From c3066408c919a9c985387f318eec35e3fd41d5b9 Mon Sep 17 00:00:00 2001 From: "Steve L. Nyemba -- The Architect" Date: Tue, 20 Nov 2018 10:26:07 -0600 Subject: [PATCH 01/23] Refactored, including population risk assessment --- src/pandas_risk.py | 101 +++++++++++++++++++- src/risk.py | 231 ++++++++++++++++++++++++++++----------------- 2 files changed, 243 insertions(+), 89 deletions(-) diff --git a/src/pandas_risk.py b/src/pandas_risk.py index 355c2fb..e3af3e0 100644 --- a/src/pandas_risk.py +++ b/src/pandas_risk.py @@ -22,16 +22,108 @@ """ import pandas as pd import numpy as np - +import time @pd.api.extensions.register_dataframe_accessor("deid") class deid : """ This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe """ def __init__(self,df): - self._df = df + self._df = df.fillna(' ') + def explore(self,**args): + """ + This function will perform experimentation by performing a random policies (combinations of attributes) + This function is intended to explore a variety of policies and evaluate their associated risk. + + @param pop|sample data-frame with popublation reference + @param id key field that uniquely identifies patient/customer ... + """ + # id = args['id'] + pop= args['pop'] if 'pop' in args else None + # if 'columns' in args : + # cols = args['columns'] + # params = {"sample":args['data'],"cols":cols} + # if pop is not None : + # params['pop'] = pop + # return self.evaluate(**params) + # else : + # + # Policies will be generated with a number of runs + # + RUNS = args['num_runs'] if 'num_runs' in args else 5 + + sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df) + + k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count']) + columns = list(set(sample.columns.tolist()) - set([id])) + o = pd.DataFrame() + # pop = args['pop'] if 'pop' in args else None + for i in np.arange(RUNS): + n = np.random.randint(2,k) + + cols = np.random.choice(columns,n,replace=False).tolist() + params = {'sample':sample,'cols':cols} + if pop is not None : + params['pop'] = pop + r = self.evaluate(**params) + # + # let's put the policy in place + p = pd.DataFrame(1*sample.columns.isin(cols)).T + p.columns = sample.columns + o = o.append(r.join(p)) + + o.index = np.arange(o.shape[0]).astype(np.int64) + + return o + def evaluate(self,**args) : + """ + This function will compute the marketer, if a population is provided it will evaluate the marketer risk relative to both the population and sample + @param smaple data-frame with the data to be processed + @param policy the columns to be considered. + @param pop population dataset + @params flag user defined flag (no computation use) + """ + if (args and 'sample' not in args) or not args : + x_i = pd.DataFrame(self._df) + elif args and 'sample' in args : + x_i = args['sample'] + if (args and 'cols' not in args) or not args : + cols = x_i.columns.tolist() + # cols = self._df.columns.tolist() + elif args and 'cols' in args : + cols = args['cols'] + flag = args['flag'] if 'flag' in args else 'UNFLAGGED' + # if args and 'sample' in args : + + # x_i = pd.DataFrame(self._df) + # else : + # cols = args['cols'] if 'cols' in args else self._df.columns.tolist() + # x_i = x_i.groupby(cols,as_index=False).size().values + x_i_values = x_i.groupby(cols,as_index=False).size().values + SAMPLE_GROUP_COUNT = x_i_values.size + SAMPLE_FIELD_COUNT = len(cols) + SAMPLE_POPULATION = x_i_values.sum() + + SAMPLE_MARKETER = SAMPLE_GROUP_COUNT / np.float64(SAMPLE_POPULATION) + SAMPLE_PROSECUTOR = 1/ np.min(x_i_values).astype(np.float64) + if 'pop' in args : + Yi = args['pop'] + y_i= pd.DataFrame({"group_size":Yi.groupby(cols,as_index=False).size()}).reset_index() + # y_i['group'] = pd.DataFrame({"group_size":args['pop'].groupby(cols,as_index=False).size().values}).reset_index() + # x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size().values}).reset_index() + x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size()}).reset_index() + SAMPLE_RATIO = int(100 * x_i.size/args['pop'].shape[0]) + r = pd.merge(x_i,y_i,on=cols,how='inner') + r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1) + r['sample %'] = np.repeat(SAMPLE_RATIO,r.shape[0]) + r['tier'] = np.repeat(flag,r.shape[0]) + r['sample marketer'] = np.repeat(SAMPLE_MARKETER,r.shape[0]) + r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']] + else: + r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]}) + return r - def risk(self,**args): + def _risk(self,**args): """ @param id name of patient field @params num_runs number of runs (default will be 100) @@ -50,7 +142,7 @@ class deid : k = len(columns) N = self._df.shape[0] tmp = self._df.fillna(' ') - np.random.seed(1) + np.random.seed(int(time.time()) ) for i in range(0,num_runs) : # @@ -85,6 +177,7 @@ class deid : [ { "group_count":x_.size, + "patient_count":N, "field_count":n, "marketer": x_.size / np.float64(np.sum(x_)), diff --git a/src/risk.py b/src/risk.py index 7bdf90a..0a1bb64 100644 --- a/src/risk.py +++ b/src/risk.py @@ -146,7 +146,7 @@ class utils : return " ".join(SQL).replace(":fields"," , ".join(fields)) -class risk : +class SQLRisk : """ This class will handle the creation of an SQL query that computes marketer and prosecutor risk (for now) """ @@ -186,102 +186,163 @@ class risk : +class UtilHandler : + def __init__(self,**args) : + """ + @param path path to the service account file + @param dataset input dataset name + @param key_field key_field (e.g person_id) + @param key_table + + """ + self.path = args['path'] + self.client = bq.Client.from_service_account_json(self.path) + dataset = args['dataset'] + self.key = args['key_field'] + + self.mytools = utils(client = self.client) + self.tables = self.mytools.get_tables(dataset=dataset,client=self.client,key=self.key) + index = [ self.tables.index(item) for item in self.tables if item['name'] == args['key_table']] [0] + if index != 0 : + first = self.tables[0] + aux = self.tables[index] + self.tables[0] = aux + self.tables[index] = first + if 'filter' in args : + self.tables = [item for item in self.tables if item['name'] in args['filter']] -if 'action' in SYS_ARGS and SYS_ARGS['action'] in ['create','compute','migrate'] : + def create_table(self,**args): + """ + @param path absolute filename to save the create statement - 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 --key --o_dataset --table [--file] --path - # - 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') + """ + create_sql = self.mytools.get_sql(tables=self.tables,key=self.key) #-- The create statement + # o_dataset = SYS_ARGS['o_dataset'] + # table = SYS_ARGS['table'] + if 'path' in args: + f = open(args['path'],'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) + return create_sql + def migrate_tables(self,**args): + """ + This function will migrate a table from one location to another + The reason for migration is to be able to reduce a candidate table to only represent a patient by her quasi-identifiers. + @param dataset target dataset + """ + o_dataset = args['dataset'] if 'dataset' in args else None + p = [] + for table in self.tables: + sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",self.mytools.get_filtered_table(table,self.key),") as ",table['name']]) + p.append(sql) + if o_dataset : + job = bq.QueryJobConfig() + job.destination = self.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(create_sql,location='US',job_config=job) + r = self.client.query(sql,location='US',job_config=job) + + print [table['full_name'],' ** ',r.job_id,' ** ',r.state] + return p + +# 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) +# 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 --key --o_dataset --table [--file] --path +# # +# 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] - elif SYS_ARGS['action'] == 'migrate' : - # - # +# 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']]) - print "" - print sql - print "" - # 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) +# 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']]) +# print "" +# print sql +# print "" +# # 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) +# # r = client.query(sql,location='US',job_config=job) - # print [table['full_name'],' ** ',r.job_id,' ** ',r.state] +# # print [table['full_name'],' ** ',r.job_id,' ** ',r.state] - pass - else: - # - # - tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ] - limit = int(SYS_ARGS['limit']) if 'limit' in SYS_ARGS else 1 - if tables : - 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'],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') - print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape] - time.sleep(2) +# pass +# else: +# # +# # +# tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ] +# limit = int(SYS_ARGS['limit']) if 'limit' in SYS_ARGS else 1 +# if tables : +# 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'],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') +# print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape] +# time.sleep(2) -else: - print 'ERROR' - 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') +# # 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') From 43cbd12a1f2f0d18f3e8adc07b7c45f937775172 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 11 Dec 2018 17:42:37 -0600 Subject: [PATCH 02/23] misc updates ... --- notebooks/registered-tier-history.ipynb | 385 +++++++++++ notebooks/registered-tier.ipynb | 859 ++++++++++++++++++++++++ src/pandas_risk.py | 11 +- src/sql/basic-observation.sql | 83 +++ src/sql/observation-registered.sql | 376 +++++++++++ 5 files changed, 1710 insertions(+), 4 deletions(-) create mode 100644 notebooks/registered-tier-history.ipynb create mode 100644 notebooks/registered-tier.ipynb create mode 100644 src/sql/basic-observation.sql create mode 100644 src/sql/observation-registered.sql diff --git a/notebooks/registered-tier-history.ipynb b/notebooks/registered-tier-history.ipynb new file mode 100644 index 0000000..3278968 --- /dev/null +++ b/notebooks/registered-tier-history.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The experiments here describe medical/family history as they associate with risk measures\n", + "Additionally we will have fractional risk assessments\n", + "\"\"\"\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pandas_risk import *\n", + "dfm = pd.read_gbq(\"SELECT * FROM deid_risk.registered_medical_history_dec_001\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", + "dff = pd.read_gbq(\"SELECT * FROM deid_risk.registered_family_history_dec_001\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", + "df = pd.read_gbq(\"SELECT person_id, birth_date,city,state,home_owner,race,ethnicity,gender,birth_place,marital_status,orientation,education,employment_status,income,travel_abroad_6_months,active_duty_status FROM deid_risk.registered_dec_01\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "med_cols = np.random.choice(list(set(dfm.columns.tolist()) - set(['person_id'])),3).tolist()\n", + "fam_cols = np.random.choice(list(set(dff.columns.tolist()) - set(['person_id'])),3).tolist()\n", + "medical = pd.merge(df,dfm[med_cols+['person_id']],on='person_id')\n", + "family = pd.merge(df,dff[fam_cols + ['person_id']],on='person_id')\n", + "_tmp = pd.merge(dfm[med_cols +['person_id']],dff[fam_cols+['person_id']])\n", + "data = pd.merge(df,_tmp,on='person_id')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
021full history1153080.9926911.00.987663
118medical1153060.9926741.00.987629
218family1153040.9926561.00.987594
315no-history1153000.9926221.00.987526
43medical-only270.0002320.50.000000
53family-only1460.0012571.00.000551
\n", + "
" + ], + "text/plain": [ + " field_count flag group_count marketer prosecutor \\\n", + "0 21 full history 115308 0.992691 1.0 \n", + "1 18 medical 115306 0.992674 1.0 \n", + "2 18 family 115304 0.992656 1.0 \n", + "3 15 no-history 115300 0.992622 1.0 \n", + "4 3 medical-only 27 0.000232 0.5 \n", + "5 3 family-only 146 0.001257 1.0 \n", + "\n", + " unique_row_ratio \n", + "0 0.987663 \n", + "1 0.987629 \n", + "2 0.987594 \n", + "3 0.987526 \n", + "4 0.000000 \n", + "5 0.000551 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([data.deid.evaluate(flag='full history',cols= list(set(data.columns.tolist()) - set(['person_id'])) )\n", + " ,medical.deid.evaluate(flag='medical',cols=list( set(medical.columns.tolist() ) - set(['person_id']) ) )\n", + " ,family.deid.evaluate(flag='family',cols=list( set(family.columns.tolist() ) - set(['person_id']) ) )\n", + " ,df.deid.evaluate(flag='no-history',cols=list( set(df.columns.tolist() ) - set(['person_id']) ) )\n", + " , dfm.deid.evaluate(flag='medical-only',cols=med_cols )\n", + " , dff.deid.evaluate(flag='family-only',cols=fam_cols )\n", + " ],ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division\n", + "def evaluate(df) :\n", + " cols = list(set(df.columns.tolist()) - set(['person_id']))\n", + " \n", + " portions = np.round(np.random.random_sample(4),3).tolist() + np.arange(5,105,5).tolist()\n", + " \n", + " N = df.shape[0] - 1\n", + " portions = np.divide(np.multiply(portions,N),100).astype(np.int64)\n", + " portions = np.unique([n for n in portions if n > 1])\n", + " \n", + " r = pd.DataFrame()\n", + " for num_rows in portions :\n", + " \n", + " indices = np.random.choice(N,num_rows,replace=False)\n", + "# print (indices.size / N)\n", + " flag = \" \".join([str( np.round(100*indices.size/ N,2)),'%'])\n", + " r = r.append(df.loc[indices].deid.evaluate(cols=cols,flag=flag,min_group_size=2))\n", + " return r" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
011UNFLAGGED1148860.9890581.00.980535
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" + ], + "text/plain": [ + " field_count flag group_count marketer prosecutor unique_row_ratio\n", + "0 11 UNFLAGGED 114886 0.989058 1.0 0.980535" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = list(set (df.columns.tolist()) - set(['person_id']))\n", + "df[['race','state','gender_identity','ethnicity','marital_status','education','orientation','sex_at_birth','birth_date','travel_abroad_6_months','active_duty_status']].deid.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['person_id',\n", + " 'HearingVision_FarSightedness',\n", + " 'HearingVision_Glaucoma',\n", + " 'Digestive_Pancreatitis']" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#\n", + "# This is the merge with medical history\n", + "\n", + "cols = ['person_id'] + np.random.choice(dfm.columns[1:],3,replace=False).tolist()\n", + "p = pd.merge(df,dfm[cols],on='person_id')\n", + "cols\n", + "# # cols = list(set(p.columns.tolist()) - set(['person_id']))\n", + "# evaluate(p) #p.deid.explore(cols=cols,num_runs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "cols = list( set(dfm.columns.tolist()) - set(['person_id']))\n", + "cols = np.random.choice(cols,3,replace=False).tolist()\n", + "p = pd.merge(dfm[['person_id']+cols],df)\n", + "fcols = list(set(p.columns.tolist()) - set(['person_id']))\n", + "# dfm[cols].deid.evaluate(cols=list( set(cols) - set(['person_id'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "variables": { + " \" ; \".join(cols)": "InfectiousDiseases_HepatitisC ; Cancer_StomachCancer ; Circulatory_Hypertension", + " p.shape[0] ": "116157", + " p[fcols].deid.evaluate() ": "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
037UNFLAGGED1153970.9934571.00.98886
\n
" + } + }, + "source": [ + "### Medical History\n", + "\n", + " We randomly select three a tributes {{ \" ; \".join(cols)}} . \n", + " The dataset associated risk evaluation contains {{ p.shape[0] }} records\n", + "{{ p[fcols].deid.evaluate() }}\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['person_id',\n", + " 'InfectiousDiseases_Tuberculosis',\n", + " 'SkeletalMuscular_Fibromyalgia',\n", + " 'Cancer_ProstateCancer']" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dfm[cols[1:]].head()\n", + "np.sum(dfm.fillna(' ').groupby(cols[1:],as_index=False).size().values <= 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/registered-tier.ipynb b/notebooks/registered-tier.ipynb new file mode 100644 index 0000000..c8acbbf --- /dev/null +++ b/notebooks/registered-tier.ipynb @@ -0,0 +1,859 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "This notebook is designed to run experiments around demographics on registered tier\n", + "The \n", + "\"\"\"\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pandas_risk import *\n", + "\n", + "ATTRIBUTES = ['race','ethnicity','birth_date','state','city','zip','marital_status','education','language','home_owner','income','employment_status','living_situation','active_duty_status','gender_identity','birth_place','death_date','death_cause','orientation']\n", + "dfs = pd.read_csv('scenario-settings.csv')\n", + "dfc = pd.read_gbq(\"SELECT * FROM deid_risk.registered_dec_01\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "cols_o = dfs.loc[(dfs.fo & dfs.fi) ==1].feature.tolist()\n", + "cols_i = dfs.loc[(dfs.fo + dfs.fi )>=1 ].feature.tolist()\n", + "cols_a = dfs.feature.tolist()\n", + "\n", + "cols_v = ['birth_date','gender_identity','race','state','city','birth_place'] #-- voter registration\n", + "#remove the dates fields because dates are shifted\n", + "cols_o = [i for i in cols_o if i not in ['birth_date','death_date']]\n", + "cols_i = [i for i in cols_i if i not in ['birth_date','death_date']]\n", + "cols_a = [i for i in cols_a if i not in ['birth_date','death_date']]\n", + "cols_v = [i for i in cols_v if i not in ['birth_date', 'death_date']]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# print(dfs)\n", + "# print(cols_o)\n", + "# print(cols_i)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
08high-conj65320.0562341.00.021368
111high-disj474470.4084731.00.278554
216all607180.5227241.00.408189
35voter-reg13160.0113291.00.002944
\n", + "
