Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are derived from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI). Naturally, the need to ensure analytical tractability in the model poses limitations that can ultimately hinder the efficiency and applicability of BO. In this paper, we cast the computation of EI as a binary classification problem, building on the well-known link between class probability estimation (CPE) and density-ratio estimation (DRE), and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints imposed on the model, this reformulation provides several natural advantages, not least in scalability, increased flexibility, and greater representational capacity.

Type

Publication

Presented at the NeurIPS2020 Workshop on Meta-Learning. Vancouver, Canada, 2020.

**B**ayesian **O**ptimization (BO) by Density-**R**atio **E**stimation (DRE),
or **BORE**, is a simple, yet effective framework for the optimization of
blackbox functions.
BORE is built upon the correspondence between *expected improvement (EI)*—arguably
the predominant *acquisition functions* used in BO—and the *density-ratio*
between two unknown distributions.

One of the far-reaching consequences of this correspondence is that we can
reduce the computation of EI to a *probabilistic classification* problem—a
problem we are well-equipped to tackle, as evidenced by the broad range of
streamlined, easy-to-use and, perhaps most importantly, battle-tested
tools and frameworks available at our disposal for applying a variety of approaches.
Notable among these are
Keras /
TensorFlow and
PyTorch Lightning /
PyTorch
for Deep Learning,
XGBoost for Gradient Tree Boosting,
not to mention
scikit-learn for just about
everything else.
The BORE framework lets us take direct advantage of these tools.

We provide an simple example with Keras to give you a taste of how BORE can
be implemented using a feed-forward *neural network (NN)* classifier.
A useful class that the
bore package provides is
`MaximizableSequential`

,
a subclass of
`Sequential`

from
Keras that inherits all of its existing functionalities, and provides just
one additional method.
We can build and compile a feed-forward NN classifier as usual:

```
from bore.models import MaximizableSequential
from tensorflow.keras.layers import Dense
# build model
classifier = MaximizableSequential()
classifier.add(Dense(16, activation="relu"))
classifier.add(Dense(16, activation="relu"))
classifier.add(Dense(1, activation="sigmoid"))
# compile model
classifier.compile(optimizer="adam", loss="binary_crossentropy")
```

See First contact with Keras from the Keras documentation if this seems unfamiliar to you.

The additional method provided is `argmax`

, which returns the *maximizer* of
the network, i.e. the input $\mathbf{x}$ that maximizes the final output of
the network:

```
x_argmax = classifier.argmax(bounds=bounds, method="L-BFGS-B", num_start_points=3)
```

Since the network is differentiable end-to-end wrt to input $\mathbf{x}$, this
method can be implemented efficiently using a *multi-started quasi-Newton
hill-climber* such as
L-BFGS.
We will see the pivotal role this method plays in the next section.

Using this classifier, the BO loop in BORE looks as follows:

```
import numpy as np
features = []
targets = []
# initialize design
features.extend(features_initial_design)
targets.extend(targets_initial_design)
for i in range(num_iterations):
# construct classification problem
X = np.vstack(features)
y = np.hstack(targets)
tau = np.quantile(y, q=0.25)
z = np.less(y, tau)
# update classifier
classifier.fit(X, z, epochs=200, batch_size=64)
# suggest new candidate
x_next = classifier.argmax(bounds=bounds, method="L-BFGS-B", num_start_points=3)
# evaluate blackbox
y_next = blackbox.evaluate(x_next)
# update dataset
features.append(x_next)
targets.append(y_next)
```

Let’s break this down a bit:

At the start of the loop, we construct the classification problem—by labeling instances $\mathbf{x}$ whose corresponding target value $y$ is in the top

`q=0.25`

quantile of all target values as*positive*, and the rest as*negative*.Next, we train the classifier to discriminate between these instances. This classifier should converge towards $$ \pi^{*}(\mathbf{x}) = \frac{\gamma \ell(\mathbf{x})}{\gamma \ell(\mathbf{x}) + (1-\gamma) g(\mathbf{x})}, $$ where $\ell(\mathbf{x})$ and $g(\mathbf{x})$ are the unknown distributions of instances belonging to the positive and negative classes, respectively, and $\gamma$ is the class balance-rate and, by construction, simply the quantile we specified (i.e. $\gamma=0.25$).

Once the classifier is a decent approximation to $\pi^{*}(\mathbf{x})$, we propose the maximizer of this classifier as the next input to evaluate. In other words, we are now using the classifier

*itself*as the acquisition function.How is it justifiable to use this in lieu of EI, or some other acquisition function we’re used to? And what is so special about $\pi^{*}(\mathbf{x})$?

*Well, as it turns out, $\pi^{*}(\mathbf{x})$ is equivalent to EI, up to some constant factors.*The remainder of the loop should now be self-explanatory. Namely, we

evaluate the blackbox function at the suggested point, and

update the dataset.

Here is a step-by-step animation of six iterations of this loop in action,
using the *Forrester* synthetic function as an example.
The noise-free function is shown as the solid gray curve in the main pane.
This procedure is warm-started with four random initial designs.

The right pane shows the empirical CDF (ECDF) of the observed $y$ values. The vertical dashed black line in this pane is located at $\Phi(y) = \gamma$, where $\gamma = 0.25$. The horizontal dashed black line is located at $\tau$, the value of $y$ such that $\Phi(y) = 0.25$, i.e. $\tau = \Phi^{-1}(0.25)$.

The instances below this horizontal line are assigned binary label $z=1$, while those above are assigned $z=0$. This is visualized in the bottom pane, alongside the probabilistic classifier $\pi_{\boldsymbol{\theta}}(\mathbf{x})$ represented by the solid gray curve, which is trained to discriminate between these instances.

Finally, the maximizer of the classifier is represented by the vertical solid green line. This is the location at which the BO procedure suggests be evaluated next.

We see that the procedure converges toward to global minimum of the blackbox function after half a dozen iterations.

To understand how and why this works in more detail, please read our paper! If you only have 15 minutes to spare, please watch the video recording of our talk!