template-adsets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\r\n",
      "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "! pip install --quiet environs cyksuid toolz psycopg2-binary typing_json backoff xxhash pyyaml geopandas facebook_business"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from environs import Env\n",
    "from adopt.malaria import window\n",
    "from adopt.malaria import get_conf, get_df, lookalike, get_ad_token\n",
    "from adopt.facebook.state import CampaignState\n",
    "from adopt.responses import get_ad_token\n",
    "\n",
    "env = Env()\n",
    "env.read_env('.env-upswell-vlab', override=True)\n",
    "\n",
    "cnf = get_conf(env)\n",
    "token = get_ad_token(cnf[\"survey_user\"], cnf[\"chatbase\"])\n",
    "state = CampaignState(env, token, window(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "state.adsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[49, 151, 200, 200, 151, 23, 26]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(a['targeting']['excluded_geo_locations']['custom_locations']) for a in state.adsets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[len(a['targeting']['geo_locations']['regions']) for a in state.adsets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'load_cities' from 'adopt.malaria' (/home/jupyter/work/adopt/adopt/malaria.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-d8272d7df5a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfacebook_business\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetinggeolocationcity\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTargetingGeoLocationCity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0madopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmalaria\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_cities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0madopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmarketing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'load_cities' from 'adopt.malaria' (/home/jupyter/work/adopt/adopt/malaria.py)"
     ]
    }
   ],
   "source": [
    "from facebook_business.adobjects.targetinggeolocationcity import TargetingGeoLocationCity\n",
    "import pandas as pd\n",
    "from adopt.malaria import load_cities\n",
    "from adopt.marketing import *\n",
    "\n",
    "def get_control_cities():\n",
    "    treatment_assignment = pd.read_csv('outs/ma-with-treatment.csv')[['disthash', 'treatment']]\n",
    "    cities = load_cities('output/cities.csv')\n",
    "\n",
    "    controls = cities \\\n",
    "        .merge(treatment_assignment) \\\n",
    "        .pipe(lambda df: df[df.treatment == 0]) \\\n",
    "        .pipe(lambda df: df.assign(rad = df.rad + 1))\n",
    "\n",
    "    return controls\n",
    "\n",
    "def lookup_region(state):\n",
    "    regions = {\n",
    "        'Uttar Pradesh': {\n",
    "            \"country\": \"IN\",\n",
    "            \"key\": \"1754\",\n",
    "            \"name\": \"Uttar Pradesh\"\n",
    "        },\n",
    "        'Jharkhand': {\n",
    "            \"country\": \"IN\",\n",
    "            \"key\": \"1756\",\n",
    "            \"name\": \"Jharkhand\"\n",
    "        },\n",
    "        'Chhatisgarh': {\n",
    "            \"country\": \"IN\",\n",
    "            \"key\": \"1755\",\n",
    "            \"name\": \"Chhattisgarh\"\n",
    "        }\n",
    "    }\n",
    "\n",
    "    regions = {**regions, 'combined': [v for v in regions.values()]}\n",
    "\n",
    "    return regions[state]\n",
    "\n",
    "\n",
    "def create_city(t):\n",
    "    return {\n",
    "        TargetingGeoLocationCity.Field.key: t['key'],\n",
    "        TargetingGeoLocationCity.Field.radius: 25.0,\n",
    "        TargetingGeoLocationCity.Field.distance_unit: 'kilometer'\n",
    "    }\n",
    "\n",
    "def template_targeting(c: AdsetConf, target_key, targets, excluded_cities=[]):\n",
    "    targeting = {}\n",
    "\n",
    "    excluded_locs = [create_location(lat, lng, rad)\n",
    "                     for lat, lng, rad in c.excluded_locs]\n",
    "\n",
    "    targeting[Targeting.Field.excluded_geo_locations] = {\n",
    "        TargetingGeoLocation.Field.location_types: ['home'],\n",
    "        TargetingGeoLocation.Field.custom_locations: excluded_locs,\n",
    "        TargetingGeoLocation.Field.cities: excluded_cities\n",
    "    }\n",
    "\n",
    "    # if targets is None:\n",
    "    #     key = \n",
    "    #     locs = lookup_region(c.cluster.name)\n",
    "\n",
    "    # else: \n",
    "    #     key = \n",
    "    #     locs = [create_city(t) for t in targets]\n",
    "        \n",
    "\n",
    "    targeting[Targeting.Field.geo_locations] = {\n",
    "        TargetingGeoLocation.Field.location_types: ['home'],\n",
    "        target_key: targets\n",
    "    }\n",
    "\n",
    "    return targeting\n",
    "\n",
