iohxplainer 0.9.1

eXplainable Benchmarking for Iterative Optimization Heuristics

IOHxplainer

eXplainable benchmarking using XAI methods for iterative optimization heuristics.

IOHxplainer builds on top of the IOH package, including IOHexperimenter and IOHprofiler.

Installation

Either install the package from source or via Pypi:

pip install iohxplainer

Quick Start

Import the package

from iohxplainer import explainer

Define the search space and the algorithm runner, basically the hyper-parameters and ranges you want to explore for a given optimization algorithm or modular algorithm. In this example we use the Differential Evolution implementation by scipy and we take a limited set of hyper-parameter options to explore.

from scipy.optimize import differential_evolution
from ConfigSpace import ConfigurationSpace
import numpy as np

confSpace = ConfigurationSpace(
    {
        "strategy": ["best1bin", "best1exp", "rand1exp", "randtobest1exp"],
        "popsize": [1, 2, 5, 10],
    }
) 

features = ["strategy", "popsize"]

def run_de(func, config, budget, dim, *args, **kwargs):
    bounds = [(-5,5)] * dim #define the boundaries
    result = differential_evolution(func, bounds, strategy=config.get("strategy"), popsize=config.get("popsize"))

Inside the algorithm runner (run_de) we define the boundaries of the to be optimized function. Since we will be using the BBOB benchmark suite for this example, we set the boundaries to -5,5 for each dimension of the problem.

Next we create the explainer object and run the experiments.

de_explainer = explainer(
    run_de,
    confSpace,
    algname="Differential Evolution",
    dims=[2],  # we test in 2D for this example.
    fids=np.arange(1, 25),  # we use all 24 BBOB functions.
    iids=[1, 2, 3, 4, 5], # we use the first 5 instances for each BBOB function.
    reps=3, # we repeat each run 3 times with different random seeds.
    sampling_method="grid",  # can also be set to random, since we have a few options we can make a full enumeration of the space.
    grid_steps_dict={}, # only needed if we have continuous parameters.
    sample_size=None,  # only used with random sampling method
    budget=10000,  # evaluation budget of an optimization run.
    seed=1, # starting random seed for reproducability.
    verbose=True,
)

#we start running the experiments in paralell and store intermediate results in a csv (to allow a restart when crashing)
de_explainer.run(paralell=True, start_index=0, checkpoint_file="checkpoint.csv")
#store the final results as pkl file.
de_explainer.save_results("de.pkl")

Finally we can analyze the run data and make various plots and reports.

import os
os.mkdir("de_report")
de_explainer.to_latex_report(filename="de_report", img_dir="de_report/")

Also see the python notebook file for additional demo material.

Experimental setup

All experiments and setup from the scientific paper can be found in the (experiments)[experiments/] folder.
The Modular CMA and Modular DE setup are specified in the config.py file.

Reproducing the experiments

Steps to reproduce the experiments from the paper. Make sure you checkout the ModularCMAES framework from Github and install the cpp version. See https://github.com/IOHprofiler/ModularCMAES for installation instructions.

The experiments are run on two modular frameworks, Modular DE and Modular CMAES. Both can be easily installed poetry add modde modcma.

1. Run all Modular DE or Modular CMA configurations using the (de|cma_es)_run-configurations.py file, writes a pkl file as result. (This step takes a few days on a supercomputer with 120 cores). The processed results of this step and the step 2 can also be downloaded (since it takes roughly a month to run on a CPU cluster): (cma_final_processed.pkl)[https://www.dropbox.com/scl/fi/ry9b1nnn7681o3b08o073/cma_final_processed.pkl?rlkey=zi9kjjs8t870ldzx9iw87fpm9&dl=0] and (de_final_processed.pkl)[https://www.dropbox.com/scl/fi/f46q2tuhylupm7vgth948/de_final_processed.pkl?rlkey=j87etm66ilvglue0l35mvw8n7&dl=0].
2. Pre-process the pickle files with (de|cma_es)_process_pkl.py.
3. Analyse the performance data of all configurations using IOH-Xplainer (de|cma_es)_analyse.py.
4. Compare the two frameworks using compare_de_cma.py. Writes the result as latex file (compare-new.tex).
5. Perform automated algorithm configuration experiment using (de|cma_es)_AAC-notebook.ipynb files.

Setting up the dev environment

• Checkout this code.
• Make sure pipx (https://github.com/pypa/pipx) is installed with python 3.8+
• Install Poetry with pipx pipx install poetry
• Install dependencies poetry install
• Run tests poetry run pytest

Cite us

TBA