         self.subgenerator = subgenerator
         self.epsilon = epsilon
+        self.lb = lb
+        self.ub = ub
 
     @classmethod
     def from_config(cls, config: Config) -> "EpsilonGreedyGenerator":
@@ -36,12 +46,14 @@ def from_config(cls, config: Config) -> "EpsilonGreedyGenerator":
             EpsilonGreedyGenerator: The generator.
         """
         classname = cls.__name__
+        lb = torch.tensor(config.getlist(classname, "lb"))
+        ub = torch.tensor(config.getlist(classname, "ub"))
         subgen_cls = config.getobj(
             classname, "subgenerator", fallback=OptimizeAcqfGenerator
         )
         subgen = subgen_cls.from_config(config)
         epsilon = config.getfloat(classname, "epsilon", fallback=0.1)
-        return cls(subgenerator=subgen, epsilon=epsilon)
+        return cls(lb=lb, ub=ub, subgenerator=subgen, epsilon=epsilon)
 
     def gen(self, num_points: int, model: ModelProtocol) -> torch.Tensor:
         """Query next point(s) to run by sampling from the subgenerator with probability 1-epsilon, and randomly otherwise.
@@ -53,7 +65,7 @@ def gen(self, num_points: int, model: ModelProtocol) -> torch.Tensor:
         if num_points > 1:
             raise NotImplementedError("Epsilon-greedy batched gen is not implemented!")
         if np.random.uniform() < self.epsilon:
-            sample = np.random.uniform(low=model.lb, high=model.ub)
+            sample = np.random.uniform(low=self.lb, high=self.ub)
             return torch.tensor(sample).reshape(1, -1)
         else:
             return self.subgenerator.gen(num_points, model)

aepsych/generators/monotonic_rejection_generator.py

@@ -12,6 +12,7 @@
 from aepsych.config import Config
 from aepsych.generators.base import AEPsychGenerator
 from aepsych.models.monotonic_rejection_gp import MonotonicRejectionGP
+from aepsych.utils import _process_bounds
 from botorch.acquisition import AcquisitionFunction
 from botorch.logging import logger
 from botorch.optim.initializers import gen_batch_initial_conditions
@@ -43,13 +44,17 @@ class MonotonicRejectionGenerator(AEPsychGenerator[MonotonicRejectionGP]):
     def __init__(
         self,
         acqf: MonotonicMCAcquisition,
+        lb: torch.Tensor,
+        ub: torch.Tensor,
         acqf_kwargs: Optional[Dict[str, Any]] = None,
         model_gen_options: Optional[Dict[str, Any]] = None,
         explore_features: Optional[Sequence[int]] = None,
     ) -> None:
         """Initialize MonotonicRejectionGenerator.
         Args:
-            acqf (MonotonicMCAcquisition): Acquisition function to use.
+            acqf (AcquisitionFunction): Acquisition function to use.
+            lb (torch.Tensor): Lower bounds for the optimization.
+            ub (torch.Tensor): Upper bounds for the optimization.
             acqf_kwargs (Dict[str, object], optional): Extra arguments to
                 pass to acquisition function. Defaults to None.
             model_gen_options (Dict[str, Any], optional): Dictionary with options for generating candidate, such as
@@ -63,6 +68,8 @@ def __init__(
         self.acqf_kwargs = acqf_kwargs
         self.model_gen_options = model_gen_options
         self.explore_features = explore_features
+        self.lb, self.ub, _ = _process_bounds(lb, ub, None)
+        self.bounds = torch.stack((self.lb, self.ub))
 
     def _instantiate_acquisition_fn(
         self, model: MonotonicRejectionGP
@@ -110,7 +117,7 @@ def gen(
         )
 
         # Augment bounds with deriv indicator
-        bounds = torch.cat((model.bounds_, torch.zeros(2, 1)), dim=1)
+        bounds = torch.cat((self.bounds, torch.zeros(2, 1)), dim=1)
         # Fix deriv indicator to 0 during optimization
         fixed_features = {(bounds.shape[1] - 1): 0.0}
         # Fix explore features to random values
@@ -192,6 +199,8 @@ def from_config(cls, config: Config) -> "MonotonicRejectionGenerator":
         classname = cls.__name__
         acqf = config.getobj("common", "acqf", fallback=None)
         extra_acqf_args = cls._get_acqf_options(acqf, config)
+        lb = torch.tensor(config.getlist(classname, "lb"))
+        ub = torch.tensor(config.getlist(classname, "ub"))
 
         options = {}
         options["num_restarts"] = config.getint(classname, "restarts", fallback=10)
@@ -217,6 +226,8 @@ def from_config(cls, config: Config) -> "MonotonicRejectionGenerator":
 
         return cls(
             acqf=acqf,
+            lb=lb,
+            ub=ub,
             acqf_kwargs=extra_acqf_args,
             model_gen_options=options,
             explore_features=explore_features,