Baseline_L63.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "id": "jEDUuvB92-Sr",
    "outputId": "1be469a0-6053-4a83-f6e0-0fe73deebff4"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import os\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "import time\n",
    "import copy\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import torch.nn.functional as F\n",
    "from pytorch_lightning import loggers as pl_loggers\n",
    "\n",
    "from sklearn import decomposition\n",
    "import scipy\n",
    "from scipy.integrate import solve_ivp\n",
    "\n",
    "from sklearn.feature_extraction import image\n",
    "\n",
    "from Metrics import *\n",
    "from DataGeneration import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h1FjHV_K3W9s"
   },
   "source": [
    "# L63 Data simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import DataGeneration\n",
    "reload(DataGeneration) \n",
    "from DataGeneration import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Metrics\n",
    "reload(Metrics) \n",
    "from Metrics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "OmfV4QU3yg-g"
   },
   "outputs": [],
   "source": [
    "Training_dataset,Val_dataset,Test_dataset = L63PatchDataExtraction(sparsity=0.5,sigma_noise=np.sqrt(2),num_variables=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 783
    },
    "id": "mG0xApc47Kge",
    "outputId": "0014e6cb-dac3-4a19-cab5-52ca2ebc3539"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation_data(Training_dataset['Truth'],Training_dataset['Obs'],Training_dataset['Mask'],Training_dataset['Init'],idx=125,path='Figures',type_LM='L63')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vhKugE5VpyZc"
   },
   "source": [
    "# Baseline 1 - 4D Var\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "dJEhltDDu1Nm"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class Int(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Int, self).__init__()\n",
    "\n",
    "        self.sigma  = torch.nn.Parameter(torch.Tensor([10.]))\n",
    "        self.rho    = torch.nn.Parameter(torch.Tensor([28.]))\n",
    "        self.beta   = torch.nn.Parameter(torch.Tensor([8./3.]))\n",
    "\n",
    "        self.dt        = 0.01\n",
    "        self.IntScheme = 0\n",
    "                        \n",
    "    def _odeL63(self, xin):\n",
    "        x1  = xin[:,:,0]\n",
    "        x2  = xin[:,:,1]\n",
    "        x3  = xin[:,:,2]\n",
    "            \n",
    "        dx_1 = (self.sigma*(x2-x1)).view(-1,xin.size(1),1)\n",
    "        dx_2 = (x1*(self.rho-x3)-x2).view(-1,xin.size(1),1)\n",
    "        dx_3 = (x1*x2 - self.beta*x3).view(-1,xin.size(1),1)\n",
    "         \n",
    "        dpred = torch.cat((dx_1,dx_2,dx_3),dim=2)\n",
    "        \n",
    "        return dpred\n",
    "\n",
    "    def _EulerSolver(self, x):\n",
    "        return x + self.dt * self._odeL63(x)\n",
    "      \n",
    "    def forward(self, x):\n",
    "            \n",
    "            xpred = self._EulerSolver( x[:,0:x.size(1)-1,:])\n",
    "            \n",
    "            \n",
    "            xnew  = torch.cat((x[:,0,:].view(-1,1,3),xpred),dim=1)\n",
    "            return xnew\n",
    "    \n",
    "modelInt      = Int()\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  use gpu if available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "modelInt          = modelInt.to(device)\n",
    "test_dataset         = torch.utils.data.TensorDataset(torch.Tensor(Test_dataset['Init']),torch.Tensor(Test_dataset['Obs']),torch.Tensor(Test_dataset['Mask']),torch.Tensor(Test_dataset['Truth'])) # create your datset\n",
    "batch_size = 128\n",
    "dataloaders = {\n",
    "    'dataset': torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)\n",
    "}            \n",
    "dataset_sizes = {'dataset': len(test_dataset)}\n",
    "\n",
    "comptUpdate = 1\n",
    "iterInit    = 0\n",
    "iterStateUpdate = 250 \n",
    "NIterGrad       = 5000\n",
    "modIter         = 100\n",
    "delta           = 1e4 \n",
    "alpha           = 1e0* np.array([.01,0.99])\n",
    "\n",
    "xhat_tot = []\n",
    "numData  = 0\n",
    "compt    = 0\n",
    "for x_init,x_obs,masks,x_GT in dataloaders['dataset']:\n",
    "    x_init    = x_init.to(device)\n",
    "    x_obs     = x_obs.to(device)\n",
    "    masks     = masks.to(device)\n",
    "    x_GT      = x_GT.to(device)\n",
    "\n",
    "    numData +=  x_GT.size(0)\n",
    "\n",
    "    # initialisation\n",
    "    xhat = x_init \n",
    "    xhat = torch.autograd.Variable(xhat, requires_grad=True)\n",
    "\n",
    "    # 4DVar assimilation using trained gradient descent\n",
    "    losses_test = []\n",
    "    compt_kk = 0\n",
    "    for kk in range(0,NIterGrad):     \n",
    "        # AE projection\n",
    "        x_pred = modelInt( xhat )\n",
    "        \n",
    "        # dynamical loss\n",
    "        loss_dyn = torch.mean((xhat - x_pred)**2 )\n",
    "    \n",
    "        # observation loss\n",
    "        loss_obs = torch.sum((xhat - x_obs)**2 * masks) / torch.sum( masks)\n",
    "    \n",
    "        ## loss wrt groudn-truth\n",
    "        loss_GT  = torch.mean((xhat - x_GT)**2)\n",
    "\n",
    "        # overall loss\n",
    "        loss = alpha[0] * loss_obs + alpha[1] * loss_dyn \n",
    "    \n",
    "        # compute gradient w.r.t. X and update X\n",
    "        loss.backward()\n",
    "        \n",
    "        #grad_X  = torch.autograd.grad(loss,X_torch,create_graph=True)\n",
    "        xhat = xhat - delta * xhat.grad.data\n",
    "        xhat = torch.autograd.Variable(xhat, requires_grad=True)                                           \n",
    "        # other loss types\n",
    "        dB          = 0\n",
    "        dT          = xhat.size(2)\n",
    "        loss_R      = torch.sum( (xhat[:,dB:dT-dB] - x_GT[:,dB:dT-dB])**2 * masks[:,dB:dT-dB] )\n",
    "        loss_R      = loss_R / torch.sum( masks[:,dB:dT-dB] )\n",
    "        loss_AE_GT  = torch.mean( (modelInt(x_GT)[:,dB:dT-dB] - x_GT[:,dB:dT-dB])**2 )        \n",
