{
    "info": {
        "author": "Laurent Vermue, Maciej Korzepa, Petr Taborsky, Morten M\u00f8rup",
        "author_email": "<lauve@dtu.dk>, <mjko@dtu.dk>, <ptab@dtu.dk>, <mmor@dtu.dk>",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "Bayesian Cut Package\n====================\n\n.. image:: https://img.shields.io/pypi/v/bayesian_cut.svg\n    :target: https://pypi.python.org/pypi/bayesian_cut\n    :alt: Pypi Version\n.. image:: https://img.shields.io/pypi/l/bayesian_cut.svg\n    :target: https://pypi.python.org/pypi/bayesian_cut\n    :alt: License\n.. image:: https://travis-ci.com/DTUComputeCognitiveSystems/bayesian_cut.svg?branch=master\n   :target: https://travis-ci.com/DTUComputeCognitiveSystems/bayesian_cut\n   :alt: Build Status\n\nThe Bayesian Cut Python package provides an easy to use API for the straight-forward application of Bayesian network\ncuts using a full Bayesian inference framework based on the Gibbs-Sampler using the degree corrected Stochastic\nBlockmodel (dc-SBM) or the Bayesian Cut (BC).\nFurthermore it provides modularity, ratio-cut and norm cut based spectral network cut methods.\nIt also provides a rich visualization library that allow an easy analysis of posterior solution landscapes and network\ncuts obtained by the various methods.\n\nJupyter Notebooks with examples on how to use the package can be found at\nhttps://github.com/DTUComputeCognitiveSystems/bayesian_cut/tree/master/examples\n\n\nInstallation\n------------\n\n-  | Install the package for Python3 using the following command. Some\n     dependencies might require an upgrade (scikit-learn, numpy and\n     scipy).\n   | ``$ pip install bayesian_cut``\n\n-  | Now you can import the bayesian cut class by typing\n   | ``from bayesian_cut.cuts import Model``\n\nQuick Start\n-----------\n\nUse the bayesian_cut package for performing graph cuts\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   # Load the package\n   from bayesian_cut.data.load import load_data\n   from bayesian_cut.cuts.bayesian_models import Model\n\n   # Load an example network\n   network_name = 'karate'\n   X, Y = load_data(network=network_name, labels=True, remove_disconnected=True)\n\n   # Set the parameters for the model\n   n_samples_per_chain = 75\n   n_chains = 15\n   C = 2\n   model_params = {\n       'alpha_in': 1e-2,\n       'beta_in': 1e-2,\n       'alpha_out': 1e-2,\n       'beta_out': 1e-2,\n       'b': 1,\n       'gamma': 3\n   }\n\n   # Define the model\n   BC = Model('ShiftedApproximateBayesianCommunityDetection',\n               X,\n               model_params,\n               Y=Y,\n               C=C,\n               block_sampling=False,\n               marginalize_phi=True\n               )\n\n   # Add the number of chains to run\n   BC.add_chains(number_of_chains=n_chains)\n\n   # Run the Gibbs sampler\n   BC.run_chains(n_samples=n_samples_per_chain,\n                 n_prior_updates=20,\n                 verbose=2,\n                 save_all_samples=False,\n                 parallel=True\n                 )\n\n   # Obtain the cut with the highest log-likelihood\n   graph_cut_z_vector = BC.get_best_chain().max_log_lik_z_\n\n   # Plot the cut as an adjacency matrix\n   from bayesian_cut.utils import utils\n   utils.cluster_plot(BC)\n\n   # Done\n\n\n",
        "description_content_type": "text/x-rst",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/DTUComputeCognitiveSystems/bayesian_cut",
        "keywords": "",
        "license": "new BSD",
        "maintainer": "",
        "maintainer_email": "",
        "name": "bayesian-cut",
        "package_url": "https://pypi.org/project/bayesian-cut/",
        "platform": "",
        "project_url": "https://pypi.org/project/bayesian-cut/",
        "project_urls": {
            "Homepage": "https://github.com/DTUComputeCognitiveSystems/bayesian_cut"
        },
        "release_url": "https://pypi.org/project/bayesian-cut/0.1.2b0/",
        "requires_dist": [
            "numpy (>=1.13.3)",
            "scipy (>=1.0.0)",
            "scikit-learn (>=0.20.0)",
            "networkx (>=2.0)",
            "joblib (>=0.12.3)",
            "sphinx (>=1.6) ; extra == 'docs'",
            "sphinx-rtd-theme ; extra == 'docs'",
