{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from src.dataset import PandemicDataset\n",
    "from src.problem import PandemicProblem\n",
    "from src.dinn import DINN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data = np.genfromtxt('./datasets/SIR_data.csv', delimiter=',')\n",
    "dataset = PandemicDataset(['S', 'I', 'R'], 7900000, *covid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SIRProblem(PandemicProblem):\n",
    "    def __init__(self, data: PandemicDataset):\n",
    "        super().__init__(data)\n",
    "\n",
    "    def residual(self, SIR_pred, data: PandemicDataset, alpha, beta):\n",
    "        S_pred, I_pred, R_pred = SIR_pred[:, 0], SIR_pred[:, 1], SIR_pred[:, 2]\n",
    "            \n",
    "        SIR_pred.backward(self.gradients[0], retain_graph=True)\n",
    "        dSdt = data.t_raw.grad.clone()\n",
    "        data.t_raw.grad.zero_()\n",
    "\n",
    "        SIR_pred.backward(self.gradients[1], retain_graph=True)\n",
    "        dIdt = data.t_raw.grad.clone()\n",
    "        data.t_raw.grad.zero_()\n",
    "\n",
    "        SIR_pred.backward(self.gradients[2], retain_graph=True)\n",
    "        dRdt = data.t_raw.grad.clone()\n",
    "        data.t_raw.grad.zero_()\n",
    "        \n",
    "        S = data.get_min('S') + (data.get_max('S') - data.get_min('S')) * S_pred\n",
    "        I = data.get_min('I') + (data.get_max('I') - data.get_min('I')) * I_pred\n",
    "        R = data.get_min('R') + (data.get_max('R') - data.get_min('R')) * R_pred\n",
    "\n",
    "        S_residual = dSdt - (-beta * ((S * I) / data.N)) / (data.get_max('S') - data.get_min('S'))\n",
    "        I_residual = dIdt - (beta * ((S * I) / data.N) - alpha * I) / (data.get_max('I') - data.get_min('I'))\n",
    "        R_residual = dRdt - (alpha * I) / (data.get_max('R') - data.get_min('R'))\n",
    "\n",
    "        return S_residual, I_residual, R_residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = SIRProblem(dataset)\n",
    "dinn = DINN(3, dataset, ['alpha', 'beta'], problem)\n",
    "# dinn.to_cuda()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch  0\n",
      "physics loss:\t\t0.00044685273344467665\n",
      "observation loss:\t1.2245409441177175\n",
      "loss:\t\t\t1.2249877968511622\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.083763487637043\n",
      "beta:\t\t\t0.6109486818313599\n",
      "#################################\n",
      "\n",
      "Epoch  1000\n",
      "physics loss:\t\t0.00013292721349644342\n",
      "observation loss:\t0.6062661553690567\n",
      "loss:\t\t\t0.6063990825825532\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08948440104722977\n",
      "beta:\t\t\t0.6072231531143188\n",
      "#################################\n",
      "\n",
      "Epoch  2000\n",
      "physics loss:\t\t0.0012941294955406924\n",
      "observation loss:\t0.46864197083496695\n",
      "loss:\t\t\t0.46993610033050764\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.10565895587205887\n",
      "beta:\t\t\t0.596127986907959\n",
      "#################################\n",
      "\n",
      "Epoch  3000\n",
      "physics loss:\t\t0.0011427965251261313\n",
      "observation loss:\t0.44917103779330514\n",
      "loss:\t\t\t0.45031383431843125\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.11779233813285828\n",
      "beta:\t\t\t0.5880351066589355\n",
      "#################################\n",
      "\n",
      "Epoch  4000\n",
      "physics loss:\t\t0.0009804308751867187\n",
      "observation loss:\t0.42587292439619107\n",
      "loss:\t\t\t0.4268533552713778\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.12778256833553314\n",
      "beta:\t\t\t0.5813132524490356\n",
      "#################################\n",
      "\n",
      "Epoch  5000\n",
      "physics loss:\t\t0.0008698020268223673\n",
      "observation loss:\t0.38923744928223314\n",
      "loss:\t\t\t0.3901072513090555\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.1374264657497406\n",
      "beta:\t\t\t0.5747601985931396\n",
      "#################################\n",
      "\n",
      "Epoch  6000\n",
      "physics loss:\t\t0.0008932732743767176\n",
      "observation loss:\t0.31268866377008186\n",
      "loss:\t\t\t0.3135819370444586\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.14699114859104156\n",
      "beta:\t\t\t0.5681848526000977\n",
      "#################################\n",
      "\n",
      "Epoch  7000\n",
      "physics loss:\t\t0.0014417972995630379\n",
      "observation loss:\t0.21859313779724948\n",
      "loss:\t\t\t0.22003493509681252\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.15807460248470306\n",
      "beta:\t\t\t0.5599933862686157\n",
      "#################################\n",
      "\n",
      "Epoch  8000\n",