    "        loss_I      = torch.sum( (xhat[:,dB:dT-dB] - x_GT[:,dB:dT-dB])**2 * (1. - masks[:,dB:dT-dB]) )\n",
    "        loss_I      = loss_I / torch.sum( (1. - masks[:,dB:dT-dB]) )\n",
    "\n",
    "        # evaluate and store losses\n",
    "        if( np.mod(kk,modIter) == 0 ):  \n",
    "            # store as np\n",
    "            if( compt_kk == 0 ):\n",
    "                losses_test = torch.cat( (loss_GT.view(1,1),loss_dyn.view(1,1),loss_obs.view(1,1),loss_R.view(1,1),loss_I.view(1,1)), dim = 1 ).cpu().detach().numpy()                                \n",
    "            else:\n",
    "                losses_test = np.concatenate( (losses_test,torch.cat( (loss_GT.view(1,1),loss_dyn.view(1,1),loss_obs.view(1,1),loss_R.view(1,1),loss_I.view(1,1)), dim = 1 ).cpu().detach().numpy()) , axis = 0)\n",
    "            compt_kk += 1\n",
    "    xhat_tot.append(xhat)\n",
    "    if compt == 0 :\n",
    "        loss4DVar = x_GT.size(0) * losses_test\n",
    "    else :\n",
    "        loss4DVar = loss4DVar + x_GT.size(0) * losses_test\n",
    "    compt += 1\n",
    "    \n",
    "\n",
    "loss4DVar =  loss4DVar / numData\n",
    "                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.38786966\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation4DVar(idx = 5,x_obs = x_obs,x_GT = x_GT,xhat= xhat, model_type = 'L63')\n",
    "R_score = reconstruction_error_4DVar(x_GT,xhat)\n",
    "print(R_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qVRQZNQ-COH-"
   },
   "source": [
    "# Baseline 2 : Model CNN\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "mcdOcXh9AKgJ"
   },
   "outputs": [],
   "source": [
    "shapeData  = Training_dataset['Truth'].shape[1:]\n",
    "dW =1\n",
    "dimCNN = 10\n",
    "class CNN(pl.LightningModule):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1  = torch.nn.Conv1d(shapeData[1],shapeData[1]*dimCNN,2*dW+1,padding=dW,bias=False)\n",
    "        self.conv2  = torch.nn.Conv1d(shapeData[1]*dimCNN,shapeData[1]*dimCNN,2*dW+1,padding=dW,bias=False)\n",
    "        self.conv3  = torch.nn.Conv1d(shapeData[1]*dimCNN,shapeData[1]*dimCNN,2*dW+1,padding=dW,bias=False)\n",
    "        #self.conv4  = torch.nn.Conv1d(shapeData[1]*dimCNN,shapeData[1]*dimCNN,2*dW+1,padding=dW,bias=False)\n",
    "        #self.conv5  = torch.nn.Conv1d(shapeData[1]*dimCNN,shapeData[1]*dimCNN,2*dW+1,padding=dW,bias=False)\n",
    "        #self.conv6  = torch.nn.Conv1d(shapeData[0]*dimCNN,shapeData[0]*dimCNN,1,padding=0,bias=False)\n",
    "        self.conv7  = torch.nn.Conv1d(shapeData[1]*dimCNN,shapeData[1],1,padding=0,bias=False)\n",
    "\n",
    "        self.tot_loss=[]\n",
    "        self.tot_val_loss=[]\n",
    "        self.batch_size=128\n",
    "        self.best_loss=1e10\n",
    "        \n",
    "    def forward(self,xinp):\n",
    "        xinp = xinp.view(-1,shapeData[1],shapeData[0])\n",
    "        x = self.conv1( xinp )\n",
    "        x = self.conv2( F.relu(x) )\n",
    "        x = self.conv3( F.relu(x) )\n",
    "        #x = self.conv4( F.relu(x) )\n",
    "        #x = self.conv5( F.relu(x) )\n",
    "        #x = self.conv6( F.relu(x) )\n",
    "        x = self.conv7( F.relu(x) )\n",
    "        x = x.view(-1,shapeData[0],shapeData[1])\n",
    "\n",
    "        return x\n",
    "    def setup(self,stage='None'):\n",
    "        batch_size=128\n",
    "        training_dataset   = torch.utils.data.TensorDataset(torch.Tensor(Training_dataset['Init']),torch.Tensor(Training_dataset['Obs']),torch.Tensor(Training_dataset['Mask']),torch.Tensor(Training_dataset['Truth'])) \n",
    "        val_dataset        = torch.utils.data.TensorDataset(torch.Tensor(Val_dataset['Init']),torch.Tensor(Val_dataset['Obs']),torch.Tensor(Val_dataset['Mask']),torch.Tensor(Val_dataset['Truth']))\n",
    "        test_dataset       = torch.utils.data.TensorDataset(torch.Tensor(Test_dataset['Init']),torch.Tensor(Test_dataset['Obs']),torch.Tensor(Test_dataset['Mask']),torch.Tensor(Test_dataset['Truth'])) \n",
    "        self.dataloaders = {\n",
    "            'train': torch.utils.data.DataLoader(training_dataset, batch_size=batch_size, shuffle=True, num_workers=0),\n",
    "            'val': torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0),\n",
    "            'test': torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)}\n",
    "\n",
    "        \n",
    "    def loss(self, x, y):\n",
    "         return torch.mean((x - y)**2 )\n",
    "    \n",
    "    def training_step(self, train_batch, batch_idx):\n",
    "        running_loss = 0.0\n",
    "        num_loss     = 0\n",
    "        \n",
    "        inputs_init,inputs_missing,masks,targets_GT  = train_batch\n",
    "        inputs_init    = torch.autograd.Variable(inputs_init, requires_grad=True)\n",
    "        outputs     = self(inputs_init)\n",
    "        loss      = torch.mean((outputs - targets_GT)**2 )\n",
    "        \n",
    "        running_loss             += loss.item() * inputs_missing.size(0)\n",
    "        num_loss                 += inputs_missing.size(0)\n",
    "        epoch_loss       = running_loss / num_loss\n",
    "        self.tot_loss.append(epoch_loss)\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.log('train_loss', epoch_loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, val_batch, batch_idx):\n",
    "        inputs_init,inputs_missing,masks,targets_GT  = val_batch\n",
    "        running_val_loss = 0.0\n",
    "        num_val_loss     = 0\n",
    "\n",
    "        outputs     = self(inputs_init)\n",
    "        loss      = torch.mean((outputs - targets_GT)**2 )\n",
    "           \n",
    "        running_val_loss             += loss.item() * inputs_missing.size(0)\n",
    "        num_val_loss                 += inputs_missing.size(0)\n",
    "        epoch_val_loss       = running_val_loss / num_val_loss\n",
    "        self.tot_val_loss.append(epoch_val_loss)\n",
    "        \n",
    "\n",
    "        \n",
    "        self.log('val_loss', loss ,prog_bar=False,logger=False)\n",
