privacykit/notebooks/.ipynb_checkpoints/risk-checkpoint.ipynb

{
 "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
}