4_simulations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03ccfde8",
   "metadata": {},
   "source": [
    "# Simulations\n",
    "\n",
    "The following Jupiter notebook contains simulations which compare the actual rate of adoption of #BlackLivesMatter to models of simple contagion, complex contagion, and a null model that I developed for my research ( what we would've expected to happen in the absence of online social contagion )."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c12e3331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raycrist/opt/anaconda3/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.2-CAPI-1.16.0). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path to Utils: /Volumes/rc/lab/S/SIGIL/crist/utils\n"
     ]
    }
   ],
   "source": [
    "from sqlitedict import SqliteDict\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from pyarrow import feather\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import autograd.numpy as np\n",
    "from autograd import grad \n",
    "import itertools\n",
    "\n",
    "from utils.common import constants as c\n",
    "from utils.data_analysis.load_edges import load_edges_df, load_geo_edges_df\n",
    "from utils.data_analysis.load_users import load_users_dfs\n",
    "from utils.data_analysis.load_tweets import load_tweets_dfs\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c9e938",
   "metadata": {},
   "source": [
    "## Load in Required Data\n",
    "\n",
    "* `geo_users_w_info_gdf` contains the relevant informtion about users with geotags who adopted (our study sample)\n",
    "* `adopters_df` has the adoption_time of each user which we need, as well as the list of actual neighbor adopters.'\n",
    "* `news_df` has the number of news articles containing hashtag by day. We use this to estimate amount of outside influence per day.\n",
    "\n",
    "If any of the above data frames have not yet been loaded and saved as feathers, this will fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "366a509b",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {0:'black', 1:'violet', 2:'orange', 3:'lightseagreen', 4:'red', 5:'blue'}\n",
    "markers = {0:'X', 1:'s', 2:'D', 3:'v', 4:'^', 5:'<'}\n",
    "labels = {0:'Rur.White', 1:'Sub.Blacker', 2:'Sub.Richer', 3:'BigCities', 4:'SubRur.Poorer', 5:'MidCities'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "003bb766",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Articles</th>\n",
       "      <th>Days_Since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>348</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>559</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-30</td>\n",
       "      <td>626</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>1087</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2181</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>3180</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Articles  Days_Since\n",
       "0 2020-05-25        13           0\n",
       "1 2020-05-26        94           1\n",
       "2 2020-05-27       288           2\n",
       "3 2020-05-28       348           3\n",
       "4 2020-05-29       559           4\n",
       "5 2020-05-30       626           5\n",
       "6 2020-05-31      1087           6\n",
       "7 2020-06-01      2181           7\n",
       "8 2020-06-02      3180           8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_df = pd.read_csv(\"utils/data_analysis/datasets/NewsData.csv\")\n",
    "news_df[\"Date\"] = pd.to_datetime(news_df[\"Date\"])\n",
    "news_df[\"Days_Since\"] = news_df.index\n",
    "news_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33af6841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adopted_time         datetime64[ns, US/Eastern]\n",
       "exposures                               float64\n",
       "Nbr Adopters                             object\n",
       "Nbr Adopter Count                         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adopters_df = feather.read_feather(\"utils/data_analysis/feathers/adopters_df.ftr\")\n",
    "adopters_df = adopters_df.set_index(\"index\")   # feather doesn't support non-default indices, must be re-set on load.\n",
    "adopters_df['adopted_time'] = adopters_df['adopted_time'].dt.tz_convert('US/Eastern')\n",
    "adopters_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5c5c497",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean-mode dev.</th>\n",
       "      <th>Geo_FIPS</th>\n",
       "      <th>Geo_STATE</th>\n",
       "      <th>Total_Pop</th>\n",
       "      <th>Pop_Dens</th>\n",
       "      <th>Land_Area</th>\n",
       "      <th>WhitePop</th>\n",
       "      <th>BlackPop</th>\n",
       "      <th>OtherRacePop</th>\n",
       "      <th>WhitePopPerc</th>\n",
       "      <th>BlackPopPerc</th>\n",
       "      <th>OtherRacePopPerc</th>\n",
       "      <th>PctLatinoPop</th>\n",
       "      <th>WhitePerCap</th>\n",
       "      <th>BlackPerCap</th>\n",
       "      <th>LatinoPerCap</th>\n",
       "      <th>PerCap</th>\n",
       "      <th>PoliceKillings</th>\n",
       "      <th>PKPerSqMile</th>\n",
       "      <th>Blck_Wht_PerCap_Ratio</th>\n",
       "      <th>Following</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Pop_Dens_std</th>\n",
       "      <th>PKPerSqMile_std</th>\n",
       "      <th>WhitePopPerc_std</th>\n",
       "      <th>BlackPopPerc_std</th>\n",
       "      <th>WhitePerCap_std</th>\n",
       "      <th>BlackPerCap_std</th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>exposures</th>\n",
       "      <th>Nbr Adopter Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34194.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34300.000000</td>\n",
       "      <td>34050.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>3.437900e+04</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "      <td>34379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.579819e+03</td>\n",
       "      <td>2.598706e+09</td>\n",
       "      <td>25.902382</td>\n",
       "      <td>8.556566e+05</td>\n",
       "      <td>8036.880040</td>\n",
       "      <td>192.025560</td>\n",
       "      <td>4.641589e+05</td>\n",
       "      <td>156743.689229</td>\n",
       "      <td>2.347540e+05</td>\n",
       "      <td>61.736517</td>\n",
       "      <td>18.027244</td>\n",
       "      <td>20.236247</td>\n",
       "      <td>23.307587</td>\n",
       "      <td>45324.986096</td>\n",
       "      <td>26451.629134</td>\n",
       "      <td>22605.882348</td>\n",
       "      <td>37659.224439</td>\n",
       "      <td>25.720033</td>\n",
       "      <td>0.160133</td>\n",
       "      <td>0.630862</td>\n",
       "      <td>1196.417231</td>\n",
       "      <td>3.282625e+03</td>\n",
       "      <td>1.658021e+04</td>\n",
       "      <td>0.680125</td>\n",
       "      <td>0.770593</td>\n",
       "      <td>-0.538963</td>\n",
       "      <td>0.341629</td>\n",
       "      <td>0.542345</td>\n",
       "      <td>0.036051</td>\n",
       "      <td>2.859624</td>\n",
       "      <td>288.139009</td>\n",
       "      <td>99.726199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.955855e+03</td>\n",
       "      <td>1.652377e+09</td>\n",
       "      <td>16.489786</td>\n",
       "      <td>9.993318e+05</td>\n",
       "      <td>13604.529302</td>\n",
       "      <td>466.710048</td>\n",
       "      <td>5.485587e+05</td>\n",
       "      <td>221619.030296</td>\n",
       "      <td>3.245024e+05</td>\n",
       "      <td>18.226162</td>\n",
       "      <td>16.546325</td>\n",
       "      <td>13.338321</td>\n",
       "      <td>18.519561</td>\n",
       "      <td>18702.669909</td>\n",
       "      <td>9201.380373</td>\n",
       "      <td>8103.623555</td>\n",
       "      <td>13530.812948</td>\n",
       "      <td>36.069957</td>\n",
       "      <td>0.161441</td>\n",
       "      <td>0.235241</td>\n",
       "      <td>4387.541000</td>\n",
       "      <td>5.706542e+04</td>\n",
       "      <td>3.645231e+04</td>\n",
       "      <td>1.949278</td>\n",
       "      <td>1.363909</td>\n",
       "      <td>0.919040</td>\n",
       "      <td>1.058868</td>\n",
       "      <td>1.318129</td>\n",
       "      <td>0.796388</td>\n",
       "      <td>1.589941</td>\n",
       "      <td>1234.590579</td>\n",
       "      <td>338.309230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.001926e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>0.434984</td>\n",
       "      <td>0.032625</td>\n",
       "      <td>1.630000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4226.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>4725.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.470243</td>\n",
       "      <td>-0.579824</td>\n",
       "      <td>-3.582392</td>\n",
       "      <td>-0.812009</td>\n",
       "      <td>-2.354235</td>\n",
       "      <td>-2.211264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.001336e+08</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.099300e+04</td>\n",
       "      <td>1933.357000</td>\n",
       "      <td>36.339705</td>\n",
       "      <td>5.553000e+04</td>\n",
       "      <td>6863.500000</td>\n",
       "      <td>9.244500e+03</td>\n",
       "      <td>47.780000</td>\n",
       "      <td>5.780000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>33715.000000</td>\n",
       "      <td>20324.000000</td>\n",
       "      <td>18142.000000</td>\n",
       "      <td>29147.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023944</td>\n",
       "      <td>0.498853</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.900000e+02</td>\n",
       "      <td>2.039000e+03</td>\n",
       "      <td>-0.193044</td>\n",
       "      <td>-0.380999</td>\n",
       "      <td>-1.242709</td>\n",
       "      <td>-0.442123</td>\n",
       "      <td>-0.275905</td>\n",
       "      <td>-0.490059</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.328306e-10</td>\n",
       "      <td>2.605938e+09</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>3.957770e+05</td>\n",
       "      <td>3570.421000</td>\n",
       "      <td>87.658840</td>\n",
       "      <td>2.094240e+05</td>\n",
       "      <td>45888.000000</td>\n",
       "      <td>6.624500e+04</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>11.880000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>41883.000000</td>\n",
       "      <td>25557.000000</td>\n",
       "      <td>20597.000000</td>\n",
       "      <td>35563.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.107292</td>\n",
       "      <td>0.611203</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>6.379000e+03</td>\n",
       "      <td>0.041726</td>\n",
       "      <td>0.326842</td>\n",
       "      <td>-0.611399</td>\n",
       "      <td>-0.051758</td>\n",
       "      <td>0.299761</td>\n",
       "      <td>-0.035321</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.275834e+02</td>\n",
       "      <td>3.906102e+09</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.579075e+06</td>\n",
       "      <td>9067.710000</td>\n",
       "      <td>235.938329</td>\n",
       "      <td>7.563910e+05</td>\n",
       "      <td>240993.000000</td>\n",
       "      <td>4.233490e+05</td>\n",
       "      <td>76.380000</td>\n",
       "      <td>28.280000</td>\n",
       "      <td>28.450000</td>\n",
       "      <td>33.035000</td>\n",
       "      <td>49305.000000</td>\n",
       "      <td>30095.000000</td>\n",
       "      <td>26033.000000</td>\n",
       "      <td>41569.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.272115</td>\n",
       "      <td>0.726091</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1.169000e+03</td>\n",
       "      <td>1.745500e+04</td>\n",
       "      <td>0.830088</td>\n",
       "      <td>1.719667</td>\n",
       "      <td>0.199423</td>\n",
       "      <td>0.997745</td>\n",
       "      <td>0.822849</td>\n",
       "      <td>0.353585</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.993679e+04</td>\n",
       "      <td>7.214985e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3.433961e+06</td>\n",
       "      <td>71488.690000</td>\n",
       "      <td>24607.898341</td>\n",
       "      <td>2.556502e+06</td>\n",
       "      <td>833683.000000</td>\n",
       "      <td>1.050342e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.340000</td>\n",
       "      <td>98.430000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>156737.000000</td>\n",
       "      <td>192355.000000</td>\n",
       "      <td>238039.000000</td>\n",
       "      <td>164307.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>2.579073</td>\n",
       "      <td>5.684942</td>\n",
       "      <td>362676.000000</td>\n",
       "      <td>6.848484e+06</td>\n",
       "      <td>2.355436e+06</td>\n",
       "      <td>9.781831</td>\n",
       "      <td>21.214474</td>\n",
       "      <td>1.390442</td>\n",
       "      <td>5.225198</td>\n",
       "      <td>8.394457</td>\n",
       "      <td>14.464471</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75224.000000</td>\n",
       "      <td>24656.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean-mode dev.      Geo_FIPS     Geo_STATE     Total_Pop      Pop_Dens     Land_Area      WhitePop       BlackPop  OtherRacePop  WhitePopPerc  BlackPopPerc  OtherRacePopPerc  PctLatinoPop    WhitePerCap    BlackPerCap   LatinoPerCap         PerCap  PoliceKillings   PKPerSqMile  Blck_Wht_PerCap_Ratio      Following     Followers        Tweets  Pop_Dens_std  PKPerSqMile_std  WhitePopPerc_std  BlackPopPerc_std  WhitePerCap_std  BlackPerCap_std  cluster_label     exposures  Nbr Adopter Count\n",
       "count    3.437900e+04  3.437900e+04  34379.000000  3.437900e+04  34300.000000  34300.000000  3.437900e+04   34379.000000  3.437900e+04  34379.000000  34379.000000      34379.000000  34379.000000   34379.000000   34050.000000   34194.000000   34379.000000    34379.000000  34300.000000           34050.000000   34379.000000  3.437900e+04  3.437900e+04  34379.000000     34379.000000      34379.000000      34379.000000     34379.000000     34379.000000   34379.000000  34379.000000       34379.000000\n",
       "mean     1.579819e+03  2.598706e+09     25.902382  8.556566e+05   8036.880040    192.025560  4.641589e+05  156743.689229  2.347540e+05     61.736517     18.027244         20.236247     23.307587   45324.986096   26451.629134   22605.882348   37659.224439       25.720033      0.160133               0.630862    1196.417231  3.282625e+03  1.658021e+04      0.680125         0.770593         -0.538963          0.341629         0.542345         0.036051       2.859624    288.139009          99.726199\n",