    "        if  epoch_val_loss < self.best_loss:\n",
    "            self.best_loss = epoch_val_loss\n",
    "            self.best_model_wts = copy.deepcopy(self.state_dict())\n",
    "\n",
    "        return epoch_val_loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        self.optimizer      = optim.Adam(self.parameters(), lr=5e-3)\n",
    "        \n",
    "        return self.optimizer\n",
    "    \n",
    "    def train_dataloader(self):\n",
    "        return self.dataloaders['train']\n",
    "    \n",
    "    def val_dataloader(self):\n",
    "        return self.dataloaders['val']\n",
    "    \n",
    "    def test_dataloader(self):\n",
    "        return self.dataloaders['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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    },
    "id": "zif1fxJhAN2Y",
    "outputId": "15226b38-e87d-43ba-ed93-8596aac80a73"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Conv1d(3, 30, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
      "  (conv2): Conv1d(30, 30, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
      "  (conv3): Conv1d(30, 30, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
      "  (conv7): Conv1d(30, 3, kernel_size=(1,), stride=(1,), bias=False)\n",
      ")\n",
      "CNN : Number of trainable parameters = 5760\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name  | Type   | Params\n",
      "---------------------------------\n",
      "0 | conv1 | Conv1d | 270   \n",
      "1 | conv2 | Conv1d | 2.7 K \n",
      "2 | conv3 | Conv1d | 2.7 K \n",
      "3 | conv7 | Conv1d | 90    \n",
      "---------------------------------\n",
      "5.8 K     Trainable params\n",
      "0         Non-trainable params\n",
      "5.8 K     Total params\n",
      "0.023     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n",
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:  83%|██████████  | 100/120 [00:04<00:00, 20.50it/s, loss=6.29, v_num=0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 0:  89%|██████████▋ | 107/120 [00:04<00:00, 21.48it/s, loss=6.29, v_num=0]\u001b[A\n",
      "Epoch 0:  95%|███████████▍| 114/120 [00:05<00:00, 22.38it/s, loss=6.29, v_num=0]\u001b[A\n",
      "Epoch 0: 100%|████████████| 120/120 [00:05<00:00, 23.05it/s, loss=6.29, v_num=0]\u001b[A\n",
      "Epoch 1:  83%|██████████  | 100/120 [00:04<00:00, 20.37it/s, loss=4.29, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 1:  88%|██████████▌ | 105/120 [00:04<00:00, 21.07it/s, loss=4.29, v_num=0]\u001b[A\n",
      "Epoch 1:  93%|███████████▏| 112/120 [00:05<00:00, 21.96it/s, loss=4.29, v_num=0]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:00<00:00, 58.98it/s]\u001b[A\n",
      "Epoch 1: 100%|████████████| 120/120 [00:05<00:00, 22.84it/s, loss=4.29, v_num=0]\u001b[A\n",
      "Epoch 2:  83%|██████████▊  | 100/120 [00:04<00:00, 20.74it/s, loss=3.3, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Epoch 7:  83%|██████████  | 100/120 [00:04<00:00, 21.09it/s, loss=2.52, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 7:  88%|██████████▌ | 105/120 [00:04<00:00, 21.81it/s, loss=2.52, v_num=0]\u001b[A\n",
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      "Epoch 8:  83%|██████████  | 100/120 [00:04<00:00, 20.94it/s, loss=2.49, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 8:  88%|██████████▌ | 105/120 [00:04<00:00, 21.67it/s, loss=2.49, v_num=0]\u001b[A\n",
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      "Epoch 9:  83%|██████████  | 100/120 [00:04<00:00, 20.76it/s, loss=2.44, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Epoch 10:  83%|██████████  | 100/120 [00:04<00:00, 21.06it/s, loss=2.4, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 10:  88%|██████████▌ | 105/120 [00:04<00:00, 21.80it/s, loss=2.4, v_num=0]\u001b[A\n",
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      "Epoch 11:  83%|█████████▏ | 100/120 [00:04<00:00, 20.98it/s, loss=2.36, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 11:  88%|█████████▋ | 105/120 [00:04<00:00, 21.70it/s, loss=2.36, v_num=0]\u001b[A\n",
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      "Epoch 12:  83%|█████████▏ | 100/120 [00:04<00:00, 20.94it/s, loss=2.35, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 12:  88%|█████████▋ | 105/120 [00:04<00:00, 21.66it/s, loss=2.35, v_num=0]\u001b[A\n",
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      "Epoch 13:  88%|█████████▋ | 105/120 [00:04<00:00, 21.67it/s, loss=2.26, v_num=0]\u001b[A\n",
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      "Epoch 14:  83%|█████████▏ | 100/120 [00:04<00:00, 21.01it/s, loss=2.23, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 14:  88%|█████████▋ | 105/120 [00:04<00:00, 21.73it/s, loss=2.23, v_num=0]\u001b[A\n",
      "Epoch 14:  93%|██████████▎| 112/120 [00:04<00:00, 22.64it/s, loss=2.23, v_num=0]\u001b[A\n",
      "Epoch 14:  99%|██████████▉| 119/120 [00:05<00:00, 23.51it/s, loss=2.23, v_num=0]\u001b[A\n",
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      "Epoch 15:  83%|█████████▏ | 100/120 [00:04<00:00, 20.47it/s, loss=2.21, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 15:  88%|█████████▋ | 105/120 [00:04<00:00, 21.19it/s, loss=2.21, v_num=0]\u001b[A\n",
      "Epoch 15:  93%|██████████▎| 112/120 [00:05<00:00, 22.07it/s, loss=2.21, v_num=0]\u001b[A\n",
      "Epoch 15:  99%|██████████▉| 119/120 [00:05<00:00, 22.92it/s, loss=2.21, v_num=0]\u001b[A\n",
      "Epoch 15: 100%|███████████| 120/120 [00:05<00:00, 22.96it/s, loss=2.21, v_num=0]\u001b[A\n",
      "Epoch 16:  83%|██████████  | 100/120 [00:04<00:00, 20.96it/s, loss=2.2, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 16:  88%|██████████▌ | 105/120 [00:04<00:00, 21.69it/s, loss=2.2, v_num=0]\u001b[A\n",
      "Epoch 16:  93%|███████████▏| 112/120 [00:04<00:00, 22.60it/s, loss=2.2, v_num=0]\u001b[A\n",
      "Epoch 16:  99%|███████████▉| 119/120 [00:05<00:00, 23.48it/s, loss=2.2, v_num=0]\u001b[A\n",
      "Epoch 16: 100%|████████████| 120/120 [00:05<00:00, 23.52it/s, loss=2.2, v_num=0]\u001b[A\n",
