BAxUS 0.0.8

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Increasing the Scope as You Learn: BAxUS

This is the code for our paper: Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces (Leonard Papenmeier, Matthias Poloczek, and Luigi Nardi)

Please see the full online documentation.

Installation

You have four options for installing BAxUS: PyPi, Docker, setup.py, or requirements.txt. Please make sure to install the following packages before running the non-Docker BAxUS installation. We assume that you have a Debian Buster or Bullseye based Linux distribution (e.g., Ubuntu 18.04 or Ubuntu 20.04). Please use a Docker image if you are working with a different distribution:

Installation from PyPi

pip install baxus

Installation from source

First install required software:

apt-get update && apt-get -y upgrade && apt-get -y install libsuitesparse-dev libatlas-base-dev swig libopenblas-dev libsdl2-mixer-2.0-0 libsdl2-image-2.0-0 libsdl2-2.0-0 libsdl2-ttf-2.0-0 libsdl2-dev

Then install with the setup.py:

cd baxus
pip install .

or with the requirements.txt:

cd baxus
pip install -r requirements.txt

Docker image

Alternatively, use the Docker installation. We do not share the Docker image to ensure anonymity. However, you can build the Docker image yourself with the provided Dockerfile:

First, install Docker. Next, build the Docker image

cd baxus
sudo docker build -t baxus

By default, BAxUS stores all results in a directory called results. To get the results on the host machine, first create this directory and mount it into the Docker container:

mkdir results
sudo docker run -v "$(pwd)/results":/app/results baxus /bin/bash -c "python benchmark_runner.py -id 100 -td 1 -f branin2 --adjust-initial-target-dimension"

After the run completed, the results can be obtained in the ./results directory.

Getting started

The main file is benchmark_runner.py in the project root. It can be configured with command line arguments (see Command Line Options)

For example, to run BAxUS for 1,000 function evaluations on a Branin2 function with input dimensionality 100 for one repetition run (for installation from source)

python3
benchmark_runner.py - id
100 - td
1 - n
10 - r
1 - m
1000 - f
branin2 - a
baxus - -adjust - initial - target - dimension

or, for PyPi installations,

benchmark_runner.py -id 100 -td 1 -n 10 -r 1 -m 1000 -f branin2 -a baxus --adjust-initial-target-dimension

For Docker, follow the instructions above.

Note that we need to pass an initial target dimensionality with -td 1 even though this is adjusted later by passing the option --adjust-initial-target-dimension-

Command line options

Name	Shortcut	Full argument	Default	Description
Algorithm	-a	--algorithms	baxus	The algorithm to run. Has tobe from baxus, embedded_turbo_target_dim, embedded_turbo_effective_dim, embedded_turbo_2_effective_dim, random_search
Function	-f	--functions	None	One ore several test functions. Has to be from lunarlander,mnist,robotpushing,roverplanning,hartmann6,branin2,rosenbrock5,rosenbrock10,ackley,rosenbrock,levy,dixonprice,griewank,michalewicz,rastrigin,bipedalnncontroller,acrobotnncontroller,svm,lasso10,mopta08,hartmann6in1000_rotated,rosenbrock5in1000_rotated.
Input dimensionality	-id	--input-dim	100	Input dimensionality of the function. This is overriden when the function has a fixed dimensionality.
Target dimensionality	-td	--target-dim	10	(Initial) targetdimensionality of the function. Whether initial or not depends on the algorithm. Initial for AdaTheSBO-1 and AdaTheSBO-m as they adapt the target dimensionality.
Acquisition function	None	--acquisition-function	ts	Either ts (Thompson sampling) or ei (Expected improvement)
Embedding type	None	--embedding-type	baxus	Either baxus (for the BAxUS embedding) or hesbo (for the HeSBO embedding)
Adjust initial target dimensionality	None	--adjust-initial-target-dimension	not set	Whether to adjust initial target dimensionality as described in the BAxUS paper.
Number of initial samples	-n	--n-init	None (set to target dimensionality + 1 if not set)	Number of samples.
Number of repetitions	-r	--num-repetitions	1	Number repetitions of the run.
Number of evaluations	-m	--max-evals	300	Number evaluations. Cma-ES might use a few more.
Initial baselength	-l	--initial-baselength	0.8	The base length of the trust region (default value is as in the TuRBO paper).
Minimum baselength	-lmin	--min-baselength	0.5^7	The base length a trust region is allowed to obtain (default value is as in the TuRBO paper).
Maximum baselength	-l_max	--max-baselength	1.6	The maximum base length a trust region is allowed obtain (default value is as in the TuRBO paper).
Noise standard deviation	None	--noise-std	0	The deviation of the noise. Whether this is used or not depends on the benchmark. It is generally only recognized synthetic benchmarks like Branin2 but also for the synthetic Lasso versions.
Results directory	None	--results-dir	results	The directory which the results are written. Relative to the path from which the run was started.
Run description	None	--run-description	None	Short description that will be added to the run directory
MLE multistart samples	None	--multistart-samples	100	Number of multistart samples for the MLE GD optimization. Samples will be drawn from latin hypercube
Multistarts after sampling	None	--multistart-after-sample	10	Only recognized for --mle-optimization sample-and-choose-best. Number of multi-start gradient descent optimization of the --multistart-samples best ones.
MLE optimization method	None	--mle-optimization	sample-and-choose-best Either multistart-gd or sample-and-choose-best.
Number of MLE gradient updates	None	--mle-training-steps	50	Number of GD steps in MLE maximization.
Budget until input dimensionality	None	--budget-until-input-dim	0	The budget after which BAxUS will roughly reach the input dimensionality (see paper for details). If 0: this is ignored
Verbose mode	-v	--verbose	not set	Whether to print verbose messages

