healthcareio
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privacykit
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bug fix and enhancements

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pull/2/head
Steve L. Nyemba 6 years ago
parent 6ff91fd031
commit 11a247d0ea
2 changed files with 125 additions and 60 deletions
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65
src/pandas_risk.py
Unescape View File

@ -45,56 +45,71 @@ class deid :
else :
num_runs = args['num_runs'] if 'num_runs' in args else 100
columns = list(set(self._df.columns) - set([id]))
r = pd.DataFrame()
r = pd.DataFrame()
k = len(columns)
N = self._df.shape[0]
tmp = self._df.fillna(' ')
np.random.seed(1)
for i in range(0,num_runs) :
#
# let's chose a random number of columns and compute marketer and prosecutor risk
# Once the fields are selected we run a groupby clause
#
if 'quasi_id' not in args :
if 'field_count' in args :
#
# We chose to limit how many fields we passin
n = np.random.randint(2,int(args['field_count'])) #-- number of random fields we are picking
else :
n = np.random.randint(2,k) #-- number of random fields we are picking
ii = np.random.choice(k,n,replace=False)
cols = np.array(columns)[ii].tolist()
policy = np.zeros(k)
policy [ii] = 1
policy = pd.DataFrame(policy).T
else:
cols = columns
policy = np.ones(k)
policy = pd.DataFrame(policy).T
n = len(cols)
x_ = self._df.groupby(cols).count()[id].values
policy.columns = columns
N = tmp.shape[0]
x_ = tmp.groupby(cols).size().values
# print [id,i,n,k,self._df.groupby(cols).count()]
r = r.append(
pd.DataFrame(
[
{
"selected":n,
"group_count":x_.size,
"patient_count":N,
"field_count":n,
"marketer": x_.size / np.float64(np.sum(x_)),
"prosecutor":1 / np.float64(np.min(x_))
}
]
).join(policy)
)
)
g_size = x_.size
n_ids = np.float64(np.sum(x_))
# g_size = x_.size
# n_ids = np.float64(np.sum(x_))
# sql = """
# SELECT COUNT(g_size) as group_count, :patient_count as patient_count,SUM(g_size) as rec_count, COUNT(g_size)/SUM(g_size) as marketer, 1/ MIN(g_size) as prosecutor, :n as field_count
# FROM (
# SELECT COUNT(*) as g_size,:key,:fields
# FROM :full_name
# GROUP BY :fields
# """.replace(":n",str(n)).replace(":fields",",".join(cols)).replace(":key",id).replace(":patient_count",str(N))
# r.append(self._df.query(sql.replace("\n"," ").replace("\r"," ") ))
return r
import pandas as pd
import numpy as np
from io import StringIO
csv = """
id,sex,age,profession,drug_test
1,M,37,doctor,-
2,F,28,doctor,+
3,M,37,doctor,-
4,M,28,doctor,+
5,M,28,doctor,-
6,M,37,doctor,-
"""
f = StringIO()
f.write(unicode(csv))
f.seek(0)
df = pd.read_csv(f)
print df.deid.risk(id='id',num_runs=2)
print " *** "
print df.deid.risk(id='id',quasi_id=['sex','age','profession'])
# df = pd.read_gbq("select * from deid_risk.risk_30k",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')
# r = df.deid.risk(id='person_id',num_runs=200)
# print r[['field_count','patient_count','marketer','prosecutor']]

