Add logic for running a decision making loop

gpjax/citation.py

@@ -10,11 +10,11 @@
 from jaxlib.xla_extension import PjitFunction
 from plum import dispatch
 
-from gpjax.decision_making.acquisition_functions import ThompsonSampling
 from gpjax.decision_making.test_functions import (
     Forrester,
     LogarithmicGoldsteinPrice,
 )
+from gpjax.decision_making.utility_functions import ThompsonSampling
 from gpjax.kernels import (
     RFF,
     ArcCosine,

gpjax/decision_making/__init__.py

@@ -12,14 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-from gpjax.decision_making.acquisition_functions import (
-    AbstractAcquisitionFunctionBuilder,
-    AcquisitionFunction,
-    ThompsonSampling,
-)
-from gpjax.decision_making.acquisition_maximizer import (
-    AbstractAcquisitionMaximizer,
-    ContinuousAcquisitionMaximizer,
+from gpjax.decision_making.decision_maker import (
+    AbstractDecisionMaker,
+    UtilityDrivenDecisionMaker,
 )
 from gpjax.decision_making.posterior_handler import PosteriorHandler
 from gpjax.decision_making.search_space import (
@@ -32,14 +27,27 @@
     LogarithmicGoldsteinPrice,
     Quadratic,
 )
+from gpjax.decision_making.utility_functions import (
+    AbstractUtilityFunctionBuilder,
+    ThompsonSampling,
+    UtilityFunction,
+)
+from gpjax.decision_making.utility_maximizer import (
+    AbstractUtilityMaximizer,
+    ContinuousUtilityMaximizer,
+)
+from gpjax.decision_making.utils import build_function_evaluator
 
 __all__ = [
-    "AbstractAcquisitionFunctionBuilder",
-    "AbstractAcquisitionMaximizer",
+    "AbstractUtilityFunctionBuilder",
+    "AbstractUtilityMaximizer",
+    "AbstractDecisionMaker",
     "AbstractSearchSpace",
-    "AcquisitionFunction",
-    "ContinuousAcquisitionMaximizer",
+    "UtilityFunction",
+    "build_function_evaluator",
+    "ContinuousUtilityMaximizer",
     "ContinuousSearchSpace",
+    "UtilityDrivenDecisionMaker",
     "AbstractContinuousTestFunction",
     "Forrester",
     "LogarithmicGoldsteinPrice",

