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bug fix: prosecutor risk, marketer risk

This commit is contained in:
Steve L. Nyemba -- The Architect 2018-09-27 10:33:52 -05:00
parent 18bfa63df1
commit 140a4c4573
3 changed files with 374 additions and 74 deletions
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203
notebooks/risk.ipynb
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@ -2,15 +2,29 @@
"cells": [
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 1,
"metadata": {},
"outputs": [],
"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"
]
}
],
"source": [
"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')"
"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"
]
},
{
@ -25,7 +39,7 @@
},
{
"cell_type": "code",
"execution_count": 181,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@ -68,7 +82,7 @@
" 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",
" fields += (['.'.join([xi['name'], name]) for name in xi['fields'] if name != args['join']])\n",
" return fields\n",
"def generate_sql(**args):\n",
" \"\"\"\n",
@ -97,7 +111,27 @@
" 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",
@ -111,24 +145,39 @@
},
{
"cell_type": "code",
"execution_count": 183,
"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": [
"'SELECT :fields FROM raw.person INNER JOIN raw.measurement ON measurement.person_id = person.person_id'"
"['person.race', 'person.value_as_number', 'measurement.value_source_value']"
]
},
"execution_count": 183,
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\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')"
"fields\n"
]
},
{
@ -179,69 +228,16 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[u'condition_occurrence.condition_occurrence_id',\n",
" u'condition_occurrence.person_id',\n",
" u'condition_occurrence.condition_concept_id',\n",
" u'condition_occurrence.condition_start_date',\n",
" u'condition_occurrence.condition_start_datetime',\n",
" u'condition_occurrence.condition_end_date',\n",
" u'condition_occurrence.condition_end_datetime',\n",
" u'condition_occurrence.condition_type_concept_id',\n",
" u'condition_occurrence.stop_reason',\n",
" u'condition_occurrence.provider_id',\n",
" u'condition_occurrence.visit_occurrence_id',\n",
" u'condition_occurrence.condition_source_value',\n",
" u'condition_occurrence.condition_source_concept_id',\n",
" u'death.death_date',\n",
" u'death.death_datetime',\n",
" u'death.death_type_concept_id',\n",
" u'death.cause_concept_id',\n",
" u'death.cause_source_value',\n",
" u'death.cause_source_concept_id',\n",
" u'device_exposure.device_exposure_id',\n",
" u'device_exposure.device_concept_id',\n",
" u'device_exposure.device_exposure_start_date',\n",
" u'device_exposure.device_exposure_start_datetime',\n",
" u'device_exposure.device_exposure_end_date',\n",
" u'device_exposure.device_exposure_end_datetime',\n",
" u'device_exposure.device_type_concept_id',\n",
" u'device_exposure.unique_device_id',\n",
" u'device_exposure.quantity',\n",
" u'device_exposure.provider_id',\n",
" u'device_exposure.visit_occurrence_id',\n",
" u'device_exposure.device_source_value',\n",
" u'device_exposure.device_source_concept_id',\n",
" u'drug_exposure.drug_exposure_id',\n",
" u'drug_exposure.drug_concept_id',\n",
" u'drug_exposure.drug_exposure_start_date',\n",
" u'drug_exposure.drug_exposure_start_datetime',\n",
" u'drug_exposure.drug_exposure_end_date',\n",
" u'drug_exposure.drug_exposure_end_datetime',\n",
" u'drug_exposure.drug_type_concept_id',\n",
" u'drug_exposure.stop_reason',\n",
" u'drug_exposure.refills',\n",
" u'drug_exposure.quantity',\n",
" u'drug_exposure.days_supply',\n",
" u'drug_exposure.sig',\n",
" u'drug_exposure.route_concept_id',\n",
" u'drug_exposure.effective_drug_dose',\n",
" u'drug_exposure.dose_unit_concept_id',\n",
" u'drug_exposure.lot_number',\n",
" u'drug_exposure.provider_id',\n",
" u'drug_exposure.visit_occurrence_id',\n",
" u'drug_exposure.drug_source_value',\n",
" u'drug_exposure.drug_source_concept_id',\n",
" u'drug_exposure.route_source_value',\n",
" u'drug_exposure.dose_unit_source_value']"
"array([1, 3, 0, 0])"
]
},
"execution_count": 111,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@ -250,12 +246,7 @@
"#\n",
