ensemble_train_world.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "source": [
    "import csv\r\n",
    "import os\r\n",
    "\r\n",
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "#from owid_downloader import GenerateTrainingData\r\n",
    "#from utils import date_today, gravity_law_commute_dist\r\n",
    "\r\n",
    "os.environ['NUMEXPR_MAX_THREADS'] = '16'\r\n",
    "os.environ['NUMEXPR_NUM_THREADS'] = '8'\r\n",
    "\r\n",
    "import pickle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import dgl\r\n",
    "import torch\r\n",
    "from torch import nn\r\n",
    "import torch.nn.functional as F\r\n",
    "from model import STAN\r\n",
    "\r\n",
    "import sklearn\r\n",
    "from sklearn.metrics import mean_absolute_error\r\n",
    "from sklearn.metrics import mean_squared_error\r\n",
    "\r\n",
    "from stan_sirvc_layer import run_layer, get_features\r\n",
    "import matplotlib.dates as mdates"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "source": [
    "#GenerateTrainingData().download_jhu_data('2020-08-01', '2020-12-01')\r\n",
    "start_date = '2021-01-01'\r\n",
    "end_date = '2021-05-31'"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "source": [
    "# data processing\r\n",
    "raw_data = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv', usecols=[\"location\", \"date\", \"total_cases\", \"new_cases_smoothed\", \"total_deaths\",\r\n",
    "                    \"new_deaths\", \"total_vaccinations\", \"people_fully_vaccinated\", \"new_vaccinations\", \"population\"])\r\n",
    "raw_data['date'] = pd.to_datetime(raw_data['date'])\r\n",
    "mask = (raw_data['date'] >= start_date) & (raw_data['date'] <= end_date)\r\n",
    "raw_data = raw_data.loc[mask]\r\n",
    "\r\n",
    "countries = []\r\n",
    "loc_list = list(raw_data['location'].unique())\r\n",
    "# only include countries that have more than 1000 total cases on start date and at least 1 death\r\n",
    "for loc in loc_list:\r\n",
    "    if raw_data[raw_data['location'] == loc][\"total_cases\"].values[0] > 1000 and \\\r\n",
    "        raw_data[raw_data['location'] == loc][\"total_deaths\"].values[0] > 0:\r\n",
    "        countries.append(loc)\r\n",
    "# hard-coded; these are problematic locations (non-countries) that need to be removed\r\n",
    "countries.remove(\"European Union\")\r\n",
    "countries.remove(\"Europe\")\r\n",
    "countries.remove(\"Africa\")\r\n",
    "countries.remove(\"Asia\")\r\n",
    "countries.remove(\"North America\")\r\n",
    "countries.remove(\"Oceania\")\r\n",
    "countries.remove(\"South America\")\r\n",
    "countries.remove(\"World\")\r\n",
    "countries.remove(\"Tajikistan\")\r\n",
    "continents = [\"Europe\", \"Africa\", \"Asia\", \"North America\", \"South America\", \"Oceania\"]\r\n",
    "world = [\"World\"]\r\n",
    "\r\n",
    "country_mask = raw_data['location'].isin(countries)\r\n",
    "continent_mask = raw_data['location'].isin(continents)\r\n",
    "world_mask = raw_data['location'].isin(world)\r\n",
    "\r\n",
    "country_raw_data = raw_data.loc[country_mask]\r\n",
    "continent_raw_data = raw_data.loc[continent_mask]\r\n",
    "world_raw_data = raw_data.loc[world_mask]\r\n",
    "\r\n",
    "print(len(country_raw_data['location'].unique()))   # 163\r\n",
    "print(len(continent_raw_data['location'].unique())) # 6\r\n",
    "print(len(world_raw_data['location'].unique()))     # 1"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "163\n",
      "6\n",
      "1\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "source": [
    "# layer 1, 2, 3\r\n",
    "test_window = 29\r\n",
    "history_window=14 # days of information\r\n",
