Metadata-Version: 1.1
Name: mindstrong
Version: 1.0
Summary: Mindstrong Digital Biomarker Model Fitting
Home-page: http://mindstronghealth.com
Author: Mindstrong Health Data Science
Author-email: datascience@mindstronghealth.com
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: =================================================
        Mindstrong Health Digital Biomarker Model Fitting
        =================================================
        
        This 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:
        
            * Paul Dagum, MD, PhD
            * Greg Ryslik, PhD, FCAS, MAAA
            * Bob Dougherty, PhD
            * Patrick Staples, PhD
        
        Please contact us at `datascience@mindstronghealth.com <datascience@mindstronghealth.com>`_.
        
        NOTE: If you use this software in your work, please cite the following `paper <https://www.nature.com/articles/s41746-018-0018-4>`_:
        
            Dagum, P. (2018) Digital biomarkers of cognitive function. npj Digital Medicine, issue 1, article 10. DOI: 10.1038/s41746-018-0018-4.
        
        Installation
        ------------
        
        The easiest way to install the package is via ``easy_install`` or ``pip``::
        
            $ pip install mindstrong_biomarker_modelfit
        
        This should also take care of the dependencies (numpy, scipy, pandas, and sklearn).
        
        Usage
        -----
        
        Simulated digital biomarker and target measure data are included with the project. To fit a model to these example data::
        
            import numpy as np
            import pandas as pd
            import os
            from mindstrong import mindstrong_modelfit as mindstrong
        
            target_file = mindstrong.get_example_data('example_targets.csv')
            feature_file = mindstrong.get_example_data('example_features.csv')
            target_colname = 'target1'
        
            # Load target data
            target_df = pd.read_csv(target_file)
            target_df.set_index('device_id', inplace=True)
        
            # Load Feature Data
            feature_df = pd.read_csv(feature_file).set_index(['device_id', 'targetDOY'])
        
            # Cross Validated supervised kernel PCA model-fitting
            cvdf, best_model = mindstrong.calculateCrossValidatedCorrelation(target_df,
                                                                             feature_df,
                                                                             target_colname,
                                                                             fold_type='n',
                                                                             n_folds=5,
                                                                             kernel_training='linear',
                                                                             kernel_training_param=1,
                                                                             kernel_target='linear',
                                                                             kernel_target_param=1,
                                                                             regularization=0.1)
        
            # Print the final results
            print(best_model)
        
        
        
        Copyright & License
        -------------------
        
        Copyright (c) 2018, `Mindstrong Health <http://mindstronghealth.com>`_. GNU Affero General Public License.
        
        
Keywords: digital biomarkers,supervised kernel PCA,machine learning,cross-validation
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Programming Language :: Python :: 3
