Consolidate recommender / strat user guides and examples

baybe/recommenders/pure/__init__.py

@@ -1,6 +1,6 @@
 """Pure recommenders.
 
-Pure recommenders implement optimization strategies and can be queried for
+Pure recommenders implement selection algorithms and can be queried for providing
 recommendations. They can be part of meta recommenders.
 """
 

baybe/recommenders/pure/nonpredictive/clustering.py

@@ -1,4 +1,4 @@
-"""Recommendation strategies based on clustering."""
+"""Recommenders based on clustering."""
 
 from abc import ABC
 from typing import ClassVar, List, Type, Union

baybe/recommenders/pure/nonpredictive/sampling.py

@@ -1,4 +1,4 @@
-"""Recommendation strategies based on sampling."""
+"""Recommenders based on sampling."""
 
 from typing import ClassVar
 

docs/userguide/campaigns.md

@@ -28,7 +28,7 @@ describe the underlying optimization problem at hand:
 Apart from this basic configuration, it is possible to further define the specific
 optimization 
 `Recommender` ([class](baybe.recommenders.pure.base.PureRecommender) 
-/ [user guide](./recommender)) to be used.
+/ [user guide](./recommenders)) to be used.
 
 ~~~python
 from baybe import Campaign

docs/userguide/recommenders.md

@@ -0,0 +1,118 @@
+# Recommenders
+
+## General Information
+
+Recommenders are an essential part of BayBE that effectively explore the search space
+and provide recommendations for the next experiment or batch of experiments.
+Available recommenders can be partitioned into the following subclasses.
+
+## Pure Recommenders
+
+Pure recommenders simply take on the task to recommend measurements. They each contain
+the inner logic to do so via different algorithms and approaches.
+While some pure recommenders are versatile and work across different types of search
+spaces, other are specifically designed for discrete or continuous spaces. The
+compatibility is indicated via the corresponding `compatibility` class variable.
+
+```{admonition} Additional Options for Discrete Search Spaces
+:class: note
+For discrete search spaces, BayBE provides additional control over pure recommenders.
+You can explicitly define whether a recommender is allowed to recommend previous 
+recommendations again via `allow_repeated_recommendations` and whether it can output
+recommendations that have already been measured via
+`allow_recommending_already_measured`. 
+```
+
+### Bayesian Recommenders
+
+The Bayesian recommenders in BayBE are built on the foundation of the
+[`BayesianRecommender`](baybe.recommenders.pure.bayesian.base.BayesianRecommender)
+class, offering an array of possibilities with internal surrogate models and support
+for various acquisition functions.
+
+* The **[`SequentialGreedyRecommender`](baybe.recommenders.pure.bayesian.sequential_greedy.SequentialGreedyRecommender)**
+  is a powerful recommender that performs sequential Greedy optimization. It can be
+  applied for discrete, continuous and hybrid search spaces. It is an implementation of
+  the BoTorch optimization functions for discrete, continuous and mixed spaces.
+  It is important to note that this recommender performs a brute-force search when
+  applied in hybrid search spaces, as it optimizes the continuous part of the space
+  while exhaustively searching choices in the discrete subspace. You can customize
+  this behavior to only sample a certain percentage of the discrete subspace via the
+  `sample_percentage` attribute and to choose different sampling algorithms via the
+  `hybrid_sampler` attribute. An example on using this recommender in a hybrid space
+  can be found [here](./../../examples/Backtesting/hybrid).
+
+* The **[`NaiveHybridSpaceRecommender`](baybe.recommenders.naive.NaiveHybridSpaceRecommender)**
+  can be applied to all search spaces, but is intended to be used in hybrid spaces.
+  This recommender combines individual recommenders for the continuous and the discrete
+  subspaces. It independently optimizes each subspace and consolidates the best results
+  to generate a candidate for the original hybrid space. An example on using this
+  recommender in a hybrid space can be found [here](./../../examples/Backtesting/hybrid).
+
+### Clustering Recommenders
+
+BayBE offers a set of recommenders leveraging techniques to facilitate point selection
+via clustering:
+* **[`PAMClusteringRecommender`](baybe.recommenders.pure.nonpredictive.clustering.PAMClusteringRecommender):**
+  This recommender utilizes partitioning around medoids.
+* **[`KMeansClusteringRecommender`](baybe.recommenders.pure.nonpredictive.clustering.KMeansClusteringRecommender):**
+  This recommender implements k-means clustering.
+* **[`GaussianMixtureClusteringRecommender`](baybe.recommenders.pure.nonpredictive.clustering.GaussianMixtureClusteringRecommender):**
+  This recommender leverages Gaussian Mixture Models for clustering.