    "pred_window=14 # predicts future # of days\r\n",
    "test_start_date = pd.to_datetime(end_date) + pd.DateOffset(days=-test_window)\r\n",
    "_, world_features, world_active_cases, world_static_feat, world_norms = get_features(world_raw_data, start_date, end_date, world, edges=False)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "done\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "loc_name = \"World\"\r\n",
    "continent_pred_prev = []\r\n",
    "continent_pred_test = []\r\n",
    "random = False\r\n",
    "for continent in continents:\r\n",
    "    if not random:\r\n",
    "        continent_pred_prev.append(pickle.load(open(f'results/trained_{continent}_pred_dI_prev', 'rb')))\r\n",
    "        continent_pred_test.append(pickle.load(open(f'results/trained_{continent}_pred_dI', 'rb')))\r\n",
    "    if random:\r\n",
    "        continent_pred_prev.append(pickle.load(open(f'results/trained_{continent}_pred_dI_prev_random', 'rb')))\r\n",
    "        continent_pred_test.append(pickle.load(open(f'results/trained_{continent}_pred_dI_random', 'rb')))\r\n",
    "\r\n",
    "continent_pred_prev = np.array(continent_pred_prev)\r\n",
    "continent_pred_test = np.array(continent_pred_test)\r\n",
    "\r\n",
    "input_continent_pred_prev = (continent_pred_prev - world_norms[0][0]) / world_norms[1][0]\r\n",
    "input_continent_pred_test = (continent_pred_test - world_norms[0][0]) / world_norms[1][0]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "source": [
    "print(continent_pred_prev.shape) # n_loc, timestep, pred_window\r\n",
    "\r\n",
    "x_train = input_continent_pred_prev.transpose(1, 0, 2) # swap locs with timesteps\r\n",
    "x_test = input_continent_pred_test.transpose(1, 0, 2)\r\n",
    "\r\n",
    "x_train = x_train.reshape(x_train.shape[0], x_train.shape[1]*x_train.shape[2]) # timesteps, locs*pred_window\r\n",
    "x_test = x_test.reshape(x_test.shape[0], x_test.shape[1]*x_test.shape[2])\r\n",
    "\r\n",
    "print(x_train.shape)\r\n",
    "\r\n",
    "# get true_dI for each continent\r\n",
    "true_dI_train = world_features[0][5] # 0=train, 5=yI; locations, timesteps, values\r\n",
    "true_dI_val = world_features[1][5]\r\n",
    "\r\n",
    "# target for train and test\r\n",
    "true_dI_prev = np.concatenate((true_dI_train, true_dI_val), axis=1)\r\n",
    "true_dI_test = world_features[2][5]\r\n",
    "\r\n",
    "y_train = true_dI_prev[0]\r\n",
    "y_test = true_dI_test[0]"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(6, 19, 14)\n",
      "(19, 84)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "source": [
    "class MLP(nn.Module):\r\n",
    "    def __init__(self, n_continents, pred_window):\r\n",
    "        super(MLP, self).__init__()\r\n",
    "        self.layers = nn.Sequential(\r\n",
    "            nn.Linear(n_continents*pred_window, 64),\r\n",
    "            nn.ReLU(),\r\n",
    "            nn.Linear(64, pred_window)\r\n",
    "        )\r\n",
    "        \r\n",
    "    def forward(self, x):\r\n",
    "        x = self.layers(x)\r\n",
    "        return x"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n",
    "\r\n",
    "x_train = torch.tensor(x_train, dtype=torch.float).to(device)\r\n",
    "x_test = torch.tensor(x_test, dtype=torch.float).to(device)\r\n",
    "\r\n",
    "y_train = torch.tensor(y_train, dtype=torch.float).to(device)\r\n",
    "y_test = torch.tensor(y_test, dtype=torch.float).to(device)\r\n",
    "\r\n",
    "model = MLP(len(continents), pred_window).to(device)\r\n",
    "print(model)\r\n",
    "\r\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\r\n",