      "Epoch 17:  83%|█████████▏ | 100/120 [00:07<00:01, 14.20it/s, loss=2.13, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 17:  88%|█████████▋ | 105/120 [00:07<00:01, 14.76it/s, loss=2.13, v_num=0]\u001b[A\n",
      "Epoch 17:  93%|██████████▎| 112/120 [00:07<00:00, 15.50it/s, loss=2.13, v_num=0]\u001b[A\n",
      "Epoch 17:  99%|██████████▉| 119/120 [00:07<00:00, 16.20it/s, loss=2.13, v_num=0]\u001b[A\n",
      "Epoch 17: 100%|███████████| 120/120 [00:07<00:00, 16.26it/s, loss=2.13, v_num=0]\u001b[A\n",
      "Epoch 18:  83%|█████████▏ | 100/120 [00:04<00:00, 20.32it/s, loss=2.32, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 18:  88%|█████████▋ | 105/120 [00:04<00:00, 21.03it/s, loss=2.32, v_num=0]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18:  93%|██████████▎| 112/120 [00:05<00:00, 21.93it/s, loss=2.32, v_num=0]\u001b[A\n",
      "Epoch 18:  99%|██████████▉| 119/120 [00:05<00:00, 22.79it/s, loss=2.32, v_num=0]\u001b[A\n",
      "Epoch 18: 100%|███████████| 120/120 [00:05<00:00, 22.83it/s, loss=2.32, v_num=0]\u001b[A\n",
      "Epoch 19:  83%|█████████▏ | 100/120 [00:04<00:00, 20.56it/s, loss=2.19, v_num=0]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 19:  88%|█████████▋ | 105/120 [00:04<00:00, 21.28it/s, loss=2.19, v_num=0]\u001b[A\n",
      "Epoch 19:  93%|██████████▎| 112/120 [00:05<00:00, 22.19it/s, loss=2.19, v_num=0]\u001b[A\n",
      "Epoch 19:  99%|██████████▉| 119/120 [00:05<00:00, 23.06it/s, loss=2.19, v_num=0]\u001b[A\n",
      "Epoch 19: 100%|███████████| 120/120 [00:05<00:00, 23.10it/s, loss=2.19, v_num=0]\u001b[A\n",
      "Epoch 19: 100%|███████████| 120/120 [00:05<00:00, 23.08it/s, loss=2.19, v_num=0]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "model_CNN = CNN()\n",
    "\n",
    "print(model_CNN)\n",
    "print('CNN : Number of trainable parameters = %d'%(sum(p.numel() for p in model_CNN.parameters() if p.requires_grad)))\n",
    "\n",
    "tb_logger = pl_loggers.TensorBoardLogger(\"logs/\")\n",
    "#Train the model\n",
    "trainer = pl.Trainer(logger=tb_logger,max_epochs=20,enable_progress_bar=True,limit_train_batches=1.0)\n",
    "trainer.fit(model_CNN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "hAsX6H4hBRSx",
    "outputId": "c2b7891b-17b0-4bd7-ef4b-1388b41356c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(model_CNN,max_epoch=20,path = 'Figures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "id": "0NHjWF-OBUK7",
    "outputId": "7eddbf50-c588-4de0-ff8a-987e347d2e18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables reconstruction score : [1.233785   1.45949025 1.50866877]\n",
      "Global reconstruction score : 1.4006480060825026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(model_CNN,idx=25,dataset = Test_dataset,path = 'Figures',type_LM = 'L63',name='CNNL1DW1')\n",
    "\n",
    "r_score = R_score(model_CNN,dataset = Test_dataset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two variables observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "Training_dataset,Val_dataset,Test_dataset = L63PatchDataExtraction(sparsity=0.5,sigma_noise=np.sqrt(2),num_variables=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation_data(Training_dataset['Truth'],Training_dataset['Obs'],Training_dataset['Mask'],Training_dataset['Init'],idx=125,path='Figures',type_LM='L63')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "\n",
      "  | Name  | Type   | Params\n",
      "---------------------------------\n",
      "0 | conv1 | Conv1d | 1.5 K \n",
      "1 | conv2 | Conv1d | 15.3 K\n",
      "2 | conv3 | Conv1d | 15.3 K\n",
      "3 | conv4 | Conv1d | 15.3 K\n",
      "4 | conv5 | Conv1d | 15.3 K\n",
      "5 | conv7 | Conv1d | 90    \n",
      "---------------------------------\n",
      "62.8 K    Trainable params\n",
      "0         Non-trainable params\n",
      "62.8 K    Total params\n",
      "0.251     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Conv1d(3, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv2): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv3): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv4): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv5): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv7): Conv1d(30, 3, kernel_size=(1,), stride=(1,), bias=False)\n",
      ")\n",
      "CNN : Number of trainable parameters = 62820\n",
      "Validation sanity check:   0%|                            | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25:   4%|▍          | 5/120 [03:50<1:28:25, 46.13s/it, loss=1.2, v_num=19]\n",
      "Epoch 0:  83%|█████████▏ | 100/120 [00:40<00:08,  2.49it/s, loss=12.2, v_num=20]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 0:  85%|█████████▎ | 102/120 [00:40<00:07,  2.53it/s, loss=12.2, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.43it/s]\u001b[A\n",
      "Epoch 0:  87%|█████████▌ | 104/120 [00:40<00:06,  2.56it/s, loss=12.2, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.57it/s]\u001b[A\n",
      "Epoch 0:  88%|█████████▋ | 106/120 [00:40<00:05,  2.59it/s, loss=12.2, v_num=20]\u001b[A\n",
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      "Epoch 4:  95%|██████████▍| 114/120 [00:41<00:02,  2.77it/s, loss=3.34, v_num=20]\u001b[A\n",
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      "Epoch 5:  88%|█████████▋ | 106/120 [00:40<00:05,  2.64it/s, loss=3.17, v_num=20]\u001b[A\n",
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      "Epoch 5:  93%|██████████▎| 112/120 [00:40<00:02,  2.74it/s, loss=3.17, v_num=20]\u001b[A\n",
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      "Epoch 5:  95%|██████████▍| 114/120 [00:41<00:02,  2.77it/s, loss=3.17, v_num=20]\u001b[A\n",
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      "Epoch 5:  97%|██████████▋| 116/120 [00:41<00:01,  2.80it/s, loss=3.17, v_num=20]\u001b[A\n",
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      "Epoch 5:  98%|██████████▊| 118/120 [00:41<00:00,  2.83it/s, loss=3.17, v_num=20]\u001b[A\n",