       "std      3.955855e+03  1.652377e+09     16.489786  9.993318e+05  13604.529302    466.710048  5.485587e+05  221619.030296  3.245024e+05     18.226162     16.546325         13.338321     18.519561   18702.669909    9201.380373    8103.623555   13530.812948       36.069957      0.161441               0.235241    4387.541000  5.706542e+04  3.645231e+04      1.949278         1.363909          0.919040          1.058868         1.318129         0.796388       1.589941   1234.590579         338.309230\n",
       "min      0.000000e+00  1.001926e+08      1.000000  1.660000e+02      0.434984      0.032625  1.630000e+02       0.000000  0.000000e+00      1.380000      0.000000          0.000000      0.000000    4226.000000     602.000000    1611.000000    4725.000000        0.000000      0.000000               0.020028       0.000000  0.000000e+00  1.000000e+00     -0.470243        -0.579824         -3.582392         -0.812009        -2.354235        -2.211264       0.000000      0.000000          -1.000000\n",
       "25%      0.000000e+00  9.001336e+08      9.000000  8.099300e+04   1933.357000     36.339705  5.553000e+04    6863.500000  9.244500e+03     47.780000      5.780000          9.550000      8.210000   33715.000000   20324.000000   18142.000000   29147.000000        1.000000      0.023944               0.498853     290.000000  1.900000e+02  2.039000e+03     -0.193044        -0.380999         -1.242709         -0.442123        -0.275905        -0.490059       2.000000     29.000000          15.000000\n",
       "50%      2.328306e-10  2.605938e+09     26.000000  3.957770e+05   3570.421000     87.658840  2.094240e+05   45888.000000  6.624500e+04     60.300000     11.880000         17.200000     18.750000   41883.000000   25557.000000   20597.000000   35563.000000       10.000000      0.107292               0.611203     597.000000  4.600000e+02  6.379000e+03      0.041726         0.326842         -0.611399         -0.051758         0.299761        -0.035321       3.000000     90.000000          41.000000\n",
       "75%      9.275834e+02  3.906102e+09     39.000000  1.579075e+06   9067.710000    235.938329  7.563910e+05  240993.000000  4.233490e+05     76.380000     28.280000         28.450000     33.035000   49305.000000   30095.000000   26033.000000   41569.000000       34.000000      0.272115               0.726091    1227.000000  1.169000e+03  1.745500e+04      0.830088         1.719667          0.199423          0.997745         0.822849         0.353585       4.000000    247.000000         100.000000\n",
       "max      2.993679e+04  7.214985e+09     72.000000  3.433961e+06  71488.690000  24607.898341  2.556502e+06  833683.000000  1.050342e+06    100.000000     94.340000         98.430000    100.000000  156737.000000  192355.000000  238039.000000  164307.000000      204.000000      2.579073               5.684942  362676.000000  6.848484e+06  2.355436e+06      9.781831        21.214474          1.390442          5.225198         8.394457        14.464471       5.000000  75224.000000       24656.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_users_w_info_gdf = gpd.read_feather(\"utils/data_analysis/feathers/geo_users_w_info_gdf.ftr\")\n",
    "geo_users_w_info_gdf = geo_users_w_info_gdf.merge(\n",
    "    adopters_df, how=\"left\", left_on=\"U_ID\", right_index=True\n",
    ")\n",
    "geo_users_w_info_gdf.fillna({\"Nbr Adopter Count\": 0, \"exposures\": 0}, inplace=True)\n",
    "geo_users_w_info_gdf.dtypes\n",
    "geo_users_w_info_gdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba82983a",
   "metadata": {},
   "source": [
    "## Plot the growth over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05db6152",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f94bbaaee50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(1,1, figsize=(16,9))\n",
    "\n",
    "clusters = geo_users_w_info_gdf.groupby(\"cluster_label\")\n",
    "\n",
    "for label, cluster in clusters:\n",
    "    \n",
    "    day_counts = cluster.groupby(\n",
    "        pd.Grouper(key=\"adopted_time\", freq='24h')\n",
    "    ).agg({\n",
    "        \"U_ID\": \"count\"\n",
    "    })\n",
    "    day_counts.reset_index(inplace=True)\n",
    "    ax.plot(\n",
    "        day_counts.index,\n",
    "        day_counts[\"U_ID\"],\n",
    "        color = colors[label],\n",
    "        label = labels[label],\n",
    "        marker = markers[label]\n",
    "    )\n",
    "    \n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(\n",
    "    news_df.index, \n",
    "    news_df[\"Articles\"], \n",
    "    color=\"grey\", \n",
    "    linestyle=\"dashed\",\n",
    "    label=\"News Articles\",\n",
    "    marker=\"o\"\n",
    ")\n",
    "\n",
    "ax.legend()\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd4d55c4",
   "metadata": {},
   "source": [
    "## Generate Activity Dataframe\n",
    "\n",
    "For users whose activity-rate is downloaded, there are 720 periods -- 30 days (default) at 1 hour granularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59873c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_logins(activity_rt):\n",
    "    '''\n",
    "    Each period in which twt.count > 0 represents a log-in.\n",
    "    '''\n",
    "    return np.count_nonzero(activity_rt)\n",
    "\n",
    "TOTAL_TIME_STEPS = 720 # 30 days at 1 hr granularity (period in which I checked if a user had tweeted w/in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b01379c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_activites_df():\n",
    "    \n",
    "    try:\n",
    "        activity_df = feather.read_feather(\"utils/data_analysis/feathers/activity_df.ftr\")\n",
    "    except Exception as e:\n",
    "        print(\"Couldn't find feathered version of activites_df. Generating now.\")\n",
    "        \n",
    "        dSeries = []\n",
    "        count = 0\n",
    "\n",
    "        with SqliteDict(c.USERS_SQL) as u_dict:\n",
    "\n",
    "            print(u_dict)\n",
    "            for _, user in u_dict.items():\n",
    "                if hasattr(user, \"activity_rate\") and len(user.geos) > 0:\n",
    "                    dSeries.append((user.user_id, user.activity_rate))\n",
    "\n",
    "            # Sometimes SQLiteDict gets stuck.\n",
    "            u_dict.close(force=True)\n",
    "            \n",
    "        activity_df = pd.DataFrame.from_dict(dSeries)\n",
    "        activity_df.columns =[c.U_ID, \"Activity Rate\"]\n",
    "        activity_df[\"Activity Rate\"] = activity_df[\"Activity Rate\"].apply(get_logins)\n",
    "        activity_df[\"Activity Rate\"] = activity_df[\"Activity Rate\"].div(TOTAL_TIME_STEPS)\n",
    "\n",
    "        activity_df.to_feather(\"utils/data_analysis/activity_df.ftr\")\n",
    "        \n",
    "    return activity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "996c90f6",
   "metadata": {
    "scrolled": true
   },
   "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>Activity Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70383.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.103414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.117661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.020833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.144444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.998611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Activity Rate\n",
       "count   70383.000000\n",
       "mean        0.103414\n",
       "std         0.117661\n",
       "min         0.000000\n",
       "25%         0.020833\n",
       "50%         0.058333\n",
       "75%         0.144444\n",
       "max         0.998611"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_df = load_activites_df()\n",
    "activity_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa8c493",
   "metadata": {},
   "source": [
    "#### View Activity Rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cef5dbcf",
   "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>Activity Rate</th>\n",
       "      <th>U_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>522.000000</td>\n",
       "      <td>522.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.366837</td>\n",
       "      <td>134.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.219958</td>\n",
       "      <td>245.150917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.180903</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.361806</td>\n",
       "      <td>39.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.542708</td>\n",
       "      <td>133.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.998611</td>\n",
       "      <td>2406.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Activity Rate         U_ID\n",
       "count     522.000000   522.000000\n",
       "mean        0.366837   134.833333\n",
       "std         0.219958   245.150917\n",
       "min         0.000000     1.000000\n",
       "25%         0.180903     9.000000\n",
       "50%         0.361806    39.500000\n",
       "75%         0.542708   133.750000\n",
       "max         0.998611  2406.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD6CAYAAABNu5eFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeAElEQVR4nO3de3RddZ338fc396RJL2mS3tuUUgqUgZZmCnLRIj62omPRQS06gi7GOoqKs/RRYMaRUViDzqMijOBC5QEdtMMjMK0IDogoIJeSQqE3C7HXNLFJm7a5Nffv88fZSU/PSZvTXM5J9vm81srKPr+zd873R8unv/z23r9t7o6IiKSHjFQXICIiyaPQFxFJIwp9EZE0otAXEUkjCn0RkTSi0BcRSSMDhr6ZzTKzZ8xsm5ltMbMbgvZbzGyfmW0Mvq6IOuYmM6sys+1mtjyqfYmZbQreu9PMbGS6JSIi/bGBrtM3s2nANHd/1cyKgA3AlcCHgWZ3/z8x+58N/AJYCkwHfguc4e7dZrYeuAF4CXgcuNPdnzjZ55eUlHh5efkguiYikr42bNhwwN1LY9uzBjrQ3WuB2mC7ycy2ATNOcshKYI27twM7zawKWGpmu4Dx7v4igJn9lMg/HicN/fLyciorKwcqU0REopjZ7v7aT2lO38zKgcXAy0HT58zsDTO7z8wmBW0zgL1Rh1UHbTOC7dh2ERFJkoRD38wKgYeBL7p7I3APMA9YROQ3ge/07trP4X6S9v4+a7WZVZpZZX19faIliojIABIKfTPLJhL4D7r7IwDuvt/du929B/gRkTl8iIzgZ0UdPhOoCdpn9tMex93vdfcKd68oLY2bkhIRkUFK5OodA34CbHP370a1T4va7QPA5mB7HbDKzHLNbC4wH1gfnBtoMrMLg595DbB2mPohIiIJGPBELnAx8HFgk5ltDNpuBq42s0VEpmh2AZ8GcPctZvYQsBXoAq539+7guM8A9wP5RE7gnvQkroiIDK8BL9lMtYqKCtfVOyIip8bMNrh7RWy77sgVEUkjoQ39+/+4k1+93u95YhGRtBXa0P/Pl/fwxObaVJchIjKqhDb0DRjlpytERJIuvKFvCn0RkVjhDX0M7/+GXxGRtBXe0NdIX0QkTmhDH06wsI+ISBoLbeibmUb6IiIxwhv6gMb6IiLHC2/oa05fRCROuEM/1UWIiIwy4Q19jNG+mJyISLKFN/Q10hcRiRPe0Edz+iIisUIb+phppC8iEiO0oR8Z6Sv2RUSihTf0LdUViIiMPuENfTSnLyISK7yhb1plU0QkVnhDH430RURihTf0tQyDiEic8Ia+HqIiIhIntKGPRvoiInFCG/qGlmEQEYkV3tBX6ouIxAlv6GtOX0QkTnhDX3P6IiJxwh36qS5CRGSUCW/o6yEqIiJxwhv6GumLiMQJbeiD5vRFRGINGPpmNsvMnjGzbWa2xcxuCNqLzewpM3sr+D4p6pibzKzKzLab2fKo9iVmtil4706zkVsA2fQQFRGROImM9LuAL7n7WcCFwPVmdjZwI/C0u88Hng5eE7y3ClgIrADuNrPM4GfdA6wG5gdfK4axL8cx0FBfRCTGgKHv7rXu/mqw3QRsA2YAK4EHgt0eAK4MtlcCa9y93d13AlXAUjObBox39xc9cob1p1HHDDvN6YuIxDulOX0zKwcWAy8DU9y9FiL/MABlwW4zgL1Rh1UHbTOC7dj2EaGllUVE4iUc+mZWCDwMfNHdG0+2az9tfpL2/j5rtZlVmlllfX19oiXG/gzdkSsiEiOh0DezbCKB/6C7PxI07w+mbAi+1wXt1cCsqMNnAjVB+8x+2uO4+73uXuHuFaWlpYn25fia0UhfRCRWIlfvGPATYJu7fzfqrXXAtcH2tcDaqPZVZpZrZnOJnLBdH0wBNZnZhcHPvCbqmGGnZRhEROJlJbDPxcDHgU1mtjFouxm4HXjIzK4D9gAfAnD3LWb2ELCVyJU/17t7d3DcZ4D7gXzgieBrhOiSTRGRWAOGvrs/T//z8QCXn+CY24Db+mmvBM45lQIHKzLSV+yLiEQL7R25I3bXl4jIGBbe0NecvohInPCGvh6iIiISJ7yhr5G+iEiccId+qosQERllwhv6eoiKiEic0IY+GumLiMQJbehHllZOdRUiIqNLeENfD1EREYkT3tBHd+SKiMQKb+hrTl9EJE54Qx9dpy8iEiu8oa+HqIiIxAlv6KORvohIrNCGPlqGQUQkTmhD37S4sohInPCGvh6iIiISJ7yhjy7ZFBGJFd7Q15y+iEic8Ia+HqIiIhInvKGvkb6ISJxwh36qixARGWVCG/pgGumLiMQIbeibFtQXEYkT3tBHc/oiIrHCG/qa0xcRiRPe0NeD0UVE4oQ39DXSFxGJE97QR3P6IiKxwhv6pukdEZFYoQ190PSOiEis0Ia+aZlNEZE4A4a+md1nZnVmtjmq7RYz22dmG4OvK6Leu8nMqsxsu5ktj2pfYmabgvfuNLMRfcpJZME1ERGJlshI/35gRT/t33P3RcHX4wBmdjawClgYHHO3mWUG+98DrAbmB1/9/cxho4eoiIjEGzD03f1ZoCHBn7cSWOPu7e6+E6gClprZNGC8u7/okST+KXDlIGtOiGZ3RETiDWVO/3Nm9kYw/TMpaJsB7I3apzpomxFsx7aPGC2tLCISb7Chfw8wD1gE1ALfCdr7m6f3k7T3y8xWm1mlmVXW19cPqkAzPURFRCTWoELf3fe7e7e79wA/ApYGb1UDs6J2nQnUBO0z+2k/0c+/190r3L2itLR0MCXq5iwRkX4MKvSDOfpeHwB6r+xZB6wys1wzm0vkhO16d68FmszswuCqnWuAtUOoO4EiNacvIhIra6AdzOwXwDKgxMyqga8Dy8xsEZFc3QV8GsDdt5jZQ8BWoAu43t27gx/1GSJXAuUDTwRfI8aU+iIicQYMfXe/up/mn5xk/9uA2/pprwTOOaXqhiCy4JpSX0QkWmjvyM3Q1TsiInFCG/qG0aPUFxE5TnhDX1P6IiJxwhv6aHpHRCRWaEOfkV3PTURkTApt6PdGvhZdExE5JryhH6S+Ml9E5Jjwhn4w1lfmi4gcE97Q7xvpK/ZFRHqFN/SD74p8EZFjwhv6mtMXEYkT4tDvndNX6ouI9Apt6PfSSF9E5JjQhr7uzRIRiRfe0O+9ZFMjfRGRPuEN/d4TuZrTFxHpE97QD75rpC8ickx4Q79vpC8iIr3CG/p9c/qKfRGRXuEN/WCk/1e3PMmqe19MbTEiIqNEaEO/s/vYCP+lHQ0prEREZPQIbei3tHelugQRkVEnvKHfcSz0JxZkp7ASEZHRI7yhH4z0JxVk09reneJqRERGh/CGfkck6KeMz6Oju4eOrp4UVyQiknqhDf3WYKRfWpQLaI5fRARCHPotwZROWVEeAF9Y81oqyxERGRVCG/r5OZnAsbV3nnvrgG7UEpG0F9rQ/86Hz+MbKxey+u2n9bXd98ddqStIRGQUCG3olxTmcs3byjlz6niuWjITgLufqUpxVSIiqRXa0I928xVnMSE/Ww9WEZG0lxahXzwuh09cVM7Blg46u3XppoikrwFD38zuM7M6M9sc1VZsZk+Z2VvB90lR791kZlVmtt3Mlke1LzGzTcF7d5old9xdWpSLOzS0dCTzY0VERpVERvr3Ayti2m4Ennb3+cDTwWvM7GxgFbAwOOZuM8sMjrkHWA3MD75if+aI6r1ev76pPZkfKyIyqgwY+u7+LBC7TOVK4IFg+wHgyqj2Ne7e7u47gSpgqZlNA8a7+4seuW7yp1HHJEVv6Nc1tSXzY0VERpXBzulPcfdagOB7WdA+A9gbtV910DYj2I5tT5righwADrV0JvNjRURGleE+kdvfPL2fpL3/H2K22swqzayyvr5+WAqbkB9ZafPIUYW+iKSvwYb+/mDKhuB7XdBeDcyK2m8mUBO0z+ynvV/ufq+7V7h7RWlp6SBLPN54hb6IyKBDfx1wbbB9LbA2qn2VmeWa2VwiJ2zXB1NATWZ2YXDVzjVRxyRFZoZRlJtFY5tCX0TSV9ZAO5jZL4BlQImZVQNfB24HHjKz64A9wIcA3H2LmT0EbAW6gOvdvXcx+88QuRIoH3gi+Eqq8fnZGumLSFobMPTd/eoTvHX5Cfa/Dbitn/ZK4JxTqm6YTcjP5vFNtXznQ+eR5NsERERGhbS4I7dXbnYGbZ09bN/flOpSRERSIq1C/6b3nAVAXaNu0BKR9JRWod97g9bBFoW+iKSntAr9yYWRG7QONmv9HRFJT2kV+kW5WeRkZrC/UUsxiEh6SqvQNzMWTC3i0df20d2jRyeKSPpJq9AH+HDFTA40d2i1TRFJS2kX+jMm5QPwuz/V6UHpIpJ20i70p02IhP7Nj25i3esnXP5HRCSU0i70p0/M79uuPnQ0hZWIiCRf2oV+7xLLANmZWopBRNJL2oU+wHNfuQyABj1QRUTSTFqG/qziAsqKcjmkh6SLSJpJy9AHKB6Xo+UYRCTtpG3ozyou4Lfb6li7cV+qSxERSZq0Df3MYD39G9ZsTG0hIiJJlLahf+Xi6akuQUQk6dI29FecM43/vXwBAK0dXSmuRkQkOdI29CFqfX0ttSwiaSKtQ78kWF9/9c820Nima/ZFJPzSPPQjI/1ttY18+aHXU1yNiMjIS+vQLyvK69t+cut+9ja0prAaEZGRl9ahP3VCHrdeeU7f672HFPoiEm5pHfoAf3fhnL7t6gatuiki4Zb2oQ+w/p8uB2CPpndEJOQU+kTm9mcXF7BboS8iIafQD2RmGL96vYYfPFOV6lJEREaMQj+Ql50JwL//z/YUVyIiMnIU+oEf/t35LC0vBuBAs5ZcFpFwUugH5kwex/XvPB2Ailt/S1tnd4orEhEZfgr9KKXBHboAVXXNKaxERGRkKPSjlBTl9G3/uV6hLyLho9CPUlxwLPS/+dg2Ht5QncJqRESG35BC38x2mdkmM9toZpVBW7GZPWVmbwXfJ0Xtf5OZVZnZdjNbPtTih1tW5rH/HAea2/nS/9MibCISLsMx0r/M3Re5e0Xw+kbgaXefDzwdvMbMzgZWAQuBFcDdZpY5DJ8/rK67ZC5Txh+b29+874hO6opIaIzE9M5K4IFg+wHgyqj2Ne7e7u47gSpg6Qh8/pB87X1n8/xX39m31v777nqeL2vELyIhMdTQd+BJM9tgZquDtinuXgsQfC8L2mcAe6OOrQ7a4pjZajOrNLPK+vr6IZZ46rIzM/jC5fP7Xv9+e/JrEBEZCVlDPP5id68xszLgKTP700n2tX7avL8d3f1e4F6AioqKfvcZaWVFx6Z49AxdEQmLIY303b0m+F4HPEpkuma/mU0DCL7XBbtXA7OiDp8J1Azl80dSaVTo9zis3bgP95T8+yMiMmwGHfpmNs7Minq3gXcDm4F1wLXBbtcCa4PtdcAqM8s1s7nAfGD9YD9/pEU/VQvghjUb+dNfmlJUjYjI8BjK9M4U4FEz6/05P3f335jZK8BDZnYdsAf4EIC7bzGzh4CtQBdwvbuP2stiokf6vVraNc0jImPboEPf3XcA5/XTfhC4/ATH3AbcNtjPTKbeVTcBFs+eyGt7DnO4tTOFFYmIDJ3uyD2J769axNrrL+aOjywC4PDRTrp7nA/c/UfWbtyX2uJERAZhqFfvhNrKRZErSo8EI/zDrR08+PJuXttzmNf3bux7X0RkrFDoJ6AoL/Kf6dZfb+trKymMn/MXERntNL2TgIyM+FsM6praeXnHwRRUIyIyeAr9BH1m2by4tuseqExBJSIig6fQT9BXli847vXFp0+mub2Lzu6eFFUkInLqFPoJMjOe/+plfH/VIp77ymV8cPFMAN79vWf5w5tam0dExgaF/imYOamAlYtmMKu4gMWzJwKw80ALn/i/63n0tWpW3PEsXRr5i8gopqt3Bum00sK+bXf4x/+KLL9cc7iN2ZMLUlWWiMhJaaQ/BLlZ8f/5dje00NOjhdlEZHRS6A/BpfNL+razMyOXde480MJpNz9O+Y2/5m/uej5VpYmI9MtG+3LBFRUVXlk5Oi+NbG7vonJXAxXlxeRnZzLv5sfj9vneR87jA8FJXxGRZDGzDVGPse2jkf4QFOZmsWxBGYW5WWT2cwMXROb69YxdERktFPpJcObXfsPPXtqd6jJERBT6w+nuj51/wve+vnYzj2+qTWI1IiLxFPrD6Iq/msau29/Lv75/IQDfvurcvvd6HD774Kt841dbuef3f+bFP2vdHhFJPp3IHQHuTkd3D7lZmbzvrufYvK+x3/02/PO7mKzVOkVkBOhEbhKZGblZkSdv/fxTF/LEDZdy5tSiuP0u/fYzPPjybtq7uqlrauNwa0eySxWRNKORfhK9vOMgudmZ7Glo5b9e2cMfqyJTPFctmckvN1RTUphL5T+/K8VVikgYaKQ/Clxw2mQWzZrI+8+bzoN/fyEfrohcv//LDdUAHGhup7FNz+EVkZGjtXdS6NtXnccTm/9CU1tXX9u5tzzJ1UtnU9/URklhLh3dPWzed4THPn8pOf0s+yAicioU+in2Hx89nzXr9/Ctq87l3FueBOAX6/fE7VdV10x9czsPvbKXO1YtIjtT/wCIyKlT6KfYO84o5R1nlALw809dwGNv1PKr12t499lTefjVavKyM2jr7GFrbSP/um4LTe1dPF91gGsvKuc950zlrGnjU9wDERlLdCJ3lOvq7uGi239HXVN73HvTJuRx19WLqSgvBqCts5s16/dw2ZllzJk8LtmlisgocqITuQr9MeDVPYf4we+qOGNqEYdaOljzyt7j3v/EReXc/8Kuvtfzywp5+LMX8aNnd7D7YCs3vudMpk/MByL/MORmZWDW/1pBIhIOCv0QcXc27TvC/S/s4pFX9w24/0XzJnP7ByN3B7/935/h5ivOZNGsSWRmGItnTSQjarG4jq4eGts6KdFNYyJjmkI/pDbsPsTf3vMCAKeVjmNHfcuAx5wxpZA39zf3vb77Y+fzjjNK2bTvCKvufQmA7beu6LvBTETGHoV+iDW0dFB75CgLp0+g+lAr19y3nh31LfzTFWextbaRR18b+LeB/vS3TERDSwdbao5w6fzSvrZdB1ooL9E5BJHR5EShr6t3QqB4XA7F43KAyMPbf/elZX3vbattpKGlg7s+uph9h46Sk5XB1/57Mxt2H+LqpbOPOxcQa8mtv+XqpbN4vuoAU4ryuOX9C3lf8DSw33zxUs6cOp6nt+3nugcqee+50/jK8gV9J5Cr6pqZVzruhOcO2jq76XGnIEd/BUWSSSN9ob2rm4PNHbyyq4EHX9rD4jkTeWLTX9jT0HrS4y6YW8zLOxuOa7vjI4v45YZqnq86wOLZExmfl83fLpnJ0vJifrO5lpaObq6/7HTe9d0/sOtAC898eRmzio89SN7dqW9up6wob0T6KpIuNL0jp6yhpYOdB1r42Yu7eHpbHQumFnH+nEms21jDXxrb+vabVJDNodbEl4/4xsqF/MvaLX2vP3FROYW5WZw1bTz7G9v4xmNb+dl1S7nk9BJ6HDbtO8L0CXmUjY//h6Cts5uaw0c5rbRwaJ0VCRmFvgyrN/c3Ud/UzqJZExmXm8XehlYu/fYzTB2fx3vPnca80kJ+8EwV+w4fHfRnZGYYWRlGe1dPX9vlZ5ZxxtQi3qg+TEt7N7VHjrK/sZ15peO46+rzae3o4rfb6vjUpXMZl5tFzeGjbNx7mIvmlWAGU8bn0dPjHD7ayc4DLSyZMwl31yWsEjqjJvTNbAXwfSAT+LG7336y/RX6Y0dLexfjco/N0feG6V+OtPGfL+3mkxeXc+uvt3HJ6SVMLMjmlV2H+HDFTDbuPczkwly++dhWquqaueT0ErbWNjJ9Yh5zJo/j4nkl/OT5Hfx5gCuTZkzMp7Gt87i1jGKVTy6g5nAbHd2Rf0hyMjOYMiGXb33wXA60dPD63sPsb2xja00jH3/bHIrH5XDm1PEU5WVx/wu7mDO5gPedO5287AwONHeQnWHH/Qbi7uw7fJQZE/N5Zdch5pcVUpSXRZaWzZAkGxWhb2aZwJvA/wKqgVeAq91964mOUeinj96/i/2Nuusa2/j1plqWL5zK09v2M6+skFmTCnhy637cnfH52XzvqTc5vayQvzlvOr/fXsehlk6mTcjjkZNcvZSfncnRYXhwfVFeFk1tXcyclE/1ofjfbopys/j0O06jKC+bCfnZPPxqNV3dzrSJeVxyegmZGcbB5g4mjcum9kgb5ZPHUXP4KG/ub6KkMJe/nlvM+LxsCnIyycww3OHI0U4Ot3aQYUZBTibPvnWA5QunsGjWRJ6vOsCCKUXUNbWzp6GVC+YWMz4/+7g1mzq7e2jv6qEw9+Qn0492dJOTlUGGHfuzcXdaOroZl5Op35JGqdES+m8DbnH35cHrmwDc/d9OdIxCX4ZqR30zUyfksan6CIdaO1i2oIzcrAz2N7YzsSCbLTWNPPdWPdmZGZw7cwLbahtZtqCMTdVH2LDnEOs21rBsQSkTC7J59NV9tHR088HzZzC7uIBXdjX0PRcBYHxeFo1Rv2lcMLeYrTWNNLWf+LeP4WYGJ/rfevqEPJrbu2hs6yIrw+h2Z/K4HDLMyDAjM8PIyCCybZGptd4pusLcLCbkZ5OTlcH+xjZaO7qZkJ9NWZFu5Bspj33hkkHfLzNaQv8qYIW7/33w+uPABe7+uZj9VgOrAWbPnr1k9+7dSatRJFZXd0/f9ExPj3O0s7tvGqunx9nT0MqcyQV09TjZmRl0dPX0TXXlZGXQ1d3DroMtVB86ymklhRw52kludgaFuVnkZWeyYfchCnIyKS3KxYD6pna21DRy/pxJPPOnOirKJ9ERhG/tkTYONLfT0dXD7oOREXxBbhYlhTk0Hu2kICeLzTVHeGt/MxefXkJJYQ5baxuZX1bEnoZWGo920t7VTWlRLuNyIp/f0NqBu9Pd43T3QE+w3eNOZoZxWkkhDS3tHD7aiQFdPU5ZUR6lRbnsaWjlyFE98W2k3PGRxYNeUn20hP6HgOUxob/U3T9/omM00hcROXWj5clZ1cCsqNczgZok1yAikraSHfqvAPPNbK6Z5QCrgHVJrkFEJG0l9R54d+8ys88B/0Pkks373H3LAIeJiMgwSfrCJ+7+OPB4sj9XRESSP70jIiIppNAXEUkjCn0RkTSi0BcRSSOjfpVNM6sHBntLbglwYBjLGQvU5/SgPqeHofR5jruXxjaO+tAfCjOr7O+OtDBTn9OD+pweRqLPmt4REUkjCn0RkTQS9tC/N9UFpID6nB7U5/Qw7H0O9Zy+iIgcL+wjfRERiRKK0DezFWa23cyqzOzGft43M7szeP8NMzs/FXUOpwT6/LGgr2+Y2Qtmdl4q6hwuA/U3ar+/NrPu4IE9Y1oifTazZWa20cy2mNkfkl3jcEvg7/UEM/uVmb0e9PmTqahzOJnZfWZWZ2abT/D+8OaXu4/pLyKrdf4ZOA3IAV4Hzo7Z5wrgCcCAC4GXU113Evp8ETAp2H7PWO5zIv2N2u93RBb0uyrVdSfhz3gisBWYHbwuS3XdSejzzcC3gu1SoAHISXXtQ+z324Hzgc0neH9Y8ysMI/2lQJW773D3DmANsDJmn5XATz3iJWCimU1LdqHDaMA+u/sL7n4oePkSkQfWjFWJ/BkDfB54GKhLZnEjJJE+fxR4xN33ALj7WO93In12oMgiT2MvJBL6yXsA8Qhw92eJ9ONEhjW/whD6M4C9Ua+rg7ZT3WcsOdX+XEdkpDBWDdhfM5sBfAD4YRLrGkmJ/BmfAUwys9+b2QYzuyZp1Y2MRPr8H8BZRJ64twm4wd17klNeygxrfiV9Pf0RYP20xV6SlMg+Y0nC/TGzy4iE/iUjWtHISqS/dwBfdffuyCBwzEukz1nAEuByIB940cxecvc3R7q4EZJIn5cDG4F3AvOAp8zsOXdvHOHaUmlY8ysMoZ/Ic3fD9mzehPpjZucCPwbe4+4Hk1TbSEikvxXAmiDwS4ArzKzL3f87KRUOv0T/Xh9w9xagxcyeBc4DxmroJ9LnTwK3e2Syu8rMdgJnAuuTU2JKDGt+hWF6J5Hn7q4DrgnOgl8IHHH32mQXOowG7LOZzQYeAT4+hkd+vQbsr7vPdfdydy8Hfgl8dgwHPiT293otcKmZZZlZAXABsC3JdQ6nRPq8h8hvNpjZFGABsCOpVSbfsObXmB/p+wmeu2tm/xC8/0MiV3NcAVQBrURGC2NWgn3+F2AycHcw+u3yMbpYVYL9DZVE+uzu28zsN8AbQA/wY3fv97K/sSDBP+dvAveb2SYi0x5fdfcxvfKmmf0CWAaUmFk18HUgG0Ymv3RHrohIGgnD9I6IiCRIoS8ikkYU+iIiaUShLyKSRhT6IiJpRKEvIpJGFPoiImlEoS8ikkb+P74SSnqR2EK1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "activity_counts = activity_df.groupby(\"Activity Rate\").count() # / 720\n",
    "activity_counts.reset_index(inplace=True)\n",
    "\n",
    "plt.plot(\n",
    "    activity_counts[\"Activity Rate\"], \n",
    "    activity_counts[\"U_ID\"]\n",
    ")\n",
    "\n",
    "activity_counts.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e065b2",
   "metadata": {},
   "source": [
    "## Construct Exposures by Hour DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62344568",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tweets_df, _ = load_tweets_dfs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07cf6ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             1.0\n",
       "1             1.0\n",
       "2             1.0\n",
       "3             1.0\n",
       "4             1.0\n",
       "            ...  \n",
       "18004744    212.0\n",
       "18004745    212.0\n",
       "18004746    212.0\n",
       "18004747    212.0\n",
       "18004748    212.0\n",
       "Name: Timestamp, Length: 18004749, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = pd.Timestamp('2020-05-25 04:00:00+00:00', tz='US/Eastern') # the timestamp must be in UTC time, then converted.\n",
    "tweets_df[c.CREATED_AT] = tweets_df[c.CREATED_AT].dt.ceil('h')\n",
    "tweets_df[c.CREATED_AT] = tweets_df[c.CREATED_AT] - start_time\n",
    "tweets_df[c.CREATED_AT] = tweets_df[c.CREATED_AT] / pd.Timedelta(1, \"h\")\n",
    "tweets_df[c.CREATED_AT]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7b32f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_grpd = tweets_df.groupby([c.CREATED_AT])\n",
    "periods = range(len(tweets_grpd)+1) # +1 because we have to account for the 0th hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb4b6e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26931489"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_edges_dict():\n",
    "    '''\n",
    "    Generates a dictionary where each key is an adopter (geotagged or not) \n",
    "    who is followed by a geotagged adopter, and each item is a set of people who follow them \n",
    "    (geotagged adopters)\n",
    "    '''\n",
    "    \n",
    "    edges_df = load_edges_df()\n",
    "    edges_df = edges_df.drop([\"Type\", \"Weight\"], axis=1)\n",
    "    edges_df = edges_df.set_index('Source')\n",
    "    edges_df = edges_df.groupby(\"Source\").agg({\n",
    "        \"Target\": lambda x: set(x)\n",
    "    })\n",
    "    \n",
    "    edges_dict = edges_df.to_dict(orient='index')\n",
    "    return edges_dict\n",
    "\n",
    "edges_dict = load_edges_dict()\n",
    "len(edges_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9df7812",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_exposures_by_pd_df():\n",
    "    \n",
    "    try:\n",
    "        exposures_by_pd_df = feather.read_feather(\"utils/data_analysis/feathers/exposures_by_pd_df.ftr\")\n",
    "    except Exception as e:\n",
    "        print(\"Couldn't find exposures_by_pd_df feather. Generating raw.\")   \n",
    "\n",
    "        nbr_adopters = dict()\n",
    "        exposures_by_pd = [dict() for _ in periods] # where each period has a dict() of users, \n",
    "        # and each user's items are ( _, _) aka nbr. adopters, and exposures.\n",
    "\n",
    "        geo_users = geo_users_w_info_gdf[\"U_ID\"]\n",
    "\n",
    "        # Initialize no exposures, no adoptions at time 0.\n",
    "        for user in geo_users:\n",
    "            exposures_by_pd[0][str(user)] = (0, 0)\n",
    "            nbr_adopters[str(user)] = set()\n",
    "\n",
    "        for time_pd, time_pd_tweets in tweets_grpd:\n",
    "\n",
    "            time_pd_int = int(time_pd) # Each time period maps to an indiced position of exposures_by_pd.\n",
    "\n",
    "            for user in geo_users:\n",
    "                exposures_by_pd[time_pd_int][user] = exposures_by_pd[time_pd_int-1][user]\n",
    "\n",
    "            for row in time_pd_tweets.index:\n",
    "                author = str(time_pd_tweets.loc[row, \"U_ID\"])\n",
    "                author_edges = edges_dict.get(author, None)\n",
    "\n",
    "                if author_edges:\n",
    "                    for target in author_edges[\"Target\"]:\n",
    "                        target = str(target) # dataframe may have converted U_ID to int.\n",
    "\n",
    "                        target_vals = exposures_by_pd[time_pd_int].get(target)\n",
    "                        if target_vals: # may be false if the target was not geolocated\n",
    "\n",
    "                            nbr_ct, expos_ct = target_vals\n",
    "                            if author not in nbr_adopters[target]:\n",
    "                                nbr_adopters[target].add(author)\n",
    "                                exposures_by_pd[time_pd_int][target] = (nbr_ct + 1, expos_ct + 1)\n",
    "                            else:\n",
    "                                exposures_by_pd[time_pd_int][target] = (nbr_ct, expos_ct + 1)\n",
    "\n",
    "            exposures_by_pd_df = pd.DataFrame.from_dict(exposures_by_pd).transpose()\n",
    "            exposures_by_pd_df.reset_index(inplace=True)\n",
    "            exposures_by_pd_df.rename({\"index\": \"U_ID\"}, axis=1, inplace=True)\n",
    "            exposures_by_pd_df.head()\n",
    "\n",
    "            exposures_by_pd_df.columns = exposures_by_pd_df.columns.astype(str)\n",
    "            exposures_by_pd_df.to_feather(\"utils/data_analysis/exposures_by_pd_df.ftr\")\n",
    "            \n",
    "    return exposures_by_pd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a3328b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "exposures_by_pd_df = load_exposures_by_pd_df()\n",
    "exposures_by_pd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e542acad",
   "metadata": {},
   "source": [
    "# Now, the actual simulations:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb88478",
   "metadata": {},
   "source": [
    "## Prepare Simulation Information\n",
    "\n",
    "For simplicity's sake, I combine the 3 dataframes that have information I need for simulations -- the ones with activity rate, with exposures by time period, and with the cluster label -- into a single dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8d4a9dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    34379.000000\n",
       "mean       141.412723\n",
       "std         49.309619\n",
       "min          2.000000\n",
       "25%        104.000000\n",
       "50%        146.000000\n",
       "75%        187.000000\n",
       "max        212.000000\n",
       "Name: adopted_hour, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = pd.Timestamp('2020-05-25 04:00:00+00:00', tz='US/Eastern') # the timestamp must be in UTC time, then converted.\n",
    "geo_users_w_info_gdf[\"adopted_hour\"] = geo_users_w_info_gdf[\"adopted_time\"].dt.ceil('h')\n",
    "geo_users_w_info_gdf[\"adopted_hour\"] = geo_users_w_info_gdf[\"adopted_hour\"] - start_time\n",
    "geo_users_w_info_gdf[\"adopted_hour\"] = geo_users_w_info_gdf[\"adopted_hour\"] / pd.Timedelta(1, \"h\")\n",
    "geo_users_w_info_gdf[\"adopted_hour\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4bdacf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "agents_df = exposures_by_pd_df.merge(activity_df, how=\"left\", on=\"U_ID\")\n",
    "agents_df = agents_df.merge(geo_users_w_info_gdf[[\"U_ID\", \"cluster_label\", \"adopted_hour\"]], on=\"U_ID\", how=\"left\")\n",
    "agents_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2458de8",
   "metadata": {},
   "source": [
    "## Construct Random Log-In Schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91911674",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_logins(agents_df):\n",
    "    \n",
    "    logins = []\n",
    "    for time_pd in range(int(agents_df[\"adopted_hour\"].max())):\n",
    "        logins.append(\n",
    "            np.random.binomial(\n",
    "                n=1, \n",
    "                p=agents_df[\"Activity Rate\"], \n",
    "                size=len(agents_df)).astype(bool)\n",
    "        )\n",
    "    return np.array(logins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d22710be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_exposures_by_login(agents_df, logins):\n",
    "    \n",
    "    # to return\n",
    "    exposures_by_login = dict()\n",
    "    \n",
    "    # running temporary dict\n",
    "    unseen_exposures = dict()\n",
    "    for agent in agents_df.index:\n",
    "        unseen_exposures[agent] = []\n",
    "        \n",
    "    for time_pd in range(logins.shape[0]):\n",
    "        for agent in range(logins.shape[1]):\n",
    "            \n",
    "            # Add last hour of exposures to unseen.\n",
    "            unseen_exposures[agent].append(\n",
    "                agents_df.loc[agent, str(time_pd)]\n",
    "            )\n",
    "            \n",
    "            if logins[time_pd, agent] == True:\n",
    "                # And if there's a login,\n",
    "                # Then save the running list of exposures, and reset unseen.\n",
    "                # First item will always be the previously-seen ones.\n",
    "                exposures_by_login[(time_pd, agent)] = unseen_exposures[agent]\n",
    "                unseen_exposures[agent] = [\n",
    "                    agents_df.loc[agent, str(time_pd)]\n",
    "                ]\n",
    "                \n",
    "\n",
    "    return exposures_by_login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b5f73d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "logins = generate_logins(agents_df)\n",
    "exposures_by_login = get_exposures_by_login(agents_df, logins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b25d614b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.659937752697868"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View average number of logins per user predicted:\n",
    "logins.sum() / logins.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bffcfc1",
   "metadata": {},
   "source": [
    "# Calculating Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bdb08af4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cum_adopts(agents_df):\n",
    "    \n",
    "    # Not every time period has an adopter, so need to create `cum_adopts`:\n",
    "    \n",
    "    cum_counts = agents_df.groupby(\"adopted_hour\").agg({\n",
    "        \"U_ID\": \"count\"\n",
    "    }).cumsum()\n",
    "    cum_adopts = [0 for _ in range(int(cum_counts.index.max()+1))]\n",
    "    \n",