" + ], + "text/plain": [ + " field_count flag group_count marketer prosecutor unique_row_ratio\n", + "0 8 high-conj 6532 0.056234 1.0 0.021368\n", + "1 11 high-disj 47447 0.408473 1.0 0.278554\n", + "2 16 all 60718 0.522724 1.0 0.408189\n", + "3 5 voter-reg 1316 0.011329 1.0 0.002944" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r = pd.concat([dfc[cols_o].deid.evaluate(),dfc[cols_i].deid.evaluate(),dfc[cols_a].deid.evaluate(),dfc[cols_v].deid.evaluate() ])\n", + "r.index = np.arange(r.shape[0]).astype(np.int64)\n", + "r['flag']=['high-conj','high-disj','all','voter-reg']\n", + "\n", + "r\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " feature fi fo\n", + "0 race 1 1\n", + "1 ethnicity 1 1\n", + "2 birth_date 1 1\n", + "3 city 1 1\n", + "4 state 1 1\n", + "5 marital_status 1 1\n", + "6 education 1 0\n", + "7 language 0 0\n", + "8 home_owner 1 1\n", + "9 income 0 1\n", + "10 employment_status 1 0\n", + "11 living_situation 0 0\n", + "12 active_duty_status 0 0\n", + "13 gender_identity 1 1\n", + "14 birth_place 0 0\n", + "15 death_date 1 1\n", + "16 death_cause 1 1\n", + "17 orientation 0 0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "names = pd.read_csv('family-history.csv').name.tolist()\n", + "path ='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json'\n", + "sql = \"\"\"\n", + "SELECT * FROM deid_risk.registered_medical_history_dec_001\n", + "\"\"\"\n", + "dfm = pd.read_gbq(\"SELECT * FROM deid_risk.registered_medical_history_dec_001\",private_key=path,dialect='standard')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9343780009344719, 1.269831148073964)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = list( set(dfm.columns.tolist()) - set(['person_id']))\n", + "r = pd.DataFrame(dfm[cols].count(),columns=['counts'])\n", + "r['attributes'] = r.index\n", + "r['rate'] = 100*(r.counts / dfm.shape[0])\n", + "r.rate.mean(),np.sqrt(r.rate.var())" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "writer = pd.ExcelWriter('/home/steve/tmp/simple.xlsx', engine='xlsxwriter')\n", + "r.to_excel(writer,sheet_name='p1')\n", + "workbook = writer.book\n", + "worksheet = workbook.add_worksheet()\n", + "b = pd.DataFrame({\"id\":np.random.choice(10,30)})" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__doc__',\n", + " '__format__',\n", + " '__getattribute__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__module__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_assemble_xml_file',\n", + " '_button_params',\n", + " '_calculate_spans',\n", + " '_calculate_x_split_width',\n", + " '_check_dimensions',\n", + " '_comment_params',\n", + " '_convert_date_time',\n", + " '_convert_name_area',\n", + " '_csv_join',\n", + " '_encode_password',\n", + " '_escape_attributes',\n", + " '_escape_data',\n", + " '_escape_url',\n", + " '_extract_filter_tokens',\n", + " '_get_palette_color',\n", + 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'orientation',\n", + " 'original_row_height',\n", + " 'outline_below',\n", + " 'outline_changed',\n", + " 'outline_col_level',\n", + " 'outline_on',\n", + " 'outline_right',\n", + " 'outline_row_level',\n", + " 'outline_settings',\n", + " 'outline_style',\n", + " 'page_order',\n", + " 'page_setup_changed',\n", + " 'page_start',\n", + " 'page_view',\n", + " 'palette',\n", + " 'panes',\n", + " 'paper_size',\n", + " 'previous_row',\n", + " 'print_across',\n", + " 'print_area',\n", + " 'print_area_range',\n", + " 'print_comments',\n", + " 'print_gridlines',\n", + " 'print_headers',\n", + " 'print_options_changed',\n", + " 'print_row_col_headers',\n", + " 'print_scale',\n", + " 'protect',\n", + " 'protect_options',\n", + " 'rel_count',\n", + " 'remove_timezone',\n", + " 'repeat_col_range',\n", + " 'repeat_columns',\n", + " 'repeat_row_range',\n", + " 'repeat_rows',\n", + " 'right_to_left',\n", + " 'row_col_headers',\n", + " 'row_data_fh',\n", + " 'row_data_fh_closed',\n", + " 'row_data_filename',\n", + " 'row_size_changed',\n", + " 'row_sizes',\n", + " 'row_spans',\n", + " 'rstring',\n", + " 'screen_gridlines',\n", + " 'select',\n", + " 'selected',\n", + " 'selections',\n", + " 'set_cols',\n", + " 'set_column',\n", + " 'set_comments_author',\n", + " 'set_default_row',\n", + " 'set_first_sheet',\n", + " 'set_footer',\n", + " 'set_h_pagebreaks',\n", + " 'set_header',\n", + " 'set_landscape',\n", + " 'set_margins',\n", + " 'set_page_view',\n", + " 'set_paper',\n", + " 'set_portrait',\n", + " 'set_print_scale',\n", + " 'set_row',\n", + " 'set_rows',\n", + " 'set_selection',\n", + " 'set_start_page',\n", + " 'set_tab_color',\n", + " 'set_v_pagebreaks',\n", + " 'set_vba_name',\n", + " 'set_zoom',\n", + " 'shape_hash',\n", + " 'shapes',\n", + " 'show_comments',\n", + " 'show_zeros',\n", + " 'sparklines',\n", + " 'split_panes',\n", + " 'str_table',\n", + " 'strings_to_formulas',\n", + " 'strings_to_numbers',\n", + " 'strings_to_urls',\n", + " 'tab_color',\n", + " 'table',\n", + " 'tables',\n", + " 'tmpdir',\n", + " 'use_data_bars_2010',\n", + " 'validations',\n", + " 'vba_codename',\n", + " 'vbreaks',\n", + " 'vcenter',\n", + " 'vertical_dpi',\n", + " 'vml_data_id',\n", + " 'vml_drawing_links',\n", + " 'vml_header_id',\n", + " 'vml_shape_id',\n", + " 'worksheet_meta',\n", + " 'write',\n", + " 'write_array_formula',\n", + " 'write_blank',\n", + " 'write_boolean',\n", + " 'write_column',\n", + " 'write_comment',\n", + " 'write_datetime',\n", + " 'write_formula',\n", + " 'write_match',\n", + " 'write_number',\n", + " 'write_rich_string',\n", + " 'write_row',\n", + " 'write_string',\n", + " 'write_url',\n", + " 'xls_colmax',\n", + " 'xls_rowmax',\n", + " 'xls_strmax',\n", + " 'zoom',\n", + " 'zoom_scale_normal']" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(worksheet)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/pandas_risk.py b/src/pandas_risk.py index e3af3e0..665582d 100644 --- a/src/pandas_risk.py +++ b/src/pandas_risk.py @@ -87,12 +87,13 @@ class deid : x_i = pd.DataFrame(self._df) elif args and 'sample' in args : x_i = args['sample'] - if (args and 'cols' not in args) or not args : + if not args or 'cols' not in args: cols = x_i.columns.tolist() # cols = self._df.columns.tolist() elif args and 'cols' in args : cols = args['cols'] flag = args['flag'] if 'flag' in args else 'UNFLAGGED' + MIN_GROUP_SIZE = args['min_group_size'] if 'min_group_size' in args else 1 # if args and 'sample' in args : # x_i = pd.DataFrame(self._df) @@ -100,15 +101,16 @@ class deid : # cols = args['cols'] if 'cols' in args else self._df.columns.tolist() # x_i = x_i.groupby(cols,as_index=False).size().values x_i_values = x_i.groupby(cols,as_index=False).size().values - SAMPLE_GROUP_COUNT = x_i_values.size + SAMPLE_GROUP_COUNT = x_i_values.size SAMPLE_FIELD_COUNT = len(cols) SAMPLE_POPULATION = x_i_values.sum() - + UNIQUE_REC_RATIO = np.divide(np.sum(x_i_values <= MIN_GROUP_SIZE) , np.float64( SAMPLE_POPULATION)) SAMPLE_MARKETER = SAMPLE_GROUP_COUNT / np.float64(SAMPLE_POPULATION) SAMPLE_PROSECUTOR = 1/ np.min(x_i_values).astype(np.float64) if 'pop' in args : Yi = args['pop'] y_i= pd.DataFrame({"group_size":Yi.groupby(cols,as_index=False).size()}).reset_index() + UNIQUE_REC_RATIO = np.sum(y_i.group_size < MIN_GROUP_SIZE) , np.float64(Yi.shape[0]) # y_i['group'] = pd.DataFrame({"group_size":args['pop'].groupby(cols,as_index=False).size().values}).reset_index() # x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size().values}).reset_index() x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size()}).reset_index() @@ -120,7 +122,8 @@ class deid : r['sample marketer'] = np.repeat(SAMPLE_MARKETER,r.shape[0]) r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']] else: - r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]}) + r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"flag":[flag],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]}) + r['unique_row_ratio'] = np.repeat(UNIQUE_REC_RATIO,r.shape[0]) return r def _risk(self,**args): diff --git a/src/sql/basic-observation.sql b/src/sql/basic-observation.sql new file mode 100644 index 0000000..96b7067 --- /dev/null +++ b/src/sql/basic-observation.sql @@ -0,0 +1,83 @@ +SELECT person.person_id,sex_at_birth,birth_date, race,zip,city,state, gender +FROM + (SELECT DISTINCT person_id from deid_tmp.observation order by person_id) as person +FULL JOIN ( + SELECT + person_id,MAX(value_as_string) as race + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRace') and value_as_string IS NOT NULL + + GROUP BY person_id + order by person_id +) as lang +ON lang.person_id = person.person_id + +FULL JOIN ( + SELECT + person_id,MAX(value_as_string) as zip + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIZIP') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id +) as work_add +ON work_add.person_id = person.person_id + + + +FULL JOIN ( + SELECT + person_id,max(value_as_string) as city + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIICity') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id +) as u_city +ON u_city.person_id = person.person_id + +FULL JOIN ( + SELECT + person_id,max(value_as_string) as state + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIState') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id + +) as p_addr_o +ON p_addr_o.person_id = person.person_id + +FULL JOIN ( + SELECT + person_id,max(value_as_string) as gender + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id + +) as p_gender +ON p_gender.person_id = person.person_id + +FULL JOIN ( + SELECT + person_id,max(value_as_string) as birth_date + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIBirthInformation_BirthDate') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id + +) as p_birth +ON p_birth.person_id = person.person_id + +FULL JOIN ( + SELECT + person_id,max(value_as_string) as sex_at_birth + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'BiologicalSexAtBirth_SexAtBirth') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id + +) as p_sex +ON p_sex.person_id = person.person_id + + +ORDER BY person.person_id + diff --git a/src/sql/observation-registered.sql b/src/sql/observation-registered.sql new file mode 100644 index 0000000..abfb249 --- /dev/null +++ b/src/sql/observation-registered.sql @@ -0,0 +1,376 @@ +SELECT * +FROM ( + SELECT person.person_id,first_name,last_name,birth_date,city,family_history_aware,current_hyper_tension,sex_at_birth, race,state, gender,ethnicity,birth_place,orientation,education,employment_status, + marital_status,language,home_owner,sd_bloodbank, nhpi, living_situation,income,death_cause, death_date, active_duty_status, + gender_identity, insurance_type, work_address_state,consent_18_years_age,person_one_state,person_two_state,sc_site, + health_abroad_6_months,travel_abroad_6_months + FROM + (SELECT DISTINCT person_id from deid_tmp.observation order by person_id) as person + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as travel_abroad_6_months + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'OutsideTravel6Month_OutsideTravel6MonthWhere') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as te_ + ON te_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as health_abroad_6_months + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'OverallHealth_OutsideTravel6Month') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as he_ + ON he_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as active_duty_status + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'ActiveDuty_AvtiveDutyServeStatus') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as mil_ + ON mil_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as sc_site + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'SouthCarolinaSitePairing_EauClaireAppointment') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as sc_ + ON sc_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as person_one_state + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PersonOneAddress_PersonOneAddressState') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as p1_ + ON p1_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as person_two_state + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'SecondContactsAddress_SecondContactState') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as p2_ + ON p2_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_as_string) as work_address_state + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'EmploymentWorkAddress_State') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as ws_ + ON ws_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as consent_18_years_age + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'ExtraConsent_18YearsofAge') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as c18_ + ON c18_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as gender_identity + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as gi_ + ON gi_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as income + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Income_AnnualIncome') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as income_ + ON income_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as living_situation + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'LivingSituation_CurrentLiving') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as living_ + ON living_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as nhpi + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'NHPI_NHPISpecific') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as nhpi_ + ON nhpi_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_as_string) as sd_bloodbank + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'SanDiegoBloodBank') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as sd + ON sd.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as education + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'EducationLevel_HighestGrade') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as edu + ON edu.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as home_owner + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'HomeOwn_CurrentHomeOwn') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as h_owner + ON h_owner.person_id = person.person_id + + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as employment_status + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Employment_EmploymentStatus') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as empl + ON empl.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as marital_status + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'MaritalStatus_CurrentMaritalStatus') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as marital + ON marital.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as language + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Language_SpokenWrittenLanguage') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as lang_ + ON lang_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as race + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRace') and value_source_value IS NOT NULL + + GROUP BY person_id + order by person_id + ) as lang + ON lang.person_id = person.person_id + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as ethnicity + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRaceEthnicity') and value_source_value IS NOT NULL + + GROUP BY person_id + order by person_id + ) as ethnic + ON ethnic.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as birth_place + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'TheBasics_Birthplace') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as birthp + ON birthp.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,MAX(value_source_value) as orientation + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'TheBasics_SexualOrientation') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + ) as sexo + ON sexo.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,max(value_source_value) as state + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIState') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + + ) as p_addr_o + ON p_addr_o.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_source_value) as gender + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + + ) as p_gender + ON p_gender.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_source_value) as sex_at_birth + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'_SexAtBirth') --and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + + ) as p_sex + ON p_sex.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_source_value) as insurance_type + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'HealthInsurance_HealthInsuranceType') and value_source_value IS NOT NULL + GROUP BY person_id + order by person_id + + ) as ins_ + ON ins_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_as_string) as last_name + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIName_Last') and value_as_string IS NOT NULL + GROUP BY person_id + order by person_id + + ) as ln_ + ON ln_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,max(value_as_string) as first_name + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIName_First') + GROUP BY person_id + order by person_id + + ) as fn_ + ON fn_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_as_string) as current_hyper_tension + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'Circulatory_HypertensionCurrently') + GROUP BY person_id + order by person_id + + ) as cht_ + ON cht_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max( cast(value_as_string as DATE)) as birth_date + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'PIIBirthInformation_BirthDate') + GROUP BY person_id + order by person_id + + ) as bd_ + ON bd_.person_id = person.person_id + + + FULL JOIN ( + SELECT + person_id,max(value_as_string) as city + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'StreetAddress_PIICity') + GROUP BY person_id + order by person_id + + ) as city_ + ON city_.person_id = person.person_id + + FULL JOIN ( + SELECT + person_id,max(value_as_string) as family_history_aware + FROM deid_tmp.observation + WHERE REGEXP_CONTAINS(observation_source_value,'FamilyHistory_FamilyMedicalHistoryAware') + GROUP BY person_id + order by person_id + + ) as bro_ + ON bro_.person_id = person.person_id + FULL JOIN ( + SELECT person_id, max(death_date) AS death_date + FROM deid_tmp.death + GROUP BY person_id + order BY person_id + + ) as death_ + ON death_.person_id = person.person_id + + FULL JOIN ( + SELECT person_id, max(cause_source_value) as death_cause + FROM deid_tmp.death + GROUP BY person_id + order BY person_id + + + ) as death_c ON death_c.person_id = person.person_id + ORDER BY person.person_id +) as frame + +-- WHERE first_name is not NULL From 0b16ce94ccf6361899e6a225d7afdee4191979df Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 11 Dec 2018 17:43:16 -0600 Subject: [PATCH 03/23] notebooks --- notebooks/Untitled.ipynb | 238 ++++++++++++ notebooks/data-analysis.ipynb | 2 +- notebooks/data-preparation.ipynb | 95 +++++ notebooks/experiments.ipynb | 610 +++++++++++++++++++++++++++++++ 4 files changed, 944 insertions(+), 1 deletion(-) create mode 100644 notebooks/Untitled.ipynb create mode 100644 notebooks/data-preparation.ipynb create mode 100644 notebooks/experiments.ipynb diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb new file mode 100644 index 0000000..1e154e2 --- /dev/null +++ b/notebooks/Untitled.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import itertools \n", + "import pandas as pd\n", + "import numpy as np\n", + "# from pandas_risk import *\n", + "from time import time\n", + "import os\n", + "\n", + "attr = ['gender','race','zip','year_of_birth']\n", + "comb_attr = [\n", + " ['zip' ,'gender', 'birth_datetime', 'race'], \n", + " ['zip', 'gender', 'year_of_birth', 'race'], \n", + " ['gender','race','zip'],\n", + " ['race','year_of_birth','zip']\n", + "]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "SQL_CONTROLLED=\"SELECT * FROM deid_risk.basic_risk60k\"\n", + "dfc = pd.read_gbq(SQL_CONTROLLED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def risk(**args):\n", + " Yi = args['data']\n", + " Yi = Yi.fillna(' ')\n", + " sizes = args['prop'] if 'prop' in args else np.arange(5,100,5)\n", + " FLAG = args['flag'] if 'flag' in args else 'UNFLAGGED'\n", + " N = args['num_runs']\n", + " if 'cols' in args :\n", + " columns = args['cols']\n", + " else:\n", + " columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", + " p = pd.DataFrame()\n", + " y_i= pd.DataFrame({\"group_size\":Yi.groupby(columns,as_index=False).size()}).reset_index()\n", + " for index in sizes :\n", + " for n in np.repeat(index,N):\n", + " \n", + " # we will randomly sample n% rows from the dataset\n", + " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", + " x_i= pd.DataFrame(Yi).loc[i] \n", + " risk = x_i.deid.risk(id='person_id',quasi_id = columns)\n", + " x_i = pd.DataFrame({\"group_size\":x_i.groupby(columns,as_index=False).size()}).reset_index()\n", + "\n", + "\n", + " r = pd.merge(x_i,y_i,on=columns,how='inner')\n", + " if r.shape[0] == 0 :\n", + " continue\n", + " r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)\n", + " r['sample %'] = np.repeat(n,r.shape[0])\n", + " r['tier'] = np.repeat(FLAG,r.shape[0])\n", + " r['sample marketer'] = np.repeat(risk['marketer'].values[0],r.shape[0])\n", + " # r['patient_count'] = np.repeat(r.shape[0],r.shape[0])\n", + " r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]\n", + " p = p.append(r)\n", + " p.index = np.arange(p.shape[0]).astype(np.int64)\n", + " return p\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas_risk import *\n", + "o = pd.DataFrame()\n", + "PATH=\"out/experiment-phase-2.xlsx\"\n", + "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", + "comb_attr = [\n", + " ['zip' ,'gender', 'birth_datetime', 'race'], \n", + " ['zip', 'gender', 'year_of_birth', 'race'], \n", + " ['gender','race','zip'],\n", + " ['race','year_of_birth','zip']\n", + "]\n", + "\n", + "for cols in comb_attr :\n", + " o = risk(data=dfc,cols=cols,flag='CONTROLLED',num_runs=5)\n", + " #\n", + " # adding the policy\n", + " x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n", + " o = o.join(pd.DataFrame(x,columns = dfc.columns))\n", + " #\n", + " # Write this to excel notebook\n", + " o.to_excel(writer,\"-\".join(cols))\n", + "# break\n", + " \n", + "\n", + "# p = p.rename(columns={'marketer_x':'sample marketer'})\n", + "# p.index = np.arange(p.shape[0]).astype(np.int64)\n", + "\n", + "writer.save()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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person_idyear_of_birthmonth_of_birthday_of_birthbirth_datetimerace_concept_idethnicity_concept_idlocation_idcare_site_idperson_source_value...gender_source_concept_idrace_source_valueethnicity_source_valuesex_at_birthbirth_dateracezipcitystategender
