bug fix and adding usage
This commit is contained in:
Steve L. Nyemba -- The Architect
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README.md
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README.md
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@ -1,34 +1,16 @@
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# deid-risk
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This project is intended to compute an estimated value of risk for a given database.
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The code below extends a data-frame by adding it the ability to compute de-identification risk (marketer, prosecutor).
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Because data-frames can connect to any database/file it will be the responsibility of the user to load the dataset into a data-frame.
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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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Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from
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[http://ehelthinformation.ca](http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf)
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The following are the dependencies needed to run the code:
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pandas
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Dependencies:
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numpy
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pandas-gbq
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google-cloud-bigquery
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pandas
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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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**Compute risk (marketer, prosecutor)**
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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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Limitations:
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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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@ -2,294 +2,121 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dev-deid-600@aou-res-deid-vumc-test.iam.gserviceaccount.com df0ac049-d5b6-416f-ab3c-6321eda919d6 2018-09-25 08:18:34.829000+00:00 DONE\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"\"\"\"\n",
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" This notebook is intended to show how to use the risk framework:\n",
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" There are two basic usages:\n",
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" 1. Experiment\n",
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" \n",
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" Here the framework will select a number of random fields other than the patient id and compute risk for the selection.\n",
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" This will repeat over a designated number of runs.\n",
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" \n",
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" The parameters to pass to enable this mode are id=<patient id>,nun_runs=<number of runs>\n",
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" 2. Assessment\n",
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" \n",
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" Here the framework assumes you are only interested in a list of quasi identifiers and will run the evaluation once for a given list of quasi identifiers.\n",
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" The parameters to enable this mode are id=<patient id>,quasi_id=<list of quasi ids>\n",
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"\"\"\"\n",
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from google.cloud import bigquery as bq\n",
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"\n",
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"client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
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"# pd.read_gbq(query=\"select * from raw.observation limit 10\",private_key='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
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"jobs = client.list_jobs()\n",
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"for job in jobs :\n",
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"# print dir(job)\n",
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" print job.user_email,job.job_id,job.started, job.state\n",
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" break"
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"\n",
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"#\n",
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"#-- Loading a template file\n",
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"# The example taken a de-identification white-paper\n",
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"# http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf\n",
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"#\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from io import StringIO\n",
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"csv = \"\"\"\n",
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"id,sex,age,profession,drug_test\n",
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"1,M,37,doctor,-\n",
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"2,F,28,doctor,+\n",
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"3,M,37,doctor,-\n",
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"4,M,28,doctor,+\n",
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"5,M,28,doctor,-\n",
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"6,M,37,doctor,-\n",
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"\"\"\"\n",
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"f = StringIO()\n",
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"f.write(unicode(csv))\n",
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"f.seek(0)\n",
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"MY_DATAFRAME = pd.read_csv(f) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"xo = ['person_id','date_of_birth','race']\n",
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"xi = ['person_id','value_as_number','value_source_value']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_tables(client,id,fields=[]):\n",
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"\"\"\"\n",
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" getting table lists from google\n",
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" Here's the pandas_risk code verbatim. \n",
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" NOTE: \n",
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"\"\"\"\n",
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" r = []\n",
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" ref = client.dataset(id)\n",
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" tables = list(client.list_tables(ref))\n",
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" for table in tables :\n",
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" ref = table.reference\n",
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" schema = client.get_table(ref).schema\n",
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" names = [f.name for f in schema]\n",
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" x = list(set(names) & set(fields))\n",
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" if x :\n",
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" r.append({\"name\":table.table_id,\"fields\":names})\n",
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" return r\n",
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"@pd.api.extensions.register_dataframe_accessor(\"deid\")\n",
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"class deid :\n",
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" \"\"\"\n",
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" This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe\n",
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" \"\"\"\n",
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" def __init__(self,df):\n",
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" self._df = df\n",
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" \n",
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"def get_fields(**args):\n",
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" def risk(self,**args):\n",
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" \"\"\"\n",
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" This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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" {name,fields:[],\"y\":}, with \n",
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" name table name (needed to generate sql query)\n",
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" fields list of field names, used in the projection\n",
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" y name of the field to be joined.\n",
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" @param xo candidate table in the join\n",
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" @param xi candidate table in the join\n",
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" @param join field by which the tables can be joined.\n",
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" @param id name of patient field \n",
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" @params num_runs number of runs (default will be 100)\n",
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" @params quasi_id \tlist of quasi identifiers to be used (this will only perform a single run)\n",
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" \"\"\"\n",
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" # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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"# xo = args['xo']['fields']\n",
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"# xi = args['xi']['fields']\n",
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"# zi = args['xi']['name']\n",
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"# return list(set([ \".\".join([args['xo']['name'],name]) for name in xo]) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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" xo = args['xo']\n",
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" fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
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" if not isinstance(args['xi'],list) :\n",
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" x_ = [args['xi']]\n",
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" \n",
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" id = args['id']\n",
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" if 'quasi_id' in args :\n",
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" num_runs = 1\n",
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" columns = list(set(args['quasi_id'])- set(id) )\n",
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" else :\n",
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" x_ = args['xi']\n",
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" for xi in x_ :\n",
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" fields += (['.'.join([xi['name'], name]) for name in xi['fields'] if name != args['join']])\n",
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" return fields\n",
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"def generate_sql(**args):\n",
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" \"\"\"\n",
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" This function will generate the SQL query for the resulting join\n",
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" \"\"\"\n",