    "    for idx in cum_counts.index:\n",
    "        cum_adopts[int(idx)] = cum_counts.loc[idx, \"U_ID\"]\n",
    "        \n",
    "    return cum_adopts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c3dcdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def least_squares(model, agents_df, news_df, first_n_hours, **kwargs):   \n",
    "    \n",
    "    cum_adopts = get_cum_adopts(agents_df)\n",
    "    predicted_cum_adopts, _ = model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours,\n",
    "        **kwargs\n",
    "    )\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(predicted_cum_adopts[:first_n_hours], cum_adopts[:first_n_hours])**2\n",
    "    )/len(cum_adopts)\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4247b0a0",
   "metadata": {},
   "source": [
    "## Broadcast Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0dd32a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def broadcast_model(agents_df, news_df, first_n_hours, q):\n",
    "    \n",
    "    periods = range(first_n_hours)\n",
    "    \n",
    "    infected_counts = []\n",
    "    infected_times = np.array([0 for _ in range(len(agents_df))], dtype=int)\n",
    "    agents = np.array([0 for _ in range(len(agents_df))], dtype=bool)\n",
    "                       \n",
    "    for time_pd in periods:\n",
    "                       \n",
    "        day = time_pd // 24 # floor division\n",
    "        news_rate = news_df.loc[news_df[\"Days_Since\"]==day, \"Articles\"]\n",
    "        infections = np.random.binomial(\n",
    "            n=1, \n",
    "            p=q*news_rate, \n",
    "            size=agents.size\n",
    "        ).astype(bool)\n",
    "        \n",
    "        new_infected = np.logical_and(np.logical_not(agents), infections)\n",
    "        infected_times = np.where(new_infected, time_pd, infected_times) # Where True, yield x, otherwise yield y.\n",
    "        \n",
    "        agents = np.logical_or(infections, agents)\n",
    "        infected_counts.append(agents.sum())\n",
    "        \n",
    "        \n",
    "\n",
    "    return infected_counts, infected_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2cde34",
   "metadata": {},
   "source": [
    "# Simple Contagion\n",
    "\n",
    "simpleProb(k) = 1-(1-p)k\n",
    "\n",
    "P(k, d) = simpleProb(k) + s * externalProb(d)\n",
    "\n",
    "where k = exposures since last hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45de720c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_model(agents_df, news_df, first_n_hours, exposures_by_login, p, q, s):\n",
    "    \n",
    "    periods = range(first_n_hours)\n",
    "    \n",
    "    infected_counts = []\n",
    "    infected_times = np.array([0 for _ in range(len(agents_df))], dtype=int)\n",
    "    agents = np.array([0 for _ in range(len(agents_df))], dtype=bool)\n",
    "                       \n",
    "    for time_pd in periods:\n",
    "        \n",
    "        probs = np.zeros(agents.size)\n",
    "        for agent in range(agents.size):\n",
    "            exposures = exposures_by_login.get((time_pd, agent))\n",
    "            if exposures:\n",
    "                k = (exposures[-1][1] - exposures[0][-1])\n",
    "                probs[agent] = 1 - (1-p)**k\n",
    "          \n",
    "        day = time_pd // 24 # floor division\n",
    "        news_rate = news_df.at[day, \"Articles\"]\n",
    "        probs = probs + s*q*news_rate\n",
    "        probs = np.clip(probs, a_min=None, a_max=1.0)\n",
    "        \n",
    "        infections = np.random.binomial(\n",
    "            n=1, \n",
    "            p=probs, \n",
    "            size=agents.size\n",
    "        ).astype(bool)\n",
    "        new_infected = np.logical_and(np.logical_not(agents), infections)\n",
    "        infected_times = np.where(new_infected, time_pd, infected_times) # Where True, yield x, otherwise yield y.\n",
    "        \n",
    "        agents = np.logical_or(infections, agents)\n",
    "        infected_counts.append(agents.sum())\n",
    "        \n",
    "    return infected_counts, infected_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d71ee0",
   "metadata": {},
   "source": [
    "## Complex Contagion\n",
    "\n",
    "complexProb(L_k) = (1 - p_k) * L_k where L_k is the number of exposures at k neighbor adopters, and p_k is sigmoid probability function's value at k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2195640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def p_k(k, k_0 = 50, p_lo=0.001, p_hi=0.4, g=0.5):\n",
    "\n",
    "    out = (p_hi - p_lo) / (1+math.e**(-g*(k-k_0)))\n",
    "    out += p_lo\n",
    "    return out\n",
    "\n",
    "p_k(k=500, k_0 = 5, p_lo=0.001, p_hi=0.5, g=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9cc8d78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fae4d9e5f10>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9jEl5uUzKy2VkvxzCJmFJgIJeRCKxqWwPtz69ikVrtrNq074mmOF9u3HMqD5MystlYl4uEwb3TKsmmLagoBeRdle4dhvfnv0GZXuqOXZUX2YcPohJ+blMGpZL75ysqMtLOQp6EWk37s7s19bys0ffZWhuV+7/xjGMG9Qj6rJSnoJeRNrFnupa/vtfbzOnsIRTxw/gt2dPolfXzlGXlRYU9CLS5kq2V/Dt2W+wdN0OLj9tDJefNiYtv88eFQW9iLSpl4u28N0H36S6po4/fbWA0w4dGHVJaUdBLyJtwt25+4XV3DBvOaMHdOeuCwoY2S8n6rLSkoJeRFpdeWUN1/zjLR57awOfOmIwN37hSHKyFTdR0Z4XkVb1/pZyvnX/Ioo27eK/Zo7n4qmj9M9NEVPQi0ireXrZRq54eDGZnYy/fv0YThzTL+qSBAW9iLSCujrnd8+s4panVnH40J7cef7RDOvdLeqyJKSgF5GDsmN3Nd9/eDFPL9/EWUcN45efO1xdFnQwCnoRabEVH+5k1uxCirdV8PMzD+P8Y4erPb4DUtCLSIs8+tZ6rpnzFjnZmTx08bEUjOgTdUnSCAW9iDRLTW0dN81fwV0vrObo4b2547yjGNAz9W/Hl8wU9CKSsG3lVXz3wTd4uWgrFxw7nP/36QlkZXaKuixpgoJeRBKytGQHs2YXsnlXJTd94Ui+WJAXdUmSIAW9iDRpTmEJP3hkKf27Z/OPWcdzxLBeUZckzaCgF5FGVdXU8fNH3+X+19Zywui+/O6cyfTVvVmTjoJeRBq0sWwP3/nbGxSu3c63po7i6k+OIzND7fHJSEEvIh+xaM02vv23NyivrOH2Lx/Fp44cHHVJchAU9CKyl7tz/2tr+dm/32VY767M1q3+UoKCXkSA4FZ/P3zkbf7xRgmnjR/Ab3Srv5ShoBcRSrZXMGt2IW+vK+OKT4zhslN1q79UoqAXSXMvrdrCdx98g5o6163+UpSCXiRN6VZ/6UNBL5KGyitruGbOWzy2dAOfOnIwN56lW/2lMh1ZkTSzp7qWs+54hZUbd/KD08dz0cd1q79Ul9B/P5jZDDNbYWZFZnZdA+PPM7O3wp9XzGxiovOKSPu675U1LP9wJ3ddUMDFUw9RyKeBJoPezDKA24GZwATgXDObEDfZ+8BJ7n4k8HPg7mbMKyLtZEdFNX94tohTxw9g2gR96JouErminwIUuftqd68CHgLOjJ3A3V9x9+3h09eAYYnOKyLt5w/PF7GzsoZrZoyLuhRpR4kE/VCgOOZ5STisMd8AnmjhvCLSRjbs2M19L6/hc5OHMn5Qz6jLkXaUyIexDTXgeYMTmp1CEPQntmDei4GLAfLz8xMoS0Sa45YnV+EO3582NupSpJ0lckVfAsTeYWAYsD5+IjM7ErgHONPdtzZnXgB3v9vdC9y9oH///onULiIJKtq0k78XFnPBccMZ1rtb1OVIO0sk6BcCY8xspJllAecAc2MnMLN84J/ABe6+sjnzikjbu3HeCnKyMrnklNFRlyIRaLLpxt1rzOxSYD6QAdzr7u+Y2axw/J3Aj4C+wB/Cr2rVhFfnDc7bRtsiIg0oXLudBe9u5KrpY+mTkxV1ORIBc2+wyTxSBQUFvmjRoqjLEEl67s7Zd73G+1vLef7qk+mWpf+RTFVmVujuBQ2N0+1iRFLYsys28Z8127j8tDEK+TSmoBdJUbV1zg1PrGBkvxzO/lhe0zNIylLQi6Sof725jhUbd3Ll9LF01r1e05qOvkgK2lNdy2+eXMmRw3px+uG632u6U9CLpKDZr61lXelurp0xXneKEgW9SKop21PNbc8W8fEx/ThhdL+oy5EOQEEvkmLufn41pRXVXDtjfNSlSAehoBdJIZvK9nDPS6v5zMQhHD60V9TlSAehoBdJIbc+vYqaWufK6eq4TPZR0IukiNWbd/HQwmLOOyaf4X11k2/ZR0EvkiJ+vWAl2ZmduPTUMVGXIh2Mgl4kBSwpLuWxpRu46OOj6N8jO+pypINR0IskOXfn+ieW0zcni4umjoq6HOmAFPQiSe6FVVt4dfVWvnvqaLpnq+My+SgFvUgSq6tzbnhiOXl9uvLlY4ZHXY50UAp6kST277fW8+6GMq6aPo6sTL2cpWE6M0SSVFVNHTcvWMGEwT0548ghUZcjHZiCXiRJPfD6Woq37ebameq4TA5MQS+ShHZV1vD7Z4o4blRfpo5Rx2VyYAp6kST0xxdWs7W8iutmjsdMV/NyYAp6kSSzeWcl97y4mtOPGMTEvNyoy5EkoKAXSTK3PbOKPTV1XDV9XNSlSJJQ0IskkbVby3ngPx9w9sfyGNW/e9TlSJJQ0IskkV8vWElmp05ccZo6LpPEKehFksTb63Ywd8l6vn7iCAb07BJ1OZJEFPQiSeKGecvJ7daZb510SNSlSJJR0IskgZeLtvDiqi1cespoenbpHHU5kmQU9CIdnLtzw7zlDM3tyvnHquMyaT4FvUgH9/jSD3mrZAffmzaWLp0zoi5HkpCCXqQDq66t46b5yxk3sAefmzw06nIkSSnoRTqwhxcWs2ZrBdfMGEeGOi6TFkoo6M1shpmtMLMiM7uugfHjzexVM6s0s6vixq0xs6VmttjMFrVW4SKprqKqhlufXsWUEX04dfyAqMuRJNbkfcfMLAO4HZgGlAALzWyuu78bM9k24DLgs40s5hR333KQtYqklXtfep/NOyu58/yj1XGZHJREruinAEXuvtrdq4CHgDNjJ3D3Te6+EKhugxpF0s628irufH410ycM5OjhvaMuR5JcIkE/FCiOeV4SDkuUAwvMrNDMLm5sIjO72MwWmdmizZs3N2PxIqnn9meLqKiq4ZoZ6rhMDl4iQd/Qe0ZvxjpOcPejgJnAJWY2taGJ3P1udy9w94L+/fs3Y/EiqaVkewX3v7qWLx6dx+gBPaIuR1JAIkFfAuTFPB8GrE90Be6+Pvy9CXiEoClIRBrxmydXYgZXTFPHZdI6Egn6hcAYMxtpZlnAOcDcRBZuZjlm1qP+MTAdeLulxYqkumUbynjkzXVceMIIBvfqGnU5kiKa/NaNu9eY2aXAfCADuNfd3zGzWeH4O81sELAI6AnUmdkVwASgH/BI+I2BTOABd5/XJlsikgJumr+CHtmZfOek0VGXIimkyaAHcPfHgcfjht0Z8/hDgiadeGXAxIMpUCRdvL56K88s38R1M8fTq5s6LpPWo/+MFekA3J3r5y1nUM8uXHj8iKjLkRSjoBfpAOa/s5E3Pyjle9PGqOMyaXUKepGI1YQdlx3SP4ezjmqoBVTk4CjoRSI2p7CE9zaXc82M8WRm6CUprU9nlUiEdlfVcstTqzgqP5fpEwZGXY6kKAW9SITue2UNH5bt4doZ49VxmbQZBb1IREorqrjjuSJOHT+AY0b1jbocSWEKepGI3PHce+ysVMdl0vYU9CIRWF+6mz+/sobPTR7K+EE9oy5HUpyCXiQCtzy1Ehy+P21s1KVIGlDQi7SzVRt3MqewhAuOG86w3t2iLkfSgIJepJ3dOH8FOVmZXHKKOi6T9qGgF2lHhWu38eS7G/nWSaPok5MVdTmSJhT0Iu3E3bn+ieX075HN108cGXU5kkYU9CLt5Jnlm1i4ZjuXnzaGblkJ9RAu0ioU9CLtoLbOuWHeckb2y+Hsj+U1PYNIK1LQi7SDR95cx8qNu7hq+jg6q+MyaWc640Ta2J7qWn6zYAUTh/Xi9CMGRV2OpCEFvUgbm/3aWtbvUMdlEh0FvUgbKttTzW3PFjF1bH+OH90v6nIkTSnoRdrQXc+/R2lFNdd8Uh2XSXQU9CJtZGPZHv700vucOWkIhw/tFXU5ksYU9CJt5NanV1Fb51w5TVfzEi0FvUgbeG/zLh5eWMx5xwwnv686LpNoKehF2sCvF6ygS2YnLj1VHZdJ9BT0Iq1scXEpjy/9kIumjqJf9+yoyxFR0Iu0pqDjsmX0zcnimx8fFXU5IoCCXqRVPb9yM6+t3sZlp42he7Y6LpOOQUEv0krq6pwb5q0gv083zp2SH3U5Insp6EVaydwl61m2oYwrp48lK1MvLek4dDaKtILKmlpuXrCCCYN7csaRQ6IuR2Q/CQW9mc0wsxVmVmRm1zUwfryZvWpmlWZ2VXPmFUkFD7z+ASXbd3PtzPF06qSOy6RjaTLozSwDuB2YCUwAzjWzCXGTbQMuA25uwbwiSW3nnmp+/0wRxx/Sl6lj1HGZdDyJXNFPAYrcfbW7VwEPAWfGTuDum9x9IVDd3HlFkt0fX3yfbeVV6oZYOqxEgn4oUBzzvCQcloiE5zWzi81skZkt2rx5c4KLF4nW5p2V3PPiaj51xGAm5uVGXY5IgxIJ+oYuUTzB5Sc8r7vf7e4F7l7Qv3//BBcvEq3fP7OKypo6rpw+NupSRBqVSNCXALF3Mx4GrE9w+Qczr0iHtnZrOQ+8/gHnfCyPUf27R12OSKMSCfqFwBgzG2lmWcA5wNwEl38w84p0aDcvWEnnjE5cftqYqEsROaAm/0fb3WvM7FJgPpAB3Ovu75jZrHD8nWY2CFgE9ATqzOwKYIK7lzU0bxtti0i7eXvdDv69ZD2XnjKaAT27RF2OyAEl1BmHuz8OPB437M6Yxx8SNMskNK9Isrth3nJ6d+vMxSep4zLp+PSfsSLN9NKqLby4aguXnDKanl06R12OSJMU9CLNEHRctpyhuV05/9jhUZcjkhAFvUgzPP72Bpau28H3p42lS+eMqMsRSYiCXiRB1bV13Dx/BeMG9uCzkxP9n0GR6CnoRRL00MJi1myt4NqZ48hQx2WSRBT0Igkor6zh1qdWMWVEH04ZNyDqckSaRUEvkoB7X3qfLbsquXamOi6T5KOgF2nCtvIq7nphNdMnDOTo4b2jLkek2RT0Ik247ZkiKqpquGbGuKhLEWkRBb3IARRvq2D2a2v54tF5jB7QI+pyRFpEQS9yAL99ciVmcMU0dVwmyUtBL9KIZRvKeGTxOi48YQSDe3WNuhyRFlPQizTixnnL6ZGdyXdOGh11KSIHRUEv0oDXVm/l2RWb+c4po+nVTR2XSXJT0IvEcXeuf2I5g3p24cLjR0RdjshBU9CLxJn/zkYWF5fyvWlj1HGZpAQFvUiMmto6bpy/nNEDunPWUQ3eS0ck6SjoRWLMKSxh9eZyrv7kODIz9PKQ1KAzWSS0u6qW3z61kqPyc5k+YWDU5Yi0GgW9SOi+V9awsayS62Yeqo7LJKUo6EWA0ooq/vBcEaeNH8CUkX2iLkekVSnoRYA7nnuPXZU1XK2OyyQFKegl7a0v3c2fX1nD5ycPY/ygnlGXI9LqFPSS9m55aiU4fE8dl0mKUtBLWlu1cSdzCkv4ynHDGda7W9TliLQJBb2ktRvnryAnK5NLTlHHZZK6FPSSthat2caT725k1smH0DsnK+pyRNqMgl7Skrtzw7zl9O+RzddOGBF1OSJtSkEvaenpZZtYuGY7V3xiDN2yMqMuR6RNKegl7dTWOTfOX87Ifjl8qSAv6nJE2pyCXtLOP98oYeXGXVz9yXF0VsdlkgYSOsvNbIaZrTCzIjO7roHxZma/C8e/ZWZHxYxbY2ZLzWyxmS1qzeJFmmtPdS2/fXIlE4f1Yubhg6IuR6RdNNk4aWYZwO3ANKAEWGhmc9393ZjJZgJjwp9jgDvC3/VOcfctrVa1SAvd/+pa1u/Yw81fmqiOyyRtJHJFPwUocvfV7l4FPAScGTfNmcBfPfAakGtmg1u5VpGDsmN3Nbc/V8TUsf05/pB+UZcj0m4SCfqhQHHM85JwWKLTOLDAzArN7OLGVmJmF5vZIjNbtHnz5gTKEmmeu55/j9KKaq5Vx2WSZhIJ+obe33ozpjnB3Y8iaN65xMymNrQSd7/b3QvcvaB///4JlCWSuI1le7j35fc5c9IQDhvSK+pyRNpVIkFfAsR+B20YsD7Rady9/vcm4BGCpiCRdlNRVcN//XMptXXOldN0NS/pJ5GgXwiMMbORZpYFnAPMjZtmLvCV8Ns3xwI73H2DmeWYWQ8AM8sBpgNvt2L9Ige0Zks5n7v9FZ5bsYkfnn4o+X3VcZmknya/dePuNWZ2KTAfyADudfd3zGxWOP5O4HHgdKAIqAC+Fs4+EHgk/HZDJvCAu89r9a0QacCzyzdx+UNv0qmTcd/XpjB1rJoEJT2Ze3xze/QKCgp80SJ95V5apq7Oue3ZIn771EoOHdSTuy44mrw+upKX1GZmhe5e0NA4dfIhKaVsTzXff3gJTy3byOcmD+V/PncEXbMyoi5LJFIKekkZKzfu5Fv3F1K8rYKfnDGBrx4/Qv8UJYKCXlLE40s3cNXfl9AtK5MHLjqWKSP7RF2SSIehoJekVlNbx00LVnDX86uZnJ/LHecdzaBeXaIuS6RDUdBL0tpWXsVlD77JS0VbOO+YfH50xgSyM9UeLxJPQS9J6e11O/jW/YVs3lnJjWcdyZc+pn7lRRqjoJek84/CEn7wyFL65GTx91nHMTEvN+qSRDo0Bb0kjaqaOn7x2Lv89dW1HDuqD7d9+Sj6dc+OuiyRDk9BL0lhU9kevvO3N1i0djsXfXwk184YT6buDiWSEAW9dHiFa7fx7dlvsHNPDb87dzKfmTgk6pJEkoqCXjosd2f26x/ws3+/w5Dcrvzl61M4dHDPqMsSSToKeumQ9lTX8t//eps5hSWcMq4/t5w9mV7dOkddlkhSUtBLh7OudDez7i9k6bodXHbqaK74xFg6dVJXBiItpaCXDuWVoi1c+uCbVNfU8cevFDBtwsCoSxJJegp66RDcnT++uJrrn1jOqP7dueuCozmkf/eoyxJJCQp6iVx5ZQ3X/OMtHntrAzMPH8RNX5xI92ydmiKtRa8midT7W8qZdX8hqzbt5NoZ45l10ih1LSzSyhT0Epmnl23kiocXk9HJ+MvXp/DxMbrVn0hbUNBLu6urc373zCpueWoVhw3pyZ3n61Z/Im1JQS/tasfuar7/8GKeXr6Jz08eyv98/gi6dFbXwiJtSUEv7WbFhzuZNTu41d9PP3MYXzluuNrjRdqBgl7axaNvreeaOW/pVn8iEVDQS5uqqqnj1wtWcNcLqzkqP5c7zj+agT11qz+R9qSgl1bj7hRv282bxdtZXFzKkuJS3l5fRlVNHecdk8+PzziMrEx1LSzS3hT00mKlFVVhoO9gcfF2lpTsYFt5FQBdOnfiiKG9+OpxwzlxTH9OGquvTopERUEvCamsqWXZhp0s/iAI9MXFpby/pRwAMxgzoDunjR/ApPxcJuXlMnZgDzrrxiAiHYKCXj7C3VmztYIlxaUsLi7lzeJSlq0vo6q2DoABPbKZlJfLF44exuS8XI4Y1oseXdSFsEhHpaAXtpVX7Q31xcWlLCkppbSiGoCunTM4YlgvvnbCCCbl5TIxL5fBvbroa5EiSURBn2b2VNfy7oYyFn+wL9TXbq0AoJPB2IE9+OSEQXubYMYM6K57s4okOQV9Cqurc97fWs7iD4JAX1xcyrINZVTXOgCDenZhUl4u507JZ+KwoAlGvUaKpB69qjugmto6yqtqKa+soaKqhl2VweNdlTWUV9bsHVc/rKKyll1VNXuHlVfWUl5Vw7ZdVeysrAEgJytogvnGiaOYlBdcrQ/qpe+zi6SDhILezGYAtwIZwD3ufn3ceAvHnw5UABe6+xuJzNtRuDu1dU6dQ134uNadurrYx+wdVrd3eqe2jpjHwbQVYdjuC+TaMLT3D+P6cXtDuqqGPdV1CdVsBjlZmeRkZ5CTnbn38ZDcLuRkZ9Kra2cOG9KTSXm9GT2gOxm6HZ9IWmoy6M0sA7gdmAaUAAvNbK67vxsz2UxgTPhzDHAHcEyC87aaT//+RSqqaoNwrg/mmMCuiwnz/YLcHfe2qGh/ZtA9K5Oc7Ey6ZWfQPQznoblZe8O6e0xg52RnhsMywmH1P8G8XTtn6ENREWlSIlf0U4Aid18NYGYPAWcCsWF9JvBXd3fgNTPLNbPBwIgE5m01o/t3p7rOyTAjo5PRyYyMTux93ClueKdOFjdt8GPGR4bvm5b9lxU3vH5Yt84xwR2Gs4JZRKKQSNAPBYpjnpcQXLU3Nc3QBOcFwMwuBi4GyM/PT6Csj7rlnMktmk9EJJUl8r25hi5B4xs6GpsmkXmDge53u3uBuxf0769/lxcRaS2JXNGXAHkxz4cB6xOcJiuBeUVEpA0lckW/EBhjZiPNLAs4B5gbN81c4CsWOBbY4e4bEpxXRETaUJNX9O5eY2aXAvMJviJ5r7u/Y2azwvF3Ao8TfLWyiODrlV870LxtsiUiItIg8/b4XmEzFRQU+KJFi6IuQ0QkaZhZobsXNDROnZiIiKQ4Bb2ISIpT0IuIpLgO2UZvZpuBtS2cvR+wpRXLSWbaF/vT/tif9sc+qbAvhrt7g/+E1CGD/mCY2aLGPpBIN9oX+9P+2J/2xz6pvi/UdCMikuIU9CIiKS4Vg/7uqAvoQLQv9qf9sT/tj31Sel+kXBu9iIjsLxWv6EVEJIaCXkQkxaVM0JvZDDNbYWZFZnZd1PW0NzPLM7NnzWyZmb1jZpeHw/uY2ZNmtir83TvqWtuLmWWY2Ztm9mj4PJ33Ra6ZzTGz5eE5clya74/vha+Tt83sQTPrksr7IyWCPubetDOBCcC5ZjYh2qraXQ1wpbsfChwLXBLug+uAp919DPB0+DxdXA4si3mezvviVmCeu48HJhLsl7TcH2Y2FLgMKHD3wwl61j2HFN4fKRH0xNzX1t2rgPp706YNd9/g7m+Ej3cSvJCHEuyHv4ST/QX4bCQFtjMzGwZ8CrgnZnC67ouewFTgTwDuXuXupaTp/ghlAl3NLBPoRnBDpJTdH6kS9I3dszYtmdkIYDLwOjAwvAkM4e8BEZbWnm4BrgHqYoal674YBWwG/hw2Zd1jZjmk6f5w93XAzcAHwAaCGyUtIIX3R6oEfcL3pk11ZtYd+AdwhbuXRV1PFMzs08Amdy+MupYOIhM4CrjD3ScD5aRQs0RzhW3vZwIjgSFAjpmdH21VbStVgj6R+9qmPDPrTBDyf3P3f4aDN5rZ4HD8YGBTVPW1oxOAz5jZGoJmvFPNbDbpuS8geH2UuPvr4fM5BMGfrvvjE8D77r7Z3auBfwLHk8L7I1WCPu3vTWtmRtAGu8zdfxMzai7w1fDxV4H/a+/a2pu7/5e7D3P3EQTnwjPufj5puC8A3P1DoNjMxoWDTgPeJU33B0GTzbFm1i183ZxG8JlWyu6PlPnPWDM7naBdtv7etL+MtqL2ZWYnAi8CS9nXLv0Dgnb6/wXyCU7wL7r7tkiKjICZnQxc5e6fNrO+pOm+MLNJBB9MZwGrCe7r3In03R8/Bc4m+Lbam8A3ge6k6P5ImaAXEZGGpUrTjYiINEJBLyKS4hT0IiIpTkEvIpLiFPQiIilOQS8ikuIU9CIiKe7/A9zh+sXKRIn7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [k for k in range(0, 100, 10)]\n",
    "y = [p_k(k=k, k_0 = 50, p_lo=0.001, p_hi=0.25, g=0.15) for k in range(0, 100, 10)]\n",
    "\n",
    "plt.title(\"Example p_k curve demonstrating threshold behavior\")\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a494fd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "def complex_model(agents_df, news_df, first_n_hours, exposures_by_login, k_0, p_hi, g, q, s):\n",
    "    \n",
    "    periods = range(first_n_hours)\n",
    "    \n",
    "    infected_counts = []\n",
    "    infected_times = np.array([0 for _ in range(len(agents_df))], dtype=int)\n",