            "nbsphinx ; extra == 'docs'",
            "nbsphinx-link ; extra == 'docs'",
            "matplotlib ; extra == 'extras'",
            "plotly ; extra == 'extras'",
            "naturalneighbor ; extra == 'extras'",
            "seaborn ; extra == 'extras'",
            "pytest ; extra == 'tests'"
        ],
        "requires_python": ">=3.5",
        "summary": "An implementation of bayesian cut methods",
        "version": "0.1.2b0"
    },
    "last_serial": 4894123,
    "releases": {
        "0.0.1b0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "78762c7b0d1ac1473094c736fddd8be0",
                    "sha256": "09f2831090ab5c946b7931298dd9e6ae58e9e908ff8469f7038711b522bf69d4"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.0.1b0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "78762c7b0d1ac1473094c736fddd8be0",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.5",
                "size": 24113,
                "upload_time": "2019-02-18T21:30:04",
                "url": "https://files.pythonhosted.org/packages/67/a3/2766e37190554962935f5e1583de5fd4a7b54fdbce2d3c28a545b0a05dae/bayesian_cut-0.0.1b0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "2bf270d362b0aef9d49c2a073a3cd773",
                    "sha256": "bfad6631904cbc864b90e7cf3b6dd1e50fd5f2c160fb343b6bf8e694d073bc29"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.0.1b0.tar.gz",
                "has_sig": false,
                "md5_digest": "2bf270d362b0aef9d49c2a073a3cd773",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.5",
                "size": 21289,
                "upload_time": "2019-02-18T21:30:06",
                "url": "https://files.pythonhosted.org/packages/16/6b/1b3df13748bcd057ddc752c9137008a09ef9bd5a44d6692f6e188f98c7fe/bayesian_cut-0.0.1b0.tar.gz"
            }
        ],
        "0.0.2b0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "358d0429c3aaad6eb6b3ccbaf7a8c075",
                    "sha256": "abd3140293204c4f16d772b4c74524bc9807a5719046783d5f925170bcf31997"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.0.2b0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "358d0429c3aaad6eb6b3ccbaf7a8c075",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.5",
                "size": 25956,
                "upload_time": "2019-02-19T08:48:07",
                "url": "https://files.pythonhosted.org/packages/38/37/2954586a28279ca0e7d82303dc3c2b3dbc04d209d1849768635497133b44/bayesian_cut-0.0.2b0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "1b3ecd91d936b767b32e39253d928c74",
                    "sha256": "aa7a66be9fc1e05be661415c4ef9f92eb72b94eb23f58be742b9afdcfc9bc2f5"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.0.2b0.tar.gz",
                "has_sig": false,
                "md5_digest": "1b3ecd91d936b767b32e39253d928c74",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.5",
                "size": 22995,
                "upload_time": "2019-02-19T08:48:09",
                "url": "https://files.pythonhosted.org/packages/1d/a8/5494ea23b41ae2e01c97970152702d96a39d70cd2ce56528c3cddb5f3ccb/bayesian_cut-0.0.2b0.tar.gz"
            }
        ],
        "0.1.0b0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "003c65fe2e32dafa23da6df8d62a72aa",
                    "sha256": "42cd764811ae2e14044c64fbb72af3f7395745599cccae67cf21252862dbc61a"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.0b0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "003c65fe2e32dafa23da6df8d62a72aa",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.5",
                "size": 28570,
                "upload_time": "2019-02-23T20:07:21",
                "url": "https://files.pythonhosted.org/packages/35/2b/5f3d53b7a96959d7ae17500852f6b9916f3d43db7b5fb5be206c70c8bd47/bayesian_cut-0.1.0b0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "23efddef4d7feb4d0f6d2cccbb810a89",
                    "sha256": "9afbf9a282b8a3bf9eaf606e64fb93bd76baf091fd737fb65bb0a2b9d12d5621"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.0b0.tar.gz",
                "has_sig": false,
                "md5_digest": "23efddef4d7feb4d0f6d2cccbb810a89",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.5",
                "size": 24454,