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      "Epoch 6:  85%|█████████▎ | 102/120 [00:39<00:07,  2.57it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  87%|█████████▌ | 104/120 [00:40<00:06,  2.60it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  88%|█████████▋ | 106/120 [00:40<00:05,  2.63it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  90%|█████████▉ | 108/120 [00:40<00:04,  2.66it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  92%|██████████ | 110/120 [00:40<00:03,  2.70it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  93%|██████████▎| 112/120 [00:41<00:02,  2.73it/s, loss=2.72, v_num=20]\u001b[A\n",
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      "Epoch 6:  95%|██████████▍| 114/120 [00:41<00:02,  2.76it/s, loss=2.72, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.62it/s]\u001b[A\n",
      "Epoch 6:  97%|██████████▋| 116/120 [00:41<00:01,  2.79it/s, loss=2.72, v_num=20]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.63it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.61it/s]\u001b[A\n",
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      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.60it/s]\u001b[A\n",
      "Epoch 19: 100%|██████████| 120/120 [00:42<00:00,  2.83it/s, loss=1.66, v_num=20]\u001b[A\n",
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      "Epoch 20:  83%|████████▎ | 100/120 [00:39<00:07,  2.51it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 20:  85%|████████▌ | 102/120 [00:40<00:07,  2.55it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.62it/s]\u001b[A\n",
      "Epoch 20:  87%|████████▋ | 104/120 [00:40<00:06,  2.58it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.62it/s]\u001b[A\n",
      "Epoch 20:  88%|████████▊ | 106/120 [00:40<00:05,  2.61it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:02,  6.79it/s]\u001b[A\n",
      "Epoch 20:  90%|█████████ | 108/120 [00:40<00:04,  2.64it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.23it/s]\u001b[A\n",
      "Epoch 20:  92%|█████████▏| 110/120 [00:41<00:03,  2.68it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.26it/s]\u001b[A\n",
      "Epoch 20:  93%|█████████▎| 112/120 [00:41<00:02,  2.71it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.42it/s]\u001b[A\n",
      "Epoch 20:  95%|█████████▌| 114/120 [00:41<00:02,  2.74it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.53it/s]\u001b[A\n",
      "Epoch 20:  97%|█████████▋| 116/120 [00:41<00:01,  2.77it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.60it/s]\u001b[A\n",
      "Epoch 20:  98%|█████████▊| 118/120 [00:42<00:00,  2.80it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.63it/s]\u001b[A\n",
      "Epoch 20: 100%|██████████| 120/120 [00:42<00:00,  2.83it/s, loss=1.71, v_num=20]\u001b[A\n",
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      "Epoch 21:  83%|████████▎ | 100/120 [00:39<00:07,  2.51it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Epoch 21:  85%|████████▌ | 102/120 [00:40<00:07,  2.55it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.63it/s]\u001b[A\n",
      "Epoch 21:  87%|████████▋ | 104/120 [00:40<00:06,  2.58it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.65it/s]\u001b[A\n",
      "Epoch 21:  88%|████████▊ | 106/120 [00:40<00:05,  2.61it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.68it/s]\u001b[A\n",
      "Epoch 21:  90%|█████████ | 108/120 [00:40<00:04,  2.65it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.66it/s]\u001b[A\n",
      "Epoch 21:  92%|█████████▏| 110/120 [00:41<00:03,  2.68it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.67it/s]\u001b[A\n",
      "Epoch 21:  93%|█████████▎| 112/120 [00:41<00:02,  2.71it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.67it/s]\u001b[A\n",
      "Epoch 21:  95%|█████████▌| 114/120 [00:41<00:02,  2.74it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.69it/s]\u001b[A\n",
      "Epoch 21:  97%|█████████▋| 116/120 [00:41<00:01,  2.77it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.69it/s]\u001b[A\n",
      "Epoch 21:  98%|█████████▊| 118/120 [00:42<00:00,  2.80it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.68it/s]\u001b[A\n",
      "Epoch 21: 100%|██████████| 120/120 [00:42<00:00,  2.83it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Epoch 21: 100%|██████████| 120/120 [00:42<00:00,  2.82it/s, loss=1.53, v_num=20]\u001b[A\n",
      "Epoch 22:  83%|████████▎ | 100/120 [00:39<00:07,  2.52it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 22:  85%|████████▌ | 102/120 [00:39<00:07,  2.56it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.62it/s]\u001b[A\n",
      "Epoch 22:  87%|████████▋ | 104/120 [00:40<00:06,  2.59it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.65it/s]\u001b[A\n",
      "Epoch 22:  88%|████████▊ | 106/120 [00:40<00:05,  2.63it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.65it/s]\u001b[A\n",
      "Epoch 22:  90%|█████████ | 108/120 [00:40<00:04,  2.66it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.65it/s]\u001b[A\n",
      "Epoch 22:  92%|█████████▏| 110/120 [00:40<00:03,  2.69it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.64it/s]\u001b[A\n",
      "Epoch 22:  93%|█████████▎| 112/120 [00:41<00:02,  2.72it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.65it/s]\u001b[A\n",
      "Epoch 22:  95%|█████████▌| 114/120 [00:41<00:02,  2.75it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.41it/s]\u001b[A\n",
      "Epoch 22:  97%|█████████▋| 116/120 [00:41<00:01,  2.78it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.43it/s]\u001b[A\n",
      "Epoch 22:  98%|█████████▊| 118/120 [00:41<00:00,  2.81it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.55it/s]\u001b[A\n",
      "Epoch 22: 100%|██████████| 120/120 [00:42<00:00,  2.84it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Epoch 22: 100%|██████████| 120/120 [00:42<00:00,  2.83it/s, loss=1.62, v_num=20]\u001b[A\n",
      "Epoch 23:  83%|████████▎ | 100/120 [00:39<00:07,  2.51it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 23:  85%|████████▌ | 102/120 [00:40<00:07,  2.55it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.62it/s]\u001b[A\n",
