{
    "info": {
        "author": "Jared Katzman",
        "author_email": "jaredleekatzman@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
        ],
        "description": "DeepSurv\n========\n\nDeepSurv implements a deep learning generalization of the Cox proportional hazards model using Theano and Lasagne. \n\nDeepSurv has an advantage over traditional Cox regression because it does not require an *a priori* selection of covariates, but learns them adaptively. \n\nDeepSurv can be used in numerous survival analysis applications. One medical application is provided, recommend_treatment, which provides treatment recommendations for a set of patient observations. \n\nInstallation:\n-------------\n\nDependencies:\n^^^^^^^^^^^^^\n\nTheano, Lasagne, lifelines, matplotlib (for visualization) and all of their respective dependencies. \n\nInstalling:\n^^^^^^^^^^^\n\nYou can install *DeepSurv* using\n\n::\n\n\tpip install deepsurv\n\nfrom the command line.\n\nRunning the tests:\n^^^^^^^^^^^^^^^^^^\nAfter installing, you can optionally run the test suite with\n\n::\n\n\tpy.test\n\nfrom the command line while in the module main directory.\n\nTraining a Network:\n-------------------\n\nTraining DeepSurv can be done in a few lines. \nFirst, all you need to do is prepare the datasets to have the following keys:\n\n::\n\n\t{ \n\t\t'x': (n,d) observations (dtype = float32), \n\t \t't': (n) event times (dtype = float32),\n\t \t'e': (n) event indicators (dtype = int32)\n\t}\n\nThen prepare a dictionary of hyper-parameters. And it takes only two lines to train a network:\n\n::\n\n\tnetwork = deepsurv.DeepSurv(**hyperparams)\n\tlog = network.train(train_data, valid_data, n_epochs=500)\n\nYou can then evaluate its success on testing data:\n\n::\n\n\tnetwork.get_concordance_index(**test_data)\n\t>> 0.62269622730138632\n\nIf you have matplotlib installed, you can visualize the training and validation curves after training the network:\n\n::\n\n\tdeepsurv.plot_log(log)\n\n\n\nLicense:\n-------- \n\nMIT License\n\nCopyright (c) 2016, Jared Katzman\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "UNKNOWN",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/jaredleekatzman/DeepSurv",
        "keywords": "survival analysis deep learning cox regression",
        "license": "MIT",
        "maintainer": null,
        "maintainer_email": null,
        "name": "deepsurv",
        "package_url": "https://pypi.org/project/deepsurv/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/deepsurv/",
        "project_urls": {
            "Download": "UNKNOWN",
            "Homepage": "https://github.com/jaredleekatzman/DeepSurv"
        },
        "release_url": "https://pypi.org/project/deepsurv/0.1.0/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "Deep Cox Proportional Hazards Network for deep learning and survival analysis.",
        "version": "0.1.0"
    },
    "last_serial": 2151880,
    "releases": {
        "0.1.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "1958b89002b903e784ea03eb8e2866e8",
                    "sha256": "6e1fc72bbe6a8c1396702005daeb56f50a0d2a3f64c67463fa87d2649926251f"
                },
                "downloads": -1,
                "filename": "deepsurv-0.1.0.tar.gz",
                "has_sig": false,
                "md5_digest": "1958b89002b903e784ea03eb8e2866e8",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 10202,
                "upload_time": "2016-06-05T19:03:34",
                "url": "https://files.pythonhosted.org/packages/cb/55/416bc3610452c54ad3028bfee3a4c69b269a2b9d026be90c7221039222cc/deepsurv-0.1.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "1958b89002b903e784ea03eb8e2866e8",
                "sha256": "6e1fc72bbe6a8c1396702005daeb56f50a0d2a3f64c67463fa87d2649926251f"
            },
            "downloads": -1,
            "filename": "deepsurv-0.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "1958b89002b903e784ea03eb8e2866e8",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 10202,
            "upload_time": "2016-06-05T19:03:34",
            "url": "https://files.pythonhosted.org/packages/cb/55/416bc3610452c54ad3028bfee3a4c69b269a2b9d026be90c7221039222cc/deepsurv-0.1.0.tar.gz"
        }
    ]
}