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" num_runs = args['num_runs'] if 'num_runs' in args else 100\n",
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" columns = list(set(self._df.columns) - set([id]))\n",
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" r = pd.DataFrame() \n",
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" k = len(columns)\n",
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" for i in range(0,num_runs) :\n",
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" #\n",
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" # let's chose a random number of columns and compute marketer and prosecutor risk\n",
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" # Once the fields are selected we run a groupby clause\n",
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" #\n",
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" if 'quasi_id' not in args :\n",
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" n = np.random.randint(2,k) #-- number of random fields we are picking\n",
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" ii = np.random.choice(k,n,replace=False)\n",
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" cols = np.array(columns)[ii].tolist()\n",
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" else:\n",
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" cols \t= columns\n",
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" n \t= len(cols)\n",
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" x_ = self._df.groupby(cols).count()[id].values\n",
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" r = r.append(\n",
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" pd.DataFrame(\n",
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" [\n",
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" {\n",
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" \"selected\":n,\n",
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" \"marketer\": x_.size / np.float64(np.sum(x_)),\n",
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" \"prosecutor\":1 / np.float64(np.min(x_))\n",
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"\n",
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" xo = args['xo']\n",
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" x_ = args['xi']\n",
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" xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
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" SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
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" if not isinstance(x_,list):\n",
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" x_ = [x_]\n",
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" f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
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" INNER_JOINS = []\n",
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" for xi in x_ :\n",
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" xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
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" JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
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" value = \".\".join([xi['name'],args['join']])\n",
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" f.append(value) \n",
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" \n",
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" ON_SQL = \"\"\n",
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" tmp = []\n",
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" for term in f :\n",
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" ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
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" ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
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" tmp.append(ON_SQL)\n",
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" INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
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" return SQL + \" \".join(INNER_JOINS)\n",
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"def get_final_sql(**args):\n",
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" xo = args['xo']\n",
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" xi = args['xi']\n",
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" join=args['join']\n",
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" prefix = args['prefix'] if 'prefix' in args else ''\n",
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" fields = get_fields (xo=xo,xi=xi,join=join)\n",
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" k = len(fields)\n",
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" n = np.random.randint(2,k) #-- number of fields to select\n",
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" i = np.random.randint(0,k,size=n)\n",
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" fields = [name for name in fields if fields.index(name) in i]\n",
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" base_sql = generate_sql(xo=xo,xi=xi,prefix)\n",
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" SQL = \"\"\"\n",
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" SELECT AVERAGE(count),size,n as selected_features,k as total_features\n",
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" FROM(\n",
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" SELECT COUNT(*) as count,count(:join) as pop,sum(:n) as N,sum(:k) as k,:fields\n",
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" FROM (:sql)\n",
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" GROUP BY :fields\n",
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" }\n",
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" ]\n",
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" )\n",
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" order by 1\n",
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" )\n",
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" g_size = x_.size\n",
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" n_ids = np.float64(np.sum(x_))\n",
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"\n",
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" \"\"\".replace(\":sql\",base_sql)\n",
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"# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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"# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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" \n",
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" \n",
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"# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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"# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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"# return sql\n",
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" \n",
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" "
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" return r"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race','value_as_number']}\n",
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"xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
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"# generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')\n",
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"fields = get_fields(xo=xo,xi=xi,join='person_id')\n",
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"ofields = list(fields)\n",
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"k = len(fields)\n",
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"n = np.random.randint(2,k) #-- number of fields to select\n",
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"i = np.random.randint(0,k,size=n)\n",
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"fields = [name for name in fields if fields.index(name) in i]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['person.race', 'person.value_as_number', 'measurement.value_source_value']"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fields\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'SELECT person_id,value_as_number,measurements.value_source_value,measurements.value_as_number,value_source_value FROM person INNER JOIN measurements ON measurements.person_id = person_id '"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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"generate_sql(xo=xo,xi=xi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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]
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},
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"execution_count": 59,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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" We are designing a process that will take two tables that will generate \n",
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"\"\"\"\n",
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"import itertools\n",
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"list(itertools.combinations(['a','b','c'],2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 3, 0, 0])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#\n",
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"# find every table with person id at the very least or a subset of fields\n",
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"#\n",
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"np.random.randint(0,4,size=4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['a']"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(set(['a','b']) & set(['a']))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 120,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_ = 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
|
||||
"outputs": [],
|
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"source": [
|
||||
"x_ = pd.DataFrame({\"group\":[1,1,1,1,1], \"size\":[2,1,1,1,1]})"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 12,
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"execution_count": 7,
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"metadata": {},
|
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"outputs": [
|
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{
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|
|
@ -313,35 +140,125 @@
|
|||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
||||
" <th>size</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>group</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th>marketer</th>\n",
|
||||
" <th>prosecutor</th>\n",
|
||||
" <th>selected</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1.2</td>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.333333</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.333333</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" size\n",
|
||||
"group \n",
|
||||
"1 1.2"
|
||||
" marketer prosecutor selected\n",
|
||||
"0 0.500000 1.0 2\n",
|
||||
"0 0.500000 1.0 3\n",
|
||||
"0 0.500000 1.0 3\n",
|
||||
"0 0.333333 1.0 2\n",
|
||||
"0 0.333333 0.5 2"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x_.groupby(['group']).mean()\n"
|
||||
"#\n",
|
||||
"# Lets us compute risk here for a random any random selection of quasi identifiers\n",
|
||||
"# We will run this experiment 5 times\n",
|
||||
"#\n",
|
||||
"MY_DATAFRAME.deid.risk(id='id',num_runs=5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>marketer</th>\n",
|
||||
" <th>prosecutor</th>\n",
|
||||
" <th>selected</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" marketer prosecutor selected\n",
|
||||
"0 0.5 1.0 3"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# In this scenario we are just interested in sex,profession,age\n",
|
||||
"#\n",
|
||||
"MY_DATAFRAME.deid.risk(id='id',quasi_id=['age','sex','profession'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Reference in New Issue