{
 "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": [
       "<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>person_id</th>\n",
       "      <th>year_of_birth</th>\n",
       "      <th>month_of_birth</th>\n",
       "      <th>day_of_birth</th>\n",
       "      <th>birth_datetime</th>\n",
       "      <th>race_concept_id</th>\n",
       "      <th>ethnicity_concept_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>care_site_id</th>\n",
       "      <th>person_source_value</th>\n",
       "      <th>...</th>\n",
       "      <th>gender_source_concept_id</th>\n",
       "      <th>race_source_value</th>\n",
       "      <th>ethnicity_source_value</th>\n",
       "      <th>sex_at_birth</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>race</th>\n",
       "      <th>zip</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "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<ipython-input-6-8e7b9895361f>\u001b[0m in \u001b[0;36m<module>\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": []
  }
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