+
+### Sampling Recommenders
+
+BayBE provides two recommenders that recommend by sampling form the search space:
+* **[`RandomRecommender`](baybe.recommenders.pure.nonpredictive.sampling.RandomRecommender):**
+  This recommender offers random recommendations for all types of search spaces.
+  It is extensively used in backtesting examples, providing a valuable comparison.
+  For detailed usage examples, refer to the list
+  [here](./../../examples/Backtesting/Backtesting).
+* **[`FPSRecommender`](baybe.recommenders.pure.nonpredictive.sampling.FPSRecommender):**
+  This recommender is only applicable for discrete search spaces, and recommends points
+  based on farthest point sampling. A practical application showcasing the usage of
+  this recommender can be found
+  [here](./../../examples/Custom_Surrogates/surrogate_params).
+
+## Meta Recommenders
+
+In analogy to meta studies, meta recommenders are wrappers that operate on a sequence
+of pure recommenders and determine when to switch between them according to different
+logics. BayBE offers three distinct kinds of meta recommenders.
+
+* The
+  [`TwoPhaseMetaRecommender`](baybe.recommenders.meta.sequential.TwoPhaseMetaRecommender)
+  employs two distinct recommenders and switches between them at a certain specified
+  point, controlled by the `switch_after` attribute. This is useful e.g. if you want a
+  different recommender for the initial recommendation when there is no data yet
+  available. This simple example would recommend randomly for the first batch and switch
+  to a Bayesian recommender as soon as measurements have been ingested:
+```python
+from baybe.recommenders import (
+    TwoPhaseMetaRecommender,
+    RandomRecommender,
+    SequentialGreedyRecommender,
+)
+
+recommender = TwoPhaseMetaRecommender(
+    initial_recommender=RandomRecommender(), recommender=SequentialGreedyRecommender()
+)
+```
+
+* The **[`SequentialMetaRecommender`](baybe.recommenders.meta.sequential.SequentialMetaRecommender)**
+  introduces a simple yet versatile approach by utilizing a predefined list of
+  recommenders. By specifying the desired behavior using the `mode` attribute, it is
+  possible to flexibly determine the strategy's response when it exhausts the available
+  recommenders. The possible choices are to either raise an error, re-use the last
+  recommender or re-start at the beginning of the sequence.
+
+* Similar to the `SequentialMetaRecommender`, the
+  **[`StreamingSequentialMetaRecommender`](baybe.recommenders.meta.sequential.StreamingSequentialMetaRecommender)**
+  enables the utilization of *arbitrary* iterables to select recommender.
+
+  ```{warning}
+  Due to the arbitrary nature of iterables that can be used, de-/serializability cannot
+  be guaranteed. As a consequence, using a `StreamingSequentialMetaRecommender` results
+  in an error if you attempt to serialize the corresponding object or higher-level
+  objects containing it.
+  ```

docs/userguide/searchspace.md

@@ -23,7 +23,7 @@ A discrete/continuous search space is a s searchspace that was constructed by on
 In addition to the ones noted above, a discrete subspace has the following attributes:
 * **The experimental representation:** A ``DataFrame`` representing the experimental representation of the subspace.
 * **The metadata:** A ``DataFrame`` keeping track of different metadata that is relevant for running a campaign.
-* **An "empty" encoding flag:** A flag denoting whether an "empty" encoding should be used. This is useful, for instance, in combination with random search strategies that do not read the actual parameter values.
+* **An "empty" encoding flag:** A flag denoting whether an "empty" encoding should be used. This is useful, for instance, in combination with random recommenders that do not read the actual parameter values.
 * **The computational representation:** The computational representation of the space. If not provided explicitly, it will be derived from the experimental representation.
 
 Although it is possible to directly create a discrete subspace via the ``__init__`` function, it is intended to create themvia the [`from_dataframe`](baybe.searchspace.discrete.SubspaceDiscrete.from_dataframe) or [`from_product`](baybe.searchspace.discrete.SubspaceDiscrete.from_product) methods. These methods either require a ``DataFrame`` containing the experimental representation of the parameters and the optional explicit list of parameters (``from_dataframe``) or a list of parameters and optional constraints (``from_product``).

docs/userguide/userguide.md

@@ -5,10 +5,9 @@ Campaigns <campaigns>
 Constraints <constraints>
 Objective <objective>
 Parameters <parameters>
-PureRecommender <recommender>
+Recommenders <recommenders>
 Search Spaces <searchspace>
 Simulation <simulation>
-Strategies <strategies>
 Surrogates <surrogates>
