Add mixed search spaces notebook

docs/notebooks/mixed_search_spaces.pct.py

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+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     custom_cell_magics: kql
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.11.2
+#   kernelspec:
+#     display_name: .venv_310
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Mixed search spaces
+#
+# This notebook demonstrates optimization of mixed search spaces in Trieste.
+#
+# The example function is defined over 2D input space that is a combination of a
+# discrete and a continuous search space. The problem is a modification of the Branin function
+# where one of the input dimensions is discretized.
+
+# %%
+import numpy as np
+import tensorflow as tf
+
+np.random.seed(1793)
+tf.random.set_seed(1793)
+
+# %% [markdown]
+# ## The problem
+#
+# The Branin function is a common optimization benchmark that has three global minima. It is normally
+# defined over a 2D continuous search space.
+#
+# We first show the Branin function over its original continuous search space.
+
+# %%
+from trieste.experimental.plotting import plot_function_plotly
+from trieste.objectives import ScaledBranin
+
+scaled_branin = ScaledBranin.objective
+
+fig = plot_function_plotly(
+    scaled_branin,
+    ScaledBranin.search_space.lower,
+    ScaledBranin.search_space.upper,
+)
+fig.show()
+
+# %% [markdown]
+# In order to convert the Branin function from a continuous to a mixed search space problem, we
+# modify it by discretizing its first input dimension.
+#
+# The discrete dimension is defined by a set of 10 points that are equally spaced, ensuring that
+# the three minimizers are included in this set. The continuous dimension is defined by the
+# interval [0, 1].
+#
+# We observe that the first and third minimizers are equidistant from the middle minimizer, so we
+# choose the discretization points to be equally spaced around the middle minimizer.
+#
+# The `TaggedProductSearchSpace` class is a convenient way to define a search space
+# that is a combination of multiple search spaces, each with an optional tag.
+# We create our mixed search space by instantiating this class with a list containing the discrete
+# and continuous spaces, without any explicit tags (hence using default tags).
+# This can be easily extended to more than two search spaces by adding more elements to the list.
+
+# %%
+from trieste.space import Box, DiscreteSearchSpace, TaggedProductSearchSpace
+
+minimizers0 = ScaledBranin.minimizers[:, 0]
+step = (minimizers0[1] - minimizers0[0]) / 4
+points = np.concatenate(
+    [
+        # Equally spaced points to the left of the middle minimizer. Skip the last point as it is
+        # the same as the first point in the next array.
+        np.flip(np.arange(minimizers0[1], 0.0, -step))[:-1],
+        # Equally spaced points to the right of the middle minimizer.
+        np.arange(minimizers0[1], 1.0, step),
+    ]
+)
+discrete_space = DiscreteSearchSpace(points[:, None])
+continuous_space = Box([0.0], [1.0])
+mixed_search_space = TaggedProductSearchSpace(
+    [discrete_space, continuous_space]
+)
+
+
+# %% [markdown]
+# Next we demonstrate the Branin function over the mixed search space, by plotting the original
+# function contours and highlighting the discretization points.
+# The discrete dimension is along the x-axis and the continuous dimension is on the y-axis, with
+# the vertical dashed lines indicating the discretization points.
+
+# %%
+from trieste.experimental.plotting import plot_function_2d
+
+# Plot over the predefined search space.
+fig, ax = plot_function_2d(
+    scaled_branin,
+    ScaledBranin.search_space.lower,
+    ScaledBranin.search_space.upper,
+    contour=True,
+)
+
+ax[0, 0].set_xlabel(r"$x_1$")
+ax[0, 0].set_ylabel(r"$x_2$")
+
+# Draw vertical lines at the discrete points.
+for point in points:
+    ax[0, 0].vlines(
+        point,
+        mixed_search_space.lower[1],
+        mixed_search_space.upper[1],
+        colors="b",
+        linestyles="dashed",
+        alpha=0.6,