    "def make_template_adsets(env, state, base_name, controls, targets, target_key, excluded_cities=[]):\n",
    "\n",
    "    locs = [Location(r.lat, r.lng, r.rad) for _, r in controls.iterrows()]\n",
    "    cluster = Cluster(base_name, base_name)\n",
    "\n",
    "    conf = AdsetConf(state.campaign, cluster, None, locs, [], \n",
    "                       10000, 'PAUSED', env('FACEBOOK_INSTA_ID'), \n",
    "                       48, None, None, None)\n",
    "\n",
    "    instructions = create_adset(base_name, conf, template_targeting(conf, target_key, targets, excluded_cities))\n",
    "\n",
    "\n",
    "    return instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from adopt.responses import query\n",
    "\n",
    "cnf = {'db': 'chatroach', 'user': 'chatroach', 'port': 5432, 'host': '0.0.0.0', 'password': None}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = treatment_assignment.merge(cities)\n",
    "m = m[m.state != 'Chhatisgarh']\n",
    "\n",
    "m = m[['disthash', 'treatment']].drop_duplicates().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import psycopg2\n",
    "\n",
    "for i, r in m.iterrows():\n",
    "    with psycopg2.connect(dbname=cnf['db'],\n",
    "                          user=cnf['user'],\n",
    "                          host=cnf['host'],\n",
    "                          port=cnf['port'],\n",
    "                          password=cnf['password']) as conn:\n",
    "        with conn.cursor() as cur:\n",
    "            vals = (r.disthash, bool(r.treatment))\n",
    "            q = 'insert into clusters(clusterid, treatment) values(%s, %s)'\n",
    "            cur.execute(q, vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cities = load_cities('output/cities.csv')\n",
    "treatment_assignment = pd.read_csv('outs/ma-with-treatment.csv')[['disthash', 'treatment']]\n",
    "cities = cities.merge(treatment_assignment)\n",
    "\n",
    "districts = cities.groupby('distname') \\\n",
    "                  .head(1) \\\n",
    "                  .reset_index(drop=True)\n",
    "\n",
    "controls = get_control_cities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "controls[controls.state == 'Chhatisgarh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from facebook_business.adobjects.targeting import Targeting\n",
    "\n",
    "\n",
    "dir(Targeting.Field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = TargetingSearch.search({'type': 'adTargetingCategory', 'country_code': 'LB', 'class': 'demographics'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<TargetingSearch> {\n",
       "     \"audience_size\": 18063462,\n",
       "     \"description\": \"People who have been married for less than 1 year\",\n",
       "     \"id\": \"6002714398172\",\n",
       "     \"name\": \"Newlywed (1 year)\",\n",
       "     \"path\": [\n",
       "         \"Newlywed (1 year)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 287925012,\n",
       "     \"description\": \"People who are likely to be parents\",\n",
       "     \"id\": \"6002714398372\",\n",
       "     \"name\": \"Parents (All)\",\n",
       "     \"path\": [\n",
       "         \"Parents (All)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"family_statuses\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 3705965,\n",
       "     \"description\": \"People who have been engaged for less than 6 months\",\n",
       "     \"id\": \"6002714398772\",\n",
       "     \"name\": \"Newly-engaged (6 months)\",\n",
       "     \"path\": [\n",
       "         \"Newly-engaged (6 months)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 73519660,\n",
       "     \"description\": \"People who are going to have their birthday within one week\",\n",
       "     \"id\": \"6002737124172\",\n",
       "     \"name\": \"Upcoming birthday\",\n",
       "     \"path\": [\n",
       "         \"Birthday\",\n",
       "         \"Upcoming birthday\"\n",
       "     ],\n",
       "     \"real_time_cluster\": true,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 799331,\n",
       "     \"description\": \"People who have updated their profile with a new current city in the last 6 months\",\n",
       "     \"id\": \"6003054185372\",\n",
       "     \"name\": \"Recently moved\",\n",
       "     \"path\": [\n",
       "         \"Recently moved\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 9176150,\n",
       "     \"description\": \"People who are in a long-distance relationship\",\n",
       "     \"id\": \"6003053984972\",\n",
       "     \"name\": \"Long-distance relationship\",\n",
       "     \"path\": [\n",
       "         \"Long-distance relationship\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 144769312,\n",
       "     \"description\": \"People who are away from their hometowns\",\n",
       "     \"id\": \"6003053860372\",\n",
       "     \"name\": \"Away from hometown\",\n",