      "physics loss:\t\t0.0025154446144873147\n",
      "observation loss:\t0.13095959697906104\n",
      "loss:\t\t\t0.13347504159354837\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.17077937722206116\n",
      "beta:\t\t\t0.5499967336654663\n",
      "#################################\n",
      "\n",
      "Epoch  9000\n",
      "physics loss:\t\t0.0022843827047599945\n",
      "observation loss:\t0.08978349230107566\n",
      "loss:\t\t\t0.09206787500583564\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.18149374425411224\n",
      "beta:\t\t\t0.5417306423187256\n",
      "#################################\n",
      "\n",
      "Epoch  10000\n",
      "physics loss:\t\t0.0021971007648884846\n",
      "observation loss:\t0.06963749098265264\n",
      "loss:\t\t\t0.07183459174754112\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.19147348403930664\n",
      "beta:\t\t\t0.5342303514480591\n",
      "#################################\n",
      "\n",
      "Epoch  11000\n",
      "physics loss:\t\t0.002168132256709243\n",
      "observation loss:\t0.05576356036942948\n",
      "loss:\t\t\t0.05793169262613872\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.20174719393253326\n",
      "beta:\t\t\t0.5264887809753418\n",
      "#################################\n",
      "\n",
      "Epoch  12000\n",
      "physics loss:\t\t0.002001979308493397\n",
      "observation loss:\t0.041449672454476234\n",
      "loss:\t\t\t0.04345165176296963\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.2119624763727188\n",
      "beta:\t\t\t0.5187211036682129\n",
      "#################################\n",
      "\n",
      "Epoch  13000\n",
      "physics loss:\t\t0.0017720226646718842\n",
      "observation loss:\t0.023669702727623157\n",
      "loss:\t\t\t0.025441725392295042\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.22207751870155334\n",
      "beta:\t\t\t0.5109759569168091\n",
      "#################################\n",
      "\n",
      "Epoch  14000\n",
      "physics loss:\t\t0.001394314179305999\n",
      "observation loss:\t0.014038377385339616\n",
      "loss:\t\t\t0.015432691564645615\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.23167401552200317\n",
      "beta:\t\t\t0.5036075115203857\n",
      "#################################\n",
      "\n",
      "Epoch  15000\n",
      "physics loss:\t\t0.0010134952351123932\n",
      "observation loss:\t0.008369291050215628\n",
      "loss:\t\t\t0.00938278628532802\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.24058616161346436\n",
      "beta:\t\t\t0.4966823160648346\n",
      "#################################\n",
      "\n",
      "Epoch  16000\n",
      "physics loss:\t\t0.0007181653170692821\n",
      "observation loss:\t0.004976857958688243\n",
      "loss:\t\t\t0.005695023275757525\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.24891901016235352\n",
      "beta:\t\t\t0.49009084701538086\n",
      "#################################\n",
      "\n",
      "Epoch  17000\n",
      "physics loss:\t\t0.0005106904051980328\n",
      "observation loss:\t0.0024606274926642835\n",
      "loss:\t\t\t0.0029713178978623164\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.25684311985969543\n",
      "beta:\t\t\t0.483694851398468\n",
      "#################################\n",
      "\n",
      "Epoch  18000\n",
      "physics loss:\t\t0.0003589468136243284\n",
      "observation loss:\t0.0010212021045718272\n",
      "loss:\t\t\t0.0013801489181961557\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.26450419425964355\n",
      "beta:\t\t\t0.4773872494697571\n",
      "#################################\n",
      "\n",
      "Epoch  19000\n",
      "physics loss:\t\t0.00024198452727457501\n",
      "observation loss:\t0.0004060045653654164\n",
      "loss:\t\t\t0.0006479890926399915\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.2719448506832123\n",
      "beta:\t\t\t0.47116783261299133\n",
      "#################################\n",
      "\n",
      "Epoch  20000\n",
      "physics loss:\t\t0.00016222554879391233\n",
      "observation loss:\t0.00018594002016422\n",
      "loss:\t\t\t0.00034816556895813234\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.27896976470947266\n",
      "beta:\t\t\t0.4652327597141266\n",
      "#################################\n",
      "\n",
      "Epoch  21000\n",
      "physics loss:\t\t0.00010232301934527376\n",
      "observation loss:\t0.00011704546882443376\n",
      "loss:\t\t\t0.00021936848816970752\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.2853754460811615\n",
      "beta:\t\t\t0.4598190188407898\n",
      "#################################\n",
      "\n",
      "Epoch  22000\n",
      "physics loss:\t\t7.19968878688629e-05\n",
      "observation loss:\t8.937707073081887e-05\n",