+    )
+
+# %% [markdown]
+# ## Sample the observer over the search space
+#
+# We begin our optimization by first collecting five function evaluations from random locations in
+# the mixed search space. Samples from the discrete dimension are drawn uniformly at random with
+# replacement, and samples from the continuous dimension are drawn from a uniform distribution.
+# Observe that the `sample` method deals with the mixed search space automatically.
+
+# %%
+from trieste.objectives import mk_observer
+
+observer = mk_observer(scaled_branin)
+
+num_initial_points = 5
+initial_query_points = mixed_search_space.sample(num_initial_points)
+initial_data = observer(initial_query_points)
+
+# %% [markdown]
+# ## Model the objective function
+#
+# We now build a Gaussian process model of the objective function using the initial data, similar
+# to the [introduction notebook](expected_improvement.ipynb).
+#
+# Since all of the data in this example is quantitative, the model does not differentiate between
+# the discrete and continuous dimensions of the search space. The Gaussian process regression model
+# treats all dimensions as continuous variables, allowing for a seamless integration of both types
+# of dimensions in the optimization process.
+
+# %%
+from trieste.models.gpflow import GaussianProcessRegression, build_gpr
+
+gpflow_model = build_gpr(
+    initial_data, mixed_search_space, likelihood_variance=1e-7
+)
+model = GaussianProcessRegression(gpflow_model)
+
+# %% [markdown]
+# ## Run the optimization loop
+#
+# The Bayesian optimization loop is run for 15 steps over the mixed search space.
+# For each step, the optimizer fixes the discrete dimension to the best points found from a random
+# initial search, and then optimizes the continuous dimension using a gradient-based method.
+# This dispatch of discrete and continuous optimization is handled by the optimizer automatically.
+
+# %%
+from trieste.bayesian_optimizer import BayesianOptimizer
+
+bo = BayesianOptimizer(observer, mixed_search_space)
+
+num_steps = 15
+result = bo.optimize(num_steps, initial_data, model)
+dataset = result.try_get_final_dataset()
+
+# %% [markdown]
+# ## Explore the results
+#
+# We can now get the best point found by the optimizer. Note that this isn't necessarily the last
+# evaluated point.
+
+# %%
+query_point, observation, arg_min_idx = result.try_get_optimal_point()
+
+print(f"query point: {query_point}")
+print(f"observation: {observation}")
+
+# %% [markdown]
+# The plot below highlights how the optimizer explored the mixed search space over the course of the
+# optimization loop. The green 'x' markers indicate the initial points, the green circles mark
+# the points evaluated during the optimization loop, and the purple circle indicates the
+# optimal point found by the optimizer.
+
+# %%
+from trieste.experimental.plotting import plot_bo_points
+
+query_points = dataset.query_points.numpy()
+observations = dataset.observations.numpy()
+
+_, ax = plot_function_2d(
+    scaled_branin,
+    ScaledBranin.search_space.lower,
+    ScaledBranin.search_space.upper,
+    contour=True,
+)
+plot_bo_points(query_points, ax[0, 0], num_initial_points, arg_min_idx)
+ax[0, 0].set_xlabel(r"$x_1$")
+ax[0, 0].set_ylabel(r"$x_2$")
+
+for point in points:
+    ax[0, 0].vlines(
+        point,
+        mixed_search_space.lower[1],
+        mixed_search_space.upper[1],
+        colors="b",
+        linestyles="dashed",
+        alpha=0.6,
+    )
+
+# %% [markdown]
+# ## LICENSE
+#
+# [Apache License 2.0](https://github.com/secondmind-labs/trieste/blob/develop/LICENSE)

docs/notebooks/quickrun/mixed_search_spaces.yaml

@@ -0,0 +1,2 @@
+replace:
+  - { from: "num_steps = \\d+", to: "num_steps = 2" }

docs/tutorials.rst

@@ -41,6 +41,7 @@ The following tutorials illustrate solving different types of optimization probl
    notebooks/multifidelity_modelling
    notebooks/rembo
    notebooks/trust_region
+   notebooks/mixed_search_spaces
 
 Frequently asked questions
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