       "     \"path\": [\n",
       "         \"Away from hometown\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 144769312,\n",
       "     \"description\": \"People who are away from their family\",\n",
       "     \"id\": \"6003053857372\",\n",
       "     \"name\": \"Away from family\",\n",
       "     \"path\": [\n",
       "         \"Away from family\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 10761622,\n",
       "     \"description\": \"People who have been married for less than 6 months\",\n",
       "     \"id\": \"6003050226972\",\n",
       "     \"name\": \"Newlywed (6 months)\",\n",
       "     \"path\": [\n",
       "         \"Newlywed (6 months)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 5833926,\n",
       "     \"description\": \"People who have been engaged for less than 1 year\",\n",
       "     \"id\": \"6003050210972\",\n",
       "     \"name\": \"Newly engaged (1 year)\",\n",
       "     \"path\": [\n",
       "         \"Newly engaged (1 year)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 3375979,\n",
       "     \"description\": \"People who have updated their profile with a new relationship in the last 6 months\",\n",
       "     \"id\": \"6005232221572\",\n",
       "     \"name\": \"New relationship\",\n",
       "     \"path\": [\n",
       "         \"New relationship\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 244422,\n",
       "     \"description\": \"People who have updated their profile with a new job position in the last 6 months\",\n",
       "     \"id\": \"6005149512172\",\n",
       "     \"name\": \"New job\",\n",
       "     \"path\": [\n",
       "         \"New job\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 6967364,\n",
       "     \"description\": \"People with roles in business and finance. Examples include: accountant, accounting manager, auditor, sales manager, financial advisor, chief financial officer, dealer, agent, etc.\",\n",
       "     \"id\": \"6009003307783\",\n",
       "     \"name\": \"Business and Finance\",\n",
       "     \"path\": [\n",
       "         \"Business and Finance\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 16133607,\n",
       "     \"description\": \"People with roles in administration. Examples include: secretary, administrative assistant, office manager, office assistant, apartment locator, personal assistant, branch manager, etc.\",\n",
       "     \"id\": \"6008888954983\",\n",
       "     \"name\": \"Administrative Services\",\n",
       "     \"path\": [\n",
       "         \"Administrative Services\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 10284930,\n",
       "     \"description\": \"People with roles in education or libraries. Examples include: educator, instructor, teacher, professor, lecturer, research assistant, tutor, librarian, principal, etc.\",\n",
       "     \"id\": \"6008888998983\",\n",
       "     \"name\": \"Education and Libraries\",\n",
       "     \"path\": [\n",
       "         \"Education and Libraries\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 11204106,\n",
       "     \"description\": \"People with roles in management. Examples include: manager, supervisor, director, president, chairman, etc.\",\n",
       "     \"id\": \"6009003311983\",\n",
       "     \"name\": \"Management\",\n",
       "     \"path\": [\n",
       "         \"Management\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 13104283,\n",
       "     \"description\": \"People with roles in sales. Examples include: sales assistant, retail manager, realtor, consultant, customer service representative, cashier, etc.\",\n",
       "     \"id\": \"6008888980183\",\n",
       "     \"name\": \"Sales\",\n",
       "     \"path\": [\n",
       "         \"Sales\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 6100329,\n",
       "     \"description\": \"People with roles in IT and technical services. Examples include: IT technician, web developer, IT consultant, etc.\",\n",
       "     \"id\": \"6008888961983\",\n",
       "     \"name\": \"IT and Technical Services\",\n",
       "     \"path\": [\n",
       "         \"IT and Technical Services\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 982953,\n",
       "     \"description\": \"People with roles in legal services. Examples include: lawyer, corporate counsel, partner, paralegal, etc.\",\n",
       "     \"id\": \"6008888972183\",\n",
       "     \"name\": \"Legal Services\",\n",
       "     \"path\": [\n",
       "         \"Legal Services\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 2157330,\n",
       "     \"description\": \"People who have been engaged for less than 3 months\",\n",
       "     \"id\": \"6012631862383\",\n",
       "     \"name\": \"Newly engaged (3 months)\",\n",
       "     \"path\": [\n",