      "loss:\t\t\t0.00016137395859968176\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.29087454080581665\n",
      "beta:\t\t\t0.4552651643753052\n",
      "#################################\n",
      "\n",
      "Epoch  23000\n",
      "physics loss:\t\t5.807239215421418e-05\n",
      "observation loss:\t7.265941862897205e-05\n",
      "loss:\t\t\t0.00013073181078318623\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.295198529958725\n",
      "beta:\t\t\t0.4519815742969513\n",
      "#################################\n",
      "\n",
      "Epoch  24000\n",
      "physics loss:\t\t4.821218433344596e-05\n",
      "observation loss:\t6.022719057350864e-05\n",
      "loss:\t\t\t0.0001084393749069546\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.2980384826660156\n",
      "beta:\t\t\t0.45059311389923096\n",
      "#################################\n",
      "\n",
      "Epoch  25000\n",
      "physics loss:\t\t4.449091137945884e-05\n",
      "observation loss:\t5.098170057183685e-05\n",
      "loss:\t\t\t9.547261195129569e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.30001339316368103\n",
      "beta:\t\t\t0.45096927881240845\n",
      "#################################\n",
      "\n",
      "Epoch  26000\n",
      "physics loss:\t\t4.025694622998121e-05\n",
      "observation loss:\t4.317158352701353e-05\n",
      "loss:\t\t\t8.342852975699474e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.30151116847991943\n",
      "beta:\t\t\t0.45318636298179626\n",
      "#################################\n",
      "\n",
      "Epoch  27000\n",
      "physics loss:\t\t3.6564605677175505e-05\n",
      "observation loss:\t3.771584987219665e-05\n",
      "loss:\t\t\t7.428045554937215e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.30391809344291687\n",
      "beta:\t\t\t0.4562521278858185\n",
      "#################################\n",
      "\n",
      "Epoch  28000\n",
      "physics loss:\t\t3.3617744952756e-05\n",
      "observation loss:\t3.3660782022684266e-05\n",
      "loss:\t\t\t6.727852697544027e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3069998323917389\n",
      "beta:\t\t\t0.46070829033851624\n",
      "#################################\n",
      "\n",
      "Epoch  29000\n",
      "physics loss:\t\t3.0720276030823006e-05\n",
      "observation loss:\t3.0367312549745195e-05\n",
      "loss:\t\t\t6.10875885805682e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.31070202589035034\n",
      "beta:\t\t\t0.46622681617736816\n",
      "#################################\n",
      "\n",
      "Epoch  30000\n",
      "physics loss:\t\t2.7238324947160958e-05\n",
      "observation loss:\t2.9429792519201082e-05\n",
      "loss:\t\t\t5.666811746636204e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.314852774143219\n",
      "beta:\t\t\t0.47219935059547424\n",
      "#################################\n",
      "\n",
      "Epoch  31000\n",
      "physics loss:\t\t2.535985384846303e-05\n",
      "observation loss:\t2.629174319527369e-05\n",
      "loss:\t\t\t5.165159704373672e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.31901052594184875\n",
      "beta:\t\t\t0.4782613515853882\n",
      "#################################\n",
      "\n",
      "Epoch  32000\n",
      "physics loss:\t\t2.330540597102229e-05\n",
      "observation loss:\t2.448878447026259e-05\n",
      "loss:\t\t\t4.779419044128488e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3226718306541443\n",
      "beta:\t\t\t0.4839823544025421\n",
      "#################################\n",
      "\n",
      "Epoch  33000\n",
      "physics loss:\t\t2.258908974031205e-05\n",
      "observation loss:\t2.3212395697529116e-05\n",
      "loss:\t\t\t4.580148543784117e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3259775936603546\n",
      "beta:\t\t\t0.4888368844985962\n",
      "#################################\n",
      "\n",
      "Epoch  34000\n",
      "physics loss:\t\t2.1864845953331e-05\n",
      "observation loss:\t2.2174527814448826e-05\n",
      "loss:\t\t\t4.4039373767779825e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3289249539375305\n",
      "beta:\t\t\t0.4930059015750885\n",
      "#################################\n",
      "\n",
      "Epoch  35000\n",
      "physics loss:\t\t2.0396523076989152e-05\n",
      "observation loss:\t2.0791412630697326e-05\n",
      "loss:\t\t\t4.1187935707686474e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3308189809322357\n",
      "beta:\t\t\t0.4963831901550293\n",
      "#################################\n",
      "\n",
      "Epoch  36000\n",
      "physics loss:\t\t1.9788849983006052e-05\n",
      "observation loss:\t1.9845080898003215e-05\n",
      "loss:\t\t\t3.963393088100927e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33232831954956055\n",
