healthcareio
/
privacykit
mirror of https://hiplab.mc.vanderbilt.edu/git/hiplab/privacykit.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity
You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '0239b0ab42'
${ noResults }
privacykit/notebooks/registered-tier-history.ipynb

386 lines
12 KiB
Plaintext
Raw Blame History

{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>field_count</th>\n",
" <th>flag</th>\n",
" <th>group_count</th>\n",
" <th>marketer</th>\n",
" <th>prosecutor</th>\n",
" <th>unique_row_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21</td>\n",
" <td>full history</td>\n",
" <td>115308</td>\n",
" <td>0.992691</td>\n",
" <td>1.0</td>\n",
" <td>0.987663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18</td>\n",
" <td>medical</td>\n",
" <td>115306</td>\n",
" <td>0.992674</td>\n",
" <td>1.0</td>\n",
" <td>0.987629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18</td>\n",
" <td>family</td>\n",
" <td>115304</td>\n",
" <td>0.992656</td>\n",
" <td>1.0</td>\n",
" <td>0.987594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15</td>\n",
" <td>no-history</td>\n",
" <td>115300</td>\n",
" <td>0.992622</td>\n",
" <td>1.0</td>\n",
" <td>0.987526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>medical-only</td>\n",
" <td>27</td>\n",
" <td>0.000232</td>\n",
" <td>0.5</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3</td>\n",
" <td>family-only</td>\n",
" <td>146</td>\n",
" <td>0.001257</td>\n",
" <td>1.0</td>\n",
" <td>0.000551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>field_count</th>\n",
" <th>flag</th>\n",
" <th>group_count</th>\n",
" <th>marketer</th>\n",
" <th>prosecutor</th>\n",
" <th>unique_row_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11</td>\n",
" <td>UNFLAGGED</td>\n",
" <td>114886</td>\n",
" <td>0.989058</td>\n",
" <td>1.0</td>\n",
" <td>0.980535</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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() ": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>field_count</th>\n <th>flag</th>\n <th>group_count</th>\n <th>marketer</th>\n <th>prosecutor</th>\n <th>unique_row_ratio</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>37</td>\n <td>UNFLAGGED</td>\n <td>115397</td>\n <td>0.993457</td>\n <td>1.0</td>\n <td>0.98886</td>\n </tr>\n </tbody>\n</table>\n</div>"
}
},
"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
}
Reference in new issue View Git Blame Copy Permalink