"# find every table with person id at the very least or a subset of fields\n",
"#\n",
"info = get_tables(client,'raw',['person_id'])\n",
"# get_fields(xo=names[0],xi=names[1:4],join='person_id')\n",
"\n",
"# q = ['person_id']\n",
"# pairs = list(itertools.combinations(names,len(names)))\n",
"# pairs[0]"
"np.random.randint(0,4,size=4)"
]
},
{
@ -287,6 +278,72 @@
"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,
"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>size</th>\n",
" </tr>\n",
" <tr>\n",
" <th>group</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" size\n",
"group \n",
"1 1.2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_.groupby(['group']).mean()\n"
]
},
{
"cell_type": "code",
"execution_count": null,

17
src/params.py Normal file
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@ -0,0 +1,17 @@
import sys
SYS_ARGS={}
if len(sys.argv) > 1 :
N = len(sys.argv)
for i in range(1,N) :
value = 1
if sys.argv[i].startswith('--') :
key = sys.argv[i].replace('-','')
if i + 1 < N and not sys.argv[i+1].startswith('--') :
value = sys.argv[i + 1].strip()
SYS_ARGS[key] = value
i += 2
elif 'action' not in SYS_ARGS:
SYS_ARGS['action'] = sys.argv[i].strip()

226
src/risk.py Normal file
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@ -0,0 +1,226 @@
"""
Steve L. Nyemba & Brad Malin
Health Information Privacy Lab.
This code is proof of concept as to how risk is computed against a database (at least a schema).
The engine will read tables that have a given criteria (patient id) and generate a dataset by performing joins.
Because joins are process intensive we decided to add a limit to the records pulled.
TL;DR:
This engine generates a dataset and computes risk (marketer and prosecutor)
Assumptions:
- We assume tables that reference patients will name the keys identically (best practice). This allows us to be able to leverage data store's that don't support referential integrity
Usage :
Limitations
- It works against bigquery for now
@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
"""
import pandas as pd
import numpy as np
from google.cloud import bigquery as bq
import time
from params import SYS_ARGS
class utils :
"""
This class is a utility class that will generate SQL-11 compatible code in order to run the risk assessment
@TODO: plugins for other data-stores
"""
def __init__(self,**args):
# self.path = args['path']
self.client = args['client']
def get_tables(self,**args): #id,key='person_id'):
"""
This function returns a list of tables given a key. The key is the name of the field that uniquely designates a patient/person
in the database. The list of tables are tables that can be joined given the provided field.
@param key name of the patient field
@param dataset dataset name
@param client initialized bigquery client ()
@return [{name,fields:[],row_count}]
"""
dataset = args['dataset']
client = args['client']
key = args['key']
r = []
ref = client.dataset(dataset)
tables = list(client.list_tables(ref))
for table in tables :
if table.table_id.strip() in ['people_seed']:
print ' skiping ...'
continue
ref = table.reference
table = client.get_table(ref)
schema = table.schema
rows = table.num_rows
if rows == 0 :
continue
names = [f.name for f in schema]
x = list(set(names) & set([key]))
if x :
full_name = ".".join([dataset,table.table_id])
r.append({"name":table.table_id,"fields":names,"row_count":rows,"full_name":full_name})
return r
def get_field_name(self,alias,field_name,index):
"""
This function will format the a field name given an index (the number of times it has occurred in projection)
The index is intended to avoid a "duplicate field" error (bigquery issue)
@param alias alias of the table
@param field_name name of the field to be formatted
@param index the number of times the field appears in the projection
"""
name = [alias,field_name]
if index > 0 :
return ".".join(name)+" AS :field_name:index".replace(":field_name",field_name).replace(":index",str(index))
else:
return ".".join(name)
def get_sql(self,**args):
"""
This function will generate that will join a list of tables given a key and a limit of records
@param tables list of tables
@param key key field to be used in the join. The assumption is that the field name is identical across tables (best practice!)
@param limit a limit imposed, in case of ristrictions considering joins are resource intensive
"""
tables = args['tables']
key = args['key']
limit = args['limit'] if 'limit' in args else 300000
limit = str(limit)
SQL = [
"""
SELECT :fields
FROM
"""]
fields = []
prev_table = None
for table in tables :
name = table['full_name'] #".".join([self.i_dataset,table['name']])
alias= table['name']
index = tables.index(table)
sql_ = """
(select * from :name limit :limit) as :alias
""".replace(":limit",limit)
sql_ = sql_.replace(":name",name).replace(":alias",alias)
fields += [self.get_field_name(alias,field_name,index) for field_name in table['fields'] if field_name != key or (field_name==key and tables.index(table) == 0) ]
if tables.index(table) > 0 :
join = """
INNER JOIN :sql ON :alias.:field = :prev_alias.:field
""".replace(":name",name)
join = join.replace(":alias",alias).replace(":field",key).replace(":prev_alias",prev_alias)
sql_ = join.replace(":sql",sql_)
# sql_ = " ".join([sql_,join])
SQL += [sql_]
if index == 0:
prev_alias = str(alias)
return " ".join(SQL).replace(":fields"," , ".join(fields))
class risk :
"""
This class will handle the creation of an SQL query that computes marketer and prosecutor risk (for now)
"""
def __init__(self):
pass
def get_sql(self,**args) :
"""
This function returns the SQL Query that will compute marketer and prosecutor risk
@param key key fields (patient identifier)
@param table table that is subject of the computation
"""
key = args['key']
table = args['table']
fields = list(set(table['fields']) - set([key]))
#-- We need to select n-fields max 64
k = len(fields)
n = np.random.randint(2,24) #-- how many random fields are we processing
ii = np.random.choice(k,n,replace=False)
fields = list(np.array(fields)[ii])
sql = """
SELECT COUNT(g_size) as group_count, SUM(g_size) as patient_count, COUNT(g_size)/SUM(g_size) as marketer, 1/ MIN(g_size) as prosecutor
FROM (
SELECT COUNT(*) as g_size,:key,:fields
FROM :full_name
GROUP BY :key,:fields
)
""".replace(":fields", ",".join(fields)).replace(":full_name",table['full_name']).replace(":key",key).replace(":n",str(n))
return sql
if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute'] :
path = SYS_ARGS['path']
client = bq.Client.from_service_account_json(path)
i_dataset = SYS_ARGS['i_dataset']
key = SYS_ARGS['key']
mytools = utils(client = client)
tables = mytools.get_tables(dataset=i_dataset,client=client,key=key)
# print len(tables)
# tables = tables[:6]
if SYS_ARGS['action'] == 'create' :
#usage:
# create --i_dataset <in dataset> --key <patient id> --o_dataset <out dataset> --table <table|file> [--file] --path <bq JSON account file>
#
create_sql = mytools.get_sql(tables=tables,key=key) #-- The create statement
o_dataset = SYS_ARGS['o_dataset']
table = SYS_ARGS['table']
if 'file' in SYS_ARGS :
f = open(table+'.sql','w')
f.write(create_sql)
f.close()
else:
job = bq.QueryJobConfig()
job.destination = client.dataset(o_dataset).table(table)
job.use_query_cache = True
job.allow_large_results = True
job.priority = 'BATCH'
job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
r = client.query(create_sql,location='US',job_config=job)
print [r.job_id,' ** ',r.state]
else:
#
#
tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ]
if tables :
risk = risk()
df = pd.DataFrame()
for i in range(0,10) :
sql = risk.get_sql(key=SYS_ARGS['key'],table=tables[0])
df = df.append(pd.read_gbq(query=sql,private_key=path,dialect='standard'))
df.to_csv(SYS_ARGS['table']+'.csv')
print [i,' ** ',df.shape[0]]
time.sleep(2)
pass
else:
print 'ERROR'
pass
# r = risk(path='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json', i_dataset='raw',o_dataset='risk_o',o_table='mo')
# tables = r.get_tables('raw','person_id')
# sql = r.get_sql(tables=tables[:3],key='person_id')
# #
# # let's post this to a designated location
# #
# f = open('foo.sql','w')
# f.write(sql)
# f.close()
# r.get_sql(tables=tables,key='person_id')
# p = r.compute()
# print p
# p.to_csv("risk.csv")
# r.write('foo.sql')