gpjax/decision_making/decision_maker.py

@@ -0,0 +1,249 @@
+# Copyright 2023 The GPJax Contributors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from dataclasses import dataclass
+
+from beartype.typing import (
+    Callable,
+    Dict,
+    List,
+    Mapping,
+)
+import jax.random as jr
+
+from gpjax.dataset import Dataset
+from gpjax.decision_making.posterior_handler import PosteriorHandler
+from gpjax.decision_making.search_space import AbstractSearchSpace
+from gpjax.decision_making.utility_functions import AbstractUtilityFunctionBuilder
+from gpjax.decision_making.utility_maximizer import AbstractUtilityMaximizer
+from gpjax.decision_making.utils import FunctionEvaluator
+from gpjax.gps import AbstractPosterior
+from gpjax.typing import (
+    Array,
+    Float,
+    KeyArray,
+)
+
+
+@dataclass
+class AbstractDecisionMaker(ABC):
+    """
+    AbstractDecisionMaker abstract base class which handles the core decision making
+    loop, where we sequentially decide on points to query our function of interest at.
+    The decision making loop is split into two key steps, `ask` and `tell`. The `ask`
+    step is typically used to decide which point to query next. The `tell` step is
+    typically used to update models and datasets with newly queried points. These steps
+    can be combined in a 'run' loop which alternates between asking which point to query
+    next and telling the decision maker about the newly queried point having evaluated
+    the black-box function of interest at this point.
+
+    Attributes:
+        search_space (AbstractSearchSpace): Search space over which we can evaluate the
+        function(s) of interest.
+        posterior_handlers (Dict[str, PosteriorHandler]): Dictionary of posterior
+            handlers, which are used to update posteriors throughout the decision making
+            loop. Note that the word `posteriors` is used for consistency with GPJax, but these
+            objects are typically referred to as `models` in the model-based decision
+            making literature. Tags are used to distinguish between posteriors. In a typical
+            Bayesian optimisation setup one of the tags will be `OBJECTIVE`, defined in
+            decision_making.utils.
+        datasets (Dict[str, Dataset]): Dictionary of datasets, which are augmented with
+            observations throughout the decision making loop. In a typical setup they are
+            also used to update the posteriors, using the `posterior_handlers`. Tags are used
+            to distinguish datasets, and correspond to tags in `posterior_handlers`.
+        key (KeyArray): JAX random key, used to generate random numbers.
+        post_ask (List[Callable]): List of functions to be executed after each ask step.
+        post_tell (List[Callable]): List of functions to be executed after each tell
+            step.
+    """
+
+    search_space: AbstractSearchSpace
+    posterior_handlers: Dict[str, PosteriorHandler]
+    datasets: Dict[str, Dataset]
+    key: KeyArray
+    post_ask: List[
+        Callable
+    ]  # Specific type is List[Callable[[AbstractDecisionMaker, Float[Array, ["1 D"]]], None]] but causes Beartype issues
+    post_tell: List[
+        Callable
+    ]  # Specific type is List[Callable[[AbstractDecisionMaker], None]] but causes Beartype issues
+
+    def __post_init__(self):
+        """
+        At initialisation we check that the posterior handlers and datasets are
+        consistent (i.e. have the same tags), and then initialise the posteriors, optimizing them using the
+        corresponding datasets.
+        """
+        # Check that posterior handlers and datasets are consistent
+        if self.posterior_handlers.keys() != self.datasets.keys():
+            raise ValueError(
+                "Posterior handlers and datasets must have the same keys. "
+                f"Got posterior handlers keys {self.posterior_handlers.keys()} and "
+                f"datasets keys {self.datasets.keys()}."
+            )
+
+        # Initialize posteriors
+        self.posteriors: Dict[str, AbstractPosterior] = {}
+        for tag, posterior_handler in self.posterior_handlers.items():
+            self.posteriors[tag] = posterior_handler.get_posterior(
+                self.datasets[tag], optimize=True, key=self.key
+            )
+
+    @abstractmethod
+    def ask(self, key: KeyArray) -> Float[Array, "1 D"]:
+        """
+        Get the point to be queried next.
+
+        Args:
+            key (KeyArray): JAX PRNG key for controlling random state.
+
+        Returns:
+            Float[Array, "1 D"]: Point to be queried next
+        """
+        raise NotImplementedError
+
+    def tell(self, observation_datasets: Mapping[str, Dataset], key: KeyArray):
+        """
+        Add newly observed data to datasets and update the corresponding posteriors.
+
+        Args:
+            observation_datasets (Mapping[str, Dataset]): Dictionary of datasets
+            containing new observations. Tags are used to distinguish datasets, and
+            correspond to tags in `posterior_handlers` and `self.datasets`.
+            key (KeyArray): JAX PRNG key for controlling random state.
+        """
+        if observation_datasets.keys() != self.datasets.keys():
+            raise ValueError(
+                "Observation datasets and existing datasets must have the same keys. "
+                f"Got observation datasets keys {observation_datasets.keys()} and "
+                f"existing datasets keys {self.datasets.keys()}."
+            )
+
+        for tag, observation_dataset in observation_datasets.items():
+            self.datasets[tag] += observation_dataset
+
+        for tag, posterior_handler in self.posterior_handlers.items():
+            key, _ = jr.split(key)
+            self.posteriors[tag] = posterior_handler.update_posterior(
+                self.datasets[tag], self.posteriors[tag], optimize=True, key=key