      "beta:\t\t\t0.4982999861240387\n",
      "#################################\n",
      "\n",
      "Epoch  37000\n",
      "physics loss:\t\t1.943067795014576e-05\n",
      "observation loss:\t1.909732067247465e-05\n",
      "loss:\t\t\t3.852799862262041e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33291828632354736\n",
      "beta:\t\t\t0.4989745616912842\n",
      "#################################\n",
      "\n",
      "Epoch  38000\n",
      "physics loss:\t\t1.9019445908630463e-05\n",
      "observation loss:\t1.9374030490937203e-05\n",
      "loss:\t\t\t3.8393476399567666e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33312395215034485\n",
      "beta:\t\t\t0.4990815818309784\n",
      "#################################\n",
      "\n",
      "Epoch  39000\n",
      "physics loss:\t\t1.892717052642293e-05\n",
      "observation loss:\t1.7712655248714085e-05\n",
      "loss:\t\t\t3.663982577513702e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33275383710861206\n",
      "beta:\t\t\t0.4991866648197174\n",
      "#################################\n",
      "\n",
      "Epoch  40000\n",
      "physics loss:\t\t1.8468896129030566e-05\n",
      "observation loss:\t1.6678314569396266e-05\n",
      "loss:\t\t\t3.514721069842683e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33309778571128845\n",
      "beta:\t\t\t0.4996398389339447\n",
      "#################################\n",
      "\n",
      "Epoch  41000\n",
      "physics loss:\t\t1.8066797288619405e-05\n",
      "observation loss:\t1.607469720781166e-05\n",
      "loss:\t\t\t3.4141494496431064e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3334086537361145\n",
      "beta:\t\t\t0.4996848702430725\n",
      "#################################\n",
      "\n",
      "Epoch  42000\n",
      "physics loss:\t\t1.7676562174428494e-05\n",
      "observation loss:\t1.5568254963102887e-05\n",
      "loss:\t\t\t3.324481713753138e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3332251012325287\n",
      "beta:\t\t\t0.49945399165153503\n",
      "#################################\n",
      "\n",
      "Epoch  43000\n",
      "physics loss:\t\t1.769992417869784e-05\n",
      "observation loss:\t1.5143310655743795e-05\n",
      "loss:\t\t\t3.284323483444164e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33318382501602173\n",
      "beta:\t\t\t0.4997192919254303\n",
      "#################################\n",
      "\n",
      "Epoch  44000\n",
      "physics loss:\t\t1.746763556208888e-05\n",
      "observation loss:\t1.4537736532634622e-05\n",
      "loss:\t\t\t3.20053720947235e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3331937789916992\n",
      "beta:\t\t\t0.49993598461151123\n",
      "#################################\n",
      "\n",
      "Epoch  45000\n",
      "physics loss:\t\t1.7231192139083542e-05\n",
      "observation loss:\t1.4043802555667369e-05\n",
      "loss:\t\t\t3.127499469475091e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33314958214759827\n",
      "beta:\t\t\t0.49945068359375\n",
      "#################################\n",
      "\n",
      "Epoch  46000\n",
      "physics loss:\t\t1.703850003978787e-05\n",
      "observation loss:\t1.3638799641327025e-05\n",
      "loss:\t\t\t3.0677299681114896e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33316555619239807\n",
      "beta:\t\t\t0.4996236562728882\n",
      "#################################\n",
      "\n",
      "Epoch  47000\n",
      "physics loss:\t\t1.6221416284236092e-05\n",
      "observation loss:\t1.3151343426034392e-05\n",
      "loss:\t\t\t2.9372759710270485e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3331262469291687\n",
      "beta:\t\t\t0.4996646046638489\n",
      "#################################\n",
      "\n",
      "Epoch  48000\n",
      "physics loss:\t\t1.5939162621853766e-05\n",
      "observation loss:\t1.280440230361293e-05\n",
      "loss:\t\t\t2.8743564925466696e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.33305150270462036\n",
      "beta:\t\t\t0.4993121922016144\n",
      "#################################\n",
      "\n",
      "Epoch  49000\n",
      "physics loss:\t\t1.601943460162945e-05\n",
      "observation loss:\t1.252566450280446e-05\n",
      "loss:\t\t\t2.8545099104433914e-05\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.3331560492515564\n",
      "beta:\t\t\t0.4992446303367615\n",
      "#################################\n"
     ]
    }
   ],
   "source": [
    "dinn.train(50000, 1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18024613611474185513\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(torch.seed())\n",
    "dinn.plot_training_graphs([1/3, 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PINN",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}