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summary-marker-1-start\n\nHAWKS is a tool for generating controllably difficult synthetic data,\nused primarily for clustering.\n\n.. summary-marker-1-end\n\nThis `repo \u003chttps://github.com/sea-shunned/hawks\u003e`_ is associated with the following papers:\n\n.. paper-marker-1-start\n\n1. `Shand, C. \u003chttp://sea-shunned.github.io/\u003e`_, `Allmendinger, R. \u003chttps://personalpages.manchester.ac.uk/staff/Richard.Allmendinger/\u003e`_, `Handl, J. \u003chttps://personalpages.manchester.ac.uk/staff/Julia.Handl/\u003e`_, `Webb, A. \u003chttp://www.awebb.info/\u003e`_, \u0026 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)**\n2. `Shand, C. \u003chttp://sea-shunned.github.io/\u003e`_, `Allmendinger, R. \u003chttps://personalpages.manchester.ac.uk/staff/Richard.Allmendinger/\u003e`_, `Handl, J. \u003chttps://personalpages.manchester.ac.uk/staff/Julia.Handl/\u003e`_, `Webb, A. \u003chttp://www.awebb.info/\u003e`_, \u0026 Keane, J. (2021, Dec.). HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis. Published in **IEEE Transactions on Evolutionary Computation**. https://doi.org/10.1109/TEVC.2021.3137369\n\nRelated BibTeX info:\n\n.. code-block:: bibtex\n\n    @inproceedings{shand2019evolving,\n        title={Evolving controllably difficult datasets for clustering},\n        author={Shand, Cameron and Allmendinger, Richard and Handl, Julia and Webb, Andrew and Keane, John},\n        booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},\n        pages={463--471},\n        year={2019},\n        url={https://doi.org/10.1145/3321707.3321761}\n    }\n    @article{shand2021hawks,\n    author={Shand, Cameron and Allmendinger, Richard and Handl, Julia and Webb, Andrew and Keane, John},\n    journal={IEEE Transactions on Evolutionary Computation},\n    title={HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis},   year={2021},\n    volume={},\n    number={},\n    pages={1-1},\n    doi={10.1109/TEVC.2021.3137369}\n    }\n\nThe academic/technical details can be found there. What follows here is\na practical guide to using HAWKS to generate synthetic data.\n\n.. paper-marker-1-end\n\nIf you use HAWKS to generate data that forms part of a paper, please\ncite the paper above and link to this repo.\n\n\n.. installation-marker-start\n\nInstallation\n------------\n\nInstallation is available through pip by:\n\n``pip install hawks``\n\n.. installation-marker-end\n\nor by cloning this repo (and installing locally using\n``pip install .``). HAWKS was written for Python 3.6+. Other dependencies are specified in the `setup.py \u003chttps://github.com/sea-shunned/hawks/blob/master/setup.py\u003e`_ file.\n\n\nRunning HAWKS\n-------------\n\nThe parameters of hawks are configured via a config file system. Details\nof the parameters are found in the `documentation \u003chttps://hawks.readthedocs.io/parameters\u003e`_. For any parameters\nthat are not specified, default values will be used (as defined in\n``hawks/defaults.json``).\n\n.. example-marker-start\n\nThe example below illustrates how to run ``hawks``. Either a dictionary\nor a path to a JSON config can be provided to override any of the\ndefault values. Further examples can be found `here \u003chttps://hawks.readthedocs.io/examples\u003e`_. \n\n.. Need to turn the bit below into an example file and then just include that\n\n.. code-block:: python\n\n    \"\"\"Single, simple HAWKS run, with KMeans applied to the best dataset\n    \"\"\"\n    import numpy as np\n    from sklearn.cluster import KMeans\n    from sklearn.metrics import adjusted_rand_score\n    import hawks\n\n    # Set the magic seed number\n    SEED_NUM = 42\n    # Set the seed number in the config\n    config = {\n        \"hawks\": {\n            \"folder_name\": \"simple_example\",\n            \"seed_num\": SEED_NUM\n        },\n        \"dataset\": {\n            \"num_clusters\": 5\n        },\n        \"objectives\": {\n            \"silhouette\": {\n                \"target\": 0.9\n            }\n        }\n    }\n    # Any missing parameters will take from hawks/defaults.json\n    generator = hawks.create_generator(config)\n    # Run the generator\n    generator.run()\n    # Let's plot the best individual found\n    generator.plot_best_indivs(show=True)\n    # Get the best dataset found and it's labels\n    datasets, label_sets = generator.get_best_dataset()\n    # Stored as a list for multiple runs\n    data, labels = datasets[0], label_sets[0]\n    # Run KMeans on the data\n    km = KMeans(\n        n_clusters=len(np.unique(labels)), random_state=SEED_NUM\n    ).fit(data)\n    # Plot the output of KMeans\n    hawks.plotting.scatter_prediction(data, km.labels_)\n    # Get the Adjusted Rand Index for KMeans on the data\n    ari = adjusted_rand_score(labels, km.labels_)\n    print(f\"ARI: {ari}\")\n\n\n.. example-marker-end\n\n\nDocumentation\n-------------\n\nFor further information about how to use HAWKS, including examples, please see the `documentation \u003chttps://hawks.readthedocs.io/\u003e`__.\n\n\nIssues\n------\n\nAs this work is still in development, plain sailing is not guaranteed.\nIf you encounter an issue, first ensure that ``hawks`` is running as\nintended by navigating to the tests directory, and running\n``python tests.py``. If any test fails, please add details of this\nalongside your original problem to an issue on the `GitHub repo \u003chttps://github.com/sea-shunned/hawks\u003e`__.\n\n\nContributing\n------------\n\n.. contributing-marker-start\n\nAt 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 \u003chttps://github.com/sea-shunned/hawks/issues\u003e`__.\n\nI 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 \u003chttps://sea-shunned.github.io/\u003e`__.\n\n.. contributing-marker-end","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsea-shunned%2Fhawks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsea-shunned%2Fhawks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsea-shunned%2Fhawks/lists"}