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bug fix and adding usage

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Steve L. Nyemba -- The Architect 2018-10-02 12:38:24 -05:00
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34
README.md
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@ -1,34 +1,16 @@
# deid-risk
This project is intended to compute an estimated value of risk for a given database.
The code below extends a data-frame by adding it the ability to compute de-identification risk (marketer, prosecutor).
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.
1. Pull meta data of the database and create a dataset via joins
2. Generate the dataset with random selection of features
3. Compute risk via SQL using group by
## Python environment
Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from
[http://ehelthinformation.ca](http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf)
The following are the dependencies needed to run the code:
Dependencies:
numpy
pandas
pandas
numpy
pandas-gbq
google-cloud-bigquery
## Usage
**Generate The merged dataset**
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 ]
**Compute risk (marketer, prosecutor)**
python risk.py compute --i_dataset <dataset> --table <name> --path <bigquery-key-file> --key <patient-id-field-name>
## Limitations
- It works against bigquery for now
Limitations:
@TODO:
- Need to write a transport layer (database interface)
- Support for referential integrity, so one table can be selected and a dataset derived given referential integrity
- Add support for journalist risk

481
notebooks/risk.ipynb
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@ -2,294 +2,121 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
}
],
"outputs": [],
"source": [
"\"\"\"\n",
" This notebook is intended to show how to use the risk framework:\n",
" There are two basic usages:\n",
" 1. Experiment\n",
" \n",
" Here the framework will select a number of random fields other than the patient id and compute risk for the selection.\n",
" This will repeat over a designated number of runs.\n",
" \n",
" The parameters to pass to enable this mode are id=<patient id>,nun_runs=<number of runs>\n",
" 2. Assessment\n",
" \n",
" 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",
" The parameters to enable this mode are id=<patient id>,quasi_id=<list of quasi ids>\n",
"\"\"\"\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"from google.cloud import bigquery as bq\n",
"\n",
"client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
"# pd.read_gbq(query=\"select * from raw.observation limit 10\",private_key='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
"jobs = client.list_jobs()\n",
"for job in jobs :\n",
"# print dir(job)\n",
" print job.user_email,job.job_id,job.started, job.state\n",
" break"
"\n",
"#\n",
"#-- Loading a template file\n",
"# The example taken a de-identification white-paper\n",
"# http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf\n",
"#\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from io import StringIO\n",
"csv = \"\"\"\n",
"id,sex,age,profession,drug_test\n",
"1,M,37,doctor,-\n",
"2,F,28,doctor,+\n",
"3,M,37,doctor,-\n",
"4,M,28,doctor,+\n",
"5,M,28,doctor,-\n",
"6,M,37,doctor,-\n",
"\"\"\"\n",
"f = StringIO()\n",
"f.write(unicode(csv))\n",
"f.seek(0)\n",
"MY_DATAFRAME = pd.read_csv(f) "
]
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"xo = ['person_id','date_of_birth','race']\n",
"xi = ['person_id','value_as_number','value_source_value']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_tables(client,id,fields=[]):\n",
" \"\"\"\n",
" getting table lists from google\n",
" \"\"\"\n",
" r = []\n",
" ref = client.dataset(id)\n",
" tables = list(client.list_tables(ref))\n",
" for table in tables :\n",
" ref = table.reference\n",
" schema = client.get_table(ref).schema\n",
" names = [f.name for f in schema]\n",
" x = list(set(names) & set(fields))\n",
" if x :\n",
" r.append({\"name\":table.table_id,\"fields\":names})\n",
" return r\n",
" \n",
"def get_fields(**args):\n",
" \"\"\"\n",
" This function will generate a random set of fields from two tables. Tables are structured as follows \n",
" {name,fields:[],\"y\":}, with \n",
" name table name (needed to generate sql query)\n",
" fields list of field names, used in the projection\n",
" y name of the field to be joined.\n",
" @param xo candidate table in the join\n",
" @param xi candidate table in the join\n",
" @param join field by which the tables can be joined.\n",
" \"\"\"\n",
" # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
"# xo = args['xo']['fields']\n",
"# xi = args['xi']['fields']\n",
"# zi = args['xi']['name']\n",
"# 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",
" xo = args['xo']\n",
" fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
" if not isinstance(args['xi'],list) :\n",
" x_ = [args['xi']]\n",
" else:\n",
" x_ = args['xi']\n",
" for xi in x_ :\n",
" fields += (['.'.join([xi['name'], name]) for name in xi['fields'] if name != args['join']])\n",
" return fields\n",
"def generate_sql(**args):\n",
" \"\"\"\n",
" This function will generate the SQL query for the resulting join\n",
" \"\"\"\n",
" \n",
" xo = args['xo']\n",
" x_ = args['xi']\n",
" xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
" SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
" if not isinstance(x_,list):\n",
" x_ = [x_]\n",
" f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
" INNER_JOINS = []\n",
" for xi in x_ :\n",
" xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
" JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
" value = \".\".join([xi['name'],args['join']])\n",
" f.append(value) \n",
" \n",
" ON_SQL = \"\"\n",
" tmp = []\n",
" for term in f :\n",
" ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
" ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
" tmp.append(ON_SQL)\n",
" INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
" return SQL + \" \".join(INNER_JOINS)\n",
"def get_final_sql(**args):\n",
" xo = args['xo']\n",
" xi = args['xi']\n",
" join=args['join']\n",
" prefix = args['prefix'] if 'prefix' in args else ''\n",
" fields = get_fields (xo=xo,xi=xi,join=join)\n",
" k = len(fields)\n",
" n = np.random.randint(2,k) #-- number of fields to select\n",
" i = np.random.randint(0,k,size=n)\n",
" fields = [name for name in fields if fields.index(name) in i]\n",
" base_sql = generate_sql(xo=xo,xi=xi,prefix)\n",
" SQL = \"\"\"\n",
" SELECT AVERAGE(count),size,n as selected_features,k as total_features\n",
" FROM(\n",
" SELECT COUNT(*) as count,count(:join) as pop,sum(:n) as N,sum(:k) as k,:fields\n",
" FROM (:sql)\n",
" GROUP BY :fields\n",
" ) \n",
" order by 1\n",
" \n",
" \"\"\".replace(\":sql\",base_sql)\n",
"# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
"# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
" \n",
" \n",
"# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
"# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
"# return sql\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race','value_as_number']}\n",
"xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
"# generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')\n",
"fields = get_fields(xo=xo,xi=xi,join='person_id')\n",
"ofields = list(fields)\n",
"k = len(fields)\n",
"n = np.random.randint(2,k) #-- number of fields to select\n",
"i = np.random.randint(0,k,size=n)\n",
"fields = [name for name in fields if fields.index(name) in i]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['person.race', 'person.value_as_number', 'measurement.value_source_value']"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fields\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'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 '"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
"generate_sql(xo=xo,xi=xi)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('a', 'b'), ('a', 'c'), ('b', 'c')]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
" We are designing a process that will take two tables that will generate \n",
" Here's the pandas_risk code verbatim. \n",
" NOTE: \n",
"\"\"\"\n",
"import itertools\n",
"list(itertools.combinations(['a','b','c'],2))"
"@pd.api.extensions.register_dataframe_accessor(\"deid\")\n",
"class deid :\n",
" \"\"\"\n",
" This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe\n",
" \"\"\"\n",
" def __init__(self,df):\n",
" self._df = df\n",
" \n",
" def risk(self,**args):\n",
" \"\"\"\n",
" @param id name of patient field \n",
" @params num_runs number of runs (default will be 100)\n",
" @params quasi_id \tlist of quasi identifiers to be used (this will only perform a single run)\n",
" \"\"\"\n",
" \n",
" id = args['id']\n",
" if 'quasi_id' in args :\n",
" num_runs = 1\n",
" columns = list(set(args['quasi_id'])- set(id) )\n",
" else :\n",
" num_runs = args['num_runs'] if 'num_runs' in args else 100\n",
" columns = list(set(self._df.columns) - set([id]))\n",
" r = pd.DataFrame() \n",
" k = len(columns)\n",
" for i in range(0,num_runs) :\n",
" #\n",
" # let's chose a random number of columns and compute marketer and prosecutor risk\n",
" # Once the fields are selected we run a groupby clause\n",
" #\n",
" if 'quasi_id' not in args :\n",
" n = np.random.randint(2,k) #-- number of random fields we are picking\n",
" ii = np.random.choice(k,n,replace=False)\n",
" cols = np.array(columns)[ii].tolist()\n",
" else:\n",
" cols \t= columns\n",
" n \t= len(cols)\n",
" x_ = self._df.groupby(cols).count()[id].values\n",
" r = r.append(\n",
" pd.DataFrame(\n",
" [\n",
" {\n",
" \"selected\":n,\n",
" \"marketer\": x_.size / np.float64(np.sum(x_)),\n",
" \"prosecutor\":1 / np.float64(np.min(x_))\n",
"\n",
" }\n",
" ]\n",
" )\n",
" )\n",
" g_size = x_.size\n",
" n_ids = np.float64(np.sum(x_))\n",
"\n",
" return r"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 3, 0, 0])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#\n",
"# find every table with person id at the very least or a subset of fields\n",
"#\n",
"np.random.randint(0,4,size=4)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['a']"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(set(['a','b']) & set(['a']))"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"x_ = 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"x_ = pd.DataFrame({\"group\":[1,1,1,1,1], \"size\":[2,1,1,1,1]})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 7,
"metadata": {},
"outputs": [
{
@ -313,35 +140,125 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <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'])"
]
},
{