Optimizing custom functions

Custom benchmark class

For practical use cases, you want to optimize your own functions instead of running benchmark functions. Let's see how we implement benchmark functions. As an example, MoptaSoftConstraints implements SyntheticBenchmark, which means in particular that it has its own __call__ function.

Let's look at the __call__ function of MoptaSoftConstraints:

def __call__(self, x):
    super(MoptaSoftConstraints, self).__call__(x)
    x = np.array(x)
    if x.ndim == 0:
        x = np.expand_dims(x, 0)
    if x.ndim == 1:
        x = np.expand_dims(x, 0)
    assert x.ndim == 2

    vals = np.array([self._call(y) for y in x]).squeeze()
    return vals

which consists of some checks that ensure that we use the internal self._call function correctly.

If you want to use BAxUS with a custom function, you can just use this implementation and replace self._call in the line vals = np.array([self._call(y) for y in x]).squeeze() with a call to your own function expecting a 1D numpy array.

Example for a custom benchmark function

A custom benchmark function could look as follows:

from typing import Union, List

import numpy as np
from baxus.benchmarks.benchmark_function import Benchmark


class Parabula(Benchmark):

    def __init__(self):
        super().__init__(dim=100, ub=10 * np.ones(100), lb=-10 * np.ones(100), noise_std=0)

    def __call__(self, x: Union[np.ndarray, List[float], List[List[float]]]):
        x = np.array(x)
        if x.ndim == 0:
            x = np.expand_dims(x, 0)
        if x.ndim == 1:
            x = np.expand_dims(x, 0)
        assert x.ndim == 2
        y = np.sum(x ** 2, axis=1)
        return y

To run BAxUS on it, either register it for the benchmark runner (see explanation below), or call BAxUS directly:

from baxus.baxus import BAxUS

baxus = BAxUS(
    run_dir="results",
    max_evals=100,
    n_init=10,
    f=Parabula(),
    target_dim=2,
    verbose=True,
)

baxus.optimize()

The results of the optimization can afterwards be obtained by

x_raw, y_raw = baxus.optimization_results_raw()  # get the points in the search space and their function values
x_inc, y_inc = baxus.optimization_results_incumbent()  # get the points in the search space and the best function value at each time step

How do I register my new function?

For this we need to look at the parsing.parse function. The first thing to do is to append your benchmark to the list of existing benchmarks, currently consisting of

required_named.add_argument(
    "-f",
    "--functions",
    nargs="+",
    choices=[
        "hartmann6",
        "branin2",
        ...,
        "MY_NEW_NAME"  # <---------------- ADD THIS LINE 
    ],
    required=True,
)

Next, we have to register the new name in the parsing.fun_mapper function:

def fun_mapper():
    return {
               **{
                   "hartmann6": Hartmann6,
                   "branin2": Branin2,
                   "rosenbrock2": functools.partial(RosenbrockEffectiveDim, effective_dim=2),
                   ...,
               "MY_NEW_NAME": MyBenchmarkImplementation  # <--------- ADD THIS LINE
           },
    ** _fun_mapper,
    }

and that's it.