       "         \"Newly engaged (3 months)\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 9596135,\n",
       "     \"description\": \"People with roles in arts, entertainment, sports and media. Examples include: photographer, artist, actor, actress, singer, etc.\",\n",
       "     \"id\": \"6012901802383\",\n",
       "     \"name\": \"Arts, Entertainment, Sports and Media\",\n",
       "     \"path\": [\n",
       "         \"Arts, Entertainment, Sports and Media\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 10660490,\n",
       "     \"description\": \"People with roles in production. Examples include: miner, blacksmith, lumberjack, etc.\",\n",
       "     \"id\": \"6012903140583\",\n",
       "     \"name\": \"Production\",\n",
       "     \"path\": [\n",
       "         \"Production\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"industries\"\n",
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       " <TargetingSearch> {\n",
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       "     \"description\": \"People with roles in transportation and moving. Examples include: Driver, operator, sea captain, flight attendant, pilot, etc.\",\n",
       "     \"id\": \"6012903320983\",\n",
       "     \"name\": \"Transportation and Moving\",\n",
       "     \"path\": [\n",
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       " <TargetingSearch> {\n",
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       "     \"description\": \"People with roles in architecture and engineering. Examples include: software engineer, technician, electrician, machinist, etc.\",\n",
       "     \"id\": \"6012903126783\",\n",
       "     \"name\": \"Architecture and Engineering\",\n",
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       " <TargetingSearch> {\n",
       "     \"audience_size\": 3951665,\n",
       "     \"description\": \"People with roles in food and restaurants. Examples include: cashier, server, waiter, waitress, chef, barista, line cook, etc.\",\n",
       "     \"id\": \"6012903127583\",\n",
       "     \"name\": \"Food and Restaurants\",\n",
       "     \"path\": [\n",
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       " <TargetingSearch> {\n",
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       "     \"description\": \"People with roles in construction and extraction. Examples include: service technician, electrician, brick layer, mechanic, machine operator, etc.\",\n",
       "     \"id\": \"6012903128783\",\n",
       "     \"name\": \"Construction and Extraction\",\n",
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       " <TargetingSearch> {\n",
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       "     \"description\": \"People with roles in healthcare and medical services. Examples include: physician, dentist, cardiologist, etc.\",\n",
       "     \"id\": \"6012903159383\",\n",
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       "     \"path\": [\n",
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       " <TargetingSearch> {\n",
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       "     \"id\": \"6012903167183\",\n",
       "     \"name\": \"Life, Physical and Social Sciences\",\n",
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       " <TargetingSearch> {\n",
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       "     \"id\": \"6018399723983\",\n",
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       " <TargetingSearch> {\n",
       "     \"audience_size\": 1700059059,\n",
       "     \"description\": \"Friends of Women with a Birthday in 7-30 days\",\n",
       "     \"id\": \"6203621025983\",\n",
       "     \"name\": \"Friends of Women with a Birthday in 7-30 days\",\n",
       "     \"path\": [\n",
       "         \"Friends of\",\n",
       "         \"Friends of Women with a Birthday in 7-30 days\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 1802084361,\n",
       "     \"description\": \"Friends of Men with a Birthday in 7-30 days\",\n",
       "     \"id\": \"6203621119983\",\n",
       "     \"name\": \"Friends of Men with a Birthday in 7-30 days\",\n",
       "     \"path\": [\n",
       "         \"Friends of\",\n",
       "         \"Friends of Men with a Birthday in 7-30 days\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 1724396467,\n",
       "     \"description\": \"Friends of people with a birthday in 0-7 days\",\n",
       "     \"id\": \"6203621218383\",\n",
       "     \"name\": \"Friends of people with birthdays in a week\",\n",
       "     \"path\": [\n",
       "         \"Friends of\",\n",
       "         \"Friends of people with birthdays in a week\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 1378217607,\n",
       "     \"description\": \"Friends of Women with a Birthday in 0-7 days\",\n",
       "     \"id\": \"6203621325983\",\n",
       "     \"name\": \"Friends of Women with a Birthday in 0-7 days\",\n",
