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        "author": "Mindstrong Health Data Science",
        "author_email": "datascience@mindstronghealth.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Affero General Public License v3",
            "Programming Language :: Python :: 3"
        ],
        "description": "=================================================\nMindstrong Health Digital Biomarker Model Fitting\n=================================================\n\nThis package uses Supervised Kernel Principal Components Analysis with cross validation to fit digital biomarker data to target measurements. The software was written by members of the Mindstrong Health Data Science team:\n\n    * Paul Dagum, MD, PhD\n    * Greg Ryslik, PhD, FCAS, MAAA\n    * Bob Dougherty, PhD\n    * Patrick Staples, PhD\n\nPlease contact us at `datascience@mindstronghealth.com <datascience@mindstronghealth.com>`_.\n\nNOTE: If you use this software in your work, please cite the following `paper <https://www.nature.com/articles/s41746-018-0018-4>`_:\n\n    Dagum, P. (2018) Digital biomarkers of cognitive function. npj Digital Medicine, issue 1, article 10. DOI: 10.1038/s41746-018-0018-4.\n\nInstallation\n------------\n\nThe easiest way to install the package is via ``easy_install`` or ``pip``::\n\n    $ pip install mindstrong_biomarker_modelfit\n\nThis should also take care of the dependencies (numpy, scipy, pandas, and sklearn).\n\nUsage\n-----\n\nSimulated digital biomarker and target measure data are included with the project. To fit a model to these example data::\n\n    import numpy as np\n    import pandas as pd\n    import os\n    from mindstrong import mindstrong_modelfit as mindstrong\n\n    target_file = mindstrong.get_example_data('example_targets.csv')\n    feature_file = mindstrong.get_example_data('example_features.csv')\n    target_colname = 'target1'\n\n    # Load target data\n    target_df = pd.read_csv(target_file)\n    target_df.set_index('device_id', inplace=True)\n\n    # Load Feature Data\n    feature_df = pd.read_csv(feature_file).set_index(['device_id', 'targetDOY'])\n\n    # Cross Validated supervised kernel PCA model-fitting\n    cvdf, best_model = mindstrong.calculateCrossValidatedCorrelation(target_df,\n                                                                     feature_df,\n                                                                     target_colname,\n                                                                     fold_type='n',\n                                                                     n_folds=5,\n                                                                     kernel_training='linear',\n                                                                     kernel_training_param=1,\n                                                                     kernel_target='linear',\n                                                                     kernel_target_param=1,\n                                                                     regularization=0.1)\n\n    # Print the final results\n    print(best_model)\n\n\n\nCopyright & License\n-------------------\n\nCopyright (c) 2018, `Mindstrong Health <http://mindstronghealth.com>`_. GNU Affero General Public License.",
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        "keywords": "digital biomarkers,supervised kernel PCA,machine learning,cross-validation",
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        "name": "mindstrong",
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