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [person_id, year_of_birth, month_of_birth, day_of_birth, birth_datetime, race_concept_id, ethnicity_concept_id, location_id, care_site_id, person_source_value, gender_source_value, gender_source_concept_id, race_source_value, ethnicity_source_value, sex_at_birth, birth_date, race, zip, city, state, gender]\n", + "Index: []\n", + "\n", + "[0 rows x 21 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n", + "o.join(pd.DataFrame(x,columns = dfc.columns))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'columns' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'columns' is not defined" + ] + } + ], + "source": [ + "columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/data-analysis.ipynb b/notebooks/data-analysis.ipynb index d7c44c2..8e4e21b 100644 --- a/notebooks/data-analysis.ipynb +++ b/notebooks/data-analysis.ipynb @@ -177,7 +177,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.15rc1" }, "varInspector": { "cols": { diff --git a/notebooks/data-preparation.ipynb b/notebooks/data-preparation.ipynb new file mode 100644 index 0000000..adbd66e --- /dev/null +++ b/notebooks/data-preparation.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " skiping ...\n", + " skiping ...\n", + " skiping ...\n", + " skiping ...\n", + " skiping ...\n", + " skiping ...\n", + " skiping ...\n" + ] + }, + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + " This notebook is designed to generate SQL syntax all the quasi-identifiers for the patients in the database\n", + " The resulting SQL will be run against bigquery to produce a table with every record mapping to a patient\n", + " \n", + "\"\"\"\n", + "\n", + "from risk import *\n", + "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-prod.json',dataset='combined20180822',key_field='person_id',key_table='person',filter=['person','observation'])\n", + "r = ihandle.migrate_tables()\n", + "len(r)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "u' SELECT person.person_id , person.year_of_birth , person.month_of_birth , person.day_of_birth , person.birth_datetime , person.race_concept_id , person.ethnicity_concept_id , person.location_id , person.care_site_id , person.person_source_value , person.gender_source_value , person.gender_source_concept_id , person.race_source_value , person.ethnicity_source_value , basic_observation.sex_at_birth AS sex_at_birth1 , basic_observation.birth_date AS birth_date1 , basic_observation.race AS race1 , basic_observation.zip AS zip1 , basic_observation.city AS city1 , basic_observation.state AS state1 , basic_observation.gender AS gender1 FROM (select * from deid_image.person ) as person INNER JOIN (select * from deid_image.basic_observation ) as basic_observation ON basic_observation.person_id = person.person_id '" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',dataset='deid_image',key_field='person_id',key_table='person',filter=['person','basic_observation'])\n", + "ihandle.create_table().replace('\\n',' ')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/experiments.ipynb b/notebooks/experiments.ipynb new file mode 100644 index 0000000..3d52a33 --- /dev/null +++ b/notebooks/experiments.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + " Health Information Privacy Lab\n", + " This notebook is intended to run experiments and generate the data to be used by another notebook\n", + "\n", + " pre-requisites:\n", + " - pandas_risk This is a custom framework that will compute risk for a given dataset\n", + " - google-cloud-bigquery\n", + " - numpy\n", + "\"\"\"\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pandas_risk import *\n", + "from time import time\n", + "import os\n", + "#\n", + "#-- Loading the dataset\n", + "class Logger :\n", + " cache = []\n", + " @staticmethod\n", + " def clear():\n", + " Logger.cache = []\n", + " @staticmethod\n", + " def log(**args) :\n", + " Logger.cache.append(args)\n", + " \n", + "SQL_CONTROLLED=\"SELECT person_id,birth_datetime,city,zip,state,race,gender FROM deid_risk.basic_risk60k\"\n", + "SQL_REGISTERED = \"SELECT person_id,birth_datetime,city,zip,state,race,gender FROM deid_risk.basic_deid_risk60k\"\n", + "dfr = pd.read_gbq(SQL_REGISTERED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", + "dfc = pd.read_gbq(SQL_CONTROLLED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sample %marketersample marketertier
050.9749450.981364controlled
150.9755130.981996controlled
250.9757980.980733controlled
350.9763640.981996controlled
450.9763640.981996controlled
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" + ], + "text/plain": [ + " sample % marketer sample marketer tier\n", + "0 5 0.974945 0.981364 controlled\n", + "1 5 0.975513 0.981996 controlled\n", + "2 5 0.975798 0.980733 controlled\n", + "3 5 0.976364 0.981996 controlled\n", + "4 5 0.976364 0.981996 controlled" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#\n", + "FLAG='REGISTERED-TIER-1'\n", + "if FLAG == 'REGISTERED-TIER' :\n", + " Yi = pd.DataFrame(dfr)\n", + " FOLDER='registered'\n", + "else:\n", + " Yi = pd.DataFrame(dfc)\n", + " FOLDER='controlled'\n", + "Yi = Yi.fillna(' ')\n", + "N = 5\n", + "N_ = str(N)\n", + "SUFFIX = FOLDER+'-tier-'+str(N)+'-experiment.xlsx'\n", + "PATH = os.sep.join(['out',SUFFIX])\n", + "\n", + "\n", + "columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", + "merged_columns = list(columns)+['field_count']\n", + "m = {}\n", + "p = pd.DataFrame()\n", + "n = 0\n", + "y_i= pd.DataFrame({\"group_size\":Yi.groupby(columns,as_index=False).size()}).reset_index()\n", + "#.deid.risk(id='person_id',quasi_id=columns)\n", + "for index in np.arange(5,105,5):\n", + " for n in np.repeat(index,N) :\n", + "# np.random.seed( np.random.randint(0,int(time())+np.random.randint(0,1000)+index+n ) \n", + " #\n", + " # we will randomly sample n% rows from the dataset\n", + " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", + " x_i= pd.DataFrame(Yi).loc[i] \n", + " risk = x_i.deid.risk(id='person_id',quasi_id = columns)\n", + " x_i = pd.DataFrame({\"group_size\":x_i.groupby(columns,as_index=False).size()}).reset_index()\n", + " \n", + "# y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", + "\n", + "\n", + " r = pd.merge(x_i,y_i,on=columns,how='inner')\n", + " if r.shape[0] == 0 :\n", + " print 'skipping ',n\n", + " continue\n", + " r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)\n", + " r['sample %'] = np.repeat(n,r.shape[0])\n", + " r['tier'] = np.repeat(FOLDER,r.shape[0])\n", + " r['sample marketer'] = np.repeat(risk['marketer'].values[0],r.shape[0])\n", + "# r['patient_count'] = np.repeat(r.shape[0],r.shape[0])\n", + " r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]\n", + "# r['marketer'] = r.apply(lambda row: (row.group_size_x / row.group_size_y) / row.patient_count_x,axis=1 )\n", + "# r = r.groupby(columns+['marketer_x'],as_index=False).sum()[columns+['marketer','marketer_x']]\n", + "# r['sample %'] = np.repeat(n,r.shape[0])\n", + "# r['tier'] = np.repeat(FOLDER,r.shape[0])\n", + " p = p.append(r)\n", + "\n", + "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", + "p = p.rename(columns={'marketer_x':'sample marketer'})\n", + "p.index = np.arange(p.shape[0]).astype(np.int64)\n", + "p.to_excel(writer,FOLDER)\n", + "writer.save()\n", + "p.head() " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "p.plot(kind='scatter',x='sample %',y='marketer', c = '#4682B4')\n", + "p.plot(kind='scatter',x='sample %',y = 'sample marketer', c='#CC0000')\n", + "ax = p.plot(kind='scatter',x='sample %',y='marketer', c = '#4682B4')\n", + "p.plot(kind='scatter',x='sample %',y = 'sample marketer', c='#CC0000',ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + " This experiment consists in :\n", + " 1: randomly selecting x % of the records to be sampled\n", + " 2: running a group by on the sample\n", + " 3: calling groupby on the population which th\n", + "\"\"\"\n", + "SQL_ORIGINAL=\"SELECT * FROM deid_risk.risk_60k2\"\n", + "SQL_DEID = \"SELECT * FROM deid_risk.deid_risk_60k limit 20000\"\n", + "# df = pd.read_gbq(SQL_DEID,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", + "\n", + "#\n", + "FLAG='REGISTERED-TIER-9'\n", + "if FLAG == 'REGISTERED-TIER' :\n", + " Yi = pd.DataFrame(dfr)\n", + " FOLDER='registered'\n", + "else:\n", + " Yi = pd.DataFrame(dfc)\n", + " FOLDER='controlled'\n", + "N = 20\n", + "N_ = str(N)\n", + "SUFFIX = FOLDER+'-tier-'+str(N)+'-experiment.xlsx'\n", + "PATH = os.sep.join(['out',SUFFIX])\n", + "\n", + "\n", + "columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", + "merged_columns = list(columns)+['field_count']\n", + "m = {}\n", + "p = pd.DataFrame()\n", + "n = 0\n", + "y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", + "for index in np.arange(5,105,5):\n", + "# np.random.seed( int(time())+np.random.randint(0,100)+index ) \n", + "# n = np.random.randint(10,35) #-- randomly pick a number within an interval\n", + " \n", + " for n in np.repeat(index,20) :\n", + "# np.random.seed( np.random.randint(0,int(time())+np.random.randint(0,1000)+index+n ) \n", + " #\n", + " # we will randomly sample n% rows from the dataset\n", + " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", + " x_i= pd.DataFrame(Yi).loc[i].deid.risk(id='person_id',quasi_id = columns)\n", + " \n", + "# y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", + "\n", + "\n", + " r = pd.merge(x_i,y_i,on=merged_columns,how='inner')\n", + " if r.shape[0] == 0 :\n", + " print 'skipping ',n\n", + " continue\n", + "\n", + " r['marketer'] = r.apply(lambda row: (row.group_size_x / row.group_size_y) / row.patient_count_x,axis=1 )\n", + " r = r.groupby(columns+['marketer_x'],as_index=False).sum()[columns+['marketer','marketer_x']]\n", + " r['sample %'] = np.repeat(n,r.shape[0])\n", + " r['tier'] = np.repeat(FOLDER,r.shape[0])\n", + " p = p.append(r)\n", + "\n", + "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", + "p = p.rename(columns={'marketer_x':'sample marketer'})\n", + "p.index = np.arange(p.shape[0]).astype(np.int64)\n", + "p.to_excel(writer,FOLDER)\n", + "writer.save()\n", + "p.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ax = p.plot(kind='scatter',x='sample %',y='marketer',c='r',ylim=[p.marketer.min(),p.marketer.max()])\n", + "p.plot(kind='scatter',x='sample %',y='sample marketer',c='#4682B4')\n", + "ax = p.plot(kind='scatter',x='sample %',y='marketer',c='r')\n", + "p.plot(kind='scatter',x='sample %',y='sample marketer',c='#4682B4',ax=ax)\n", + "\n", + "_p = pd.DataFrame(p)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p.head()\n", + "\n", + "# writer = pd.ExcelWriter('out/foo.xlsx',engine='xlsxwriter')\n", + "# workbook = writer.book\n", + "# r.groupby('field_count',as_index=False).sum()[['field_count','marketer_x']].to_excel(writer,'page-0')\n", + "# chart = workbook.add_chart({'type':'line'})\n", + "# o = r.groupby('field_count',as_index=False).sum()[['field_count','marketer_x']]\n", + "# # values = o.marketer_x.tolist()\n", + "# # values = [['page-0',item] for item in values]\n", + "# # chart.add_series({\"values\":values})\n", + "# # chart.add_series({'values':'=page-0!$B$2:$B$5'})\n", + "\n", + "# worksheet = writer.sheets['page-0']\n", + "# worksheet.insert_chart('G2',chart)\n", + "# writer.save()\n", + "\n", + "str(10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(chart.add_series)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols = list(set(dfr.columns.tolist()) - set(['person_id'])) + ['field_count']\n", + "r = pd.merge(x_i,y_i,on=cols,how='inner')\n", + "r['marketer'] = r.apply(lambda row: (row.group_count_x/row.group_count_y)/row.patient_count_y ,axis=1)\n", + "# r['field_count'] = r['field_count_x']\n", + "o = r.groupby(cols,as_index=False).sum()[cols+['marketer']]\n", + "o.groupby(['field_count'],as_index=False).mean()\n", + "# o.groupby('field_count',as_index=False).mean().plot.line(x='field_count',y='marketer')\n", + "# r.head()\n", + "# N = r.patient_count_y.mean()\n", + "# r['marketer'] = r.apply(lambda row: row.group_count_x / row.group_count_y,axis=1)\n", + "# m = r.groupby(['field_count'],as_index=False).mean()[['field_count','marketer']]\n", + "# m.marketer = m.marketer / N\n", + "# m.groupby(['field_count']).mean().plot.line()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p.to_csv('out/x-2/single-runs-deid.csv',index=False)\n", + "p.groupby(['sample %']).mean()['marketer'].plot.line()\n", + "p.groupby(['sample %'],as_index=False).mean().plot.scatter(x='sample %',y='marketer')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = pd.DataFrame({\"name\":['d','e','f','g'],\"age\":[12,40,20,30],\"income\":[100,200,300,400]})\n", + "x = pd.DataFrame({\"name\":['a','b','c'],\"age\":[10,20,40],\"income\":[120,100,200]})\n", + "\n", + "# x.join(y,how='outer',on='age')\n", + "x_ = pd.merge(x,y,on=['age','income'],how='outer')\n", + "Logger.log(action='merge',value=x_.shape)\n", + "Logger.cache" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# EXP_0\n", + "# Running the experiment on the Original dataset, with all the attributes\n", + "SCHEMA = \"deid_risk\"\n", + "df = pd.read_gbq(\"select person_id,birth_datetime,race,gender,sex_at_birth, city,state,zip from deid_risk.basic_risk60k \",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", + " dialect='standard')\n", + "\n", + "RUNS = 500\n", + "FLAG = 'basic-features'\n", + "r = df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", + "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", + "compiled = r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", + "fi = compiled[['marketer','prosecutor']].plot.line().get_figure()\n", + "# fo\n", + "# r.plot.line(x='field_count',y='marketer')\n", + "compiled = r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", + "fig_i = r.plot.scatter(x='field_count',y='marketer').get_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# EXP_2 :\n", + "# This experiment will run the marketer risk against individual attributes\n", + "deid_df = pd.read_gbq(\"select person_id,birth_datetime,race,gender,sex_at_birth, city,state,zip from deid_risk.basic_deid_risk60k\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", + " dialect='standard')\n", + "RUNS = 500\n", + "FLAG = 'basic-deid-features'\n", + "deid_r = deid_df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", + "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", + "deid_compiled = deid_r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", + "fo = deid_compiled[['marketer','prosecutor']].plot.line().get_figure()\n", + "# fo\n", + "# r.plot.line(x='field_count',y='marketer')\n", + "# deid_compiled = deid_r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", + "fig_o = deid_r.plot.scatter(x='field_count',y='marketer').get_figure()\n", + "\n", + "# orig_df = pd.read_gbq(\"select * from deid_risk.risk_60k2\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", + "# dialect='standard')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# deid_r.to_csv('out/basic-attributes-deid-data-60k-patients.csv')\n", + "# r.to_csv('out/basic-attributes-raw-data-60k-patients.csv')\n", + "# deid_r.head()\n", + "p = pd.DataFrame()\n", + "p = deid_df.deid.risk(id='person_id',quasi_id=['birth_datetime','race','gender','sex_at_birth', 'city','state','zip'])\n", + "p = p.append(df.deid.risk(id='person_id',quasi_id=['birth_datetime','race','gender','sex_at_birth', 'city','state','zip']))\n", + "p.index = ['deid data','raw data']\n", + "p.to_csv('out/basic_run-7-fields.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols = deid_r.columns[5:]\n", + "deid_r.index = np.arange(deid_r.shape[0]).astype(np.int64)\n", + "xdeid_ = deid_r[cols].sum().tolist()\n", + "xraw_ = r[cols].sum().tolist()\n", + "o = pd.DataFrame()\n", + "o['name'] = cols\n", + "o['raw'] = xraw_\n", + "o['deid']= xdeid_\n", + "\n", + "\n", + "o\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "columns = list( set(orig_df.columns) - set(['person_id']))\n", + "xo = pd.DataFrame()\n", + "xi = pd.DataFrame()\n", + "#\n", + "# Let's compute the risk for every attribute given the list of attributes we've gathered\n", + "#\n", + "for name in columns :\n", + " xo = xo.append(deid_df.deid.risk(id='person_id',quasi_id=[name])[['marketer','prosecutor']],sort=False)\n", + " xi = xi.append(orig_df.deid.risk(id='person_id',quasi_id=[name])[['marketer','prosecutor']],sort=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# The following shows how much the deid process has affected each attributes\n", + "#\n", + "\n", + "RISK_THRESHOLD = 0.5\n", + "xo.index = columns\n", + "xi.index = columns\n", + "\n", + "ii = xi[xi.marketer > RISK_THRESHOLD].index\n", + "# zo = pd.concat([xi.loc[ii],xo.loc[ii]])\n", + "\n", + "zo = xi.loc[ii].join(xo.loc[ii],rsuffix='_deid')\n", + "#\n", + "# heatmap for original data\n", + "# fig_o = sns.heatmap(xi.loc[ii], cmap='RdYlGn_r', linewidths=0.5, annot=True).get_figure()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Running the experiment on the DEID dataset, with all the attributes\n", + "#\n", + "df = pd.read_gbq(\"select * from deid_risk.deid_risk_60k\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", + " dialect='standard')\n", + "\n", + "RUNS = 1500\n", + "FLAG = 'deid-full-attr-dataset'\n", + "r = df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", + "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", + "compiled = r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", + "fo = compiled[['marketer','prosecutor']].plot.line().get_figure()\n", + "# fo\n", + "# r.plot.line(x='field_count',y='marketer')\n", + "compiled = r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", + "fig_o = r.plot.scatter(x='field_count',y='marketer').get_figure()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r.groupby('field_count',as_index=False)['marketer','prosecutor'].var()[['marketer','prosecutor']].plot.line()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# We are going to look into the attributes with a risk of a given threshold\n", + "# We will run the experiment (varied combinations of the list of attributes)\n", + "# The experiment is intended to capture the attributes responsible for increasing the marketer risk\n", + "#\n", + "DEID_DATASET = 'deid_risk.deid_risk_60k2'\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0239b0ab42f6f0555cbeaf61f12e59010b939d8e Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 11:08:47 -0600 Subject: [PATCH 04/23] added pitman risk, and refactored some code --- src/out/risk_xoi.csv | 501 -------------------------- src/out/risk_xoi2.csv | 681 ----------------------------------- src/out/risk_xoi2_stream.csv | 681 ----------------------------------- src/out/risk_xoi_stream.csv | 501 -------------------------- src/pandas_risk.py | 313 +++++++++------- 5 files changed, 174 insertions(+), 2503 deletions(-) delete mode 100644 src/out/risk_xoi.csv delete mode 100644 src/out/risk_xoi2.csv delete mode 100644 src/out/risk_xoi2_stream.csv delete mode 100644 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-0,65657934,4936,79080802,0.8302638862969548,1.0,48 diff --git a/src/out/risk_xoi2_stream.csv b/src/out/risk_xoi2_stream.csv deleted file mode 100644 index 77ee1a4..0000000 --- a/src/out/risk_xoi2_stream.csv +++ /dev/null @@ -1,681 +0,0 @@ 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424be77..0000000 --- a/src/out/risk_xoi_stream.csv +++ /dev/null @@ -1,501 +0,0 @@ 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a/src/pandas_risk.py +++ b/src/pandas_risk.py @@ -1,13 +1,27 @@ """ Health Information Privacy Lab - Steve L. Nyemba & Brad. Malin + Brad. Malin, Weiyi Xia, Steve L. Nyemba + This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas) + The framework will compute the following risk measures: + - marketer + - prosecutor + - pitman - This is an extension to the pandas data-frame that will perform a risk assessment on a variety of attributes - This implementation puts the responsibility on the user of the framework to join datasets and load the final results into a pandas data-frame. + References : + https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf - The code will randomly select fields and compute the risk (marketer and prosecutor) and perform a given number of runs. + This framework integrates pandas (for now) as an extension and can be used in two modes : + Experimental mode + Here the assumption is that we are not sure of the attributes to be disclosed, the framework will explore a variety of combinations and associate risk measures every random combinations + Evaluation mode + The evaluation mode assumes the set of attributes given are known and thus will evaluate risk for a subset of attributes. + + features : + - determine viable fields (quantifiable in terms of uniqueness). This is a way to identify fields that can act as identifiers. + - explore and evaluate risk of a sample dataset against a known population dataset + - explore and evaluate risk on a sample dataset Usage: from pandas_risk import * @@ -22,14 +36,21 @@ """ import pandas as pd import numpy as np -import time +import logging +import json +from datetime import datetime +import sys + +sys.setrecursionlimit(3000) @pd.api.extensions.register_dataframe_accessor("deid") class deid : + """ - This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe + This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe """ def __init__(self,df): self._df = df.fillna(' ') + def explore(self,**args): """ This function will perform experimentation by performing a random policies (combinations of attributes) @@ -38,15 +59,15 @@ class deid : @param pop|sample data-frame with popublation reference @param id key field that uniquely identifies patient/customer ... """ - # id = args['id'] + id = args['id'] pop= args['pop'] if 'pop' in args else None - # if 'columns' in args : - # cols = args['columns'] - # params = {"sample":args['data'],"cols":cols} - # if pop is not None : - # params['pop'] = pop - # return self.evaluate(**params) - # else : + + if 'pop_size' in args : + pop_size = np.float64(args['pop_size']) + else: + pop_size = -1 + + # # Policies will be generated with a number of runs # @@ -57,7 +78,7 @@ class deid : k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count']) columns = list(set(sample.columns.tolist()) - set([id])) o = pd.DataFrame() - # pop = args['pop'] if 'pop' in args else None + for i in np.arange(RUNS): n = np.random.randint(2,k) @@ -65,6 +86,9 @@ class deid : params = {'sample':sample,'cols':cols} if pop is not None : params['pop'] = pop + if pop_size > 0 : + params['pop_size'] = pop_size + r = self.evaluate(**params) # # let's put the policy in place @@ -75,137 +99,148 @@ class deid : o.index = np.arange(o.shape[0]).astype(np.int64) return o - def evaluate(self,**args) : + def evaluate(self, **args): """ - This function will compute the marketer, if a population is provided it will evaluate the marketer risk relative to both the population and sample - @param smaple data-frame with the data to be processed - @param policy the columns to be considered. - @param pop population dataset - @params flag user defined flag (no computation use) + This function has the ability to evaluate risk associated with either a population or a sample dataset + :sample sample dataset + :pop population dataset + :cols list of columns of interest or policies + :flag user provided flag for the context of the evaluation """ - if (args and 'sample' not in args) or not args : - x_i = pd.DataFrame(self._df) - elif args and 'sample' in args : - x_i = args['sample'] - if not args or 'cols' not in args: - cols = x_i.columns.tolist() - # cols = self._df.columns.tolist() - elif args and 'cols' in args : - cols = args['cols'] - flag = args['flag'] if 'flag' in args else 'UNFLAGGED' - MIN_GROUP_SIZE = args['min_group_size'] if 'min_group_size' in args else 1 - # if args and 'sample' in args : - - # x_i = pd.DataFrame(self._df) - # else : - # cols = args['cols'] if 'cols' in args else self._df.columns.tolist() - # x_i = x_i.groupby(cols,as_index=False).size().values - x_i_values = x_i.groupby(cols,as_index=False).size().values - SAMPLE_GROUP_COUNT = x_i_values.size - SAMPLE_FIELD_COUNT = len(cols) - SAMPLE_POPULATION = x_i_values.sum() - UNIQUE_REC_RATIO = np.divide(np.sum(x_i_values <= MIN_GROUP_SIZE) , np.float64( SAMPLE_POPULATION)) - SAMPLE_MARKETER = SAMPLE_GROUP_COUNT / np.float64(SAMPLE_POPULATION) - SAMPLE_PROSECUTOR = 1/ np.min(x_i_values).astype(np.float64) - if 'pop' in args : - Yi = args['pop'] - y_i= pd.DataFrame({"group_size":Yi.groupby(cols,as_index=False).size()}).reset_index() - UNIQUE_REC_RATIO = np.sum(y_i.group_size < MIN_GROUP_SIZE) , np.float64(Yi.shape[0]) - # y_i['group'] = pd.DataFrame({"group_size":args['pop'].groupby(cols,as_index=False).size().values}).reset_index() - # x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size().values}).reset_index() - x_i = pd.DataFrame({"group_size":x_i.groupby(cols,as_index=False).size()}).reset_index() - SAMPLE_RATIO = int(100 * x_i.size/args['pop'].shape[0]) - r = pd.merge(x_i,y_i,on=cols,how='inner') - r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1) - r['sample %'] = np.repeat(SAMPLE_RATIO,r.shape[0]) - r['tier'] = np.repeat(flag,r.shape[0]) - r['sample marketer'] = np.repeat(SAMPLE_MARKETER,r.shape[0]) - r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']] + if 'sample' in args : + sample = pd.DataFrame(args['sample']) else: - r = pd.DataFrame({"marketer":[SAMPLE_MARKETER],"flag":[flag],"prosecutor":[SAMPLE_PROSECUTOR],"field_count":[SAMPLE_FIELD_COUNT],"group_count":[SAMPLE_GROUP_COUNT]}) - r['unique_row_ratio'] = np.repeat(UNIQUE_REC_RATIO,r.shape[0]) - return r - - def _risk(self,**args): - """ - @param id name of patient field - @params num_runs number of runs (default will be 100) - @params quasi_id list of quasi identifiers to be used (this will only perform a single run) - """ - - id = args['id'] - if 'quasi_id' in args : - num_runs = 1 - columns = list(set(args['quasi_id'])- set(id) ) - else : - num_runs = args['num_runs'] if 'num_runs' in args else 100 - columns = list(set(self._df.columns) - set([id])) - - r = pd.DataFrame() - k = len(columns) - N = self._df.shape[0] - tmp = self._df.fillna(' ') - np.random.seed(int(time.time()) ) - for i in range(0,num_runs) : + sample = pd.DataFrame(self._df) + + if not args or 'cols' not in args: + cols = sample.columns.tolist() + elif args and 'cols' in args: + cols = args['cols'] + flag = 'UNFLAGGED' if 'flag' not in args else args['flag'] + # + # @TODO: auto select the columns i.e removing the columns that will have the effect of an identifier + # + # if 'population' in args : + # pop = pd.DataFrame(args['population']) + r = {"flag":flag} + # if sample : + + handle_sample = Sample() + xi = sample.groupby(cols,as_index=False).size().values + + handle_sample.set('groups',xi) + if 'pop_size' in args : + pop_size = np.float64(args['pop_size']) + else: + pop_size = -1 + # + #-- The following conditional line is to address the labels that will be returned + # @TODO: Find a more elegant way of doing this. + # + if 'pop' in args : + r['sample marketer'] = handle_sample.marketer() + r['sample prosecutor'] = handle_sample.prosecutor() + r['sample unique ratio'] = handle_sample.unique_ratio() + r['sample group count'] = xi.size + else: + r['marketer'] = handle_sample.marketer() + r['prosecutor'] = handle_sample.prosecutor() + r['unique ratio'] = handle_sample.unique_ratio() + r['group count'] = xi.size + if pop_size > 0 : + handle_sample.set('pop_size',pop_size) + r['pitman risk'] = handle_sample.pitman() + if 'pop' in args : + xi = pd.DataFrame({"sample_group_size":sample.groupby(cols,as_index=False).size()}).reset_index() + yi = pd.DataFrame({"population_group_size":args['population'].groupby(cols,as_index=False).size()}).reset_index() + merged_groups = pd.merge(xi,yi,on=cols,how='inner') + handle_population= Population() + handle_population.set('merged_groups',merged_groups) - # - # 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 + r['pop. marketer'] = handle_population.marketer() + r['pitman risk'] = handle_population.pitman() + r['pop. group size'] = np.unique(yi.population_group_size).size + # + # At this point we have both columns for either sample,population or both + # + r['field count'] = len(cols) + return pd.DataFrame([r]) - else: - cols = columns - policy = np.ones(k) - policy = pd.DataFrame(policy).T - n = len(cols) - policy.columns = columns - N = tmp.shape[0] +class Risk : + """ + This class is an abstraction of how we chose to structure risk computation i.e in 2 sub classes: + - Sample computes risk associated with a sample dataset only + - Population computes risk associated with a population + """ + def __init__(self): + self.cache = {} + def set(self,key,value): + if id not in self.cache : + self.cache[id] = {} + self.cache[key] = value - x_ = tmp.groupby(cols).size().values - # print [id,i,n,k,self._df.groupby(cols).count()] - r = r.append( - pd.DataFrame( - [ - { - "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_)) +class Sample(Risk): + """ + This class will compute risk for the sample dataset: the marketer and prosecutor risk are computed by default. + This class can optionally add pitman risk if the population size is known. + """ + def __init__(self): + Risk.__init__(self) + def marketer(self): + """ + computing marketer risk for sample dataset + """ + groups = self.cache['groups'] + group_count = groups.size + row_count = groups.sum() + return group_count / np.float64(row_count) - } - ] - ).join(policy) - ) - # 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"," ") )) + def prosecutor(self): + """ + The prosecutor risk consists in determining 1 over the smallest group size + It identifies if there is at least one record that is unique + """ + groups = self.cache['groups'] + return 1 / np.float64(groups.min()) + def unique_ratio(self): + groups = self.cache['groups'] + row_count = groups.sum() + return groups[groups == 1].sum() / np.float64(row_count) - return r - - -# 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']] + def pitman(self): + """ + This function will approximate pitman de-identification risk based on pitman sampling + """ + groups = self.cache['groups'] + si = groups[groups == 1].size + u = groups.size + alpha = np.divide(si , np.float64(u) ) + f = np.divide(groups.sum(), np.float64(self.cache['pop_size'])) + return np.power(f,1-alpha) +class Population(Sample): + """ + This class will compute risk for datasets that have population information or datasets associated with them. + This computation includes pitman risk (it requires minimal information about population) + """ + def __init__(self,**args): + Sample.__init__(self) + def set(self,key,value): + Sample.set(key,value) + if key == 'merged_groups' : + Sample.set('pop_size',np.float64(r.population_group_sizes.sum()) ) + """ + This class will measure risk and account for the existance of a population + :merged_groups {sample_group_size, population_group_size} is a merged dataset with group sizes of both population and sample + """ + def marketer(self): + """ + This function requires + """ + r = self.cache['merged_groups'] + sample_row_count = r.sample_group_size.sum() + # + # @TODO : make sure the above line is size (not sum) + # sample_row_count = r.sample_group_size.size + return r.apply(lambda row: (row.sample_group_size / np.float64(row.population_group_size)) /np.float64(sample_row_count) ,axis=1).sum() From 0456a77beb1721a6ce7b5da534590d070cca368b Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 11:09:21 -0600 Subject: [PATCH 05/23] removing SQL files --- src/sql/basic-observation.sql | 83 ------- src/sql/observation-registered.sql | 376 ----------------------------- 2 files changed, 459 deletions(-) delete mode 100644 src/sql/basic-observation.sql delete mode 100644 src/sql/observation-registered.sql diff --git a/src/sql/basic-observation.sql b/src/sql/basic-observation.sql deleted file mode 100644 index 96b7067..0000000 --- a/src/sql/basic-observation.sql +++ /dev/null @@ -1,83 +0,0 @@ -SELECT person.person_id,sex_at_birth,birth_date, race,zip,city,state, gender -FROM - (SELECT DISTINCT person_id from deid_tmp.observation order by person_id) as person -FULL JOIN ( - SELECT - person_id,MAX(value_as_string) as race - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRace') and value_as_string IS NOT NULL - - GROUP BY person_id - order by person_id -) as lang -ON lang.person_id = person.person_id - -FULL JOIN ( - SELECT - person_id,MAX(value_as_string) as zip - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIZIP') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id -) as work_add -ON work_add.person_id = person.person_id - - - -FULL JOIN ( - SELECT - person_id,max(value_as_string) as city - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIICity') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id -) as u_city -ON u_city.person_id = person.person_id - -FULL JOIN ( - SELECT - person_id,max(value_as_string) as state - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIState') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id - -) as p_addr_o -ON p_addr_o.person_id = person.person_id - -FULL JOIN ( - SELECT - person_id,max(value_as_string) as gender - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id - -) as p_gender -ON p_gender.person_id = person.person_id - -FULL JOIN ( - SELECT - person_id,max(value_as_string) as birth_date - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIBirthInformation_BirthDate') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id - -) as p_birth -ON p_birth.person_id = person.person_id - -FULL JOIN ( - SELECT - person_id,max(value_as_string) as sex_at_birth - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'BiologicalSexAtBirth_SexAtBirth') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id - -) as p_sex -ON p_sex.person_id = person.person_id - - -ORDER BY person.person_id - diff --git a/src/sql/observation-registered.sql b/src/sql/observation-registered.sql deleted file mode 100644 index abfb249..0000000 --- a/src/sql/observation-registered.sql +++ /dev/null @@ -1,376 +0,0 @@ -SELECT * -FROM ( - SELECT person.person_id,first_name,last_name,birth_date,city,family_history_aware,current_hyper_tension,sex_at_birth, race,state, gender,ethnicity,birth_place,orientation,education,employment_status, - marital_status,language,home_owner,sd_bloodbank, nhpi, living_situation,income,death_cause, death_date, active_duty_status, - gender_identity, insurance_type, work_address_state,consent_18_years_age,person_one_state,person_two_state,sc_site, - health_abroad_6_months,travel_abroad_6_months - FROM - (SELECT DISTINCT person_id from deid_tmp.observation order by person_id) as person - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as travel_abroad_6_months - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'OutsideTravel6Month_OutsideTravel6MonthWhere') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as te_ - ON te_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as health_abroad_6_months - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'OverallHealth_OutsideTravel6Month') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as he_ - ON he_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as active_duty_status - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'ActiveDuty_AvtiveDutyServeStatus') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as mil_ - ON mil_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as sc_site - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'SouthCarolinaSitePairing_EauClaireAppointment') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as sc_ - ON sc_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as person_one_state - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PersonOneAddress_PersonOneAddressState') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as p1_ - ON p1_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as person_two_state - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'SecondContactsAddress_SecondContactState') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as p2_ - ON p2_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_as_string) as work_address_state - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'EmploymentWorkAddress_State') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as ws_ - ON ws_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as consent_18_years_age - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'ExtraConsent_18YearsofAge') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as c18_ - ON c18_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as gender_identity - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as gi_ - ON gi_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as income - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Income_AnnualIncome') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as income_ - ON income_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as living_situation - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'LivingSituation_CurrentLiving') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as living_ - ON living_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as nhpi - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'NHPI_NHPISpecific') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as nhpi_ - ON nhpi_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_as_string) as sd_bloodbank - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'SanDiegoBloodBank') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as sd - ON sd.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as education - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'EducationLevel_HighestGrade') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as edu - ON edu.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as home_owner - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'HomeOwn_CurrentHomeOwn') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as h_owner - ON h_owner.person_id = person.person_id - - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as employment_status - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Employment_EmploymentStatus') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as empl - ON empl.