 Targets <targets>
 Transfer Learning <transfer_learning>

examples/Backtesting/custom_analytical.py

@@ -77,24 +77,24 @@ def sum_of_squares(*x: float) -> float:
 
 ### Constructing campaigns for the simulation loop
 
-# To simplify adjusting the example for other strategies, we construct some recommender objects.
-# For details on recommender objects, we refer to [`strategies`](./../Basics/strategies.md).
+# To simplify adjusting the example for other recommenders, we construct some recommender objects.
+# For details on recommender objects, we refer to [`recommenders`](./../Basics/recommenders.md).
 
-seq_greedy_EI_strategy = TwoPhaseMetaRecommender(
+seq_greedy_EI_recommender = TwoPhaseMetaRecommender(
     recommender=SequentialGreedyRecommender(acquisition_function_cls="qEI"),
 )
-random_strategy = TwoPhaseMetaRecommender(recommender=RandomRecommender())
+random_recommender = TwoPhaseMetaRecommender(recommender=RandomRecommender())
 
 # We now create one campaign per recommender.
 
 seq_greedy_EI_campaign = Campaign(
     searchspace=searchspace,
-    recommender=seq_greedy_EI_strategy,
+    recommender=seq_greedy_EI_recommender,
     objective=objective,
 )
 random_campaign = Campaign(
     searchspace=searchspace,
-    recommender=random_strategy,
+    recommender=random_recommender,
     objective=objective,
 )
 

examples/Backtesting/hybrid.py

@@ -124,31 +124,31 @@ def sum_of_squares(*x: float) -> float:
 # Note that the recommender performs one optimization of the continuous subspace per sampled point.
 # We thus recommend to keep this parameter rather low.
 
-seq_greedy_strategy = TwoPhaseMetaRecommender(
+seq_greedy_recommender = TwoPhaseMetaRecommender(
     recommender=SequentialGreedyRecommender(
         hybrid_sampler="Farthest", sampling_percentage=0.3
     ),
 )
-naive_hybrid_strategy = TwoPhaseMetaRecommender(
+naive_hybrid_recommender = TwoPhaseMetaRecommender(
     recommender=NaiveHybridSpaceRecommender()
 )
-random_strategy = TwoPhaseMetaRecommender(recommender=RandomRecommender())
+random_recommender = TwoPhaseMetaRecommender(recommender=RandomRecommender())
 
 # We now create one campaign per recommender.
 
 seq_greedy_campaign = Campaign(
     searchspace=searchspace,
-    recommender=seq_greedy_strategy,
+    recommender=seq_greedy_recommender,
     objective=objective,
 )
 naive_hybrid_campaign = Campaign(
     searchspace=searchspace,
-    recommender=naive_hybrid_strategy,
+    recommender=naive_hybrid_recommender,
     objective=objective,
 )
 random_campaign = Campaign(
     searchspace=searchspace,
-    recommender=random_strategy,
+    recommender=random_recommender,
     objective=objective,
 )
 

examples/Basics/Basics_Header.md

@@ -2,4 +2,4 @@
 
 These examples demonstrate the most basic aspects of BayBE: How to set up a
 {doc}`Campaign </userguide/campaigns>` and how to configure an optimization
-{doc}`Strategy </userguide/strategies>`.
+{doc}`Recommenders </userguide/recommenders>`.

examples/Basics/strategies.py → examples/Basics/recommenders.py

@@ -34,7 +34,7 @@
 from baybe.targets import NumericalTarget
 from baybe.utils.dataframe import add_fake_results
 
-### Available initial strategies
+### Available recommenders suitable for initial recommendation
 
 # For the first recommendation, the user can specify which recommender to use.
 # The following initial recommenders are available.
@@ -110,7 +110,7 @@
 # Note that they all have default values.
 # Therefore one does not need to specify all of them to create a recommender object.
 
-strategy = TwoPhaseMetaRecommender(
+recommender = TwoPhaseMetaRecommender(
     initial_recommender=INITIAL_RECOMMENDER,
     recommender=SequentialGreedyRecommender(
         surrogate_model=SURROGATE_MODEL,
@@ -120,7 +120,7 @@
     ),
 )
 
-print(strategy)
+print(recommender)
 
 # Note that there are the additional keywords `hybrid_sampler` and `sampling_percentag`.
 # Their meaning and how to use and define it are explained in the hybrid backtesting example.
@@ -177,7 +177,7 @@
 
 campaign = Campaign(
     searchspace=searchspace,
-    recommender=strategy,
+    recommender=recommender,
     objective=objective,
 )
 

examples/Searchspaces/hybrid_space.py

@@ -111,15 +111,14 @@
 # recommenders for the corresponding subspaces.
 # We use the default choices, which is the `SequentialGreedyRecommender`.
 
-hybrid_recommender = NaiveHybridSpaceRecommender()
-hybrid_strategy = TwoPhaseMetaRecommender(recommender=hybrid_recommender)
+hybrid_recommender = TwoPhaseMetaRecommender(recommender=NaiveHybridSpaceRecommender())
 
 ### Constructing the campaign and performing a recommendation
 
 campaign = Campaign(
     searchspace=searchspace,
     objective=objective,
-    recommender=hybrid_strategy,
+    recommender=hybrid_recommender,
 )
 
 # Get a recommendation for a fixed batch size.