+            )
+
+    def run(
+        self, n_steps: int, black_box_function_evaluator: FunctionEvaluator
+    ) -> Mapping[str, Dataset]:
+        """
+        Run the decision making loop continuously for for `n_steps`. This is broken down
+        into three main steps:
+        1. Call the `ask` method to get the point to be queried next.
+        2. Call the `black_box_function_evaluator` to evaluate the black box functions
+        of interest at the point chosen to be queried.
+        3. Call the `tell` method to update the datasets and posteriors with the newly
+        observed data.
+
+        In addition to this, after the `ask` step, the functions in the `post_ask` list
+        are executed, taking as arguments the decision maker and the point chosen to be
+        queried next. Similarly, after the `tell` step, the functions in the `post_tell`
+        list are executed, taking the decision maker as the sole argument.
+
+        Args:
+            n_steps (int): Number of steps to run the decision making loop for.
+            black_box_function_evaluator (FunctionEvaluator): Function evaluator which
+                evaluates the black box functions of interest at supplied points.
+
+        Returns:
+            Mapping[str, Dataset]: Dictionary of datasets containing the observations
+            made throughout the decision making loop, as well as the initial data
+            supplied when initialising the `DecisionMaker`.
+        """
+        for _ in range(n_steps):
+            query_point = self.ask(self.key)
+
+            for post_ask_method in self.post_ask:
+                post_ask_method(self, query_point)
+
+            self.key, _ = jr.split(self.key)
+            observation_datasets = black_box_function_evaluator(query_point)
+            self.tell(observation_datasets, self.key)
+
+            for post_tell_method in self.post_tell:
+                post_tell_method(self)
+
+        return self.datasets
+
+
+@dataclass
+class UtilityDrivenDecisionMaker(AbstractDecisionMaker):
+    """
+    UtilityDrivenDecisionMaker class which handles the core decision making loop in a
+    typical model-based decision making setup. In this setup we use surrogate model(s)
+    for the function(s) of interest, and define a utility function (often called the
+    'acquisition function' in the context of Bayesian optimisation) which characterises
+    how useful it would be to query a given point within the search space given the data
+    we have observed so far. This can then be used to decide which point(s) to query
+    next.
+
+    The decision making loop is split into two key steps, `ask` and `tell`. The `ask`
+    step forms a `UtilityFunction` from the current `posteriors` and `datasets` and
+    returns the point which maximises it. It also stores the formed utility function
+    under the attribute `self.current_utility_function` so that it can be called,
+    for instance for plotting, after the `ask` function has been called. The `tell` step
+    adds a newly queried point to the `datasets` and updates the `posteriors`.
+
+    This can be run as a typical ask-tell loop, or the `run` method can be used to run
+    the decision making loop for a fixed number of steps. Moreover, the `run` method executes
+    the functions in `post_ask` and `post_tell` after each ask and tell step
+    respectively. This enables the user to add custom functionality, such as the ability
+    to plot values of interest during the optimization process.
+
+    Attributes:
+        utility_function_builder (AbstractUtilityFunctionBuilder): Object which
+                builds utility functions from posteriors and datasets, to decide where
+                to query next. In a typical Bayesian optimisation setup the point chosen to
+                be queried next is the point which maximizes the utility function.
+        utility_maximizer (AbstractUtilityMaximizer): Object which maximizes
+            utility functions over the search space.
+    """
+
+    utility_function_builder: AbstractUtilityFunctionBuilder
+    utility_maximizer: AbstractUtilityMaximizer
+
+    def ask(self, key: KeyArray) -> Float[Array, "1 D"]:
+        """
+        Get updated utility function and return the point which maximises it. This
+        method also stores the utility function in
+        `self.current_utility_function` so that it can be accessed after the ask
+        function has been called. This is useful for non-deterministic utility
+        functions, which will differ between calls to `ask` due to the splitting of
+        `self.key`.
+
+        Args:
+            key (KeyArray): JAX PRNG key for controlling random state.
+
+        Returns:
+            Float[Array, "1 D"]: Point to be queried next.
+        """
+        self.current_utility_function = (
+            self.utility_function_builder.build_utility_function(
+                self.posteriors, self.datasets, key
+            )
+        )
+
+        key, _ = jr.split(key)
+        return self.utility_maximizer.maximize(
+            self.current_utility_function, self.search_space, key
+        )

gpjax/decision_making/acquisition_functions/__init__.py → gpjax/decision_making/utility_functions/__init__.py

@@ -12,16 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-from gpjax.decision_making.acquisition_functions.base import (
-    AbstractAcquisitionFunctionBuilder,
-    AcquisitionFunction,
-)
-from gpjax.decision_making.acquisition_functions.thompson_sampling import (
-    ThompsonSampling,
+from gpjax.decision_making.utility_functions.base import (
+    AbstractUtilityFunctionBuilder,
+    UtilityFunction,
 )
+from gpjax.decision_making.utility_functions.thompson_sampling import ThompsonSampling
 
 __all__ = [
-    "AcquisitionFunction",
-    "AbstractAcquisitionFunctionBuilder",
+    "UtilityFunction",
+    "AbstractUtilityFunctionBuilder",
     "ThompsonSampling",
 ]