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as marital_status - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'MaritalStatus_CurrentMaritalStatus') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as marital - ON marital.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as language - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Language_SpokenWrittenLanguage') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as lang_ - ON lang_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as race - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRace') and value_source_value IS NOT NULL - - GROUP BY person_id - order by person_id - ) as lang - ON lang.person_id = person.person_id - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as ethnicity - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Race_WhatRaceEthnicity') and value_source_value IS NOT NULL - - GROUP BY person_id - order by person_id - ) as ethnic - ON ethnic.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as birth_place - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'TheBasics_Birthplace') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as birthp - ON birthp.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,MAX(value_source_value) as orientation - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'TheBasics_SexualOrientation') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - ) as sexo - ON sexo.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,max(value_source_value) as state - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIState') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - - ) as p_addr_o - ON p_addr_o.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_source_value) as gender - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Gender_GenderIdentity') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - - ) as p_gender - ON p_gender.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_source_value) as sex_at_birth - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'_SexAtBirth') --and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - - ) as p_sex - ON p_sex.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_source_value) as insurance_type - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'HealthInsurance_HealthInsuranceType') and value_source_value IS NOT NULL - GROUP BY person_id - order by person_id - - ) as ins_ - ON ins_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_as_string) as last_name - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIName_Last') and value_as_string IS NOT NULL - GROUP BY person_id - order by person_id - - ) as ln_ - ON ln_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,max(value_as_string) as first_name - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIName_First') - GROUP BY person_id - order by person_id - - ) as fn_ - ON fn_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_as_string) as current_hyper_tension - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'Circulatory_HypertensionCurrently') - GROUP BY person_id - order by person_id - - ) as cht_ - ON cht_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max( cast(value_as_string as DATE)) as birth_date - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'PIIBirthInformation_BirthDate') - GROUP BY person_id - order by person_id - - ) as bd_ - ON bd_.person_id = person.person_id - - - FULL JOIN ( - SELECT - person_id,max(value_as_string) as city - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'StreetAddress_PIICity') - GROUP BY person_id - order by person_id - - ) as city_ - ON city_.person_id = person.person_id - - FULL JOIN ( - SELECT - person_id,max(value_as_string) as family_history_aware - FROM deid_tmp.observation - WHERE REGEXP_CONTAINS(observation_source_value,'FamilyHistory_FamilyMedicalHistoryAware') - GROUP BY person_id - order by person_id - - ) as bro_ - ON bro_.person_id = person.person_id - FULL JOIN ( - SELECT person_id, max(death_date) AS death_date - FROM deid_tmp.death - GROUP BY person_id - order BY person_id - - ) as death_ - ON death_.person_id = person.person_id - - FULL JOIN ( - SELECT person_id, max(cause_source_value) as death_cause - FROM deid_tmp.death - GROUP BY person_id - order BY person_id - - - ) as death_c ON death_c.person_id = person.person_id - ORDER BY person.person_id -) as frame - --- WHERE first_name is not NULL From b724353155a6bd8bbc2d9c896407fe06be814a8e Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 11:58:48 -0600 Subject: [PATCH 06/23] deployment branch --- src/params.py | 17 -- src/risk.py | 348 --------------------------- src/risk/__init__.py | 51 ++++ src/{pandas_risk.py => risk/risk.py} | 21 +- src/setup.py | 10 + 5 files changed, 75 insertions(+), 372 deletions(-) delete mode 100644 src/params.py delete mode 100644 src/risk.py create mode 100644 src/risk/__init__.py rename src/{pandas_risk.py => risk/risk.py} (93%) create mode 100644 src/setup.py diff --git a/src/params.py b/src/params.py deleted file mode 100644 index 428ff00..0000000 --- a/src/params.py +++ /dev/null @@ -1,17 +0,0 @@ -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() - diff --git a/src/risk.py b/src/risk.py deleted file mode 100644 index 0a1bb64..0000000 --- a/src/risk.py +++ /dev/null @@ -1,348 +0,0 @@ -""" - 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)) - TERMS = ['type','unit','count','refills','stop','supply','quantity'] - for table in tables : - - 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 - table = client.get_table(ref) - schema = table.schema - rows = table.num_rows - if rows == 0 : - continue - - 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) - 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_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 - @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 10000 - 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 ) as :alias - """.replace(":limit",limit) - # 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 = """ - 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 SQLRisk : - """ - 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) - 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 - stream = pd.DataFrame(stream.tolist()).T - stream.columns = args['table']['fields'] - 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, :n as field_count - FROM ( - SELECT COUNT(*) as g_size,:fields - FROM :full_name - 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} - - - -class UtilHandler : - def __init__(self,**args) : - """ - @param path path to the service account file - @param dataset input dataset name - @param key_field key_field (e.g person_id) - @param key_table - - """ - self.path = args['path'] - self.client = bq.Client.from_service_account_json(self.path) - dataset = args['dataset'] - self.key = args['key_field'] - - self.mytools = utils(client = self.client) - self.tables = self.mytools.get_tables(dataset=dataset,client=self.client,key=self.key) - index = [ self.tables.index(item) for item in self.tables if item['name'] == args['key_table']] [0] - if index != 0 : - first = self.tables[0] - aux = self.tables[index] - self.tables[0] = aux - self.tables[index] = first - if 'filter' in args : - self.tables = [item for item in self.tables if item['name'] in args['filter']] - - - def create_table(self,**args): - """ - @param path absolute filename to save the create statement - - """ - create_sql = self.mytools.get_sql(tables=self.tables,key=self.key) #-- The create statement - # o_dataset = SYS_ARGS['o_dataset'] - # table = SYS_ARGS['table'] - if 'path' in args: - f = open(args['path'],'w') - f.write(create_sql) - f.close() - return create_sql - def migrate_tables(self,**args): - """ - This function will migrate a table from one location to another - The reason for migration is to be able to reduce a candidate table to only represent a patient by her quasi-identifiers. - @param dataset target dataset - """ - o_dataset = args['dataset'] if 'dataset' in args else None - p = [] - for table in self.tables: - sql = " ".join(["SELECT ",",".join(table['fields']) ," FROM (",self.mytools.get_filtered_table(table,self.key),") as ",table['name']]) - p.append(sql) - if o_dataset : - job = bq.QueryJobConfig() - job.destination = self.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 = self.client.query(sql,location='US',job_config=job) - - print [table['full_name'],' ** ',r.job_id,' ** ',r.state] - return p - -# 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) -# 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 --key --o_dataset --table [--file] --path -# # -# 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] -# 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']]) -# print "" -# print sql -# print "" -# # 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: -# # -# # -# tables = [tab for tab in tables if tab['name'] == SYS_ARGS['table'] ] -# limit = int(SYS_ARGS['limit']) if 'limit' in SYS_ARGS else 1 -# if tables : -# 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'],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') -# print [i,' ** ',df.shape[0],pd.DataFrame(r['stream']).shape] -# time.sleep(2) - - -# 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') diff --git a/src/risk/__init__.py b/src/risk/__init__.py new file mode 100644 index 0000000..0814772 --- /dev/null +++ b/src/risk/__init__.py @@ -0,0 +1,51 @@ +""" +(c) 2019, Health Information Privacy Lab +Brad. Malin, Weiyi Xia, Steve L. Nyemba + +This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas) +The framework will compute the following risk measures: + - marketer + - prosecutor + - pitman + +References : + https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf + +This framework integrates pandas (for now) as an extension and can be used in two modes : +1. explore: + Here the assumption is that we are not sure of the attributes to be disclosed, + The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with + +2. evaluation + Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. + + +Four risk measures are computed : + - Marketer risk + - Prosecutor risk + - Journalist risk + - Pitman Risk + +Usage: +import numpy as np +import pandas as pd +from pandas_risk import * + +mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50) }) +print mydf.risk.evaluate() + + + +# +# computing journalist and pitman +# - Insure the population size is much greater than the sample size +# - Insure the fields are identical in both sample and population +# +pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) +mydf.risk.evaluate(pop=pop) + +@TODO: + - Evaluation of how sparse attributes are (the ratio of non-null over rows) + - Have a smart way to drop attributes (based on the above in random policy search) +""" + diff --git a/src/pandas_risk.py b/src/risk/risk.py similarity index 93% rename from src/pandas_risk.py rename to src/risk/risk.py index baec653..b848eed 100644 --- a/src/pandas_risk.py +++ b/src/risk/risk.py @@ -26,7 +26,8 @@ from pandas_risk import * mydataframe = pd.DataFrame('/myfile.csv') - risk = mydataframe.deid.risk(id=,num_runs=) + resp = mydataframe.risk.evaluate(id=,num_runs=,cols=[]) + resp = mydataframe.risk.explore(id=,num_runs=,cols=[]) @TODO: @@ -41,8 +42,8 @@ import json from datetime import datetime import sys -sys.setrecursionlimit(3000) -@pd.api.extensions.register_dataframe_accessor("deid") + +@pd.api.extensions.register_dataframe_accessor("risk") class deid : """ @@ -151,8 +152,10 @@ class deid : handle_sample.set('pop_size',pop_size) r['pitman risk'] = handle_sample.pitman() if 'pop' in args : + print cols + print args['pop'].columns xi = pd.DataFrame({"sample_group_size":sample.groupby(cols,as_index=False).size()}).reset_index() - yi = pd.DataFrame({"population_group_size":args['population'].groupby(cols,as_index=False).size()}).reset_index() + yi = pd.DataFrame({"population_group_size":args['pop'].groupby(cols,as_index=False).size()}).reset_index() merged_groups = pd.merge(xi,yi,on=cols,how='inner') handle_population= Population() handle_population.set('merged_groups',merged_groups) @@ -227,9 +230,11 @@ class Population(Sample): Sample.__init__(self) def set(self,key,value): - Sample.set(key,value) - if key == 'merged_groups' : - Sample.set('pop_size',np.float64(r.population_group_sizes.sum()) ) + Sample.set(self,key,value) + if key == 'merged_groups' : + + Sample.set(self,'pop_size',np.float64(value.population_group_size.sum()) ) + Sample.set(self,'groups',value.sample_group_size) """ This class will measure risk and account for the existance of a population :merged_groups {sample_group_size, population_group_size} is a merged dataset with group sizes of both population and sample @@ -244,3 +249,5 @@ class Population(Sample): # @TODO : make sure the above line is size (not sum) # sample_row_count = r.sample_group_size.size return r.apply(lambda row: (row.sample_group_size / np.float64(row.population_group_size)) /np.float64(sample_row_count) ,axis=1).sum() + + diff --git a/src/setup.py b/src/setup.py new file mode 100644 index 0000000..156b6c9 --- /dev/null +++ b/src/setup.py @@ -0,0 +1,10 @@ +""" +This is a build file for the +""" +from setuptools import setup, find_packages + +setup( + name = "risk", + version = "0.1", + packages = find_packages() + ) \ No newline at end of file From 111d672d3c728f34b76c34e8bda0f8404c683409 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 12:07:09 -0600 Subject: [PATCH 07/23] removing notebooks --- .../.ipynb_checkpoints/risk-checkpoint.ipynb | 273 ------ notebooks/Untitled.ipynb | 238 ----- notebooks/data-analysis.ipynb | 214 ----- notebooks/data-preparation.ipynb | 95 -- notebooks/experiments.ipynb | 610 ------------- notebooks/registered-tier-history.ipynb | 385 -------- notebooks/registered-tier.ipynb | 859 ------------------ notebooks/risk.ipynb | 293 ------ 8 files changed, 2967 deletions(-) delete mode 100644 notebooks/.ipynb_checkpoints/risk-checkpoint.ipynb delete mode 100644 notebooks/Untitled.ipynb delete mode 100644 notebooks/data-analysis.ipynb delete mode 100644 notebooks/data-preparation.ipynb delete mode 100644 notebooks/experiments.ipynb delete mode 100644 notebooks/registered-tier-history.ipynb delete mode 100644 notebooks/registered-tier.ipynb delete mode 100644 notebooks/risk.ipynb diff --git a/notebooks/.ipynb_checkpoints/risk-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/risk-checkpoint.ipynb deleted file mode 100644 index dd5cf33..0000000 --- a/notebooks/.ipynb_checkpoints/risk-checkpoint.ipynb +++ /dev/null @@ -1,273 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "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')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "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": 53, - "metadata": {}, - "outputs": [], - "source": [ - "def get_tables(client,did,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.field_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(xo) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n", - "def generate_sql(**args):\n", - " \"\"\"\n", - " This function will generate the SQL query for the resulting join\n", - " \"\"\"\n", - " xo = args['xo']\n", - " xi = args['xi']\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": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['person_id',\n", - " 'measurements.value_as_number',\n", - " 'date_of_birth',\n", - " 'race',\n", - " 'measurements.value_source_value']" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\n", - "xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value']}\n", - "get_fields(xo=xo,xi=xi,join=\"person_id\")" - ] - }, - { - "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", - "\"\"\"\n", - "import itertools\n", - "list(itertools.combinations(['a','b','c'],2))" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "TableReference(DatasetReference(u'aou-res-deid-vumc-test', u'raw'), 'care_site')" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref = client.dataset('raw')\n", - "tables = list(client.list_tables(ref))\n", - "names = [table.table_id for table in tables]\n", - "(tables[0].reference)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(u'care_site',\n", - " u'concept',\n", - " u'concept_ancestor',\n", - " u'concept_class',\n", - " u'concept_relationship',\n", - " u'concept_synonym',\n", - " u'condition_occurrence',\n", - " u'criteria',\n", - " u'death',\n", - " u'device_exposure',\n", - " u'domain',\n", - " u'drug_exposure',\n", - " u'drug_strength',\n", - " u'location',\n", - " u'measurement',\n", - " u'note',\n", - " u'observation',\n", - " u'people_seed',\n", - " u'person',\n", - " u'procedure_occurrence',\n", - " u'relationship',\n", - " u'visit_occurrence',\n", - " u'vocabulary')" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#\n", - "# find every table with person id at the very least or a subset of fields\n", - "#\n", - "def get_tables\n", - "q = ['person_id']\n", - "pairs = list(itertools.combinations(names,len(names)))\n", - "pairs[0]" - ] - }, - { - "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": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb deleted file mode 100644 index 1e154e2..0000000 --- a/notebooks/Untitled.ipynb +++ /dev/null @@ -1,238 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import itertools \n", - "import pandas as pd\n", - "import numpy as np\n", - "# from pandas_risk import *\n", - "from time import time\n", - "import os\n", - "\n", - "attr = ['gender','race','zip','year_of_birth']\n", - "comb_attr = [\n", - " ['zip' ,'gender', 'birth_datetime', 'race'], \n", - " ['zip', 'gender', 'year_of_birth', 'race'], \n", - " ['gender','race','zip'],\n", - " ['race','year_of_birth','zip']\n", - "]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "SQL_CONTROLLED=\"SELECT * FROM deid_risk.basic_risk60k\"\n", - "dfc = pd.read_gbq(SQL_CONTROLLED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def risk(**args):\n", - " Yi = args['data']\n", - " Yi = Yi.fillna(' ')\n", - " sizes = args['prop'] if 'prop' in args else np.arange(5,100,5)\n", - " FLAG = args['flag'] if 'flag' in args else 'UNFLAGGED'\n", - " N = args['num_runs']\n", - " if 'cols' in args :\n", - " columns = args['cols']\n", - " else:\n", - " columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", - " p = pd.DataFrame()\n", - " y_i= pd.DataFrame({\"group_size\":Yi.groupby(columns,as_index=False).size()}).reset_index()\n", - " for index in sizes :\n", - " for n in np.repeat(index,N):\n", - " \n", - " # we will randomly sample n% rows from the dataset\n", - " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", - " x_i= pd.DataFrame(Yi).loc[i] \n", - " risk = x_i.deid.risk(id='person_id',quasi_id = columns)\n", - " x_i = pd.DataFrame({\"group_size\":x_i.groupby(columns,as_index=False).size()}).reset_index()\n", - "\n", - "\n", - " r = pd.merge(x_i,y_i,on=columns,how='inner')\n", - " if r.shape[0] == 0 :\n", - " continue\n", - " r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)\n", - " r['sample %'] = np.repeat(n,r.shape[0])\n", - " r['tier'] = np.repeat(FLAG,r.shape[0])\n", - " r['sample marketer'] = np.repeat(risk['marketer'].values[0],r.shape[0])\n", - " # r['patient_count'] = np.repeat(r.shape[0],r.shape[0])\n", - " r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]\n", - " p = p.append(r)\n", - " p.index = np.arange(p.shape[0]).astype(np.int64)\n", - " return p\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from pandas_risk import *\n", - "o = pd.DataFrame()\n", - "PATH=\"out/experiment-phase-2.xlsx\"\n", - "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", - "comb_attr = [\n", - " ['zip' ,'gender', 'birth_datetime', 'race'], \n", - " ['zip', 'gender', 'year_of_birth', 'race'], \n", - " ['gender','race','zip'],\n", - " ['race','year_of_birth','zip']\n", - "]\n", - "\n", - "for cols in comb_attr :\n", - " o = risk(data=dfc,cols=cols,flag='CONTROLLED',num_runs=5)\n", - " #\n", - " # adding the policy\n", - " x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n", - " o = o.join(pd.DataFrame(x,columns = dfc.columns))\n", - " #\n", - " # Write this to excel notebook\n", - " o.to_excel(writer,\"-\".join(cols))\n", - "# break\n", - " \n", - "\n", - "# p = p.rename(columns={'marketer_x':'sample marketer'})\n", - "# p.index = np.arange(p.shape[0]).astype(np.int64)\n", - "\n", - "writer.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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person_idyear_of_birthmonth_of_birthday_of_birthbirth_datetimerace_concept_idethnicity_concept_idlocation_idcare_site_idperson_source_value...gender_source_concept_idrace_source_valueethnicity_source_valuesex_at_birthbirth_dateracezipcitystategender
