hawks 0.2.0

A package for generating synthetic clusters, with parameters to customize different aspects of the complexity of the cluster structure

HAWKS Data Generator
====================

.. image:: docs/source/images/hawks_animation.gif
:alt: Example gif of HAWKS
:scale: 65 %
:align: center

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HAWKS is a tool for generating controllably difficult synthetic data,
used primarily for clustering.

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This `repo <https://github.com/sea-shunned/hawks>`_ is associated with the following paper:

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1. `Shand, C. <http://sea-shunned.github.io/>`_, `Allmendinger, R. <https://personalpages.manchester.ac.uk/staff/Richard.Allmendinger/>`_, `Handl, J. <https://personalpages.manchester.ac.uk/staff/Julia.Handl/>`_, `Webb, A. <http://www.awebb.info/>`_, & Keane, J. (2019, July). Evolving controllably difficult datasets for clustering. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 463-471). https://doi.org/10.1145/3321707.3321761 **(Nominated for best paper on the evolutionary machine learning track at GECCO'19)**

The academic/technical details can be found there. What follows here is
a practical guide to using HAWKS to generate synthetic data.

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If you use HAWKS to generate data that forms part of a paper, please
cite the paper above and link to this repo.


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Installation
------------

Installation is available through pip by:

``pip install hawks``

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or by cloning this repo (and installing locally using
``pip install .``). HAWKS was written for Python 3.6+. Other dependencies are specified in the `setup.py <https://github.com/sea-shunned/hawks/blob/master/setup.py>`_ file.


Running HAWKS
-------------

The parameters of hawks are configured via a config file system. Details
of the parameters are found in the `documentation <https://hawks.readthedocs.io/parameters>`_. For any parameters
that are not specified, default values will be used (as defined in
``hawks/defaults.json``).

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The example below illustrates how to run ``hawks``. Either a dictionary
or a path to a JSON config can be provided to override any of the
default values. Further examples can be found `here <https://hawks.readthedocs.io/examples>`_.

.. Need to turn the bit below into an example file and then just include that

.. code-block:: python

"""Single, simple HAWKS run, with KMeans applied to the best dataset
"""
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
import hawks

# Set the magic seed number
SEED_NUM = 42
# Set the seed number in the config
config = {
"hawks": {
"folder_name": "simple_example",
"seed_num": SEED_NUM
},
"dataset": {
"num_clusters": 5
},
"objectives": {
"silhouette": {
"target": 0.9
}
}
}
# Any missing parameters will take from hawks/defaults.json
generator = hawks.create_generator(config)
# Run the generator
generator.run()
# Let's plot the best individual found
generator.plot_best_indivs(show=True)
# Get the best dataset found and it's labels
datasets, label_sets = generator.get_best_dataset()
# Stored as a list for multiple runs
data, labels = datasets[0], label_sets[0]
# Run KMeans on the data
km = KMeans(
n_clusters=len(np.unique(labels)), random_state=SEED_NUM
).fit(data)
# Plot the output of KMeans
hawks.plotting.scatter_prediction(data, km.labels_)
# Get the Adjusted Rand Index for KMeans on the data
ari = adjusted_rand_score(labels, km.labels_)
print(f"ARI: {ari}")


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Documentation
-------------

For further information about how to use HAWKS, including examples, please see the `documentation <https://hawks.readthedocs.io/>`__.


Issues
------

As this work is still in development, plain sailing is not guaranteed.
If you encounter an issue, first ensure that ``hawks`` is running as
intended by navigating to the tests directory, and running
``python tests.py``. If any test fails, please add details of this
alongside your original problem to an issue on the `GitHub repo <https://github.com/sea-shunned/hawks>`__.


Contributing
------------

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At present, this is primarily academic work, so future developments will be released here after they have been published. If you have any suggestions or simple feature requests for HAWKS as a tool to use, please raise that on the `GitHub repo <https://github.com/sea-shunned/hawks/issues>`__.

I have various directions for HAWKS at present, and can only work on a subset of them, and so involvement with more people would be great. If you would like to extend this work or collaborate, please `contact me <https://sea-shunned.github.io/>`__.

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