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