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [person_id, year_of_birth, month_of_birth, day_of_birth, birth_datetime, race_concept_id, ethnicity_concept_id, location_id, care_site_id, person_source_value, gender_source_value, gender_source_concept_id, race_source_value, ethnicity_source_value, sex_at_birth, birth_date, race, zip, city, state, gender]\n", - "Index: []\n", - "\n", - "[0 rows x 21 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n", - "o.join(pd.DataFrame(x,columns = dfc.columns))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'columns' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'columns' is not defined" - ] - } - ], - "source": [ - "columns\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/data-analysis.ipynb b/notebooks/data-analysis.ipynb deleted file mode 100644 index 8e4e21b..0000000 --- a/notebooks/data-analysis.ipynb +++ /dev/null @@ -1,214 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "df = pd.read_csv('../src/out/risk_xoi.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0group_countrow_countmarketerprosecutorfield_count
00432512790808020.005469110
1017824004790808020.225390128
2043538084790808020.550552138
3064042788790808020.809840146
406866070790808020.086823117
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" - ], - "text/plain": [ - " Unnamed: 0 group_count row_count marketer prosecutor field_count\n", - "0 0 432512 79080802 0.005469 1 10\n", - "1 0 17824004 79080802 0.225390 1 28\n", - "2 0 43538084 79080802 0.550552 1 38\n", - "3 0 64042788 79080802 0.809840 1 46\n", - "4 0 6866070 79080802 0.086823 1 17" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "compiled = df.groupby('field_count')[['field_count','marketer','prosecutor']].mean()\n", - "figure = compiled[['marketer','prosecutor']].plot.line().get_figure()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "variables": { - " figure ": "" - } - }, - "source": [ - "# Dataset Used\n", - "---\n", - "\n", - "We performed joins against all the tables from all-of-us and truncated records while randomly selecting on record per every join. As a result we have roughly a dataset of about **80 million** records and about **5000** distinct patients.\n", - "\n", - "## Expriment Design\n", - "---\n", - "\n", - "We compute both marketer and prosecutor risk computation while randomly selecting the number of attributes out of **111**. This selection is between ***2*** and **111** attributes. The number of maximum number of attributes that can be computed at any time is **64** : limitations of Google's Big-query. We performed **500** runs.\n", - "\n", - "## Results\n", - "---\n", - "\n", - "The results show the prosecutor risk is unchanging perhaps as an artifact of the number of runs **500** or the dataset curation: The joins we performed. The prosecutor risk shows there is at least one record that vulnerable.\n", - "\n", - "The marketer risk seems to increase as the number of randomly selected attributes increases as a general trend. \n", - "\n", - "{{ figure }} \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/data-preparation.ipynb b/notebooks/data-preparation.ipynb deleted file mode 100644 index adbd66e..0000000 --- a/notebooks/data-preparation.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " skiping ...\n", - " skiping ...\n", - " skiping ...\n", - " skiping ...\n", - " skiping ...\n", - " skiping ...\n", - " skiping ...\n" - ] - }, - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"\n", - " This notebook is designed to generate SQL syntax all the quasi-identifiers for the patients in the database\n", - " The resulting SQL will be run against bigquery to produce a table with every record mapping to a patient\n", - " \n", - "\"\"\"\n", - "\n", - "from risk import *\n", - "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-prod.json',dataset='combined20180822',key_field='person_id',key_table='person',filter=['person','observation'])\n", - "r = ihandle.migrate_tables()\n", - "len(r)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "u' SELECT person.person_id , person.year_of_birth , person.month_of_birth , person.day_of_birth , person.birth_datetime , person.race_concept_id , person.ethnicity_concept_id , person.location_id , person.care_site_id , person.person_source_value , person.gender_source_value , person.gender_source_concept_id , person.race_source_value , person.ethnicity_source_value , basic_observation.sex_at_birth AS sex_at_birth1 , basic_observation.birth_date AS birth_date1 , basic_observation.race AS race1 , basic_observation.zip AS zip1 , basic_observation.city AS city1 , basic_observation.state AS state1 , basic_observation.gender AS gender1 FROM (select * from deid_image.person ) as person INNER JOIN (select * from deid_image.basic_observation ) as basic_observation ON basic_observation.person_id = person.person_id '" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',dataset='deid_image',key_field='person_id',key_table='person',filter=['person','basic_observation'])\n", - "ihandle.create_table().replace('\\n',' ')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/experiments.ipynb b/notebooks/experiments.ipynb deleted file mode 100644 index 3d52a33..0000000 --- a/notebooks/experiments.ipynb +++ /dev/null @@ -1,610 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - " Health Information Privacy Lab\n", - " This notebook is intended to run experiments and generate the data to be used by another notebook\n", - "\n", - " pre-requisites:\n", - " - pandas_risk This is a custom framework that will compute risk for a given dataset\n", - " - google-cloud-bigquery\n", - " - numpy\n", - "\"\"\"\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pandas_risk import *\n", - "from time import time\n", - "import os\n", - "#\n", - "#-- Loading the dataset\n", - "class Logger :\n", - " cache = []\n", - " @staticmethod\n", - " def clear():\n", - " Logger.cache = []\n", - " @staticmethod\n", - " def log(**args) :\n", - " Logger.cache.append(args)\n", - " \n", - "SQL_CONTROLLED=\"SELECT person_id,birth_datetime,city,zip,state,race,gender FROM deid_risk.basic_risk60k\"\n", - "SQL_REGISTERED = \"SELECT person_id,birth_datetime,city,zip,state,race,gender FROM deid_risk.basic_deid_risk60k\"\n", - "dfr = pd.read_gbq(SQL_REGISTERED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", - "dfc = pd.read_gbq(SQL_CONTROLLED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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sample %marketersample marketertier
050.9749450.981364controlled
150.9755130.981996controlled
250.9757980.980733controlled
350.9763640.981996controlled
450.9763640.981996controlled
\n", - "
" - ], - "text/plain": [ - " sample % marketer sample marketer tier\n", - "0 5 0.974945 0.981364 controlled\n", - "1 5 0.975513 0.981996 controlled\n", - "2 5 0.975798 0.980733 controlled\n", - "3 5 0.976364 0.981996 controlled\n", - "4 5 0.976364 0.981996 controlled" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "#\n", - "FLAG='REGISTERED-TIER-1'\n", - "if FLAG == 'REGISTERED-TIER' :\n", - " Yi = pd.DataFrame(dfr)\n", - " FOLDER='registered'\n", - "else:\n", - " Yi = pd.DataFrame(dfc)\n", - " FOLDER='controlled'\n", - "Yi = Yi.fillna(' ')\n", - "N = 5\n", - "N_ = str(N)\n", - "SUFFIX = FOLDER+'-tier-'+str(N)+'-experiment.xlsx'\n", - "PATH = os.sep.join(['out',SUFFIX])\n", - "\n", - "\n", - "columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", - "merged_columns = list(columns)+['field_count']\n", - "m = {}\n", - "p = pd.DataFrame()\n", - "n = 0\n", - "y_i= pd.DataFrame({\"group_size\":Yi.groupby(columns,as_index=False).size()}).reset_index()\n", - "#.deid.risk(id='person_id',quasi_id=columns)\n", - "for index in np.arange(5,105,5):\n", - " for n in np.repeat(index,N) :\n", - "# np.random.seed( np.random.randint(0,int(time())+np.random.randint(0,1000)+index+n ) \n", - " #\n", - " # we will randomly sample n% rows from the dataset\n", - " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", - " x_i= pd.DataFrame(Yi).loc[i] \n", - " risk = x_i.deid.risk(id='person_id',quasi_id = columns)\n", - " x_i = pd.DataFrame({\"group_size\":x_i.groupby(columns,as_index=False).size()}).reset_index()\n", - " \n", - "# y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", - "\n", - "\n", - " r = pd.merge(x_i,y_i,on=columns,how='inner')\n", - " if r.shape[0] == 0 :\n", - " print 'skipping ',n\n", - " continue\n", - " r['marketer'] = r.apply(lambda row: (row.group_size_x / np.float64(row.group_size_y)) /np.sum(x_i.group_size) ,axis=1)\n", - " r['sample %'] = np.repeat(n,r.shape[0])\n", - " r['tier'] = np.repeat(FOLDER,r.shape[0])\n", - " r['sample marketer'] = np.repeat(risk['marketer'].values[0],r.shape[0])\n", - "# r['patient_count'] = np.repeat(r.shape[0],r.shape[0])\n", - " r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]\n", - "# r['marketer'] = r.apply(lambda row: (row.group_size_x / row.group_size_y) / row.patient_count_x,axis=1 )\n", - "# r = r.groupby(columns+['marketer_x'],as_index=False).sum()[columns+['marketer','marketer_x']]\n", - "# r['sample %'] = np.repeat(n,r.shape[0])\n", - "# r['tier'] = np.repeat(FOLDER,r.shape[0])\n", - " p = p.append(r)\n", - "\n", - "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", - "p = p.rename(columns={'marketer_x':'sample marketer'})\n", - "p.index = np.arange(p.shape[0]).astype(np.int64)\n", - "p.to_excel(writer,FOLDER)\n", - "writer.save()\n", - "p.head() " - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "p.plot(kind='scatter',x='sample %',y='marketer', c = '#4682B4')\n", - "p.plot(kind='scatter',x='sample %',y = 'sample marketer', c='#CC0000')\n", - "ax = p.plot(kind='scatter',x='sample %',y='marketer', c = '#4682B4')\n", - "p.plot(kind='scatter',x='sample %',y = 'sample marketer', c='#CC0000',ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - " This experiment consists in :\n", - " 1: randomly selecting x % of the records to be sampled\n", - " 2: running a group by on the sample\n", - " 3: calling groupby on the population which th\n", - "\"\"\"\n", - "SQL_ORIGINAL=\"SELECT * FROM deid_risk.risk_60k2\"\n", - "SQL_DEID = \"SELECT * FROM deid_risk.deid_risk_60k limit 20000\"\n", - "# df = pd.read_gbq(SQL_DEID,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", - "\n", - "#\n", - "FLAG='REGISTERED-TIER-9'\n", - "if FLAG == 'REGISTERED-TIER' :\n", - " Yi = pd.DataFrame(dfr)\n", - " FOLDER='registered'\n", - "else:\n", - " Yi = pd.DataFrame(dfc)\n", - " FOLDER='controlled'\n", - "N = 20\n", - "N_ = str(N)\n", - "SUFFIX = FOLDER+'-tier-'+str(N)+'-experiment.xlsx'\n", - "PATH = os.sep.join(['out',SUFFIX])\n", - "\n", - "\n", - "columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n", - "merged_columns = list(columns)+['field_count']\n", - "m = {}\n", - "p = pd.DataFrame()\n", - "n = 0\n", - "y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", - "for index in np.arange(5,105,5):\n", - "# np.random.seed( int(time())+np.random.randint(0,100)+index ) \n", - "# n = np.random.randint(10,35) #-- randomly pick a number within an interval\n", - " \n", - " for n in np.repeat(index,20) :\n", - "# np.random.seed( np.random.randint(0,int(time())+np.random.randint(0,1000)+index+n ) \n", - " #\n", - " # we will randomly sample n% rows from the dataset\n", - " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n", - " x_i= pd.DataFrame(Yi).loc[i].deid.risk(id='person_id',quasi_id = columns)\n", - " \n", - "# y_i= pd.DataFrame(Yi).deid.risk(id='person_id',quasi_id=columns)\n", - "\n", - "\n", - " r = pd.merge(x_i,y_i,on=merged_columns,how='inner')\n", - " if r.shape[0] == 0 :\n", - " print 'skipping ',n\n", - " continue\n", - "\n", - " r['marketer'] = r.apply(lambda row: (row.group_size_x / row.group_size_y) / row.patient_count_x,axis=1 )\n", - " r = r.groupby(columns+['marketer_x'],as_index=False).sum()[columns+['marketer','marketer_x']]\n", - " r['sample %'] = np.repeat(n,r.shape[0])\n", - " r['tier'] = np.repeat(FOLDER,r.shape[0])\n", - " p = p.append(r)\n", - "\n", - "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n", - "p = p.rename(columns={'marketer_x':'sample marketer'})\n", - "p.index = np.arange(p.shape[0]).astype(np.int64)\n", - "p.to_excel(writer,FOLDER)\n", - "writer.save()\n", - "p.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "ax = p.plot(kind='scatter',x='sample %',y='marketer',c='r',ylim=[p.marketer.min(),p.marketer.max()])\n", - "p.plot(kind='scatter',x='sample %',y='sample marketer',c='#4682B4')\n", - "ax = p.plot(kind='scatter',x='sample %',y='marketer',c='r')\n", - "p.plot(kind='scatter',x='sample %',y='sample marketer',c='#4682B4',ax=ax)\n", - "\n", - "_p = pd.DataFrame(p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.head()\n", - "\n", - "# writer = pd.ExcelWriter('out/foo.xlsx',engine='xlsxwriter')\n", - "# workbook = writer.book\n", - "# r.groupby('field_count',as_index=False).sum()[['field_count','marketer_x']].to_excel(writer,'page-0')\n", - "# chart = workbook.add_chart({'type':'line'})\n", - "# o = r.groupby('field_count',as_index=False).sum()[['field_count','marketer_x']]\n", - "# # values = o.marketer_x.tolist()\n", - "# # values = [['page-0',item] for item in values]\n", - "# # chart.add_series({\"values\":values})\n", - "# # chart.add_series({'values':'=page-0!$B$2:$B$5'})\n", - "\n", - "# worksheet = writer.sheets['page-0']\n", - "# worksheet.insert_chart('G2',chart)\n", - "# writer.save()\n", - "\n", - "str(10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "help(chart.add_series)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols = list(set(dfr.columns.tolist()) - set(['person_id'])) + ['field_count']\n", - "r = pd.merge(x_i,y_i,on=cols,how='inner')\n", - "r['marketer'] = r.apply(lambda row: (row.group_count_x/row.group_count_y)/row.patient_count_y ,axis=1)\n", - "# r['field_count'] = r['field_count_x']\n", - "o = r.groupby(cols,as_index=False).sum()[cols+['marketer']]\n", - "o.groupby(['field_count'],as_index=False).mean()\n", - "# o.groupby('field_count',as_index=False).mean().plot.line(x='field_count',y='marketer')\n", - "# r.head()\n", - "# N = r.patient_count_y.mean()\n", - "# r['marketer'] = r.apply(lambda row: row.group_count_x / row.group_count_y,axis=1)\n", - "# m = r.groupby(['field_count'],as_index=False).mean()[['field_count','marketer']]\n", - "# m.marketer = m.marketer / N\n", - "# m.groupby(['field_count']).mean().plot.line()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.to_csv('out/x-2/single-runs-deid.csv',index=False)\n", - "p.groupby(['sample %']).mean()['marketer'].plot.line()\n", - "p.groupby(['sample %'],as_index=False).mean().plot.scatter(x='sample %',y='marketer')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y = pd.DataFrame({\"name\":['d','e','f','g'],\"age\":[12,40,20,30],\"income\":[100,200,300,400]})\n", - "x = pd.DataFrame({\"name\":['a','b','c'],\"age\":[10,20,40],\"income\":[120,100,200]})\n", - "\n", - "# x.join(y,how='outer',on='age')\n", - "x_ = pd.merge(x,y,on=['age','income'],how='outer')\n", - "Logger.log(action='merge',value=x_.shape)\n", - "Logger.cache" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# EXP_0\n", - "# Running the experiment on the Original dataset, with all the attributes\n", - "SCHEMA = \"deid_risk\"\n", - "df = pd.read_gbq(\"select person_id,birth_datetime,race,gender,sex_at_birth, city,state,zip from deid_risk.basic_risk60k \",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", - " dialect='standard')\n", - "\n", - "RUNS = 500\n", - "FLAG = 'basic-features'\n", - "r = df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", - "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", - "compiled = r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", - "fi = compiled[['marketer','prosecutor']].plot.line().get_figure()\n", - "# fo\n", - "# r.plot.line(x='field_count',y='marketer')\n", - "compiled = r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", - "fig_i = r.plot.scatter(x='field_count',y='marketer').get_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# EXP_2 :\n", - "# This experiment will run the marketer risk against individual attributes\n", - "deid_df = pd.read_gbq(\"select person_id,birth_datetime,race,gender,sex_at_birth, city,state,zip from deid_risk.basic_deid_risk60k\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", - " dialect='standard')\n", - "RUNS = 500\n", - "FLAG = 'basic-deid-features'\n", - "deid_r = deid_df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", - "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", - "deid_compiled = deid_r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", - "fo = deid_compiled[['marketer','prosecutor']].plot.line().get_figure()\n", - "# fo\n", - "# r.plot.line(x='field_count',y='marketer')\n", - "# deid_compiled = deid_r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", - "fig_o = deid_r.plot.scatter(x='field_count',y='marketer').get_figure()\n", - "\n", - "# orig_df = pd.read_gbq(\"select * from deid_risk.risk_60k2\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", - "# dialect='standard')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# deid_r.to_csv('out/basic-attributes-deid-data-60k-patients.csv')\n", - "# r.to_csv('out/basic-attributes-raw-data-60k-patients.csv')\n", - "# deid_r.head()\n", - "p = pd.DataFrame()\n", - "p = deid_df.deid.risk(id='person_id',quasi_id=['birth_datetime','race','gender','sex_at_birth', 'city','state','zip'])\n", - "p = p.append(df.deid.risk(id='person_id',quasi_id=['birth_datetime','race','gender','sex_at_birth', 'city','state','zip']))\n", - "p.index = ['deid data','raw data']\n", - "p.to_csv('out/basic_run-7-fields.