78
src/risk.py
Unescape View File

@ -51,9 +51,10 @@ class utils :
r = []
ref = client.dataset(dataset)
tables = list(client.list_tables(ref))
TERMS = ['type','unit','count','refills','stop','supply','quantity']
for table in tables :
if table.table_id.strip() in ['people_seed']:
if table.table_id.strip() in ['people_seed','measurement','drug_exposure','procedure_occurrence','visit_occurrence','condition_occurrence','device_exposure']:
print ' skiping ...'
continue
ref = table.reference
@ -62,12 +63,15 @@ class utils :
rows = table.num_rows
if rows == 0 :
continue
names = [f.name for f in schema]
names = [f.name for f in schema if len (set(TERMS) & set(f.name.strip().split("_"))) == 0 ]
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)
@ -82,6 +86,25 @@ class utils :
return ".".join(name)+" AS :field_name:index".replace(":field_name",field_name).replace(":index",str(index))
else:
return ".".join(name)
def get_filtered_table(self,table,key):
"""
This function will return a table with a single record per individual patient
"""
return """
SELECT :table.* FROM (
SELECT row_number() over () as top, * FROM :full_name ) as :table
INNER JOIN (
SELECT MAX(top) as top, :key FROM (
SELECT row_number() over () as top,:key from :full_name ) GROUP BY :key
)as filter
ON filter.top = :table.top and filter.:key = :table.:key
""".replace(":key",key).replace(":full_name",table['full_name']).replace(":table",table['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
@ -91,7 +114,7 @@ class utils :
"""
tables = args['tables']
key = args['key']
limit = args['limit'] if 'limit' in args else 300000
limit = args['limit'] if 'limit' in args else 10000
limit = str(limit)
SQL = [
"""
@ -105,9 +128,10 @@ class utils :
alias= table['name']
index = tables.index(table)
sql_ = """
(select * from :name limit :limit) as :alias
(select * from :name ) as :alias
""".replace(":limit",limit)
sql_ = sql_.replace(":name",name).replace(":alias",alias)
# sql_ = " ".join(["(",self.get_filtered_table(table,key)," ) as :alias"])
sql_ = sql_.replace(":name",name).replace(":alias",alias).replace(":limit",limit)
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 = """
@ -139,7 +163,10 @@ class risk :
fields = list(set(table['fields']) - set([key]))
#-- We need to select n-fields max 64
k = len(fields)
n = np.random.randint(2,64) #-- how many random fields are we processing
if 'field_count' in args :
n = np.random.randint(2, int(args['field_count']) ) #-- number of random fields we are picking
else:
n = np.random.randint(2,k) #-- how many random fields are we processing
ii = np.random.choice(k,n,replace=False)
stream = np.zeros(len(fields) + 1)
stream[ii] = 1
@ -148,11 +175,11 @@ class risk :
fields = list(np.array(fields)[ii])
sql = """
SELECT COUNT(g_size) as group_count, COUNT( DISTINCT :key) as patient_count,SUM(g_size) as rec_count, COUNT(g_size)/SUM(g_size) as marketer, 1/ MIN(g_size) as prosecutor, :n as field_count
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, :n as field_count
FROM (
SELECT COUNT(*) as g_size,:key,:fields
SELECT COUNT(*) as g_size,:fields
FROM :full_name
GROUP BY :key,:fields
GROUP BY :fields
)
""".replace(":fields", ",".join(fields)).replace(":full_name",table['full_name']).replace(":key",key).replace(":n",str(n))
return {"sql":sql,"stream":stream}
@ -161,7 +188,7 @@ class risk :
if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute'] :
if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute','migrate'] :
path = SYS_ARGS['path']
client = bq.Client.from_service_account_json(path)
@ -195,6 +222,27 @@ if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute'] :
r = client.query(create_sql,location='US',job_config=job)
print [r.job_id,' ** ',r.state]
elif SYS_ARGS['action'] == 'migrate' :
#
#
o_dataset = SYS_ARGS['o_dataset']
for table in tables:
sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",mytools.get_filtered_table(table,key),") as ",table['name']])
job = bq.QueryJobConfig()
job.destination = client.dataset(o_dataset).table(table['name'])
job.use_query_cache = True
job.allow_large_results = True
job.priority = 'INTERACTIVE'
job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
r = client.query(sql,location='US',job_config=job)
print [table['full_name'],' ** ',r.job_id,' ** ',r.state]
pass
else:
#
#
@ -204,17 +252,19 @@ if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute'] :
risk= risk()
df = pd.DataFrame()
dfs = pd.DataFrame()
np.random.seed(1)
for i in range(0,limit) :
r = risk.get_sql(key=SYS_ARGS['key'],table=tables[0])
sql = r['sql']
dfs = dfs.append(r['stream'])
df = df.append(pd.read_gbq(query=sql,private_key=path,dialect='standard'))
dfs = dfs.append(r['stream'],sort=True)
df = df.append(pd.read_gbq(query=sql,private_key=path,dialect='standard').join(dfs))
# df = df.join(dfs,sort=True)
df.to_csv(SYS_ARGS['table']+'.csv')
dfs.to_csv(SYS_ARGS['table']+'_stream.csv')
# dfs.to_csv(SYS_ARGS['table']+'_stream.csv')
print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape]
time.sleep(2)
pass
else:
print 'ERROR'
pass

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