    "    agents = np.array([0 for _ in range(len(agents_df))], dtype=bool)\n",
    "                       \n",
    "    for time_pd in periods:\n",
    "        \n",
    "        probs = np.zeros(agents.size)\n",
    "        \n",
    "        for agent in range(agents.size):\n",
    "            exposures = exposures_by_login.get((time_pd, agent))\n",
    "            if exposures:\n",
    "                \n",
    "                agent_not_adopting_prob = 1\n",
    "                for idx in range(1, len(exposures)):\n",
    "                    \n",
    "                    k_adptrs, exp_cum_k = exposures[idx]\n",
    "                    _, exp_pre_k = exposures[idx-1]\n",
    "                    exp_at_k = exp_cum_k - exp_pre_k\n",
    "                    \n",
    "                    agent_not_adopting_prob *= (1 - p_k(k_adptrs, k_0, p_hi=p_hi, g=g))**exp_at_k   \n",
    "                    \n",
    "                probs[agent] = 1 - agent_not_adopting_prob\n",
    "          \n",
    "        day = time_pd // 24 # floor division\n",
    "        news_rate = news_df.at[day, \"Articles\"]\n",
    "        probs = probs + s*q*news_rate\n",
    "        probs = np.clip(probs, a_min=None, a_max=1.0)\n",
    "        \n",
    "        infections = np.random.binomial(\n",
    "            n=1, \n",
    "            p=probs, \n",
    "            size=agents.size\n",
    "        ).astype(bool)\n",
    "        new_infected = np.logical_and(np.logical_not(agents), infections)\n",
    "        infected_times = np.where(new_infected, time_pd, infected_times) # Where True, yield x, otherwise yield y.\n",
    "        \n",
    "        agents = np.logical_or(infections, agents)\n",
    "        infected_counts.append(agents.sum())\n",
    "        \n",
    "    return infected_counts, infected_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a404e1",
   "metadata": {},
   "source": [
    "## Grid-Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef7fc9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "options_broadcast = {\n",
    "    'q': [0.000001, 0.000005, 0.00001, 0.000015, 0.00002, 0.00005],\n",
    "}\n",
    "options_broadcast_thin = {\n",
    "    'q': [0.00001, 0.000011, 0.000012, 0.000013, 0.000014, 0.000015],\n",
    "}\n",
    "\n",
    "options_simple = {\n",
    "    'p': [0.0001, 0.001, 0.003, 0.005, 0.01, 0.015],\n",
    "    'q': [0.000013],\n",
    "    's': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
    "}\n",
    "options_simple_thin = {\n",
    "    'p': [0.0001, 0.00025, 0.0004, 0.000625, 0.00085, 0.001],\n",
    "    'q': [0.000013],\n",
    "    's': [0.7, 0.75, 0.8, 0.85, 0.9],\n",
    "}\n",
    "\n",
    "options_complex = {\n",
    "    'k_0': [5, 20, 50, 100, 200, 300],\n",
    "    'p_hi': [0.05, 0.1, 0.2, 0.3, 0.5],\n",
    "    'g': [0.15],      # range of roughly 50, 10% of median followers.\n",
    "    'q': [0.000013],\n",
    "    's': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
    "}\n",
    "\n",
    "options_complex_thin = {\n",
    "    'k_0': [200, 220, 240, 260, 280, 300],\n",
    "    'p_hi': [0.30, 0.35, 0.4, 0.45, 0.50],\n",
    "    'q': [0.000013],\n",
    "    'g': [0.15],\n",
    "    's': [0.5, 0.55, 0.6, 0.65, 0.7]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396cddb3",
   "metadata": {},
   "source": [
    "#### Find Best Broadcast Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b93ec935",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_broadcast_params(options, m_agents_df, first_n_hours = None):\n",
    "    \n",
    "    if not first_n_hours:\n",
    "        first_n_hours = int(m_agents_df[\"adopted_hour\"].max() + 1)\n",
    "\n",
    "    broadcast_costs = []\n",
    "\n",
    "    for q in options['q']:\n",
    "\n",
    "        cost = least_squares(broadcast_model, m_agents_df, news_df, first_n_hours, q = q)\n",
    "        broadcast_costs.append(\n",
    "            (q, cost)\n",
    "        )\n",
    "        \n",
    "    out = pd.DataFrame.from_dict(broadcast_costs)\n",
    "    out.columns = [\"q\", \"cost\"]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fbf3e81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>q</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1.606248e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>5.339827e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>1.196721e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>5.084512e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>1.053369e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>1.799437e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          q          cost\n",
       "3  0.000015  1.606248e+06\n",
       "2  0.000010  5.339827e+06\n",
       "4  0.000020  1.196721e+07\n",
       "1  0.000005  5.084512e+07\n",
       "5  0.000050  1.053369e+08\n",
       "0  0.000001  1.799437e+08"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_broadcast_df = test_broadcast_params(options_broadcast, agents_df)\n",
    "costs_broadcast_df.to_csv(\"sim_results/broadcast_costs_first_round.csv\")\n",
    "costs_broadcast_df.sort_values(by=\"cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32e8b569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>q</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>4.351138e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000014</td>\n",
       "      <td>6.474173e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000012</td>\n",
       "      <td>9.339375e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1.469730e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000011</td>\n",
       "      <td>2.688677e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>4.971224e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          q          cost\n",
       "3  0.000013  4.351138e+05\n",
       "4  0.000014  6.474173e+05\n",
       "2  0.000012  9.339375e+05\n",
       "5  0.000015  1.469730e+06\n",
       "1  0.000011  2.688677e+06\n",
       "0  0.000010  4.971224e+06"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_broadcast_thin_df = test_broadcast_params(options_broadcast_thin, agents_df)\n",
    "costs_broadcast_thin_df.to_csv(\"sim_results/broadcast_costs_second_round.csv\")\n",
    "costs_broadcast_thin_df.sort_values(by=\"cost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f408a3",
   "metadata": {},
   "source": [
    "#### Find Best Simple Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23d0c232",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_simple_params(options, m_agents_df, m_exposures_by_login, first_n_hours = None):\n",
    "    \n",
    "    if not first_n_hours:\n",
    "        first_n_hours = int(m_agents_df[\"adopted_hour\"].max() + 1)\n",
    "        \n",
    "    simple_costs = []\n",
    "\n",
    "    for q in options['q']:\n",
    "        for s in options['s']:\n",
    "            for p in options['p']:\n",
    "\n",
    "                cost = least_squares(\n",
    "                    simple_model, \n",
    "                    m_agents_df, \n",
    "                    news_df, \n",
    "                    first_n_hours,\n",
    "                    exposures_by_login = m_exposures_by_login,\n",
    "                    p=p, \n",
    "                    q=q, \n",
    "                    s=s                                       \n",
    "                )\n",
    "                simple_costs.append(\n",
    "                    (p, q, s, cost)\n",
    "                )\n",
    "           \n",
    "    out = pd.DataFrame.from_dict(simple_costs)\n",
    "    out.columns = [\"p\", \"q\", \"s\", \"cost\"]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5ba236ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>p</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>7.694375e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.234539e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.251256e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.668899e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.686596e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         p         q    s          cost\n",
       "24  0.0001  0.000013  0.9  7.694375e+05\n",
       "19  0.0010  0.000013  0.7  1.234539e+06\n",
       "25  0.0010  0.000013  0.9  1.251256e+06\n",
       "14  0.0030  0.000013  0.5  1.668899e+06\n",
       "20  0.0030  0.000013  0.7  2.686596e+06"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_simple_df = test_simple_params(options_simple, agents_df, exposures_by_login)\n",
    "costs_simple_df.to_csv(\"sim_results/simple_costs_first_round.csv\")\n",
    "costs_simple_df.sort_values(by=\"cost\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5de0e726",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>p</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.275207e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4.820951e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.148330e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.366781e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.580601e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.710729e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.767760e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>6.188520e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>6.942738e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>7.342142e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>7.943234e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>8.430520e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.437030e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.074808e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.131865e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.141865e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.156547e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.306915e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.318712e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.342078e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.513276e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.694635e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.256231e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.551349e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.110101e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.251548e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3.597941e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.847162e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5.125131e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.70</td>\n",
       "      <td>7.170501e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           p         q     s          cost\n",
       "26  0.000400  0.000013  0.90  4.275207e+05\n",
       "21  0.000625  0.000013  0.85  4.820951e+05\n",
       "16  0.000850  0.000013  0.80  5.148330e+05\n",
       "27  0.000625  0.000013  0.90  5.366781e+05\n",
       "25  0.000250  0.000013  0.90  5.580601e+05\n",
       "22  0.000850  0.000013  0.85  5.710729e+05\n",
       "20  0.000400  0.000013  0.85  5.767760e+05\n",
       "17  0.001000  0.000013  0.80  6.188520e+05\n",
       "11  0.001000  0.000013  0.75  6.942738e+05\n",
       "15  0.000625  0.000013  0.80  7.342142e+05\n",
       "10  0.000850  0.000013  0.75  7.943234e+05\n",
       "23  0.001000  0.000013  0.85  8.430520e+05\n",
       "24  0.000100  0.000013  0.90  8.437030e+05\n",
       "28  0.000850  0.000013  0.90  1.074808e+06\n",
       "5   0.001000  0.000013  0.70  1.131865e+06\n",
       "19  0.000250  0.000013  0.85  1.141865e+06\n",
       "29  0.001000  0.000013  0.90  1.156547e+06\n",
       "9   0.000625  0.000013  0.75  1.306915e+06\n",
       "14  0.000400  0.000013  0.80  1.318712e+06\n",
       "18  0.000100  0.000013  0.85  1.342078e+06\n",
       "4   0.000850  0.000013  0.70  1.513276e+06\n",
       "13  0.000250  0.000013  0.80  1.694635e+06\n",
       "8   0.000400  0.000013  0.75  2.256231e+06\n",
       "3   0.000625  0.000013  0.70  2.551349e+06\n",
       "12  0.000100  0.000013  0.80  3.110101e+06\n",
       "7   0.000250  0.000013  0.75  3.251548e+06\n",
       "2   0.000400  0.000013  0.70  3.597941e+06\n",
       "6   0.000100  0.000013  0.75  4.847162e+06\n",
       "1   0.000250  0.000013  0.70  5.125131e+06\n",
       "0   0.000100  0.000013  0.70  7.170501e+06"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_simple_thin_df = test_simple_params(options_simple_thin, agents_df, exposures_by_login)\n",
    "costs_simple_thin_df.to_csv(\"sim_results/simple_costs_second_round.csv\")\n",
    "costs_simple_thin_df.sort_values(by=\"cost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3383ca",
   "metadata": {},
   "source": [
    "#### Find Best Complex Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2137210e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_complex_params(options, m_agents_df, m_exposures_by_login, first_n_hours = None):\n",
    "    '''\n",
    "    Allow variable agents, exposures by login to filter based on time.\n",
    "    '''\n",
    "    \n",
    "    if not first_n_hours:\n",
    "        first_n_hours = int(m_agents_df[\"adopted_hour\"].max() + 1)\n",
    "\n",
    "    complex_costs = []\n",
    "\n",
    "    for k_0 in options['k_0']:\n",
    "        for p_hi in options['p_hi']:\n",
    "            for g in options['g']:\n",
    "                for q in options['q']:\n",
    "                    for s in options['s']:\n",
    "\n",
    "                        cost = least_squares(\n",
    "                            complex_model, \n",
    "                            m_agents_df, \n",
    "                            news_df, \n",
    "                            first_n_hours,\n",
    "                            exposures_by_login = m_exposures_by_login,\n",
    "                            k_0 = k_0,\n",
    "                            p_hi = p_hi,\n",
    "                            g = g,\n",
    "                            q = q, \n",
    "                            s = s,                                                        \n",
    "                        )\n",
    "                        complex_costs.append(\n",
    "                            (k_0, p_hi, g, q, s, cost)\n",
    "                        )\n",
    "    \n",
    "    out = pd.DataFrame.from_dict(complex_costs)\n",
    "    out.columns = [\"k_0\", \"p_hi\", \"g\", \"q\", \"s\", \"cost\"]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9e22fceb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k_0</th>\n",
       "      <th>p_hi</th>\n",
       "      <th>g</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
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       "      <th>118</th>\n",