      "Epoch 23:  87%|████████▋ | 104/120 [00:40<00:06,  2.58it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.63it/s]\u001b[A\n",
      "Epoch 23:  88%|████████▊ | 106/120 [00:40<00:05,  2.61it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.64it/s]\u001b[A\n",
      "Epoch 23:  90%|█████████ | 108/120 [00:40<00:04,  2.64it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.64it/s]\u001b[A\n",
      "Epoch 23:  92%|█████████▏| 110/120 [00:41<00:03,  2.68it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.65it/s]\u001b[A\n",
      "Epoch 23:  93%|█████████▎| 112/120 [00:41<00:02,  2.71it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.64it/s]\u001b[A\n",
      "Epoch 23:  95%|█████████▌| 114/120 [00:41<00:02,  2.74it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.64it/s]\u001b[A\n",
      "Epoch 23:  97%|█████████▋| 116/120 [00:41<00:01,  2.77it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.53it/s]\u001b[A\n",
      "Epoch 23:  98%|█████████▊| 118/120 [00:42<00:00,  2.80it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.56it/s]\u001b[A\n",
      "Epoch 23: 100%|██████████| 120/120 [00:42<00:00,  2.83it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Epoch 23: 100%|██████████| 120/120 [00:42<00:00,  2.82it/s, loss=1.61, v_num=20]\u001b[A\n",
      "Epoch 24:  83%|████████▎ | 100/120 [00:39<00:07,  2.52it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 24:  85%|████████▌ | 102/120 [00:39<00:07,  2.56it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.66it/s]\u001b[A\n",
      "Epoch 24:  87%|████████▋ | 104/120 [00:40<00:06,  2.59it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.69it/s]\u001b[A\n",
      "Epoch 24:  88%|████████▊ | 106/120 [00:40<00:05,  2.63it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.69it/s]\u001b[A\n",
      "Epoch 24:  90%|█████████ | 108/120 [00:40<00:04,  2.66it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.70it/s]\u001b[A\n",
      "Epoch 24:  92%|█████████▏| 110/120 [00:40<00:03,  2.69it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.70it/s]\u001b[A\n",
      "Epoch 24:  93%|█████████▎| 112/120 [00:41<00:02,  2.72it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.70it/s]\u001b[A\n",
      "Epoch 24:  95%|█████████▌| 114/120 [00:41<00:02,  2.75it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.71it/s]\u001b[A\n",
      "Epoch 24:  97%|█████████▋| 116/120 [00:41<00:01,  2.79it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.71it/s]\u001b[A\n",
      "Epoch 24:  98%|█████████▊| 118/120 [00:41<00:00,  2.82it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.71it/s]\u001b[A\n",
      "Epoch 24: 100%|██████████| 120/120 [00:42<00:00,  2.85it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Epoch 24: 100%|██████████| 120/120 [00:42<00:00,  2.84it/s, loss=1.71, v_num=20]\u001b[A\n",
      "Epoch 24: 100%|██████████| 120/120 [00:42<00:00,  2.84it/s, loss=1.71, v_num=20]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "model_CNN = CNN()\n",
    "\n",
    "print(model_CNN)\n",
    "print('CNN : Number of trainable parameters = %d'%(sum(p.numel() for p in model_CNN.parameters() if p.requires_grad)))\n",
    "\n",
    "tb_logger = pl_loggers.TensorBoardLogger(\"logs/\")\n",
    "#Train the model\n",
    "trainer = pl.Trainer(logger=tb_logger,max_epochs=25,enable_progress_bar=True,limit_train_batches=1.0)\n",
    "trainer.fit(model_CNN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(model_CNN,max_epoch=20,path = 'Figures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables reconstruction score : [1.3288454 1.1927805 1.2180362]\n",
      "Global reconstruction score : 1.2465540170669556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(model_CNN,idx=25,dataset = Test_dataset,path = 'Figures',type_LM = 'L63',name='CNNL1DW1')\n",
    "\n",
    "r_score = R_score(model_CNN,dataset = Test_dataset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One variable observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "Training_dataset,Val_dataset,Test_dataset = L63PatchDataExtraction(sparsity=0.5,sigma_noise=np.sqrt(2),num_variables=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation_data(Training_dataset['Truth'],Training_dataset['Obs'],Training_dataset['Mask'],Training_dataset['Init'],idx=125,path='Figures',type_LM='L63')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "\n",
      "  | Name  | Type   | Params\n",
      "---------------------------------\n",
      "0 | conv1 | Conv1d | 1.5 K \n",
      "1 | conv2 | Conv1d | 15.3 K\n",
      "2 | conv3 | Conv1d | 15.3 K\n",
      "3 | conv4 | Conv1d | 15.3 K\n",
      "4 | conv5 | Conv1d | 15.3 K\n",
      "5 | conv7 | Conv1d | 90    \n",
      "---------------------------------\n",
      "62.8 K    Trainable params\n",
      "0         Non-trainable params\n",
      "62.8 K    Total params\n",
      "0.251     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Conv1d(3, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv2): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv3): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv4): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv5): Conv1d(30, 30, kernel_size=(17,), stride=(1,), padding=(8,), bias=False)\n",
      "  (conv7): Conv1d(30, 3, kernel_size=(1,), stride=(1,), bias=False)\n",
      ")\n",
      "CNN : Number of trainable parameters = 62820\n",
      "Validation sanity check:   0%|                            | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlottedurand/opt/anaconda3/envs/tf_2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:  83%|█████████▏ | 100/120 [00:39<00:07,  2.52it/s, loss=55.2, v_num=21]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 0:  85%|█████████▎ | 102/120 [00:39<00:07,  2.56it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.46it/s]\u001b[A\n",
      "Epoch 0:  87%|█████████▌ | 104/120 [00:40<00:06,  2.59it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.51it/s]\u001b[A\n",
