From 9e2537091702a1d3acca24cc7fcf57991c9e5967 Mon Sep 17 00:00:00 2001
From: uri-granta <50578464+uri-granta@users.noreply.github.com>
Date: Tue, 18 Jul 2023 13:52:13 +0100
Subject: [PATCH] Fix README code example (#768)

Co-authored-by: Uri Granta <uri.granta@secondmind.ai>
---
 README.md                       |  6 +++---
 trieste/acquisition/__init__.py | 11 +++++++++++
 trieste/objectives/__init__.py  |  1 +
 3 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 76789879d8..39d4ea0545 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ Trieste (pronounced tree-est) is a Bayesian optimization toolbox built on [Tenso
 
 ## Getting started
 
-Here's a quick overview of the main components of a Bayesian optimization loop. For more details, see our [Documentation](https://secondmind-labs.github.io/trieste) where we have multiple [Tutorials](https://secondmind-labs.github.io/trieste/tutorials.html) covering both the basic functionalities of the toolbox, as well as more advanced usage.
+Here's a quick overview of the main components of a Bayesian optimization loop. For more details, see our <span style="font-variant:small-caps;">[Documentation](https://secondmind-labs.github.io/trieste)</span> where we have multiple <span style="font-variant:small-caps;">[Tutorials](https://secondmind-labs.github.io/trieste/tutorials.html)</span> covering both the basic functionalities of the toolbox, as well as more advanced usage.
 
 Let's set up a synthetic black-box objective function we wish to minimize, for example, a popular Branin optimization function, and generate some initial data
 ```python
@@ -42,7 +42,7 @@ initial_data = observer(initial_query_points)
 
 First step is to create a probabilistic model of the objective function, for example a Gaussian Process model
 ```python
-from trieste.models import build_gpr, GaussianProcessRegression
+from trieste.models.gpflow import build_gpr, GaussianProcessRegression
 
 gpflow_model = build_gpr(initial_data, Branin.search_space)
 model = GaussianProcessRegression(gpflow_model)
@@ -59,7 +59,7 @@ Finally, we optimize the acquisition function using our model for a number of st
 ```python
 from trieste.bayesian_optimizer import BayesianOptimizer
 
-bo = BayesianOptimizer(observer, search_space)
+bo = BayesianOptimizer(observer, Branin.search_space)
 num_steps = 15
 result = bo.optimize(num_steps, initial_data, model)
 query_point, observation, arg_min_idx = result.try_get_optimal_point()
diff --git a/trieste/acquisition/__init__.py b/trieste/acquisition/__init__.py
index b6413b39bb..57191bd065 100644
--- a/trieste/acquisition/__init__.py
+++ b/trieste/acquisition/__init__.py
@@ -95,6 +95,17 @@
     UpdatablePenalizationFunction,
     VectorizedAcquisitionFunctionBuilder,
 )
+from .rule import (
+    TURBO,
+    AcquisitionRule,
+    AsynchronousGreedy,
+    AsynchronousOptimization,
+    BatchHypervolumeSharpeRatioIndicator,
+    DiscreteThompsonSampling,
+    EfficientGlobalOptimization,
+    RandomSampling,
+    TrustRegion,
+)
 from .sampler import (
     ExactThompsonSampler,
     GumbelSampler,
diff --git a/trieste/objectives/__init__.py b/trieste/objectives/__init__.py
index 8d78ee9ef4..9d21b3408b 100644
--- a/trieste/objectives/__init__.py
+++ b/trieste/objectives/__init__.py
@@ -44,3 +44,4 @@
     SingleObjectiveTestProblem,
     Trid10,
 )
+from .utils import mk_multi_observer, mk_observer