       "      <td>200</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>661335.018779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>200</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>664205.131455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>699676.549296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>200</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>716500.079812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>200</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>723999.638498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>300</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>880902.140845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>300</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>887857.718310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>300</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>907456.089202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>300</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>916234.633803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>300</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>927153.624413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k_0  p_hi     g         q    s           cost\n",
       "118  200  0.30  0.15  0.000013  0.7  661335.018779\n",
       "123  200  0.50  0.15  0.000013  0.7  664205.131455\n",
       "108  200  0.10  0.15  0.000013  0.7  699676.549296\n",
       "113  200  0.20  0.15  0.000013  0.7  716500.079812\n",
       "103  200  0.05  0.15  0.000013  0.7  723999.638498\n",
       "138  300  0.20  0.15  0.000013  0.7  880902.140845\n",
       "133  300  0.10  0.15  0.000013  0.7  887857.718310\n",
       "143  300  0.30  0.15  0.000013  0.7  907456.089202\n",
       "148  300  0.50  0.15  0.000013  0.7  916234.633803\n",
       "128  300  0.05  0.15  0.000013  0.7  927153.624413"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_complex_df = test_complex_params(options_complex, agents_df, exposures_by_login)\n",
    "costs_complex_df.to_csv(\"sim_results/complex_costs_first_round.csv\")\n",
    "costs_complex_df.sort_values(by=\"cost\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ca67f0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k_0</th>\n",
       "      <th>p_hi</th>\n",
       "      <th>g</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>200</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>637557.751174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>220</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>664774.075117</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>673846.967136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>200</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>676734.164319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>200</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>679174.323944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>220</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>680460.225352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>220</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>707085.028169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>716478.258216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>260</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>730838.873239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>220</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>738096.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>240</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>746686.413146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>220</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>759930.150235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>240</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>765341.807512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>260</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>767583.126761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>240</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>769498.563380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>240</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>774980.755869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>280</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>800415.812207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>260</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>809502.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>280</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>825970.873239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>260</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>843188.140845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k_0  p_hi     g         q    s           cost\n",
       "19   200  0.45  0.15  0.000013  0.7  637557.751174\n",
       "39   220  0.40  0.15  0.000013  0.7  664774.075117\n",
       "4    200  0.30  0.15  0.000013  0.7  673846.967136\n",
       "14   200  0.40  0.15  0.000013  0.7  676734.164319\n",
       "24   200  0.50  0.15  0.000013  0.7  679174.323944\n",
       "29   220  0.30  0.15  0.000013  0.7  680460.225352\n",
       "49   220  0.50  0.15  0.000013  0.7  707085.028169\n",
       "9    200  0.35  0.15  0.000013  0.7  716478.258216\n",
       "99   260  0.50  0.15  0.000013  0.7  730838.873239\n",
       "44   220  0.45  0.15  0.000013  0.7  738096.690141\n",
       "54   240  0.30  0.15  0.000013  0.7  746686.413146\n",
       "34   220  0.35  0.15  0.000013  0.7  759930.150235\n",
       "74   240  0.50  0.15  0.000013  0.7  765341.807512\n",
       "79   260  0.30  0.15  0.000013  0.7  767583.126761\n",
       "59   240  0.35  0.15  0.000013  0.7  769498.563380\n",
       "64   240  0.40  0.15  0.000013  0.7  774980.755869\n",
       "114  280  0.40  0.15  0.000013  0.7  800415.812207\n",
       "89   260  0.40  0.15  0.000013  0.7  809502.816901\n",
       "104  280  0.30  0.15  0.000013  0.7  825970.873239\n",
       "84   260  0.35  0.15  0.000013  0.7  843188.140845"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_complex_thin_df = test_complex_params(options_complex_thin, agents_df, exposures_by_login)\n",
    "costs_complex_thin_df.to_csv(\"sim_results/complex_costs_second_round.csv\")\n",
    "costs_complex_thin_df.sort_values(by=\"cost\").head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c60a858",
   "metadata": {},
   "source": [
    "### View Best Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54cd649b",
   "metadata": {},
   "outputs": [],
   "source": [
    "costs_simple_thin_df = pd.read_csv(\"sim_results/simple_costs_second_round.csv\")\n",
    "costs_complex_thin_df = pd.read_csv(\"sim_results/complex_costs_second_round.csv\")\n",
    "costs_broadcast_thin_df = pd.read_csv(\"sim_results/broadcast_costs_second_round.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "acda72fa",
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>q</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>435113.835681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0         q           cost\n",
       "0      3           3  0.000013  435113.835681"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_ref = costs_broadcast_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_ref.reset_index(inplace=True)\n",
    "best_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dd573ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "477310.84245283017"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title(\"5 Broadcast Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "plt.xlabel(\"Cumulative Adopters\")\n",
    "plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "true_counts = get_cum_adopts(agents_df)[1:]\n",
    "costs = []\n",
    "for i in range(5):\n",
    "    \n",
    "    pred_counts, pred_times = broadcast_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 212,\n",
    "        q = best_ref.loc[0, \"q\"],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(pred_counts, true_counts)**2\n",
    "    )/len(true_counts)\n",
    "    costs.append(cost)\n",
    "    \n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\")  \n",
    "np.mean(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d40d5756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "\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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>427520.70892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0       p         q    s          cost\n",
       "0     26          26  0.0004  0.000013  0.9  427520.70892"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_simple = costs_simple_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_simple.reset_index(inplace=True)\n",
    "best_simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6f760f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "397871.79154929577"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title(\"5 Simple Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "plt.xlabel(\"Cumulative Adopters\")\n",
    "plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "costs = []\n",
    "true_counts = get_cum_adopts(agents_df)\n",
    "\n",
    "for i in range(5):\n",
    "    \n",
    "    logins = generate_logins(agents_df)\n",
    "    exposures_by_login = get_exposures_by_login(agents_df, logins)\n",
    "    \n",
    "    pred_counts, pred_times = simple_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 212,\n",
    "        exposures_by_login = exposures_by_login,\n",
    "        p = best_simple.loc[0, \"p\"],\n",
    "        q=0.000013,\n",
    "        s= best_simple.loc[0, \"s\"],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(pred_counts[:212], true_counts[:212])**2\n",
    "    )/len(true_counts)\n",
    "    costs.append(cost)\n",
    "    \n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\")  \n",
    "np.mean(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5059621",
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>k_0</th>\n",
       "      <th>p_hi</th>\n",
       "      <th>g</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>200</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>637557.751174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0  k_0  p_hi     g         q    s           cost\n",
       "0     19          19  200  0.45  0.15  0.000013  0.7  637557.751174"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_complex = costs_complex_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_complex.reset_index(inplace=True)\n",
    "best_complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6cdbe0a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "610377.2131455399"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title(\"5 Complex Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "plt.xlabel(\"Cumulative Adopters\")\n",
    "plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "costs = []\n",
    "true_counts = get_cum_adopts(agents_df)\n",
    "\n",
    "for i in range(5):\n",
    "\n",
    "    logins = generate_logins(agents_df)\n",
    "    exposures_by_login = get_exposures_by_login(agents_df, logins)\n",
    "    \n",
    "    pred_counts, pred_times = complex_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 212,\n",
    "        exposures_by_login = exposures_by_login,\n",
    "        k_0 = best_complex.loc[0, \"k_0\"],\n",
    "        p_hi = best_complex.loc[0, \"p_hi\"],\n",
    "        g = 0.15,\n",
    "        q=0.000013,\n",
    "        s= best_complex.loc[0, \"s\"],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(pred_counts[:212], true_counts[:212])**2\n",
    "    )/len(true_counts)\n",
    "    costs.append(cost)\n",
    "    \n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\") \n",
    "np.mean(costs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21072397",
   "metadata": {},
   "source": [
    "## Examining the First 48 Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2b7075da",
   "metadata": {},
   "outputs": [],
   "source": [
    "options_broadcast_thin_48 = {\n",
    "    'q': [0.000010, 0.000011, 0.000012, 0.000013, 0.000014, 0.000015],\n",
    "}\n",
    "\n",
    "options_simple_thin_48 = {\n",
    "    'p': [0.003, 0.0035, 0.004, 0.0045, 0.005],\n",
    "    'q': [0.000013],\n",
    "    's': [0.1, 0.15, 0.2, 0.25, 0.3],\n",
    "}\n",
    "\n",
    "options_complex_thin_48 = {\n",
    "    'k_0': [50, 67.5, 75, 82.5, 100],\n",
    "    'p_hi': [0.05, 0.0625, 0.075, 0.0825, 0.1],\n",
    "    'q': [0.000013],\n",
    "    'g': [0.15],\n",
    "    's': [0.1, 0.15, 0.2, 0.25, 0.3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c983d4ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>q</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>9604.328638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>19598.732394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>21877.051643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>37346.032864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>37770.112676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>555141.323944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          q           cost\n",
       "2  0.000010    9604.328638\n",
       "1  0.000005   19598.732394\n",
       "3  0.000015   21877.051643\n",
       "4  0.000020   37346.032864\n",
       "0  0.000001   37770.112676\n",
       "5  0.000050  555141.323944"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_broadcast_48_df = test_broadcast_params(options_broadcast, agents_df, first_n_hours = 48)\n",
    "costs_broadcast_48_df.to_csv(\"sim_results/broadcast_costs_48_first_round.csv\")\n",
    "costs_broadcast_48_df.sort_values(by=\"cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3a57e392",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>9737.389671</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000011</td>\n",
       "      <td>10656.460094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000012</td>\n",
       "      <td>12561.835681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>14742.225352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000014</td>\n",
       "      <td>14840.140845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>16882.957746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          q          cost\n",
       "0  0.000010   9737.389671\n",
       "1  0.000011  10656.460094\n",
       "2  0.000012  12561.835681\n",
       "3  0.000013  14742.225352\n",
       "4  0.000014  14840.140845\n",
       "5  0.000015  16882.957746"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_broadcast_48_thin_df = test_broadcast_params(options_broadcast_thin_48, agents_df, first_n_hours = 48)\n",
    "costs_broadcast_48_thin_df.to_csv(\"sim_results/broadcast_costs_48_second_round.csv\")\n",
    "costs_broadcast_48_thin_df.sort_values(by=\"cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fd8dfd6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>cost</th>\n",