    "loss_fn = nn.MSELoss()\r\n",
    "\r\n",
    "for epoch in range(2000):\r\n",
    "    model.train()\r\n",
    "\r\n",
    "    optimizer.zero_grad()\r\n",
    "    train_pred = model(x_train)\r\n",
    "    loss = loss_fn(train_pred, y_train)\r\n",
    "\r\n",
    "    loss.backward()\r\n",
    "    optimizer.step()\r\n",
    "\r\n",
    "    if epoch % 10 == 0:\r\n",
    "        print('Epoch {}: train loss: {}'.format(epoch, loss.item()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "MLP(\n",
      "  (layers): Sequential(\n",
      "    (0): Linear(in_features=84, out_features=64, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=64, out_features=14, bias=True)\n",
      "  )\n",
      ")\n",
      "Epoch 0: train loss: 0.6462457776069641\n",
      "Epoch 10: train loss: 0.5104310512542725\n",
      "Epoch 20: train loss: 0.3557394742965698\n",
      "Epoch 30: train loss: 0.20883233845233917\n",
      "Epoch 40: train loss: 0.1203899011015892\n",
      "Epoch 50: train loss: 0.08025120198726654\n",
      "Epoch 60: train loss: 0.0683147981762886\n",
      "Epoch 70: train loss: 0.060771435499191284\n",
      "Epoch 80: train loss: 0.05626152455806732\n",
      "Epoch 90: train loss: 0.05363292619585991\n",
      "Epoch 100: train loss: 0.05123676359653473\n",
      "Epoch 110: train loss: 0.04928186908364296\n",
      "Epoch 120: train loss: 0.04753967374563217\n",
      "Epoch 130: train loss: 0.045754991471767426\n",
      "Epoch 140: train loss: 0.043975263833999634\n",
      "Epoch 150: train loss: 0.042244065552949905\n",
      "Epoch 160: train loss: 0.040511004626750946\n",
      "Epoch 170: train loss: 0.038788117468357086\n",
      "Epoch 180: train loss: 0.037102844566106796\n",
      "Epoch 190: train loss: 0.03543810918927193\n",
      "Epoch 200: train loss: 0.03380044177174568\n",
      "Epoch 210: train loss: 0.0321914479136467\n",
      "Epoch 220: train loss: 0.03063727542757988\n",
      "Epoch 230: train loss: 0.02915012277662754\n",
      "Epoch 240: train loss: 0.02772979997098446\n",
      "Epoch 250: train loss: 0.026383092626929283\n",
      "Epoch 260: train loss: 0.025104336440563202\n",
      "Epoch 270: train loss: 0.023887071758508682\n",
      "Epoch 280: train loss: 0.022774644196033478\n",
      "Epoch 290: train loss: 0.02173186093568802\n",
      "Epoch 300: train loss: 0.020752735435962677\n",
      "Epoch 310: train loss: 0.019836490973830223\n",
      "Epoch 320: train loss: 0.018975723534822464\n",
      "Epoch 330: train loss: 0.018166638910770416\n",
      "Epoch 340: train loss: 0.017406804487109184\n",
      "Epoch 350: train loss: 0.016693323850631714\n",
      "Epoch 360: train loss: 0.01602180115878582\n",
      "Epoch 370: train loss: 0.015391400083899498\n",
      "Epoch 380: train loss: 0.014798006974160671\n",
      "Epoch 390: train loss: 0.01424330286681652\n",
      "Epoch 400: train loss: 0.01371988095343113\n",
      "Epoch 410: train loss: 0.013225856237113476\n",
      "Epoch 420: train loss: 0.012752062641084194\n",
      "Epoch 430: train loss: 0.012302662245929241\n",
      "Epoch 440: train loss: 0.011876623146235943\n",
      "Epoch 450: train loss: 0.011473331600427628\n",
      "Epoch 460: train loss: 0.011093652807176113\n",
      "Epoch 470: train loss: 0.010732105933129787\n",
      "Epoch 480: train loss: 0.01038957666605711\n",
      "Epoch 490: train loss: 0.010060521773993969\n",
      "Epoch 500: train loss: 0.009744711220264435\n",
      "Epoch 510: train loss: 0.009440049529075623\n",
      "Epoch 520: train loss: 0.009146488271653652\n",
      "Epoch 530: train loss: 0.008861257694661617\n",