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols = deid_r.columns[5:]\n", - "deid_r.index = np.arange(deid_r.shape[0]).astype(np.int64)\n", - "xdeid_ = deid_r[cols].sum().tolist()\n", - "xraw_ = r[cols].sum().tolist()\n", - "o = pd.DataFrame()\n", - "o['name'] = cols\n", - "o['raw'] = xraw_\n", - "o['deid']= xdeid_\n", - "\n", - "\n", - "o\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "columns = list( set(orig_df.columns) - set(['person_id']))\n", - "xo = pd.DataFrame()\n", - "xi = pd.DataFrame()\n", - "#\n", - "# Let's compute the risk for every attribute given the list of attributes we've gathered\n", - "#\n", - "for name in columns :\n", - " xo = xo.append(deid_df.deid.risk(id='person_id',quasi_id=[name])[['marketer','prosecutor']],sort=False)\n", - " xi = xi.append(orig_df.deid.risk(id='person_id',quasi_id=[name])[['marketer','prosecutor']],sort=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# The following shows how much the deid process has affected each attributes\n", - "#\n", - "\n", - "RISK_THRESHOLD = 0.5\n", - "xo.index = columns\n", - "xi.index = columns\n", - "\n", - "ii = xi[xi.marketer > RISK_THRESHOLD].index\n", - "# zo = pd.concat([xi.loc[ii],xo.loc[ii]])\n", - "\n", - "zo = xi.loc[ii].join(xo.loc[ii],rsuffix='_deid')\n", - "#\n", - "# heatmap for original data\n", - "# fig_o = sns.heatmap(xi.loc[ii], cmap='RdYlGn_r', linewidths=0.5, annot=True).get_figure()\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# Running the experiment on the DEID dataset, with all the attributes\n", - "#\n", - "df = pd.read_gbq(\"select * from deid_risk.deid_risk_60k\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',\n", - " dialect='standard')\n", - "\n", - "RUNS = 1500\n", - "FLAG = 'deid-full-attr-dataset'\n", - "r = df.deid.risk(id='person_id',num_runs=RUNS) #,field_count=11)\n", - "# r.to_csv('out/pandas-60k-'+FLAG+'-patients-'+str(RUNS)+'-x-runs.csv')\n", - "compiled = r.groupby('field_count',as_index=False)['marketer','prosecutor'].mean()\n", - "fo = compiled[['marketer','prosecutor']].plot.line().get_figure()\n", - "# fo\n", - "# r.plot.line(x='field_count',y='marketer')\n", - "compiled = r.groupby('field_count',as_index=False)['field_count','marketer','prosecutor'].mean()\n", - "fig_o = r.plot.scatter(x='field_count',y='marketer').get_figure()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r.groupby('field_count',as_index=False)['marketer','prosecutor'].var()[['marketer','prosecutor']].plot.line()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# We are going to look into the attributes with a risk of a given threshold\n", - "# We will run the experiment (varied combinations of the list of attributes)\n", - "# The experiment is intended to capture the attributes responsible for increasing the marketer risk\n", - "#\n", - "DEID_DATASET = 'deid_risk.deid_risk_60k2'\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/registered-tier-history.ipynb b/notebooks/registered-tier-history.ipynb deleted file mode 100644 index 3278968..0000000 --- a/notebooks/registered-tier-history.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "The experiments here describe medical/family history as they associate with risk measures\n", - "Additionally we will have fractional risk assessments\n", - "\"\"\"\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pandas_risk import *\n", - "dfm = pd.read_gbq(\"SELECT * FROM deid_risk.registered_medical_history_dec_001\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", - "dff = pd.read_gbq(\"SELECT * FROM deid_risk.registered_family_history_dec_001\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n", - "df = pd.read_gbq(\"SELECT person_id, birth_date,city,state,home_owner,race,ethnicity,gender,birth_place,marital_status,orientation,education,employment_status,income,travel_abroad_6_months,active_duty_status FROM deid_risk.registered_dec_01\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "med_cols = np.random.choice(list(set(dfm.columns.tolist()) - set(['person_id'])),3).tolist()\n", - "fam_cols = np.random.choice(list(set(dff.columns.tolist()) - set(['person_id'])),3).tolist()\n", - "medical = pd.merge(df,dfm[med_cols+['person_id']],on='person_id')\n", - "family = pd.merge(df,dff[fam_cols + ['person_id']],on='person_id')\n", - "_tmp = pd.merge(dfm[med_cols +['person_id']],dff[fam_cols+['person_id']])\n", - "data = pd.merge(df,_tmp,on='person_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
021full history1153080.9926911.00.987663
118medical1153060.9926741.00.987629
218family1153040.9926561.00.987594
315no-history1153000.9926221.00.987526
43medical-only270.0002320.50.000000
53family-only1460.0012571.00.000551
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" - ], - "text/plain": [ - " field_count flag group_count marketer prosecutor \\\n", - "0 21 full history 115308 0.992691 1.0 \n", - "1 18 medical 115306 0.992674 1.0 \n", - "2 18 family 115304 0.992656 1.0 \n", - "3 15 no-history 115300 0.992622 1.0 \n", - "4 3 medical-only 27 0.000232 0.5 \n", - "5 3 family-only 146 0.001257 1.0 \n", - "\n", - " unique_row_ratio \n", - "0 0.987663 \n", - "1 0.987629 \n", - "2 0.987594 \n", - "3 0.987526 \n", - "4 0.000000 \n", - "5 0.000551 " - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.concat([data.deid.evaluate(flag='full history',cols= list(set(data.columns.tolist()) - set(['person_id'])) )\n", - " ,medical.deid.evaluate(flag='medical',cols=list( set(medical.columns.tolist() ) - set(['person_id']) ) )\n", - " ,family.deid.evaluate(flag='family',cols=list( set(family.columns.tolist() ) - set(['person_id']) ) )\n", - " ,df.deid.evaluate(flag='no-history',cols=list( set(df.columns.tolist() ) - set(['person_id']) ) )\n", - " , dfm.deid.evaluate(flag='medical-only',cols=med_cols )\n", - " , dff.deid.evaluate(flag='family-only',cols=fam_cols )\n", - " ],ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import division\n", - "def evaluate(df) :\n", - " cols = list(set(df.columns.tolist()) - set(['person_id']))\n", - " \n", - " portions = np.round(np.random.random_sample(4),3).tolist() + np.arange(5,105,5).tolist()\n", - " \n", - " N = df.shape[0] - 1\n", - " portions = np.divide(np.multiply(portions,N),100).astype(np.int64)\n", - " portions = np.unique([n for n in portions if n > 1])\n", - " \n", - " r = pd.DataFrame()\n", - " for num_rows in portions :\n", - " \n", - " indices = np.random.choice(N,num_rows,replace=False)\n", - "# print (indices.size / N)\n", - " flag = \" \".join([str( np.round(100*indices.size/ N,2)),'%'])\n", - " r = r.append(df.loc[indices].deid.evaluate(cols=cols,flag=flag,min_group_size=2))\n", - " return r" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
011UNFLAGGED1148860.9890581.00.980535
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" - ], - "text/plain": [ - " field_count flag group_count marketer prosecutor unique_row_ratio\n", - "0 11 UNFLAGGED 114886 0.989058 1.0 0.980535" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cols = list(set (df.columns.tolist()) - set(['person_id']))\n", - "df[['race','state','gender_identity','ethnicity','marital_status','education','orientation','sex_at_birth','birth_date','travel_abroad_6_months','active_duty_status']].deid.evaluate()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['person_id',\n", - " 'HearingVision_FarSightedness',\n", - " 'HearingVision_Glaucoma',\n", - " 'Digestive_Pancreatitis']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#\n", - "# This is the merge with medical history\n", - "\n", - "cols = ['person_id'] + np.random.choice(dfm.columns[1:],3,replace=False).tolist()\n", - "p = pd.merge(df,dfm[cols],on='person_id')\n", - "cols\n", - "# # cols = list(set(p.columns.tolist()) - set(['person_id']))\n", - "# evaluate(p) #p.deid.explore(cols=cols,num_runs=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "cols = list( set(dfm.columns.tolist()) - set(['person_id']))\n", - "cols = np.random.choice(cols,3,replace=False).tolist()\n", - "p = pd.merge(dfm[['person_id']+cols],df)\n", - "fcols = list(set(p.columns.tolist()) - set(['person_id']))\n", - "# dfm[cols].deid.evaluate(cols=list( set(cols) - set(['person_id'])))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "variables": { - " \" ; \".join(cols)": "InfectiousDiseases_HepatitisC ; Cancer_StomachCancer ; Circulatory_Hypertension", - " p.shape[0] ": "116157", - " p[fcols].deid.evaluate() ": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
field_countflaggroup_countmarketerprosecutorunique_row_ratio
037UNFLAGGED1153970.9934571.00.98886
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" - } - }, - "source": [ - "### Medical History\n", - "\n", - " We randomly select three a tributes {{ \" ; \".join(cols)}} . \n", - " The dataset associated risk evaluation contains {{ p.shape[0] }} records\n", - "{{ p[fcols].deid.evaluate() }}\n", - "\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['person_id',\n", - " 'InfectiousDiseases_Tuberculosis',\n", - " 'SkeletalMuscular_Fibromyalgia',\n", - " 'Cancer_ProstateCancer']" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cols" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# dfm[cols[1:]].head()\n", - "np.sum(dfm.fillna(' ').groupby(cols[1:],as_index=False).size().values <= 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/registered-tier.ipynb b/notebooks/registered-tier.ipynb deleted file mode 100644 index c8acbbf..0000000 --- a/notebooks/registered-tier.ipynb +++ /dev/null @@ -1,859 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "This notebook is designed to run experiments around demographics on registered tier\n", - "The \n", - "\"\"\"\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pandas_risk import *\n", - "\n", - "ATTRIBUTES = ['race','ethnicity','birth_date','state','city','zip','marital_status','education','language','home_owner','income','employment_status','living_situation','active_duty_status','gender_identity','birth_place','death_date','death_cause','orientation']\n", - "dfs = pd.read_csv('scenario-settings.csv')\n", - "dfc = pd.read_gbq(\"SELECT * FROM deid_risk.registered_dec_01\",private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "cols_o = dfs.loc[(dfs.fo & dfs.fi) ==1].feature.tolist()\n", - "cols_i = dfs.loc[(dfs.fo + dfs.fi )>=1 ].feature.tolist()\n", - "cols_a = dfs.feature.tolist()\n", - "\n", - "cols_v = ['birth_date','gender_identity','race','state','city','birth_place'] #-- voter registration\n", - "#remove the dates fields because dates are shifted\n", - "cols_o = [i for i in cols_o if i not in ['birth_date','death_date']]\n", - "cols_i = [i for i in cols_i if i not in ['birth_date','death_date']]\n", - "cols_a = [i for i in cols_a if i not in ['birth_date','death_date']]\n", - "cols_v = [i for i in cols_v if i not in ['birth_date', 'death_date']]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# print(dfs)\n", - "# print(cols_o)\n", - "# print(cols_i)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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field_countflaggroup_countmarketerprosecutorunique_row_ratio
08high-conj65320.0562341.00.021368
111high-disj474470.4084731.00.278554
216all607180.5227241.00.408189
35voter-reg13160.0113291.00.002944
\n", - "
" - ], - "text/plain": [ - " field_count flag group_count marketer prosecutor unique_row_ratio\n", - "0 8 high-conj 6532 0.056234 1.0 0.021368\n", - "1 11 high-disj 47447 0.408473 1.0 0.278554\n", - "2 16 all 60718 0.522724 1.0 0.408189\n", - "3 5 voter-reg 1316 0.011329 1.0 0.002944" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r = pd.concat([dfc[cols_o].deid.evaluate(),dfc[cols_i].deid.evaluate(),dfc[cols_a].deid.evaluate(),dfc[cols_v].deid.evaluate() ])\n", - "r.index = np.arange(r.shape[0]).astype(np.int64)\n", - "r['flag']=['high-conj','high-disj','all','voter-reg']\n", - "\n", - "r\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " feature fi fo\n", - "0 race 1 1\n", - "1 ethnicity 1 1\n", - "2 birth_date 1 1\n", - "3 city 1 1\n", - "4 state 1 1\n", - "5 marital_status 1 1\n", - "6 education 1 0\n", - "7 language 0 0\n", - "8 home_owner 1 1\n", - "9 income 0 1\n", - "10 employment_status 1 0\n", - "11 living_situation 0 0\n", - "12 active_duty_status 0 0\n", - "13 gender_identity 1 1\n", - "14 birth_place 0 0\n", - "15 death_date 1 1\n", - "16 death_cause 1 1\n", - "17 orientation 0 0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfs\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "names = pd.read_csv('family-history.csv').name.tolist()\n", - "path ='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json'\n", - "sql = \"\"\"\n", - "SELECT * FROM deid_risk.registered_medical_history_dec_001\n", - "\"\"\"\n", - "dfm = pd.read_gbq(\"SELECT * FROM deid_risk.registered_medical_history_dec_001\",private_key=path,dialect='standard')" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9343780009344719, 1.269831148073964)" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cols = list( set(dfm.columns.tolist()) - set(['person_id']))\n", - "r = pd.DataFrame(dfm[cols].count(),columns=['counts'])\n", - "r['attributes'] = r.index\n", - "r['rate'] = 100*(r.counts / dfm.shape[0])\n", - "r.rate.mean(),np.sqrt(r.rate.var())" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "writer = pd.ExcelWriter('/home/steve/tmp/simple.xlsx', engine='xlsxwriter')\n", - "r.to_excel(writer,sheet_name='p1')\n", - "workbook = writer.book\n", - "worksheet = workbook.add_worksheet()\n", - "b = pd.DataFrame({\"id\":np.random.choice(10,30)})" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__doc__',\n", - " '__format__',\n", - " '__getattribute__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__module__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_assemble_xml_file',\n", - " '_button_params',\n", - " '_calculate_spans',\n", - " '_calculate_x_split_width',\n", - " '_check_dimensions',\n", - " '_comment_params',\n", - " '_convert_date_time',\n", - " '_convert_name_area',\n", - " '_csv_join',\n", - " '_encode_password',\n", - " '_escape_attributes',\n", - " '_escape_data',\n", - " '_escape_url',\n", - " '_extract_filter_tokens',\n", - " '_get_palette_color',\n", - " '_get_range_data',\n", - " '_initialize',\n", - 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" '_write_formula',\n", - " '_write_formula_1',\n", - " '_write_formula_2',\n", - " '_write_formula_element',\n", - " '_write_freeze_panes',\n", - " '_write_header_footer',\n", - " '_write_hyperlink_external',\n", - " '_write_hyperlink_internal',\n", - " '_write_hyperlinks',\n", - " '_write_icon_set',\n", - " '_write_legacy_drawing',\n", - " '_write_legacy_drawing_hf',\n", - " '_write_merge_cell',\n", - " '_write_merge_cells',\n", - " '_write_number',\n", - " '_write_odd_footer',\n", - " '_write_odd_header',\n", - " '_write_optimized_sheet_data',\n", - " '_write_outline_pr',\n", - " '_write_page_margins',\n", - " '_write_page_set_up_pr',\n", - " '_write_page_setup',\n", - " '_write_panes',\n", - " '_write_phonetic_pr',\n", - " '_write_print_options',\n", - " '_write_rich_string',\n", - " '_write_row',\n", - " '_write_row_breaks',\n", - " '_write_rows',\n", - " '_write_rstring_color',\n", - " '_write_selection',\n", - " '_write_selections',\n", - " '_write_sheet_data',\n", - " '_write_sheet_format_pr',\n", - 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" 'default_row_zeroed',\n", - " 'default_url_format',\n", - " 'dim_colmax',\n", - " 'dim_colmin',\n", - " 'dim_rowmax',\n", - " 'dim_rowmin',\n", - " 'draft_quality',\n", - " 'drawing',\n", - " 'drawing_links',\n", - " 'dxf_priority',\n", - " 'escapes',\n", - " 'excel2003_style',\n", - " 'excel_version',\n", - " 'ext_sheets',\n", - " 'external_comment_links',\n", - " 'external_drawing_links',\n", - " 'external_hyper_links',\n", - " 'external_table_links',\n", - " 'external_vml_links',\n", - " 'fh',\n", - " 'fileclosed',\n", - " 'filter_cols',\n", - " 'filter_column',\n", - " 'filter_column_list',\n", - " 'filter_on',\n", - " 'filter_range',\n", - " 'filter_type',\n", - " 'fit_height',\n", - " 'fit_page',\n", - " 'fit_to_pages',\n", - " 'fit_width',\n", - " 'footer',\n", - " 'footer_images',\n", - " 'freeze_panes',\n", - " 'get_name',\n", - " 'has_comments',\n", - " 'has_header_vml',\n", - " 'has_vml',\n", - " 'hbreaks',\n", - " 'hcenter',\n", - " 'header',\n", - " 'header_footer_aligns',\n", - " 'header_footer_changed',\n", - " 'header_footer_scales',\n", - " 'header_images',\n", - " 'header_images_list',\n", - " 'hidden',\n", - " 'hide',\n", - " 'hide_gridlines',\n", - " 'hide_row_col_headers',\n", - " 'hide_zero',\n", - " 'hlink_count',\n", - " 'hlink_refs',\n", - " 'horizontal_dpi',\n", - " 'hyperlinks',\n", - " 'images',\n", - " 'index',\n", - " 'insert_button',\n", - " 'insert_chart',\n", - " 'insert_image',\n", - " 'insert_textbox',\n", - " 'internal_fh',\n", - " 'is_chartsheet',\n", - " 'is_right_to_left',\n", - " 'last_shape_id',\n", - " 'leading_zeros',\n", - " 'margin_bottom',\n", - " 'margin_footer',\n", - " 'margin_header',\n", - " 'margin_left',\n", - " 'margin_right',\n", - " 'margin_top',\n", - " 'merge',\n", - " 'merge_range',\n", - " 'name',\n", - " 'names',\n", - " 'nan_inf_to_errors',\n", - " 'orientation',\n", - " 'original_row_height',\n", - " 'outline_below',\n", - " 'outline_changed',\n", - " 'outline_col_level',\n", - " 'outline_on',\n", - " 'outline_right',\n", - " 'outline_row_level',\n", - " 'outline_settings',\n", - " 'outline_style',\n", - " 'page_order',\n", - " 'page_setup_changed',\n", - " 'page_start',\n", - " 'page_view',\n", - " 'palette',\n", - " 'panes',\n", - " 'paper_size',\n", - " 'previous_row',\n", - " 'print_across',\n", - " 'print_area',\n", - " 'print_area_range',\n", - " 'print_comments',\n", - " 'print_gridlines',\n", - " 'print_headers',\n", - " 'print_options_changed',\n", - " 'print_row_col_headers',\n", - " 'print_scale',\n", - " 'protect',\n", - " 'protect_options',\n", - " 'rel_count',\n", - " 'remove_timezone',\n", - " 'repeat_col_range',\n", - " 'repeat_columns',\n", - " 'repeat_row_range',\n", - " 'repeat_rows',\n", - " 'right_to_left',\n", - " 'row_col_headers',\n", - " 'row_data_fh',\n", - " 'row_data_fh_closed',\n", - " 'row_data_filename',\n", - " 'row_size_changed',\n", - " 'row_sizes',\n", - " 'row_spans',\n", - " 'rstring',\n", - " 'screen_gridlines',\n", - " 'select',\n", - " 'selected',\n", - " 'selections',\n", - " 'set_cols',\n", - " 'set_column',\n", - " 'set_comments_author',\n", - " 'set_default_row',\n", - " 'set_first_sheet',\n", - " 'set_footer',\n", - " 'set_h_pagebreaks',\n", - " 'set_header',\n", - " 'set_landscape',\n", - " 'set_margins',\n", - " 'set_page_view',\n", - " 'set_paper',\n", - " 'set_portrait',\n", - " 'set_print_scale',\n", - " 'set_row',\n", - " 'set_rows',\n", - " 'set_selection',\n", - " 'set_start_page',\n", - " 'set_tab_color',\n", - " 'set_v_pagebreaks',\n", - " 'set_vba_name',\n", - " 'set_zoom',\n", - " 'shape_hash',\n", - " 'shapes',\n", - " 'show_comments',\n", - " 'show_zeros',\n", - " 'sparklines',\n", - " 'split_panes',\n", - " 'str_table',\n", - " 'strings_to_formulas',\n", - " 'strings_to_numbers',\n", - " 'strings_to_urls',\n", - " 'tab_color',\n", - " 'table',\n", - " 'tables',\n", - " 'tmpdir',\n", - " 'use_data_bars_2010',\n", - " 'validations',\n", - " 'vba_codename',\n", - " 'vbreaks',\n", - " 'vcenter',\n", - " 'vertical_dpi',\n", - " 'vml_data_id',\n", - " 'vml_drawing_links',\n", - " 'vml_header_id',\n", - " 'vml_shape_id',\n", - " 'worksheet_meta',\n", - " 'write',\n", - " 'write_array_formula',\n", - " 'write_blank',\n", - " 'write_boolean',\n", - " 'write_column',\n", - " 'write_comment',\n", - " 'write_datetime',\n", - " 'write_formula',\n", - " 'write_match',\n", - " 'write_number',\n", - " 'write_rich_string',\n", - " 'write_row',\n", - " 'write_string',\n", - " 'write_url',\n", - " 'xls_colmax',\n", - " 'xls_rowmax',\n", - " 'xls_strmax',\n", - " 'zoom',\n", - " 'zoom_scale_normal']" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir(worksheet)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/risk.ipynb b/notebooks/risk.ipynb deleted file mode 100644 index 299bd35..0000000 --- a/notebooks/risk.ipynb +++ /dev/null @@ -1,293 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "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=,nun_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=,quasi_id=\n", - "\"\"\"\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "\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": 2, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - " Here's the pandas_risk code verbatim. \n", - " NOTE: \n", - "\"\"\"\n", - "@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": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "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'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15rc1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 