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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from google.cloud import bigquery as bq\n",
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"\n",
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"client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"xo = ['person_id','date_of_birth','race']\n",
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"xi = ['person_id','value_as_number','value_source_value']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_tables(client,did,fields=[]):\n",
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" \"\"\"\n",
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" getting table lists from google\n",
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" \"\"\"\n",
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" r = []\n",
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" ref = client.dataset(id)\n",
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" tables = list(client.list_tables(ref))\n",
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" for table in tables :\n",
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" ref = table.reference\n",
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" schema = client.get_table(ref).schema\n",
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" names = [f.field_name for f in schema]\n",
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" x = list(set(names) & set(fields))\n",
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" if x :\n",
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" r.append({\"name\":table.table_id,\"fields\":names})\n",
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" return r\n",
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" \n",
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"def get_fields(**args):\n",
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" \"\"\"\n",
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" This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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" {name,fields:[],\"y\":}, with \n",
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" name table name (needed to generate sql query)\n",
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" fields list of field names, used in the projection\n",
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" y name of the field to be joined.\n",
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" @param xo candidate table in the join\n",
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" @param xi candidate table in the join\n",
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" @param join field by which the tables can be joined.\n",
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" \"\"\"\n",
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" # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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" xo = args['xo']['fields']\n",
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" xi = args['xi']['fields']\n",
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" zi = args['xi']['name']\n",
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" return list(set(xo) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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"def generate_sql(**args):\n",
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" \"\"\"\n",
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" This function will generate the SQL query for the resulting join\n",
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" \"\"\"\n",
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" xo = args['xo']\n",
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" xi = args['xi']\n",
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" sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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" fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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" \n",
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" \n",
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" sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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" sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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" return sql\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['person_id',\n",
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" 'measurements.value_as_number',\n",
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" 'date_of_birth',\n",
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" 'race',\n",
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" 'measurements.value_source_value']"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value']}\n",
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"get_fields(xo=xo,xi=xi,join=\"person_id\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'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 '"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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"generate_sql(xo=xo,xi=xi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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]
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},
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"execution_count": 59,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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" We are designing a process that will take two tables that will generate \n",
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"\"\"\"\n",
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"import itertools\n",
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"list(itertools.combinations(['a','b','c'],2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"TableReference(DatasetReference(u'aou-res-deid-vumc-test', u'raw'), 'care_site')"
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]
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},
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"execution_count": 87,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ref = client.dataset('raw')\n",
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"tables = list(client.list_tables(ref))\n",
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"names = [table.table_id for table in tables]\n",
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"(tables[0].reference)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(u'care_site',\n",
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" u'concept',\n",
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" u'concept_ancestor',\n",
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" u'concept_class',\n",
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" u'concept_relationship',\n",
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" u'concept_synonym',\n",
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" u'condition_occurrence',\n",
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" u'criteria',\n",
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" u'death',\n",
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" u'device_exposure',\n",
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" u'domain',\n",
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" u'drug_exposure',\n",
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" u'drug_strength',\n",
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" u'location',\n",
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" u'measurement',\n",
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" u'note',\n",
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" u'observation',\n",
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" u'people_seed',\n",
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" u'person',\n",
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" u'procedure_occurrence',\n",
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" u'relationship',\n",
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" u'visit_occurrence',\n",
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" u'vocabulary')"
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]
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},
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"execution_count": 85,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#\n",
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"# find every table with person id at the very least or a subset of fields\n",
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"#\n",
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"def get_tables\n",
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"q = ['person_id']\n",
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"pairs = list(itertools.combinations(names,len(names)))\n",
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"pairs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['a']"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(set(['a','b']) & set(['a']))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.15rc1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" skiping ...\n",
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" skiping ...\n",
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" skiping ...\n",
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" skiping ...\n",
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" skiping ...\n",
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" skiping ...\n",
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" skiping ...\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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" This notebook is designed to generate SQL syntax all the quasi-identifiers for the patients in the database\n",
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" The resulting SQL will be run against bigquery to produce a table with every record mapping to a patient\n",
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" \n",
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"\"\"\"\n",
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"\n",
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"from risk import *\n",
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"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",
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"r = ihandle.migrate_tables()\n",
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"len(r)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"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 '"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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",
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"ihandle.create_table().replace('\\n',' ')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.15rc1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"The experiments here describe medical/family history as they associate with risk measures\n",
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"Additionally we will have fractional risk assessments\n",
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"\"\"\"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from pandas_risk import *\n",
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"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",
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"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",
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"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')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"med_cols = np.random.choice(list(set(dfm.columns.tolist()) - set(['person_id'])),3).tolist()\n",
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"fam_cols = np.random.choice(list(set(dff.columns.tolist()) - set(['person_id'])),3).tolist()\n",
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"medical = pd.merge(df,dfm[med_cols+['person_id']],on='person_id')\n",
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"family = pd.merge(df,dff[fam_cols + ['person_id']],on='person_id')\n",
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"_tmp = pd.merge(dfm[med_cols +['person_id']],dff[fam_cols+['person_id']])\n",
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"data = pd.merge(df,_tmp,on='person_id')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>field_count</th>\n",
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" <th>flag</th>\n",
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" <th>group_count</th>\n",
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" <th>marketer</th>\n",
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" <th>prosecutor</th>\n",
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" <th>unique_row_ratio</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>21</td>\n",
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" <td>full history</td>\n",
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" <td>115308</td>\n",
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" <td>0.992691</td>\n",
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" <td>1.0</td>\n",
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" <td>0.987663</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>18</td>\n",
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" <td>medical</td>\n",
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" <td>115306</td>\n",
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" <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
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -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=<patient id>,nun_runs=<number of 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=<patient id>,quasi_id=<list of quasi ids>\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": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
||||
" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>marketer</th>\n",
|
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" <th>prosecutor</th>\n",
|
||||
" <th>selected</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
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" <td>0.500000</td>\n",
|
||||
" <td>1.0</td>\n",
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" <td>2</td>\n",
|
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|
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" <tr>\n",
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" <td>3</td>\n",
|
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|
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" <tr>\n",
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" <th>0</th>\n",
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|
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" <td>3</td>\n",
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|
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.333333</td>\n",
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" <td>1.0</td>\n",
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" <td>2</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0.333333</td>\n",
|
||||
" <td>0.5</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" marketer prosecutor selected\n",
|
||||
"0 0.500000 1.0 2\n",
|
||||
"0 0.500000 1.0 3\n",
|
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"0 0.500000 1.0 3\n",
|
||||
"0 0.333333 1.0 2\n",
|
||||
"0 0.333333 0.5 2"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Lets us compute risk here for a random any random selection of quasi identifiers\n",
|
||||
"# We will run this experiment 5 times\n",
|
||||
"#\n",
|
||||
"MY_DATAFRAME.deid.risk(id='id',num_runs=5)"
|
||||
]
|
||||
},
|
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
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"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
"data": {
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <th>selected</th>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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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,
|
||||
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|
||||
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|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
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||||
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||||
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|
||||
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||||
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||||
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||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
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||||
"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
|
||||
}
|
||||
Loading…
Reference in new issue