      "Epoch 540: train loss: 0.008583374321460724\n",
      "Epoch 550: train loss: 0.008313351310789585\n",
      "Epoch 560: train loss: 0.008048656396567822\n",
      "Epoch 570: train loss: 0.007789588533341885\n",
      "Epoch 580: train loss: 0.0075360313057899475\n",
      "Epoch 590: train loss: 0.007285814732313156\n",
      "Epoch 600: train loss: 0.007041922304779291\n",
      "Epoch 610: train loss: 0.006800142582505941\n",
      "Epoch 620: train loss: 0.006562459748238325\n",
      "Epoch 630: train loss: 0.006329293828457594\n",
      "Epoch 640: train loss: 0.006099614780396223\n",
      "Epoch 650: train loss: 0.005874311551451683\n",
      "Epoch 660: train loss: 0.005651973653584719\n",
      "Epoch 670: train loss: 0.005435182712972164\n",
      "Epoch 680: train loss: 0.005221586674451828\n",
      "Epoch 690: train loss: 0.005012847483158112\n",
      "Epoch 700: train loss: 0.004808229859918356\n",
      "Epoch 710: train loss: 0.00460813008248806\n",
      "Epoch 720: train loss: 0.00441317493095994\n",
      "Epoch 730: train loss: 0.004222588613629341\n",
      "Epoch 740: train loss: 0.004038889892399311\n",
      "Epoch 750: train loss: 0.003858750220388174\n",
      "Epoch 760: train loss: 0.0036849246826022863\n",
      "Epoch 770: train loss: 0.0035168721806257963\n",
      "Epoch 780: train loss: 0.0033539175055921078\n",
      "Epoch 790: train loss: 0.003198034595698118\n",
      "Epoch 800: train loss: 0.0030464916490018368\n",
      "Epoch 810: train loss: 0.002901143627241254\n",
      "Epoch 820: train loss: 0.002760865492746234\n",
      "Epoch 830: train loss: 0.002626395784318447\n",
      "Epoch 840: train loss: 0.0024976986460387707\n",
      "Epoch 850: train loss: 0.0023738944437354803\n",
      "Epoch 860: train loss: 0.002256352687254548\n",
      "Epoch 870: train loss: 0.0021443027071654797\n",
      "Epoch 880: train loss: 0.0020374255254864693\n",
      "Epoch 890: train loss: 0.0019358970457687974\n",
      "Epoch 900: train loss: 0.001838996191509068\n",
      "Epoch 910: train loss: 0.0017469741869717836\n",
      "Epoch 920: train loss: 0.0016595602501183748\n",
      "Epoch 930: train loss: 0.0015765068819746375\n",
      "Epoch 940: train loss: 0.0014975201338529587\n",
      "Epoch 950: train loss: 0.0014226260827854276\n",
      "Epoch 960: train loss: 0.0013518987689167261\n",
      "Epoch 970: train loss: 0.0012847156031057239\n",
      "Epoch 980: train loss: 0.00122117530554533\n",
      "Epoch 990: train loss: 0.0011609346838667989\n",
      "Epoch 1000: train loss: 0.0011041397228837013\n",
      "Epoch 1010: train loss: 0.0010498063638806343\n",
      "Epoch 1020: train loss: 0.0009981743060052395\n",
      "Epoch 1030: train loss: 0.0009490512893535197\n",
      "Epoch 1040: train loss: 0.0009022860322147608\n",
      "Epoch 1050: train loss: 0.0008580040885135531\n",
      "Epoch 1060: train loss: 0.0008157183765433729\n",
      "Epoch 1070: train loss: 0.0007756686536595225\n",
      "Epoch 1080: train loss: 0.0007374625420197845\n",
      "Epoch 1090: train loss: 0.000701023789588362\n",
      "Epoch 1100: train loss: 0.0006663889507763088\n",
      "Epoch 1110: train loss: 0.0006336576770991087\n",
      "Epoch 1120: train loss: 0.0006023385794833302\n",
      "Epoch 1130: train loss: 0.0005725229275412858\n",
      "Epoch 1140: train loss: 0.0005441308603622019\n",
      "Epoch 1150: train loss: 0.0005171673255972564\n",
      "Epoch 1160: train loss: 0.0004914577002637088\n",
      "Epoch 1170: train loss: 0.0004670626949518919\n",
      "Epoch 1180: train loss: 0.00044381053885445\n",
      "Epoch 1190: train loss: 0.0004216497763991356\n",
