{ "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 }