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@ -5,3 +5,28 @@ This project is intended to compute an estimated value of risk for a given datab
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1. Pull meta data of the database and create a dataset via joins
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2. Generate the dataset with random selection of features
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3. Compute risk via SQL using group by
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## Python environment
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The following are the dependencies needed to run the code:
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pandas
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numpy
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pandas-gbq
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google-cloud-bigquery
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## Usage
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*Generate The merged dataset
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python risk.py create --i_dataset <in dataset|schema> --o_dataset <out dataset|schema> --table <name> --path <bigquery-key-file> --key <patient-id-field-name> [--file ]
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* Cmpute risk
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python risk.py compute --i_dataset <dataset> --table <name> --path <bigquery-key-file> --key <patient-id-field-name>
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## Limitations
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- It works against bigquery for now
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@TODO:
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- Need to write a transport layer (database interface)
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- Support for referential integrity, so one table can be selected and a dataset derived given referential integrity
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- Add support for journalist risk
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