942e3bd8129d1186f974641df406cbf44ddf7e40 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 12:10:40 -0600 Subject: [PATCH 08/23] updated readme --- README.md | 54 ++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 50 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index ab29cc5..a8427de 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,56 @@ -# deid-risk +# Re-Identification Risk -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. +This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas) +The framework will compute the following risk measures: + - marketer + - prosecutor + - pitman +References : + + [http://ehelthinformation.ca](http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) + [https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf) + +This framework integrates pandas (for now) as an extension and can be used in two modes : +* 1. explore: * + Here the assumption is that we are not sure of the attributes to be disclosed, + The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with + +* 2. evaluation: * + Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. + + +# Four risk measures are computed : + + - Marketer risk + - Prosecutor risk + - Journalist risk + - Pitman Risk + +# Usage: + + import numpy as np + import pandas as pd + from pandas_risk import * + + mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50) }) + print mydf.risk.evaluate() + + + + # + # computing journalist and pitman + # - Insure the population size is much greater than the sample size + # - Insure the fields are identical in both sample and population + # + pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) + mydf.risk.evaluate(pop=pop) + +@TODO: + - Evaluation of how sparse attributes are (the ratio of non-null over rows) + - Have a smart way to drop attributes (based on the above in random policy search) 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) + Dependencies: numpy From 7d82cc6e3a1515c4b4200d59aef819a1beb2b1c4 Mon Sep 17 00:00:00 2001 From: steve Date: Tue, 5 Mar 2019 12:18:11 -0600 Subject: [PATCH 09/23] Update 'README.md' --- README.md | 44 +++++++++++++++++++------------------------- 1 file changed, 19 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index a8427de..5bab252 100644 --- a/README.md +++ b/README.md @@ -1,33 +1,29 @@ # Re-Identification Risk -This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas) -The framework will compute the following risk measures: - - marketer - - prosecutor - - pitman +This framework computes re-identification risk of a dataset by extending pandas. It works like a pandas **add-on** +The framework will compute the following risk measures: marketer, prosecutor, journalist and pitman risk. +There are two modes available : + +**explore:** -References : + Here the assumption is that we are not sure of the attributes to be disclosed, + The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with - [http://ehelthinformation.ca](http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) - [https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf) +**evaluation** -This framework integrates pandas (for now) as an extension and can be used in two modes : -* 1. explore: * - Here the assumption is that we are not sure of the attributes to be disclosed, - The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with - -* 2. evaluation: * - Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. + Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. -# Four risk measures are computed : +### Four risk measures are computed : - Marketer risk - Prosecutor risk - Journalist risk - Pitman Risk -# Usage: +### Usage: + +The framework will depend on pandas and numpy (for now) import numpy as np import pandas as pd @@ -46,17 +42,15 @@ This framework integrates pandas (for now) as an extension and can be used in tw pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) mydf.risk.evaluate(pop=pop) +### References : + +[http://ehelthinformation.ca] (http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) + +[https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf) + @TODO: - Evaluation of how sparse attributes are (the ratio of non-null over rows) - Have a smart way to drop attributes (based on the above in random policy search) Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from - -Dependencies: - numpy - pandas -Limitations: - - @TODO: - - Add support for journalist risk From fe3ce6f2d7b7a8288777cf94874a82d4e63acca4 Mon Sep 17 00:00:00 2001 From: steve Date: Tue, 5 Mar 2019 12:23:35 -0600 Subject: [PATCH 10/23] Update 'README.md' --- README.md | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 5bab252..3448e23 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,17 @@ # Re-Identification Risk This framework computes re-identification risk of a dataset by extending pandas. It works like a pandas **add-on** -The framework will compute the following risk measures: marketer, prosecutor, journalist and pitman risk. +The framework will compute the following risk measures: marketer, prosecutor, journalist and pitman risk. References for the risk measures can be found on [http://ehelthinformation.ca] (http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) and [https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf) + There are two modes available : **explore:** - Here the assumption is that we are not sure of the attributes to be disclosed, - The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with +Here the assumption is that we are not sure of the attributes to be disclosed, the framework will randomly generate random combinations of attributes and evaluate them accordingly as it provides all the measures of risk. **evaluation** - Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. +Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. ### Four risk measures are computed : @@ -23,7 +23,8 @@ There are two modes available : ### Usage: -The framework will depend on pandas and numpy (for now) +The framework will depend on pandas and numpy (for now). Below is a basic sample to get started quickly. + import numpy as np import pandas as pd @@ -42,11 +43,6 @@ The framework will depend on pandas and numpy (for now) pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) mydf.risk.evaluate(pop=pop) -### References : - -[http://ehelthinformation.ca] (http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) - -[https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf) @TODO: - Evaluation of how sparse attributes are (the ratio of non-null over rows) From e8555520d949683465c31f5df9de60ce7e562b7b Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 12:44:44 -0600 Subject: [PATCH 11/23] refactored for pip --- {src/risk => risk}/__init__.py | 0 {src/risk => risk}/risk.py | 0 src/setup.py => setup.py | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename {src/risk => risk}/__init__.py (100%) rename {src/risk => risk}/risk.py (100%) rename src/setup.py => setup.py (100%) diff --git a/src/risk/__init__.py b/risk/__init__.py similarity index 100% rename from src/risk/__init__.py rename to risk/__init__.py diff --git a/src/risk/risk.py b/risk/risk.py similarity index 100% rename from src/risk/risk.py rename to risk/risk.py diff --git a/src/setup.py b/setup.py similarity index 100% rename from src/setup.py rename to setup.py From 7590b12ecec967575763c9c3a3bbefa4130ff503 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 13:01:11 -0600 Subject: [PATCH 12/23] adding dependencies --- setup.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 156b6c9..5211770 100644 --- a/setup.py +++ b/setup.py @@ -6,5 +6,10 @@ from setuptools import setup, find_packages setup( name = "risk", version = "0.1", - packages = find_packages() + author = "Health Information Privacy Lab", + author_email = "steve.l.nyemba@vanderbilt.edu", + license = "MIT", + + packages = ['numpy','pandas'], + install_requires = ['numpy','pandas'] ) \ No newline at end of file From 9ee279f4866d6bcafcf51a70a648c2987820362d Mon Sep 17 00:00:00 2001 From: steve Date: Tue, 5 Mar 2019 13:05:05 -0600 Subject: [PATCH 13/23] Update 'setup.py' --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 5211770..ecbe6e5 100644 --- a/setup.py +++ b/setup.py @@ -10,6 +10,5 @@ setup( author_email = "steve.l.nyemba@vanderbilt.edu", license = "MIT", - packages = ['numpy','pandas'], - install_requires = ['numpy','pandas'] + packages = ['numpy','pandas'] ) \ No newline at end of file From dc43c81d267be3fba373f162f35ccad7e7551b0a Mon Sep 17 00:00:00 2001 From: steve Date: Tue, 5 Mar 2019 13:06:51 -0600 Subject: [PATCH 14/23] Update 'setup.py' --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index ecbe6e5..af6b425 100644 --- a/setup.py +++ b/setup.py @@ -10,5 +10,5 @@ setup( author_email = "steve.l.nyemba@vanderbilt.edu", license = "MIT", - packages = ['numpy','pandas'] + install_requires = ['numpy','pandas'] ) \ No newline at end of file From ee59d223af5396f411b777ee75307a64954071ed Mon Sep 17 00:00:00 2001 From: steve Date: Tue, 5 Mar 2019 13:20:43 -0600 Subject: [PATCH 15/23] Update 'README.md' --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index 3448e23..ef46bab 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,17 @@ Here the assumption is that we are clear on the sets of attributes to be used an ### Usage: +Install this package using pip as follows : + +Stable : + + pip install git+https://hiplab.mc.vanderbilt.edu/git/steve/deid-risk.git + + +Latest Development (not fully tested): + + pip install git+https://hiplab.mc.vanderbilt.edu/git/steve/deid-risk.git@risk + The framework will depend on pandas and numpy (for now). Below is a basic sample to get started quickly. From 593648db78c29a77a4c97afe4e1bccf809efacaa Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 14:46:41 -0600 Subject: [PATCH 16/23] bug fix --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index af6b425..e9c9505 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,6 @@ setup( author = "Health Information Privacy Lab", author_email = "steve.l.nyemba@vanderbilt.edu", license = "MIT", - + packages=['risk'], install_requires = ['numpy','pandas'] ) \ No newline at end of file From c74e844939bc056224dc1305dc0d2ef51cc11de1 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 14:50:24 -0600 Subject: [PATCH 17/23] bug fix --- risk/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/risk/__init__.py b/risk/__init__.py index 0814772..88a4af9 100644 --- a/risk/__init__.py +++ b/risk/__init__.py @@ -48,4 +48,4 @@ mydf.risk.evaluate(pop=pop) - Evaluation of how sparse attributes are (the ratio of non-null over rows) - Have a smart way to drop attributes (based on the above in random policy search) """ - +from risk import * From 5a09e8996538acf8b298539b71689a937ee789e0 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 14:52:34 -0600 Subject: [PATCH 18/23] bug fix -- again --- risk/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/risk/__init__.py b/risk/__init__.py index 88a4af9..2134f25 100644 --- a/risk/__init__.py +++ b/risk/__init__.py @@ -48,4 +48,4 @@ mydf.risk.evaluate(pop=pop) - Evaluation of how sparse attributes are (the ratio of non-null over rows) - Have a smart way to drop attributes (based on the above in random policy search) """ -from risk import * +from risk import risk From 22b2cb0af37c6c34776874574bcf2a491156f1f4 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 15:15:16 -0600 Subject: [PATCH 19/23] update and making it 3.x compatible --- README.md | 4 ++-- risk/risk.py | 10 ++++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index ef46bab..96103de 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample import pandas as pd from pandas_risk import * - mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50) }) + mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50) }) print mydf.risk.evaluate() @@ -51,7 +51,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample # - Insure the population size is much greater than the sample size # - Insure the fields are identical in both sample and population # - pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) + pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)}) mydf.risk.evaluate(pop=pop) diff --git a/risk/risk.py b/risk/risk.py index b848eed..00e22fc 100644 --- a/risk/risk.py +++ b/risk/risk.py @@ -60,7 +60,7 @@ class deid : @param pop|sample data-frame with popublation reference @param id key field that uniquely identifies patient/customer ... """ - id = args['id'] + pop= args['pop'] if 'pop' in args else None if 'pop_size' in args : @@ -77,7 +77,11 @@ class deid : sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df) k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count']) - columns = list(set(sample.columns.tolist()) - set([id])) + if 'id' in args : + id = args['id'] + columns = list(set(sample.columns.tolist()) - set([id])) + else: + columns = sample.columns.tolist() o = pd.DataFrame() for i in np.arange(RUNS): @@ -152,8 +156,6 @@ class deid : handle_sample.set('pop_size',pop_size) r['pitman risk'] = handle_sample.pitman() if 'pop' in args : - print cols - print args['pop'].columns xi = pd.DataFrame({"sample_group_size":sample.groupby(cols,as_index=False).size()}).reset_index() yi = pd.DataFrame({"population_group_size":args['pop'].groupby(cols,as_index=False).size()}).reset_index() merged_groups = pd.merge(xi,yi,on=cols,how='inner') From bd7348fe20ce0d76fec9b2befcf73561948a661a Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 15:31:14 -0600 Subject: [PATCH 20/23] fixing documentation --- risk/__init__.py | 76 ++++++++++++++++++++++++++++++------------------ 1 file changed, 47 insertions(+), 29 deletions(-) diff --git a/risk/__init__.py b/risk/__init__.py index 2134f25..414f60b 100644 --- a/risk/__init__.py +++ b/risk/__init__.py @@ -1,51 +1,69 @@ """ -(c) 2019, Health Information Privacy Lab -Brad. Malin, Weiyi Xia, Steve L. Nyemba +# Re-Identification Risk -This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas) -The framework will compute the following risk measures: - - marketer - - prosecutor - - pitman +This framework computes re-identification risk of a dataset by extending pandas. It works like a pandas **add-on** +The framework will compute the following risk measures: marketer, prosecutor, journalist and pitman risk. +References for the risk measures can be found on + - http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf + - https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf -References : - https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf +There are two modes available : + +**explore:** -This framework integrates pandas (for now) as an extension and can be used in two modes : -1. explore: - Here the assumption is that we are not sure of the attributes to be disclosed, - The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with +Here the assumption is that we are not sure of the attributes to be disclosed, the framework will randomly generate random combinations of attributes and evaluate them accordingly as it provides all the measures of risk. -2. evaluation - Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. +**evaluation** + +Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk. -Four risk measures are computed : +### Four risk measures are computed : + - Marketer risk - Prosecutor risk - Journalist risk - Pitman Risk -Usage: -import numpy as np -import pandas as pd -from pandas_risk import * +### Usage: -mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50) }) -print mydf.risk.evaluate() +Install this package using pip as follows : + +Stable : + + pip install git+https://hiplab.mc.vanderbilt.edu/git/steve/deid-risk.git + + +Latest Development (not fully tested): + + pip install git+https://hiplab.mc.vanderbilt.edu/git/steve/deid-risk.git@risk + +The framework will depend on pandas and numpy (for now). Below is a basic sample to get started quickly. + + + import numpy as np + import pandas as pd + from pandas_risk import * + + mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50) }) + print mydf.risk.evaluate() -# -# computing journalist and pitman -# - Insure the population size is much greater than the sample size -# - Insure the fields are identical in both sample and population -# -pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)}) -mydf.risk.evaluate(pop=pop) + # + # computing journalist and pitman + # - Insure the population size is much greater than the sample size + # - Insure the fields are identical in both sample and population + # + pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)}) + mydf.risk.evaluate(pop=pop) + @TODO: - Evaluation of how sparse attributes are (the ratio of non-null over rows) - Have a smart way to drop attributes (based on the above in random policy search) +Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from + + """ from risk import risk From 3ff382ad7190ddfaeaf83e3937ac0a1656bfcab5 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 15:38:48 -0600 Subject: [PATCH 21/23] bug fix --- risk/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/risk/__init__.py b/risk/__init__.py index 414f60b..fff471f 100644 --- a/risk/__init__.py +++ b/risk/__init__.py @@ -66,4 +66,4 @@ Basic examples that illustrate usage of the the framework are in the notebook fo """ -from risk import risk +from risk import deid From 30fe9cf9fced66641db585738f15272b76ce0617 Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 15:45:51 -0600 Subject: [PATCH 22/23] bug fix with explore policy --- risk/risk.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/risk/risk.py b/risk/risk.py index 00e22fc..05a0896 100644 --- a/risk/risk.py +++ b/risk/risk.py @@ -76,7 +76,7 @@ class deid : sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df) - k = sample.columns.size -1 if 'field_count' not in args else int(args['field_count']) + k = sample.columns.size if 'field_count' not in args else int(args['field_count']) + 1 if 'id' in args : id = args['id'] columns = list(set(sample.columns.tolist()) - set([id])) From 25b30d2c2be7ad6e2034b72028ef5da799c160ca Mon Sep 17 00:00:00 2001 From: Steve Nyemba Date: Tue, 5 Mar 2019 15:47:34 -0600 Subject: [PATCH 23/23] bug fix with documentation --- README.md | 6 +++--- risk/__init__.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 96103de..36cdb6d 100644 --- a/README.md +++ b/README.md @@ -39,10 +39,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample import numpy as np import pandas as pd - from pandas_risk import * + import risk mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50) }) - print mydf.risk.evaluate() + print (mydf.risk.evaluate()) @@ -52,7 +52,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample # - Insure the fields are identical in both sample and population # pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)}) - mydf.risk.evaluate(pop=pop) + print (mydf.risk.evaluate(pop=pop)) @TODO: diff --git a/risk/__init__.py b/risk/__init__.py index fff471f..57e7ee1 100644 --- a/risk/__init__.py +++ b/risk/__init__.py @@ -43,10 +43,10 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample import numpy as np import pandas as pd - from pandas_risk import * + import risk mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50) }) - print mydf.risk.evaluate() + print (mydf.risk.evaluate()) @@ -56,7 +56,7 @@ The framework will depend on pandas and numpy (for now). Below is a basic sample # - Insure the fields are identical in both sample and population # pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)}) - mydf.risk.evaluate(pop=pop) + print (mydf.risk.evaluate(pop=pop)) @TODO: