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@@ -2,6 +2,31 @@
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This project is intended to compute an estimated value of risk for a given database.
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- 1. Pull meta data of the database and create a dataset via joins
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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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+ 3. Compute risk via SQL using group by
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+## Python environment
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+
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+ The following are the dependencies needed to run the code:
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+
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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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+
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+
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+## Usage
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+
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+ *Generate The merged dataset
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+
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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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+
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+ * Cmpute risk
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+
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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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