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@@ -1,238 +0,0 @@
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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": 1,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import itertools \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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- "from time import time\n",
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- "import os\n",
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- "\n",
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- "attr = ['gender','race','zip','year_of_birth']\n",
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- "comb_attr = [\n",
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- " ['zip' ,'gender', 'birth_datetime', 'race'], \n",
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- " ['zip', 'gender', 'year_of_birth', 'race'], \n",
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- " ['gender','race','zip'],\n",
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- " ['race','year_of_birth','zip']\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": 2,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "SQL_CONTROLLED=\"SELECT * FROM deid_risk.basic_risk60k\"\n",
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- "dfc = pd.read_gbq(SQL_CONTROLLED,private_key='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json')\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": 3,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "def risk(**args):\n",
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- " Yi = args['data']\n",
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- " Yi = Yi.fillna(' ')\n",
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- " sizes = args['prop'] if 'prop' in args else np.arange(5,100,5)\n",
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- " FLAG = args['flag'] if 'flag' in args else 'UNFLAGGED'\n",
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- " N = args['num_runs']\n",
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- " if 'cols' in args :\n",
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- " columns = args['cols']\n",
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- " else:\n",
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- " columns = list(set(Yi.columns.tolist()) - set(['person_id']))\n",
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- " p = pd.DataFrame()\n",
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- " y_i= pd.DataFrame({\"group_size\":Yi.groupby(columns,as_index=False).size()}).reset_index()\n",
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- " for index in sizes :\n",
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- " for n in np.repeat(index,N):\n",
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- " \n",
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- " # we will randomly sample n% rows from the dataset\n",
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- " i = np.random.choice(Yi.shape[0],((Yi.shape[0] * n)/100),replace=False)\n",
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- " x_i= pd.DataFrame(Yi).loc[i] \n",
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- " risk = x_i.deid.risk(id='person_id',quasi_id = columns)\n",
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- " x_i = pd.DataFrame({\"group_size\":x_i.groupby(columns,as_index=False).size()}).reset_index()\n",
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- "\n",
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- "\n",
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- " r = pd.merge(x_i,y_i,on=columns,how='inner')\n",
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- " if r.shape[0] == 0 :\n",
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- " continue\n",
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- " 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",
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- " r['sample %'] = np.repeat(n,r.shape[0])\n",
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- " r['tier'] = np.repeat(FLAG,r.shape[0])\n",
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- " r['sample marketer'] = np.repeat(risk['marketer'].values[0],r.shape[0])\n",
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- " # r['patient_count'] = np.repeat(r.shape[0],r.shape[0])\n",
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- " r = r.groupby(['sample %','tier','sample marketer'],as_index=False).sum()[['sample %','marketer','sample marketer','tier']]\n",
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- " p = p.append(r)\n",
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- " p.index = np.arange(p.shape[0]).astype(np.int64)\n",
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- " return p\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": 4,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "from pandas_risk import *\n",
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- "o = pd.DataFrame()\n",
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- "PATH=\"out/experiment-phase-2.xlsx\"\n",
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- "writer = pd.ExcelWriter(PATH,engine='xlsxwriter')\n",
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- "comb_attr = [\n",
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- " ['zip' ,'gender', 'birth_datetime', 'race'], \n",
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- " ['zip', 'gender', 'year_of_birth', 'race'], \n",
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- " ['gender','race','zip'],\n",
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- " ['race','year_of_birth','zip']\n",
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- "]\n",
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- "\n",
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- "for cols in comb_attr :\n",
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- " o = risk(data=dfc,cols=cols,flag='CONTROLLED',num_runs=5)\n",
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- " #\n",
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- " # adding the policy\n",
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- " x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n",
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- " o = o.join(pd.DataFrame(x,columns = dfc.columns))\n",
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- " #\n",
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- " # Write this to excel notebook\n",
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- " o.to_excel(writer,\"-\".join(cols))\n",
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- "# break\n",
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- " \n",
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- "\n",
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- "# p = p.rename(columns={'marketer_x':'sample marketer'})\n",
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- "# p.index = np.arange(p.shape[0]).astype(np.int64)\n",
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- "\n",
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- "writer.save()"
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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": 20,
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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>person_id</th>\n",
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- " <th>year_of_birth</th>\n",
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- " <th>month_of_birth</th>\n",
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- " <th>day_of_birth</th>\n",
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- " <th>birth_datetime</th>\n",
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- " <th>race_concept_id</th>\n",
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- " <th>ethnicity_concept_id</th>\n",
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- " <th>location_id</th>\n",
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- " <th>care_site_id</th>\n",
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- " <th>person_source_value</th>\n",
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- " <th>...</th>\n",
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- " <th>gender_source_concept_id</th>\n",
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- " <th>race_source_value</th>\n",
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- " <th>ethnicity_source_value</th>\n",
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- " <th>sex_at_birth</th>\n",
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- " <th>birth_date</th>\n",
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- " <th>race</th>\n",
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- " <th>zip</th>\n",
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- " <th>city</th>\n",
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- " <th>state</th>\n",
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- " <th>gender</th>\n",
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- " </tr>\n",
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- " </thead>\n",
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- " <tbody>\n",
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- " </tbody>\n",
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- "</table>\n",
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- "<p>0 rows × 21 columns</p>\n",
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- "</div>"
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- ],
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- "text/plain": [
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- "Empty DataFrame\n",
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- "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",
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- "Index: []\n",
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- "\n",
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- "[0 rows x 21 columns]"
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- ]
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- },
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- "execution_count": 20,
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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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- "x = [1* dfc.columns.isin(cols) for i in range(o.shape[0])]\n",
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- "o.join(pd.DataFrame(x,columns = dfc.columns))\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": 6,
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- "metadata": {},
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- "outputs": [
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- {
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- "ename": "NameError",
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- "evalue": "name 'columns' is not defined",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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- "\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",
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- "\u001b[0;31mNameError\u001b[0m: name 'columns' is not defined"
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- ]
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- }
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- ],
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- "source": [
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- "columns\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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