Multi-Fidelity Optimization Benchmark APIs

This repository provides APIs for the MLP benchmark in HPOBench, HPOlib, JAHS-Bench-201, and LCBench in YAHPOGym. Additionally, we provide multi-fidelity version of Hartmann and Branin functions.

Install

As JAHS-Bench-201 and LCBench are surrogate benchmarks and they may conflict your project, we provide separate installation:

# Minimal install
$ pip install mfhpo-benchmark-api

# Minimal install + LCBench
$ pip install mfhpo-benchmark-api[lcbench]

# Minimal install + JAHS-Bench-201
$ pip install mfhpo-benchmark-api[jahs]

# Full install (Minimal + LCBench + JAHS-Bench-201)
$ pip install mfhpo-benchmark-api[full]

Note that each benchmark requires the download of tabular or surrogate data.

For HPOBench and HPOlib, please follow README of this repository. In the instruction, <YOUR_DATA_PATH> should be replaced with ~/hpo_benchmarks/hpolib for HPOlib and ~/hpo_benchmarks/hpobench for HPOBench.

NOTE If we set an environment variable as follows, we can give some flexibility to the data path:

# In this example, we use datasets in /root/path/to/datasets/hpo_benchmarks/.
$ export BENCHMARK_ROOT_PATH=/root/path/to/datasets/

For JAHS-Bench-201, run the following command:

$ cd ~/hpo_benchmarks/jahs
$ wget https://ml.informatik.uni-freiburg.de/research-artifacts/jahs_bench_201/v1.1.0/assembled_surrogates.tar
# Uncompress assembled_surrogates.tar

For LCBench, access to the website and download lcbench.zip. Then you need to unzip the lcbench.zip in ~/hpo_benchmarks/lcbench.

Examples

Examples are available in examples. For example, you can test with the following code:

from benchmark_apis import MFBranin


bench = MFBranin()
for i in range(10):
    config = bench.config_space.sample_configuration().get_dictionary()
    output = bench(eval_config=config, fidels={"z0": 100})
    print(output)