      "Epoch 1200: train loss: 0.00040053061093203723\n",
      "Epoch 1210: train loss: 0.00038051154115237296\n",
      "Epoch 1220: train loss: 0.0003614351153373718\n",
      "Epoch 1230: train loss: 0.0003433664096519351\n",
      "Epoch 1240: train loss: 0.000326176465023309\n",
      "Epoch 1250: train loss: 0.00030981985037215054\n",
      "Epoch 1260: train loss: 0.0002942953142337501\n",
      "Epoch 1270: train loss: 0.0002796711341943592\n",
      "Epoch 1280: train loss: 0.0002656364522408694\n",
      "Epoch 1290: train loss: 0.00025242252741008997\n",
      "Epoch 1300: train loss: 0.00023977454111445695\n",
      "Epoch 1310: train loss: 0.00022784229076933116\n",
      "Epoch 1320: train loss: 0.00021651785937137902\n",
      "Epoch 1330: train loss: 0.0002057154633803293\n",
      "Epoch 1340: train loss: 0.00019547717238310724\n",
      "Epoch 1350: train loss: 0.00018583591736387461\n",
      "Epoch 1360: train loss: 0.00017661627498455346\n",
      "Epoch 1370: train loss: 0.00016782668535597622\n",
      "Epoch 1380: train loss: 0.00015940571029204875\n",
      "Epoch 1390: train loss: 0.00015147447993513197\n",
      "Epoch 1400: train loss: 0.0001439611951354891\n",
      "Epoch 1410: train loss: 0.00013678622781299055\n",
      "Epoch 1420: train loss: 0.00013001250044908375\n",
      "Epoch 1430: train loss: 0.0001236127718584612\n",
      "Epoch 1440: train loss: 0.00011748249380616471\n",
      "Epoch 1450: train loss: 0.00011168935452587903\n",
      "Epoch 1460: train loss: 0.00010618102533044294\n",
      "Epoch 1470: train loss: 0.00010095901234308258\n",
      "Epoch 1480: train loss: 9.598236647434533e-05\n",
      "Epoch 1490: train loss: 9.127214434556663e-05\n",
      "Epoch 1500: train loss: 8.679062739247456e-05\n",
      "Epoch 1510: train loss: 8.250681275967509e-05\n",
      "Epoch 1520: train loss: 7.84301373641938e-05\n",
      "Epoch 1530: train loss: 7.457742322003469e-05\n",
      "Epoch 1540: train loss: 7.088200800353661e-05\n",
      "Epoch 1550: train loss: 6.737351213814691e-05\n",
      "Epoch 1560: train loss: 6.403897714335471e-05\n",
      "Epoch 1570: train loss: 6.0869857406942174e-05\n",
      "Epoch 1580: train loss: 5.784839959233068e-05\n",
      "Epoch 1590: train loss: 5.497406891663559e-05\n",
      "Epoch 1600: train loss: 5.2236326155252755e-05\n",
      "Epoch 1610: train loss: 4.962995808455162e-05\n",
      "Epoch 1620: train loss: 4.7145033022388816e-05\n",
      "Epoch 1630: train loss: 4.478674236452207e-05\n",
      "Epoch 1640: train loss: 4.253450606483966e-05\n",
      "Epoch 1650: train loss: 4.038958650198765e-05\n",
      "Epoch 1660: train loss: 3.834895323961973e-05\n",
      "Epoch 1670: train loss: 3.640923387138173e-05\n",
      "Epoch 1680: train loss: 3.4563272492960095e-05\n",
      "Epoch 1690: train loss: 3.280064629507251e-05\n",
      "Epoch 1700: train loss: 3.112605554633774e-05\n",
      "Epoch 1710: train loss: 2.9532193366321735e-05\n",
      "Epoch 1720: train loss: 2.801414484565612e-05\n",
      "Epoch 1730: train loss: 2.6570656700641848e-05\n",
      "Epoch 1740: train loss: 2.5197261493303813e-05\n",
      "Epoch 1750: train loss: 2.388926804997027e-05\n",
      "Epoch 1760: train loss: 2.2644906493951567e-05\n",
      "Epoch 1770: train loss: 2.146091719623655e-05\n",
      "Epoch 1780: train loss: 2.0334999135229737e-05\n",
      "Epoch 1790: train loss: 1.9265076844021678e-05\n",
      "Epoch 1800: train loss: 1.824860373744741e-05\n",
      "Epoch 1810: train loss: 1.7281090549658984e-05\n",
      "Epoch 1820: train loss: 1.6361493180738762e-05\n",
      "Epoch 1830: train loss: 1.5488098142668605e-05\n",