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       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>541.070423</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1250.014085</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2542.615023</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3067.408451</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4970.225352</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5737.568075</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5804.093897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8735.192488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>9386.267606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10327.882629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>11236.309859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12950.474178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13501.408451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15644.713615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>17852.568075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21647.225352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.9</td>\n",
       "      <td>23799.131455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>27228.934272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.7</td>\n",
       "      <td>29417.915493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>32763.906103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         p         q    s          cost\n",
       "3   0.0050  0.000013  0.1    541.070423\n",
       "8   0.0030  0.000013  0.3   1250.014085\n",
       "2   0.0030  0.000013  0.1   2542.615023\n",
       "14  0.0030  0.000013  0.5   3067.408451\n",
       "9   0.0050  0.000013  0.3   4970.225352\n",
       "19  0.0010  0.000013  0.7   5737.568075\n",
       "13  0.0010  0.000013  0.5   5804.093897\n",
       "7   0.0010  0.000013  0.3   8735.192488\n",
       "18  0.0001  0.000013  0.7   9386.267606\n",
       "24  0.0001  0.000013  0.9  10327.882629\n",
       "20  0.0030  0.000013  0.7  11236.309859\n",
       "12  0.0001  0.000013  0.5  12950.474178\n",
       "25  0.0010  0.000013  0.9  13501.408451\n",
       "15  0.0050  0.000013  0.5  15644.713615\n",
       "1   0.0010  0.000013  0.1  17852.568075\n",
       "6   0.0001  0.000013  0.3  21647.225352\n",
       "26  0.0030  0.000013  0.9  23799.131455\n",
       "4   0.0100  0.000013  0.1  27228.934272\n",
       "21  0.0050  0.000013  0.7  29417.915493\n",
       "0   0.0001  0.000013  0.1  32763.906103"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_simple_48_df = test_simple_params(options_simple, agents_df, exposures_by_login, first_n_hours = 48)\n",
    "costs_simple_48_df.to_csv(\"sim_results/simple_costs_48_first_round.csv\")\n",
    "costs_simple_48_df.sort_values(by=\"cost\").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a257944a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.10</td>\n",
       "      <td>265.239437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.10</td>\n",
       "      <td>389.699531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.15</td>\n",
       "      <td>455.657277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.15</td>\n",
       "      <td>593.497653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.20</td>\n",
       "      <td>620.084507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.10</td>\n",
       "      <td>728.197183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.15</td>\n",
       "      <td>776.107981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.25</td>\n",
       "      <td>907.356808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.20</td>\n",
       "      <td>918.004695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.10</td>\n",
       "      <td>925.262911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.25</td>\n",
       "      <td>969.488263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1267.661972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1308.403756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1314.253521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1352.755869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1387.788732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1900.572770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1948.422535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2054.070423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2206.384977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         p         q     s         cost\n",
       "3   0.0045  0.000013  0.10   265.239437\n",
       "2   0.0040  0.000013  0.10   389.699531\n",
       "7   0.0040  0.000013  0.15   455.657277\n",
       "8   0.0045  0.000013  0.15   593.497653\n",
       "12  0.0040  0.000013  0.20   620.084507\n",
       "4   0.0050  0.000013  0.10   728.197183\n",
       "6   0.0035  0.000013  0.15   776.107981\n",
       "16  0.0035  0.000013  0.25   907.356808\n",
       "11  0.0035  0.000013  0.20   918.004695\n",
       "1   0.0035  0.000013  0.10   925.262911\n",
       "15  0.0030  0.000013  0.25   969.488263\n",
       "9   0.0050  0.000013  0.15  1267.661972\n",
       "21  0.0035  0.000013  0.30  1308.403756\n",
       "10  0.0030  0.000013  0.20  1314.253521\n",
       "17  0.0040  0.000013  0.25  1352.755869\n",
       "20  0.0030  0.000013  0.30  1387.788732\n",
       "23  0.0045  0.000013  0.30  1900.572770\n",
       "22  0.0040  0.000013  0.30  1948.422535\n",
       "5   0.0030  0.000013  0.15  2054.070423\n",
       "18  0.0045  0.000013  0.25  2206.384977"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_simple_48_thin_df = test_simple_params(options_simple_thin_48, agents_df, exposures_by_login, first_n_hours = 48)\n",
    "costs_simple_48_thin_df.to_csv(\"sim_results/simple_costs_48_second_round.csv\")\n",
    "costs_simple_48_thin_df.sort_values(by=\"cost\").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "368827fe",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     k_0  p_hi     g         q    s         cost\n",
       "55    50  0.10  0.15  0.000013  0.1   341.413146\n",
       "50    50  0.05  0.15  0.000013  0.1   686.215962\n",
       "51    50  0.05  0.15  0.000013  0.3  1535.826291\n",
       "60    50  0.20  0.15  0.000013  0.1  1744.812207\n",
       "91   100  0.30  0.15  0.000013  0.3  2068.732394\n",
       "96   100  0.50  0.15  0.000013  0.3  2116.507042\n",
       "87   100  0.20  0.15  0.000013  0.5  2169.460094\n",
       "92   100  0.30  0.15  0.000013  0.5  2353.652582\n",
       "82   100  0.10  0.15  0.000013  0.5  2375.685446\n",
       "86   100  0.20  0.15  0.000013  0.3  2506.544601\n",
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       "81   100  0.10  0.15  0.000013  0.3  2836.046948\n",
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      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_complex_48_df = test_complex_params(options_complex, agents_df, exposures_by_login, first_n_hours = 48)\n",
    "costs_complex_48_df.to_csv(\"sim_results/complex_costs_48_first_round.csv\")\n",
    "costs_complex_48_df.sort_values(by=\"cost\").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3920c67a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.5</td>\n",
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       "      <td>0.10</td>\n",
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       "      <td>0.000013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2837.427230</td>\n",
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       "      <th>82</th>\n",
       "      <td>100</td>\n",
       "      <td>0.10</td>\n",
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       "      <td>0.000013</td>\n",
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       "      <td>0.15</td>\n",
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       "      <td>0.30</td>\n",
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       "      <td>0.30</td>\n",
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       "      <td>0.000013</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
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       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4185.605634</td>\n",
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       "      <th>122</th>\n",
       "      <td>200</td>\n",
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       "      <td>0.000013</td>\n",
       "      <td>0.5</td>\n",
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      ],
      "text/plain": [
       "     k_0  p_hi     g         q    s         cost\n",
       "55    50  0.10  0.15  0.000013  0.1   585.915493\n",
       "50    50  0.05  0.15  0.000013  0.1   615.647887\n",
       "51    50  0.05  0.15  0.000013  0.3  1196.422535\n",
       "91   100  0.30  0.15  0.000013  0.3  1691.995305\n",
       "96   100  0.50  0.15  0.000013  0.3  1845.887324\n",
       "60    50  0.20  0.15  0.000013  0.1  1932.234742\n",
       "77   100  0.05  0.15  0.000013  0.5  2400.737089\n",
       "86   100  0.20  0.15  0.000013  0.3  2611.164319\n",
       "97   100  0.50  0.15  0.000013  0.5  2671.295775\n",
       "81   100  0.10  0.15  0.000013  0.3  2837.427230\n",
       "82   100  0.10  0.15  0.000013  0.5  2841.469484\n",
       "56    50  0.10  0.15  0.000013  0.3  3195.657277\n",
       "87   100  0.20  0.15  0.000013  0.5  3221.840376\n",
       "92   100  0.30  0.15  0.000013  0.5  3294.474178\n",
       "65    50  0.30  0.15  0.000013  0.1  3313.957746\n",
       "76   100  0.05  0.15  0.000013  0.3  3445.610329\n",
       "117  200  0.30  0.15  0.000013  0.5  3962.863850\n",
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       "122  200  0.50  0.15  0.000013  0.5  4219.107981"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_complex_48_thin_df = test_complex_params(options_complex, agents_df, exposures_by_login, first_n_hours = 48)\n",
    "costs_complex_48_thin_df.to_csv(\"sim_results/complex_costs_48_second_round.csv\")\n",
    "costs_complex_48_thin_df.sort_values(by=\"cost\").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "857697fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "959761ab",
   "metadata": {},
   "source": [
    "### View Best Options for 48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ce56cb16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_best_ref(model_params, m_agents_df, first_n_hours=212):\n",
    "    \n",
    "    plt.figure(figsize=(16, 9))\n",
    "    plt.title(\"5 Broadcast Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "    plt.xlabel(\"Cumulative Adopters\")\n",
    "    plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "    true_counts = get_cum_adopts(agents_df)[1:first_n_hours+1]\n",
    "    costs = []\n",
    "    \n",
    "    for i in range(5):\n",
    "\n",
    "        pred_counts, pred_times = broadcast_model(\n",
    "            agents_df, \n",
    "            news_df, \n",
    "            first_n_hours = first_n_hours,\n",
    "            q = model_params[\"q\"],\n",
    "        )\n",
    "        plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "\n",
    "        cost = (\n",
    "            np.sum(\n",
    "                np.subtract(pred_counts, true_counts)**2\n",
    "            ) / first_n_hours\n",
    "        )\n",
    "        costs.append(cost)\n",
    "\n",
    "    plt.plot(range(len(true_counts)), true_counts, color=\"red\")  \n",
    "    print(\"Mean (over 5) Cost:\", np.mean(costs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a114c75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_best_simple(model_params, m_agents_df, first_n_hours=212):\n",
    "    \n",
    "    plt.figure(figsize=(16, 9))\n",
    "    plt.title(\"5 Simple Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "    plt.xlabel(\"Cumultive Adopters\")\n",
    "    plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "    costs = []\n",
    "    true_counts = get_cum_adopts(agents_df)[1:first_n_hours+1]\n",
    "\n",
    "    for i in range(5):\n",
    "\n",
    "        logins = generate_logins(m_agents_df)\n",
    "        exposures_by_login = get_exposures_by_login(m_agents_df, logins)\n",
    "\n",
    "        pred_counts, pred_times = simple_model(\n",
    "            m_agents_df, \n",
    "            news_df, \n",
    "            first_n_hours = first_n_hours,\n",
    "            exposures_by_login = exposures_by_login,\n",
    "            p = model_params[\"p\"],\n",
    "            q = model_params[\"q\"],\n",
    "            s = model_params[\"s\"],\n",
    "        )\n",
    "        plt.plot(range(first_n_hours), pred_counts, color=\"blue\")\n",
    "\n",
    "        cost = (\n",
    "            np.sum(\n",
    "                np.subtract(pred_counts, true_counts)**2\n",
    "            ) / first_n_hours\n",
    "        )\n",
    "        costs.append(cost)\n",
    "\n",
    "    plt.plot(range(first_n_hours), true_counts, color=\"red\")  \n",
    "    print(\"Mean (over 5) Cost:\", np.mean(costs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "27940029",
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_best_complex(model_params, m_agents_df, first_n_hours=212):\n",
    "    \n",
    "    plt.figure(figsize=(16, 9))\n",
    "    plt.title(\"5 Complex Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "    plt.xlabel(\"Cumultive Adopters\")\n",
    "    plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "    costs = []\n",
    "    true_counts = get_cum_adopts(agents_df)[1:49]\n",
    "\n",
    "    for i in range(5):\n",
    "\n",
    "        logins = generate_logins(agents_df)\n",
    "        exposures_by_login = get_exposures_by_login(agents_df, logins)\n",
    "\n",
    "        pred_counts, pred_times = complex_model(\n",
    "            agents_df, \n",
    "            news_df, \n",
    "            first_n_hours = first_n_hours,\n",
    "            exposures_by_login = exposures_by_login,\n",
    "            k_0 = model_params[\"k_0\"],\n",
    "            p_hi = model_params[\"p_hi\"],\n",
    "            g = model_params[\"g\"],\n",
    "            q= model_params[\"q\"],\n",
    "            s= model_params[\"s\"],\n",
    "        )\n",
    "        plt.plot(range(first_n_hours), pred_counts, color=\"blue\")\n",
    "\n",
    "        cost = (\n",
    "            np.sum(\n",
    "                np.subtract(pred_counts, true_counts)**2\n",
    "            )/len(true_counts)\n",
    "        )\n",
    "        costs.append(cost)\n",
    "\n",
    "    plt.plot(range(len(true_counts)), true_counts, color=\"red\") \n",
    "    print(\"Mean (over 5) Cost:\", np.mean(costs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9e1dd5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "costs_simple_thin_df = pd.read_csv(\"sim_results/simple_costs_48_second_round.csv\")\n",
    "costs_complex_thin_df = pd.read_csv(\"sim_results/complex_costs_48_second_round.csv\")\n",