      "Epoch 0:  88%|█████████▋ | 106/120 [00:40<00:05,  2.63it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.59it/s]\u001b[A\n",
      "Epoch 0:  90%|█████████▉ | 108/120 [00:40<00:04,  2.66it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.63it/s]\u001b[A\n",
      "Epoch 0:  92%|██████████ | 110/120 [00:40<00:03,  2.69it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.68it/s]\u001b[A\n",
      "Epoch 0:  93%|██████████▎| 112/120 [00:41<00:02,  2.72it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.70it/s]\u001b[A\n",
      "Epoch 0:  95%|██████████▍| 114/120 [00:41<00:02,  2.75it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.71it/s]\u001b[A\n",
      "Epoch 0:  97%|██████████▋| 116/120 [00:41<00:01,  2.78it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.73it/s]\u001b[A\n",
      "Epoch 0:  98%|██████████▊| 118/120 [00:41<00:00,  2.82it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.75it/s]\u001b[A\n",
      "Epoch 0: 100%|███████████| 120/120 [00:42<00:00,  2.85it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Epoch 0: 100%|███████████| 120/120 [00:42<00:00,  2.84it/s, loss=55.2, v_num=21]\u001b[A\n",
      "Epoch 1:  83%|█████████▏ | 100/120 [00:39<00:07,  2.55it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 1:  85%|█████████▎ | 102/120 [00:39<00:06,  2.59it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.70it/s]\u001b[A\n",
      "Epoch 1:  87%|█████████▌ | 104/120 [00:39<00:06,  2.63it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.72it/s]\u001b[A\n",
      "Epoch 1:  88%|█████████▋ | 106/120 [00:39<00:05,  2.66it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.74it/s]\u001b[A\n",
      "Epoch 1:  90%|█████████▉ | 108/120 [00:40<00:04,  2.69it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.75it/s]\u001b[A\n",
      "Epoch 1:  92%|██████████ | 110/120 [00:40<00:03,  2.73it/s, loss=50.7, v_num=21]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.74it/s]\u001b[A\n",
      "Epoch 1:  93%|██████████▎| 112/120 [00:40<00:02,  2.76it/s, loss=50.7, v_num=21]\u001b[A\n",
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      "Epoch 1:  95%|██████████▍| 114/120 [00:40<00:02,  2.79it/s, loss=50.7, v_num=21]\u001b[A\n",
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      "Epoch 1:  98%|██████████▊| 118/120 [00:41<00:00,  2.85it/s, loss=50.7, v_num=21]\u001b[A\n",
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      "Epoch 2:  87%|█████████▌ | 104/120 [00:39<00:06,  2.60it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  88%|█████████▋ | 106/120 [00:40<00:05,  2.63it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  90%|█████████▉ | 108/120 [00:40<00:04,  2.67it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  92%|██████████ | 110/120 [00:40<00:03,  2.70it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  95%|██████████▍| 114/120 [00:41<00:02,  2.76it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  97%|██████████▋| 116/120 [00:41<00:01,  2.79it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 2:  98%|██████████▊| 118/120 [00:41<00:00,  2.82it/s, loss=49.8, v_num=21]\u001b[A\n",
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      "Epoch 3:  95%|██████████▍| 114/120 [00:40<00:02,  2.78it/s, loss=49.6, v_num=21]\u001b[A\n",
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      "Epoch 3:  98%|██████████▊| 118/120 [00:41<00:00,  2.84it/s, loss=49.6, v_num=21]\u001b[A\n",
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      "Epoch 4:  95%|██████████▍| 114/120 [00:41<00:02,  2.78it/s, loss=49.6, v_num=21]\u001b[A\n",
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      "Epoch 4:  98%|██████████▊| 118/120 [00:41<00:00,  2.84it/s, loss=49.6, v_num=21]\u001b[A\n",
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      "Epoch 5:  85%|█████████▎ | 102/120 [00:39<00:06,  2.58it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  87%|█████████▌ | 104/120 [00:39<00:06,  2.62it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  88%|█████████▋ | 106/120 [00:39<00:05,  2.65it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  90%|█████████▉ | 108/120 [00:40<00:04,  2.68it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  92%|██████████ | 110/120 [00:40<00:03,  2.72it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  93%|██████████▎| 112/120 [00:40<00:02,  2.75it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  95%|██████████▍| 114/120 [00:41<00:02,  2.78it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 5:  97%|██████████▋| 116/120 [00:41<00:01,  2.81it/s, loss=49.4, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.73it/s]\u001b[A\n",
      "Epoch 5:  98%|██████████▊| 118/120 [00:41<00:00,  2.84it/s, loss=49.4, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.72it/s]\u001b[A\n",
      "Epoch 5: 100%|███████████| 120/120 [00:41<00:00,  2.87it/s, loss=49.4, v_num=21]\u001b[A\n",
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      "Epoch 6:  83%|█████████▏ | 100/120 [00:39<00:07,  2.54it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Epoch 6:  85%|█████████▎ | 102/120 [00:39<00:06,  2.59it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 6:  87%|█████████▌ | 104/120 [00:39<00:06,  2.62it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.72it/s]\u001b[A\n",
      "Epoch 6:  88%|█████████▋ | 106/120 [00:39<00:05,  2.65it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 6:  90%|█████████▉ | 108/120 [00:40<00:04,  2.68it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 6:  92%|██████████ | 110/120 [00:40<00:03,  2.72it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 6:  93%|██████████▎| 112/120 [00:40<00:02,  2.75it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 6:  95%|██████████▍| 114/120 [00:41<00:02,  2.78it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.72it/s]\u001b[A\n",
      "Epoch 6:  97%|██████████▋| 116/120 [00:41<00:01,  2.81it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.73it/s]\u001b[A\n",
      "Epoch 6:  98%|██████████▊| 118/120 [00:41<00:00,  2.84it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 7:  83%|█████████▏ | 100/120 [00:39<00:07,  2.54it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