       "     \"path\": [\n",
       "         \"Friends of\",\n",
       "         \"Friends of Women with a Birthday in 0-7 days\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " },\n",
       " <TargetingSearch> {\n",
       "     \"audience_size\": 1502761571,\n",
       "     \"description\": \"Friends of Men with a Birthday in 0-7 days\",\n",
       "     \"id\": \"6203621472783\",\n",
       "     \"name\": \"Friends of Men with a Birthday in 0-7 days\",\n",
       "     \"path\": [\n",
       "         \"Friends of\",\n",
       "         \"Friends of Men with a Birthday in 0-7 days\"\n",
       "     ],\n",
       "     \"real_time_cluster\": false,\n",
       "     \"type\": \"life_events\"\n",
       " }]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from facebook_business.adobjects.targetingsearch import TargetingSearch\n",
    "\n",
    "with open('outs/cities.txt') as f:\n",
    "    cities = [l.strip() for l in f]\n",
    "\n",
    "city_locs = [(city, TargetingSearch.search({'type': 'adgeolocation', 'q': city, 'location_types': ['city'], 'country_code': 'IN'}))\n",
    "             for city in cities]\n",
    "city_cities = [l[0] for name, l in city_locs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "city_locs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "others = [{'key': '1724', 'name': 'Andhra Pradesh', 'country': 'IN'},\n",
    "          {'key': '1725', 'name': 'Assam', 'country': 'IN'},\n",
    "          {'key': '1729', 'name': 'Gujarat', 'country': 'IN'},\n",
    "          {'key': '1735', 'name': 'Maharashtra', 'country': 'IN'},\n",
    "          {'key': '1736', 'name': 'Manipur', 'country': 'IN'},\n",
    "          {'key': '1737', 'name': 'Meghalaya', 'country': 'IN'},\n",
    "          {'key': '1738', 'name': 'Karnataka', 'country': 'IN'},\n",
    "          {'key': '1739', 'name': 'Nagaland', 'country': 'IN'},\n",
    "          {'key': '1743', 'name': 'Rajasthan', 'country': 'IN'},\n",
    "          {'key': '1744', 'name': 'Tamil Nadu', 'country': 'IN'},\n",
    "          {'key': '1745', 'name': 'Tripura', 'country': 'IN'},\n",
    "          {'key': '1746', 'name': 'West Bengal', 'country': 'IN'},\n",
    "          {'key': '1747', 'name': 'Sikkim', 'country': 'IN'},\n",
    "          {'key': '1748', 'name': 'Arunachal Pradesh', 'country': 'IN'},\n",
    "          {'key': '1749', 'name': 'Mizoram', 'country': 'IN'},\n",
    "          {'key': '1752', 'name': 'Bihar', 'country': 'IN'},\n",
    "          {'key': '1753', 'name': 'Madhya Pradesh', 'country': 'IN'},\n",
    "          {'key': '1755', 'name': 'Chhattisgarh', 'country': 'IN'},\n",
    "          {'key': '1756', 'name': 'Jharkhand', 'country': 'IN'}]\n",
    "\n",
    "dats = {\n",
    "    'UP': {\n",
    "        'controls': controls[controls.state == 'Uttar Pradesh'],\n",
    "        'targets': [{\"country\": \"IN\", \"key\": \"1754\", \"name\": \"Uttar Pradesh\"}],\n",
    "        'excluded_cities': [c for c in city_cities if c['region'] == 'Uttar Pradesh' \n",
    "                            and c['name'] not in ['Meerut', 'Varanasi']], # are in the cities already\n",
    "        'target_key': TargetingGeoLocation.Field.regions,\n",
    "    },\n",
    "    'NOT-UP': {\n",
    "        'controls': controls[controls.state != 'Uttar Pradesh'],\n",
    "        'targets': others,\n",
    "        'excluded_cities': [c for c in city_cities if c['region'] != 'Uttar Pradesh'],\n",
    "        'target_key': TargetingGeoLocation.Field.regions,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from adopt.facebook.update import GraphUpdater\n",
    "\n",
    "instruction = make_template_adsets(env, \n",
    "                                   state, \n",
    "                                   'ie-rural-template-not-UP', \n",
    "                                   **dats['NOT-UP'])\n",
    "\n",
    "updater = GraphUpdater(state)\n",
    "report = updater.execute(instruction)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key': '1035921', 'radius': 25.0, 'distance_unit': 'kilometer'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    " # 'country',\n",
    " # 'distance_unit',\n",
    " # 'key',\n",
    " # 'name',\n",
    " # 'radius',\n",
    " # 'region',\n",
    " # 'region_id'\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "gdf = gpd.read_file('outs/clusters.shp').drop(columns=['treatment'])\n",
    "\n",
    "gdf = gdf.drop(columns=['st_areasha']).merge(districts[['distcode', 'treatment']], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gdf.to_file('outs/clusters.shp')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "python",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "name": "python3"
  },
  "name": "template-adsets.ipynb"
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 "nbformat": 4,
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