    "costs_broadcast_thin_df = pd.read_csv(\"sim_results/broadcast_costs_48_second_round.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b94ff636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>q</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>9737.389671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0        q         cost\n",
       "0      0           0  0.00001  9737.389671"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_ref = costs_broadcast_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_ref.reset_index(inplace=True)\n",
    "best_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b3b2630c",
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>265.239437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0       p         q    s        cost\n",
       "0      3           3  0.0045  0.000013  0.1  265.239437"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_simple = costs_simple_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_simple.reset_index(inplace=True)\n",
    "best_simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "93f4b3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3978.0666666666666"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title(\"5 Simple Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "plt.xlabel(\"Cumulative Adopters\")\n",
    "plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "costs = []\n",
    "true_counts = get_cum_adopts(agents_df)[1:49]\n",
    "\n",
    "for i in range(5):\n",
    "    \n",
    "    logins = generate_logins(agents_df)\n",
    "    exposures_by_login = get_exposures_by_login(agents_df, logins)\n",
    "    \n",
    "    pred_counts, pred_times = simple_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 48,\n",
    "        exposures_by_login = exposures_by_login,\n",
    "        p = best_simple.loc[0, \"p\"],\n",
    "        q=0.000013,\n",
    "        s= best_simple.loc[0, \"s\"],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(pred_counts, true_counts)**2\n",
    "    )/len(true_counts)\n",
    "    costs.append(cost)\n",
    "    \n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\")  \n",
    "np.mean(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "47607512",
   "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>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>k_0</th>\n",
       "      <th>p_hi</th>\n",
       "      <th>g</th>\n",
       "      <th>q</th>\n",
       "      <th>s</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>585.915493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0  k_0  p_hi     g         q    s        cost\n",
       "0     55          55   50   0.1  0.15  0.000013  0.1  585.915493"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_complex = costs_complex_thin_df.sort_values(by=\"cost\").head(1)\n",
    "best_complex.reset_index(inplace=True)\n",
    "best_complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "36748533",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4995.750000000001"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "plt.title(\"5 Complex Model Runs (Blue) vs. Actual (Red) Cumulative Adoption Rate\")\n",
    "plt.xlabel(\"Cumulative Adopters\")\n",
    "plt.ylabel(\"Hours Since Start\")\n",
    "\n",
    "costs = []\n",
    "true_counts = get_cum_adopts(agents_df)[1:49]\n",
    "\n",
    "for i in range(5):\n",
    "\n",
    "    logins = generate_logins(agents_df)\n",
    "    exposures_by_login = get_exposures_by_login(agents_df, logins)\n",
    "    \n",
    "    pred_counts, pred_times = complex_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 48,\n",
    "        exposures_by_login = exposures_by_login,\n",
    "        k_0 = best_complex.loc[0, \"k_0\"],\n",
    "        p_hi = best_complex.loc[0, \"p_hi\"],\n",
    "        g = 0.15,\n",
    "        q=0.000013,\n",
    "        s= best_complex.loc[0, \"s\"],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "    cost = np.sum(\n",
    "        np.subtract(pred_counts, true_counts)**2\n",
    "    )/len(true_counts)\n",
    "    costs.append(cost)\n",
    "    \n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\") \n",
    "np.mean(costs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d044762d",
   "metadata": {},
   "source": [
    "### Examine First 48 by Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b1b8531",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "costs_simple_48_df\n",
    "costs_complex_48_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b2092307",
   "metadata": {},
   "outputs": [],
   "source": [
    "def thin_broadcast_search(costs_broadcast_df):\n",
    "    \n",
    "    \n",
    "    costs_broadcast_df = costs_broadcast_df.sort_values(by=\"cost\")    \n",
    "    best_qs = costs_broadcast_df[\"q\"].drop_duplicates().head(2)\n",
    "    \n",
    "    return {\n",
    "        \"q\": np.linspace(start=best_qs.min(), stop=best_qs.max(), num=6) \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "045d4783",
   "metadata": {},
   "outputs": [],
   "source": [
    "def thin_simple_search(costs_simple_df):\n",
    "    \n",
    "    costs_simple_df = costs_simple_df.sort_values(by=\"cost\")  \n",
    "    q = costs_simple_df.loc[0, \"q\"]\n",
    "    \n",
    "    best_ps = costs_simple_df[\"p\"].drop_duplicates().head(2)\n",
    "    best_ss = costs_simple_df[\"s\"].drop_duplicates().head(2)\n",
    "    \n",
    "    return {\n",
    "        \"q\": [q],\n",
    "        \"p\": np.linspace(start=best_ps.min(), stop=best_ps.max(), num=6),\n",
    "        \"s\": np.linspace(start=best_ss.min(), stop=best_ss.max(), num=5) \n",
    "    }    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c44b67c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def thin_complex_search(costs_complex_df):\n",
    "    \n",
    "    costs_complex_df = costs_complex_df.sort_values(by=\"cost\")\n",
    "    \n",
    "    q = costs_complex_df.loc[0, \"q\"]\n",
    "    g = costs_complex_df.loc[0, \"g\"]\n",
    "    \n",
    "    best_k_0s = costs_complex_df[\"k_0\"].drop_duplicates().head(2)\n",
    "    best_p_his = costs_complex_df[\"p_hi\"].drop_duplicates().head(2)\n",
    "    best_ss = costs_complex_df[\"s\"].drop_duplicates().head(2)\n",
    "    \n",
    "    return {\n",
    "        \"q\": [q],\n",
    "        \"g\": [g],\n",
    "        \"k_0\": np.linspace(start=best_k_0s.min(), stop=best_k_0s.max(), num=6),\n",
    "        \"p_hi\": np.linspace(start=best_p_his.min(), stop=best_p_his.max(), num=6),\n",
    "        \"s\": np.linspace(start=best_ss.min(), stop=best_ss.max(), num=5) \n",
    "    }    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7dfb5140",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_params(m_agents_df, first_n_hours):\n",
    "    \n",
    "    options_broadcast = {\n",
    "        'q': [0.000001, 0.000005, 0.00001, 0.000015, 0.00002, 0.00005],\n",
    "    }\n",
    "    \n",
    "    m_agents_df.reset_index(inplace=True)\n",
    "    m_logins = generate_logins(m_agents_df)\n",
    "    m_exposures_by_login = get_exposures_by_login(m_agents_df, m_logins)\n",
    "        \n",
    "    costs_broadcast_df = test_broadcast_params(\n",
    "        options_broadcast, \n",
    "        m_agents_df, \n",
    "        first_n_hours = first_n_hours\n",
    "    )    \n",
    "    costs_broadcast_thin_df = test_broadcast_params(\n",
    "        thin_broadcast_search(costs_broadcast_df),\n",
    "        m_agents_df,\n",
    "        first_n_hours = first_n_hours\n",
    "    )\n",
    "    costs_broadcast_thin_df.sort_values(by=\"cost\", inplace=True)\n",
    "    print(costs_broadcast_thin_df.head(1))\n",
    "    \n",
    "    # To return:\n",
    "    best_broadcast = {\n",
    "        'q': costs_broadcast_thin_df[\"q\"][0]\n",
    "    }\n",
    "    \n",
    "    options_simple = {\n",
    "        'p': [0.0001, 0.001, 0.003, 0.005, 0.01, 0.015],\n",
    "        's': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
    "        'q': [best_broadcast['q']]\n",
    "    }\n",
    "    \n",
    "    costs_simple_df = test_simple_params(\n",
    "        options_simple, \n",
    "        m_agents_df, \n",
    "        m_exposures_by_login, \n",
    "        first_n_hours = first_n_hours\n",
    "    )\n",
    "    costs_simple_thin_df = test_simple_params(\n",
    "        thin_simple_search(costs_simple_df),\n",
    "        m_agents_df,\n",
    "        m_exposures_by_login,\n",
    "        first_n_hours = first_n_hours\n",
    "    ) \n",
    "    costs_simple_thin_df.sort_values(by=\"cost\", inplace=True)\n",
    "    print(costs_simple_thin_df.head(1))\n",
    "    \n",
    "    # To return:\n",
    "    best_simple = {\n",
    "        'p': costs_simple_thin_df[\"p\"][0],\n",
    "        'q': costs_simple_thin_df[\"q\"][0],\n",
    "        's': costs_simple_thin_df[\"s\"][0]\n",
    "    }\n",
    "    \n",
    "    options_complex = {\n",
    "        'k_0': [5, 20, 50, 100, 200, 300],\n",
    "        'p_hi': [0.05, 0.1, 0.2, 0.3, 0.5],\n",
    "        'g': [0.15],      # range of roughly 50, 10% of median followers.\n",
    "        's': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
    "        'q': [best_broadcast['q']]\n",
    "    }\n",
    "\n",
    "    costs_complex_df = test_complex_params(\n",
    "        options_complex, \n",
    "        m_agents_df, \n",
    "        m_exposures_by_login, \n",
    "        first_n_hours = first_n_hours\n",
    "    )\n",
    "    costs_complex_thin_df = test_complex_params(\n",
    "        thin_complex_search(costs_complex_df),\n",
    "        m_agents_df,\n",
    "        m_exposures_by_login,\n",
    "        first_n_hours = first_n_hours\n",
    "    )    \n",
    "    \n",
    "    print(costs_complex_thin_df.sort_values(by=\"cost\").head(1))\n",
    "    print(\"_______________\")\n",
    "    \n",
    "    # To return:\n",
    "    best_complex = {\n",
    "        'k_0': costs_complex_thin_df[\"k_0\"][0],\n",
    "        'p_hi': costs_complex_thin_df[\"p_hi\"][0],\n",
    "        'g': costs_complex_thin_df[\"g\"][0],\n",
    "        'q': costs_complex_thin_df[\"q\"][0],        \n",
    "        's': costs_complex_thin_df[\"s\"][0]\n",
    "    }\n",
    "    \n",
    "    return best_broadcast, best_simple, best_complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3bc3b329",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          q       cost\n",
      "2  0.000007  66.037559\n",
      "        p         q    s      cost\n",
      "4  0.0026  0.000005  0.1  2.192488\n",
      "      k_0  p_hi     g         q    s      cost\n",
      "79  140.0  0.38  0.15  0.000005  0.5  3.661972\n",
      "_______________\n",
      "          q        cost\n",
      "3  0.000013  312.892019\n",
      "         p        q    s      cost\n",
      "16  0.0046  0.00001  0.2  9.122066\n",
      "    k_0  p_hi     g        q     s       cost\n",
      "6  50.0  0.22  0.15  0.00001  0.15  14.431925\n",
      "_______________\n",
      "          q        cost\n",
      "4  0.000009  141.699531\n",
      "        p         q    s      cost\n",
      "12  0.003  0.000005  0.4  8.103286\n",
      "     k_0  p_hi     g         q    s      cost\n",
      "75  70.0  0.32  0.15  0.000005  0.1  3.892019\n",
      "_______________\n",
      "          q        cost\n",
      "2  0.000012  925.079812\n",
      "        p        q     s       cost\n",
      "8  0.0038  0.00001  0.15  50.643192\n",
      "    k_0  p_hi     g        q     s       cost\n",
      "6  50.0  0.06  0.15  0.00001  0.15  36.507042\n",
      "_______________\n",
      "          q       cost\n",
      "1  0.000006  94.389671\n",
      "        p         q    s      cost\n",
      "8  0.0038  0.000005  0.2  4.399061\n",
      "     k_0  p_hi     g         q    s      cost\n",
      "54  60.0  0.17  0.15  0.000005  0.3  4.774648\n",
      "_______________\n",
      "          q        cost\n",
      "1  0.000011  592.957746\n",
      "         p        q    s      cost\n",
      "28  0.0046  0.00001  0.3  26.28169\n",
      "    k_0  p_hi     g        q    s       cost\n",
      "5  50.0  0.22  0.15  0.00001  0.1  14.220657\n",
      "_______________\n"
     ]
    }
   ],
   "source": [
    "for cluster_label, cl_agents_df in agents_df.groupby(\"cluster_label\"):\n",
    "    \n",
    "    best_broadcast, best_simple, best_complex = find_best_params(cl_agents_df, 48)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3875618",
   "metadata": {},
   "source": [
    "### View Best Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6d728d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fadf55e4d60>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options = [50, 0.1, 0.15, 0.00013, 0.1]\n",
    "\n",
    "for i in range(1):\n",
    "    pred_counts, pred_times = complex_model(\n",
    "        agents_df, \n",
    "        news_df, \n",
    "        first_n_hours = 48,\n",
    "        exposures_by_login = exposures_by_login,\n",
    "        k_0 = options[0],\n",
    "        p_hi = options[1],\n",
    "        g = 0.15,\n",
    "        q=0.000013,\n",
    "        s=options[4],\n",
    "    )\n",
    "    plt.plot(range(len(pred_counts)), pred_counts, color=\"blue\")\n",
    "    \n",
    "true_counts = get_cum_adopts(agents_df)[:48]\n",
    "plt.title(\"Predicted vs Actual for Best Complex Model over 48 Hrs\")\n",
    "plt.plot(range(len(true_counts)), true_counts, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7d215177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "options = [50, 0.1, 0.15, 0.00013, 0.1]\n",
    "\n",
    "\n",
    "for cluster_label, cl_agents_df in agents_df.groupby(\"cluster_label\"):\n",
    "    \n",
    "    cl_agents_df.reset_index(inplace=True)\n",
    "    \n",
    "    cl_logins = generate_logins(cl_agents_df)\n",
    "    cl_exposures_by_login = get_exposures_by_login(cl_agents_df, cl_logins)\n",
    "    \n",
    "    true_counts = get_cum_adopts(cl_agents_df)\n",
    "    plt.plot(range(48), true_counts[:48], color=\"red\")\n",
    "    \n",
    "    for i in range(10):\n",
    "        \n",
    "        pred_counts, pred_times = complex_model(\n",
    "            cl_agents_df, \n",
    "            news_df, \n",
    "            first_n_hours = 48,\n",
    "            exposures_by_login = cl_exposures_by_login,\n",
    "            k_0 = options[0],\n",
    "            p_hi = options[1],\n",
    "            g = 0.15,\n",
    "            q=0.000013,\n",
    "            s=options[4],\n",
    "        )\n",
    "        plt.title(f\"Predicted vs Actual for Best Complex Model over 48 Hrs, Cluster {labels[cluster_label]}\")\n",
    "        plt.plot(range(len(pred_counts)), pred_counts, color=\"black\")\n",
    "        \n",
    "    i += 1\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd8590a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}