{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from src.dataset import PandemicDataset\n",
    "from src.problem import ReducedSIRProblem\n",
    "from src.dinn import DINN\n",
    "from src.plotter import Plotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data = np.genfromtxt('./datasets/I_data.csv', delimiter=',')\n",
    "dataset = PandemicDataset('synth_sir', ['I'], 7900000, *covid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learning Rate:\t0.001\n",
      "Optimizer:\tAdam\n",
      "Scheduler:\tPolynomialLR\n",
      "\n"
     ]
    }
   ],
   "source": [
    "problem = ReducedSIRProblem(dataset, 1/3)\n",
    "plotter = Plotter()\n",
    "\n",
    "dinn = DINN(2, dataset, [], problem, plotter, state_variables=['R_t'], hidden_size=100, hidden_layers=4, activation_layer=torch.nn.Tanh())\n",
    "dinn.configure_training(1e-3, 10000, scheduler_name='PolynomialLR', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch seed: 17713276445092292082\n",
      "\n",
      "Epoch 0 | LR 0.0009999\n",
      "physics loss:\t\t200.75545144230463\n",
      "observation loss:\t0.15717132075120926\n",
      "loss:\t\t\t200.91262276305585\n",
      "---------------------------------\n",
      "\n",
      "Epoch 1000 | LR 0.0008998999999999944\n",
      "physics loss:\t\t0.0013854617050044237\n",
      "observation loss:\t0.16383294738830706\n",
      "loss:\t\t\t0.16521840909331148\n",
      "---------------------------------\n",
      "\n",
      "Epoch 2000 | LR 0.0007998999999999963\n",
      "physics loss:\t\t1.974399793653932e-05\n",
      "observation loss:\t0.16385064102067798\n",
      "loss:\t\t\t0.1638703850186145\n",
      "---------------------------------\n",
      "\n",
      "Epoch 3000 | LR 0.000699900000000002\n",
      "physics loss:\t\t1.2178864909808234e-05\n",
      "observation loss:\t0.16385186159520498\n",
      "loss:\t\t\t0.1638640404601148\n",
      "---------------------------------\n",
      "\n",
      "Epoch 4000 | LR 0.0005999000000000103\n",
      "physics loss:\t\t1.0886936385578106e-05\n",
      "observation loss:\t0.16385196829927146\n",
      "loss:\t\t\t0.16386285523565702\n",
      "---------------------------------\n",
      "\n",
      "Epoch 5000 | LR 0.0004999000000000169\n",
      "physics loss:\t\t9.390655151544926e-06\n",
      "observation loss:\t0.16385207283922268\n",
      "loss:\t\t\t0.16386146349437422\n",
      "---------------------------------\n",
      "\n",
      "Epoch 6000 | LR 0.0003999000000000128\n",
      "physics loss:\t\t7.872390751664819e-06\n",
      "observation loss:\t0.16385219529103864\n",
      "loss:\t\t\t0.16386006768179032\n",
      "---------------------------------\n",
      "\n",
      "Epoch 7000 | LR 0.00029990000000000366\n",
      "physics loss:\t\t6.570482698598201e-06\n",
      "observation loss:\t0.16385232556587376\n",
      "loss:\t\t\t0.16385889604857234\n",
      "---------------------------------\n",
      "\n",
      "Epoch 8000 | LR 0.0001998999999999976\n",
      "physics loss:\t\t5.6515586628487274e-06\n",
      "observation loss:\t0.16385244002240354\n",
      "loss:\t\t\t0.1638580915810664\n",
      "---------------------------------\n",
      "\n",
      "Epoch 9000 | LR 9.989999999999895e-05\n",
      "physics loss:\t\t5.136971268701286e-06\n",
      "observation loss:\t0.16385251824906355\n",
      "loss:\t\t\t0.16385765522033224\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "# TODO: physics loss is way of, look at the denormalized I curve, it seems to be the problem of way to high values\n",
    "\n",
    "dinn.train(create_animation=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dinn.plot_training_graphs()\n",
    "dinn.plot_state_variables()"
   ]
  },
  {
   "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
}