Density-PINNs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44da58aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mport warnings\n",
    "#arnings.filterwarnings(action='ignore')\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98bde0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"data/\"\n",
    "model_dir = \"model/\"\n",
    "\n",
    "\n",
    "data_name = \"strongly_bimodal\" # write down your data in the second string\n",
    "save_file_name = data_name # \n",
    "\n",
    "K = 4 ## latent space dim\n",
    "h = 16 ## num. nodes in hidden layers\n",
    "M=80 ## number of kernel\n",
    "s_min = 0.5 ## minimum size of scale params\n",
    "s_max = 2 ## maximum size of scale params\n",
    "\n",
    "n_col_pts = 100\n",
    "learning_rate = 1e-4\n",
    "EPOCH = 2000000 # maximum numbe of iteration\n",
    "early_stopping = True\n",
    "if early_stopping == True:\n",
    "    n_hist = 30  ## number of \n",
    "    epsilon = 1e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2405ccde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"device: {}\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acfac1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_time, time_trace, scaled_trace, mean_scaled_trace = data_preprocessing(data_name, data_dir, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f47a94c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model created\n"
     ]
    }
   ],
   "source": [
    "net_MDN = MDN(d = len(obs_time), final_time = obs_time[-1].item(), K = K, h = h, M = M, s_min = s_min, s_max = s_max, device = device)\n",
    "net_MDN = net_MDN.to(device)\n",
    "net_MDN.data = scaled_trace\n",
    "net_MDN.train()\n",
    "print(\"model created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8121cf48",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt=torch.optim.Adam([{'params': net_MDN.parameters(), 'lr':learning_rate}], amsgrad = True)\n",
    "\n",
    "r1, r2 = obs_time[0].item(), obs_time[-1].item()\n",
    "col_time = torch.linspace(r1,r2,n_col_pts).to(device).reshape(-1,1)\n",
    "t_v = Variable(col_time, requires_grad = True)\n",
    "\n",
    "beta = derivative_penalty(time_trace, M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f428034",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_profile = []\n",
    "w_list_mean = []\n",
    "w_list_std = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93d786d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6844436495f426c94d7b05cb1339fae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2000000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for n_iter in tqdm(range(1, EPOCH+1)) :    \n",
    "    loss1, loss2, loss3 = train(model=net_MDN, optimizer=opt, loss_f=nn.L1Loss(), loss_kl=kl_divergence, \n",
    "                       col_time = col_time, obs_time = obs_time,\n",
    "                       time_trace = time_trace, beta = beta, device = device, s_min=0.5, s_max=2)\n",
    "    # Print Log\n",
    "    if n_iter%1000 == 0 :\n",
    "        loss_profile.append(loss1+loss2+loss3)\n",
    "        print(\"{}/{}: P_loss:{}, D_loss:{}, R_loss:{}\".format(n_iter, EPOCH, np.round(loss1,3), np.round(loss2,3), np.round(loss3,3)))\n",
    "        w_mean, w_std = mean_w(net_MDN, mean_scaled_trace, time_trace)\n",
    "        w_list_mean.append(w_mean)\n",
    "        w_list_std.append(w_std)\n",
    " \n",
    "        if early_stopping == True:\n",
    "\n",
    "            if n_iter>n_hist*1000:\n",
    "                w_mean = np.array(w_list_mean).T\n",
    "                w_std = np.array(w_list_std).T\n",
    "                w_mean = w_mean/w_mean.sum(axis=0)\n",
    "\n",
    "                w_cv = np.zeros_like(w_mean)\n",
    "                for k in range(n_hist,w_mean.shape[1]):\n",
    "                    w_cv[:,k] = w_mean[:,k-n_hist:k].std(axis=1)/(w_mean[:,k-n_hist:k].mean(axis=1)+epsilon)\n",
    "\n",
    "                w_cv_traj = (w_cv<0.01).sum(axis=0)/M\n",
    "                w_cv_traj[:n_hist] = 0\n",
    "\n",
    "\n",
    "                if (n_iter>n_hist*1000):\n",
    "                    if (w_cv_traj[-1] > .99):\n",
    "                        print(\"training stopped (w> 99%)\")\n",
    "                        break\n",
    "\n",
    "     # Save Model\n",
    "        if loss_profile[-1] <= np.min(loss_profile):\n",
    "                torch.save([net_MDN, loss_profile], model_dir+'/'+save_file_name)\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d1c2826",
   "metadata": {},
   "outputs": [],
   "source": [
    "exit()"
   ]
  }
 ],
 "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": "3.7.13"
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