qdpy 0.1.2

Quality-Diversity algorithms in Python

Package qdpy implements recent Quality-Diversity algorithms: Map-Elites, CVT-Map-Elites, NSLC, SAIL, etc. QD algorithms can be accessed directly, but qdpy also includes building blocks that can be easily assembled together to build your own QD algorithms. It can be used with parallelism mechanisms and in distributed environments.

This package requires Python 3.6+.

qdpy includes the following features:

• Generic support for diverse Containers: Grids, Novelty-Archives, Populations, etc

• Optimisation algorithms for QD: random search methods, quasi-random methods, evolutionary algorithms

• Support for multi-objective optimisation methods

• Possible to use optimisation methods not designed for QD, such as [CMA-ES](https://arxiv.org/pdf/1604.00772.pdf)

• Parallelisation of evaluations, using parallelism libraries, such as multiprocessing, concurrent.futures or [SCOOP](https://github.com/soravux/scoop)

• Easy integration with the popular [DEAP](https://github.com/DEAP/deap) evolutionary computation framework

Install

qdpy requires Python 3.6+. It can be installed with:

pip3 install qdpy

qdpy includes optional features that need extra packages to be installed:

• cma for CMA-ES support

• deap to integrate with the DEAP library

• tables to output results files in the HDF5 format

• tqdm to display a progress bar showing optimisation progress

• colorama to add colours to pretty-printed outputs

You can install qdpy and all of these optional dependencies with:

pip3 install qdpy[all]

The latest version can be installed from the GitLab repository:

pip3 install git+https://gitlab.com/leo.cazenille/qdpy.git@master

Example

From a python shell:

from qdpy import algorithms, containers, benchmarks, plots

# Create container and algorithm. Here we use MAP-Elites, by illuminating a Grid container by evolution.
grid = containers.Grid(shape=(64,64), max_items_per_bin=1, fitness_domain=((0., 1.),), features_domain=((0., 1.), (0., 1.)))
algo = algorithms.RandomSearchMutPolyBounded(grid, budget=60000, batch_size=500,
        dimension=3, optimisation_task="maximisation")

# Create a logger to pretty-print everything and generate output data files
logger = algorithms.AlgorithmLogger(algo)

# Define evaluation function
eval_fn = algorithms.partial(benchmarks.illumination_rastrigin_normalised,
        nb_features = len(grid.shape))

# Run illumination process !
best = algo.optimise(eval_fn)

# Print results info
print(algo.summary())

# Plot the results
plots.default_plots_grid(logger)

print("All results are available in the '%s' pickle file." % logger.final_filename)

Usage, Documentation

Please to go the GitLab repository main page (https://gitlab.com/leo.cazenille/qdpy) and the documentation main page (https://leo.cazenille.gitlab.io/qdpy/).

Author:

Leo Cazenille, 2018-*

License:

LGPLv3, see LICENSE file.