      "Epoch 1840: train loss: 1.4657798601547256e-05\n",
      "Epoch 1850: train loss: 1.386931398883462e-05\n",
      "Epoch 1860: train loss: 1.3121301890350878e-05\n",
      "Epoch 1870: train loss: 1.241019072040217e-05\n",
      "Epoch 1880: train loss: 1.1735568477888592e-05\n",
      "Epoch 1890: train loss: 1.1095564332208596e-05\n",
      "Epoch 1900: train loss: 1.0488049156265333e-05\n",
      "Epoch 1910: train loss: 9.91498654911993e-06\n",
      "Epoch 1920: train loss: 9.372553904540837e-06\n",
      "Epoch 1930: train loss: 8.85896497493377e-06\n",
      "Epoch 1940: train loss: 8.371745025215205e-06\n",
      "Epoch 1950: train loss: 7.911347893241327e-06\n",
      "Epoch 1960: train loss: 7.475437541870633e-06\n",
      "Epoch 1970: train loss: 7.0624760155624244e-06\n",
      "Epoch 1980: train loss: 6.671219580312027e-06\n",
      "Epoch 1990: train loss: 6.301105713646393e-06\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "source": [
    "test_pred = model(x_test)\r\n",
    "train_pred = model(x_train)\r\n",
    "\r\n",
    "# Cumulate predicted dI\r\n",
    "pred_I = (test_pred.detach().cpu().numpy() * world_norms[1][0]) + world_norms[0][0]\r\n",
    "pred_I_prev = (train_pred.detach().cpu().numpy() * world_norms[1][0]) + world_norms[0][0]\r\n",
    "# pickle.dump(pred_I, open(f\"./results/trained_World_pred_dI\", 'wb'), 2)\r\n",
    "# pickle.dump(pred_I_prev, open(f\"./results/trained_World_pred_dI_prev\", 'wb'), 2)\r\n",
    "pred_I = np.cumsum(pred_I)\r\n",
    "\r\n",
    "test_I = world_features[2][1]\r\n",
    "\r\n",
    "pred_I = pred_I + test_I[0]\r\n",
    "print(pred_I)\r\n",
    "\r\n",
    "pred_I = np.array(pred_I)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[10150457. 10016774.  9908096.  9867610.  9759169.  9628968.  9572678.\n",
      "  9417512.  9226887.  9178077.  9034421.  8908541.  8705593.  8596234.]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "source": [
    "I_true = world_active_cases[0][-test_window:]\r\n",
    "I_true = I_true[history_window:history_window+pred_window]\r\n",
    "print(I_true)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[10058856.  9873921.  9704407.  9113707.  8906597.  8698972.  8533923.\n",
      "  8367378.  8160793.  7969293.  7799285.  7581473.  7434742.  7277611.]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "source": [
    "import matplotlib.dates as mdates\r\n",
    "dates = [test_start_date + pd.DateOffset(days=i+history_window) for i in range(pred_window)]\r\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\r\n",
    "plt.plot(dates, I_true, c='r', label='Ground truth')\r\n",
    "plt.plot(dates, pred_I, c='b', label='Proposed')\r\n",
    "# stan_res = pickle.load(open(f\"stan_results/{loc_name}_stan_results_new\", \"rb\"))\r\n",
    "# stan_sirv_res = pickle.load(open(f\"stan_results/{loc_name}_stan_results_sirv\", \"rb\"))\r\n",
    "# plt.plot(dates, stan_res[-1, :], c='g', label=\"STAN (SIRV)\")\r\n",
    "# plt.plot(dates, sir_I[-1, :], c='purple', label=\"SIRVIC\")\r\n",
    "plt.gcf().autofmt_xdate()\r\n",
    "plt.legend()\r\n",
    "plt.ylabel(\"Number of Active Cases\")\r\n",
    "plt.title(\"World\")\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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  {
   "cell_type": "code",
   "execution_count": 83,
   "source": [
    "prop_mse = ((pred_I - I_true)**2).mean()\r\n",
    "print(f\"Proposed: {prop_mse}\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Proposed: 975031148504.5\n"
     ]
    }
   ],
   "metadata": {}
  }
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