                "upload_time": "2019-02-23T20:07:22",
                "url": "https://files.pythonhosted.org/packages/4a/11/3d08a6a1d1f61987dbe4e091a0d3c9107137958e438312b9fda3027f0a23/bayesian_cut-0.1.0b0.tar.gz"
            }
        ],
        "0.1.1b0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "b650485817487f811c2d2ae2190ca26a",
                    "sha256": "b222fa23504eed879b8ddf540c5c4f406b91a77a02ae28ee56739afb85f1ee5b"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.1b0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "b650485817487f811c2d2ae2190ca26a",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.5",
                "size": 28594,
                "upload_time": "2019-02-24T00:07:50",
                "url": "https://files.pythonhosted.org/packages/fd/14/a44c9daa9fd733268eb0ef6c86a6aca531b3e9ce92ff27e576bcf18493c5/bayesian_cut-0.1.1b0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "70feefe0d5fd88f769b46c99d91ab9b6",
                    "sha256": "0c42c6215b226207cf3dd0fa8b70540cde9366f60ff39a69d0a6e8cffe196aeb"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.1b0.tar.gz",
                "has_sig": false,
                "md5_digest": "70feefe0d5fd88f769b46c99d91ab9b6",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.5",
                "size": 24473,
                "upload_time": "2019-02-24T00:07:52",
                "url": "https://files.pythonhosted.org/packages/d2/1c/486ca8d2547e3029a991d0426ab7c8a163b1da7e9eefefc71285dec8c899/bayesian_cut-0.1.1b0.tar.gz"
            }
        ],
        "0.1.2b0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "604b841094814b4d13151bf0daee829a",
                    "sha256": "5fa5c7616a411bdb89b09ddebdf487c754f96be40a04534b5ab6baf5ddc68379"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.2b0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "604b841094814b4d13151bf0daee829a",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.5",
                "size": 29468,
                "upload_time": "2019-03-04T12:12:03",
                "url": "https://files.pythonhosted.org/packages/8e/96/8f429a2111ae2e097d6e0b7c998185d5074f1010e348d9da181de7270b30/bayesian_cut-0.1.2b0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "d1bc3977190f8530993072ebd4666eab",
                    "sha256": "45eebf057ade356b71bcb401aff9a4ce8f8f8a73b7c16f471489146040bce5e2"
                },
                "downloads": -1,
                "filename": "bayesian_cut-0.1.2b0.tar.gz",
                "has_sig": false,
                "md5_digest": "d1bc3977190f8530993072ebd4666eab",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.5",
                "size": 25098,
                "upload_time": "2019-03-04T12:12:05",
                "url": "https://files.pythonhosted.org/packages/8d/f8/c8782d08f573699b184dd8f581ed4eb593e1d6290c67a8deace245ebc273/bayesian_cut-0.1.2b0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "604b841094814b4d13151bf0daee829a",
                "sha256": "5fa5c7616a411bdb89b09ddebdf487c754f96be40a04534b5ab6baf5ddc68379"
            },
            "downloads": -1,
            "filename": "bayesian_cut-0.1.2b0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "604b841094814b4d13151bf0daee829a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.5",
            "size": 29468,
            "upload_time": "2019-03-04T12:12:03",
            "url": "https://files.pythonhosted.org/packages/8e/96/8f429a2111ae2e097d6e0b7c998185d5074f1010e348d9da181de7270b30/bayesian_cut-0.1.2b0-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "d1bc3977190f8530993072ebd4666eab",
                "sha256": "45eebf057ade356b71bcb401aff9a4ce8f8f8a73b7c16f471489146040bce5e2"
            },
            "downloads": -1,
            "filename": "bayesian_cut-0.1.2b0.tar.gz",
            "has_sig": false,
            "md5_digest": "d1bc3977190f8530993072ebd4666eab",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.5",
            "size": 25098,
            "upload_time": "2019-03-04T12:12:05",
            "url": "https://files.pythonhosted.org/packages/8d/f8/c8782d08f573699b184dd8f581ed4eb593e1d6290c67a8deace245ebc273/bayesian_cut-0.1.2b0.tar.gz"
        }
    ]
}