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.