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      "Epoch 7:  85%|█████████▎ | 102/120 [00:39<00:06,  2.58it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 7:  87%|█████████▌ | 104/120 [00:39<00:06,  2.61it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 7:  88%|█████████▋ | 106/120 [00:40<00:05,  2.65it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.63it/s]\u001b[A\n",
      "Epoch 7:  90%|█████████▉ | 108/120 [00:40<00:04,  2.68it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 7:  92%|██████████ | 110/120 [00:40<00:03,  2.71it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 7:  93%|██████████▎| 112/120 [00:40<00:02,  2.74it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.71it/s]\u001b[A\n",
      "Epoch 7:  95%|██████████▍| 114/120 [00:41<00:02,  2.78it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.72it/s]\u001b[A\n",
      "Epoch 7:  97%|██████████▋| 116/120 [00:41<00:01,  2.81it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.72it/s]\u001b[A\n",
      "Epoch 7:  98%|██████████▊| 118/120 [00:41<00:00,  2.84it/s, loss=49.3, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.69it/s]\u001b[A\n",
      "Epoch 7: 100%|███████████| 120/120 [00:41<00:00,  2.87it/s, loss=49.3, v_num=21]\u001b[A\n",
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      "Epoch 8:  83%|██████████▊  | 100/120 [00:39<00:07,  2.54it/s, loss=49, v_num=21]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8:  95%|████████████▎| 114/120 [00:41<00:02,  2.78it/s, loss=49, v_num=21]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17:  88%|████████▊ | 106/120 [00:40<00:05,  2.62it/s, loss=48.7, v_num=21]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25: 100%|██████████| 120/120 [00:42<00:00,  2.84it/s, loss=48.7, v_num=21]\u001b[A\n",
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      "Epoch 27:  95%|█████████▌| 114/120 [00:41<00:02,  2.76it/s, loss=48.7, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.62it/s]\u001b[A\n",
      "Epoch 27:  97%|█████████▋| 116/120 [00:41<00:01,  2.79it/s, loss=48.7, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.65it/s]\u001b[A\n",
      "Epoch 27:  98%|█████████▊| 118/120 [00:41<00:00,  2.82it/s, loss=48.7, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.68it/s]\u001b[A\n",
      "Epoch 27: 100%|██████████| 120/120 [00:42<00:00,  2.85it/s, loss=48.7, v_num=21]\u001b[A\n",
      "Epoch 27: 100%|██████████| 120/120 [00:42<00:00,  2.84it/s, loss=48.7, v_num=21]\u001b[A\n",
      "Epoch 28:  83%|████████▎ | 100/120 [00:39<00:07,  2.54it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 28:  85%|████████▌ | 102/120 [00:39<00:06,  2.58it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.65it/s]\u001b[A\n",
      "Epoch 28:  87%|████████▋ | 104/120 [00:39<00:06,  2.61it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.52it/s]\u001b[A\n",
      "Epoch 28:  88%|████████▊ | 106/120 [00:40<00:05,  2.65it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.38it/s]\u001b[A\n",
      "Epoch 28:  90%|█████████ | 108/120 [00:40<00:04,  2.68it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.46it/s]\u001b[A\n",
      "Epoch 28:  92%|█████████▏| 110/120 [00:40<00:03,  2.71it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.56it/s]\u001b[A\n",
      "Epoch 28:  93%|█████████▎| 112/120 [00:40<00:02,  2.74it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.59it/s]\u001b[A\n",
      "Epoch 28:  95%|█████████▌| 114/120 [00:41<00:02,  2.77it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.63it/s]\u001b[A\n",
      "Epoch 28:  97%|█████████▋| 116/120 [00:41<00:01,  2.80it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.67it/s]\u001b[A\n",
      "Epoch 28:  98%|█████████▊| 118/120 [00:41<00:00,  2.83it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.67it/s]\u001b[A\n",
      "Epoch 28: 100%|██████████| 120/120 [00:41<00:00,  2.86it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Epoch 28: 100%|██████████| 120/120 [00:42<00:00,  2.86it/s, loss=48.8, v_num=21]\u001b[A\n",
      "Epoch 29:  83%|████████▎ | 100/120 [00:39<00:07,  2.54it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Validating:   0%|                                        | 0/20 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 29:  85%|████████▌ | 102/120 [00:39<00:06,  2.58it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  10%|███▏                            | 2/20 [00:00<00:02,  7.65it/s]\u001b[A\n",
      "Epoch 29:  87%|████████▋ | 104/120 [00:39<00:06,  2.61it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  20%|██████▍                         | 4/20 [00:00<00:02,  7.66it/s]\u001b[A\n",
      "Epoch 29:  88%|████████▊ | 106/120 [00:40<00:05,  2.64it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  30%|█████████▌                      | 6/20 [00:00<00:01,  7.65it/s]\u001b[A\n",
      "Epoch 29:  90%|█████████ | 108/120 [00:40<00:04,  2.68it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  40%|████████████▊                   | 8/20 [00:01<00:01,  7.66it/s]\u001b[A\n",
      "Epoch 29:  92%|█████████▏| 110/120 [00:40<00:03,  2.71it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  50%|███████████████▌               | 10/20 [00:01<00:01,  7.66it/s]\u001b[A\n",
      "Epoch 29:  93%|█████████▎| 112/120 [00:40<00:02,  2.74it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  60%|██████████████████▌            | 12/20 [00:01<00:01,  7.66it/s]\u001b[A\n",
      "Epoch 29:  95%|█████████▌| 114/120 [00:41<00:02,  2.77it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  70%|█████████████████████▋         | 14/20 [00:01<00:00,  7.65it/s]\u001b[A\n",
      "Epoch 29:  97%|█████████▋| 116/120 [00:41<00:01,  2.80it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  80%|████████████████████████▊      | 16/20 [00:02<00:00,  7.66it/s]\u001b[A\n",
      "Epoch 29:  98%|█████████▊| 118/120 [00:41<00:00,  2.83it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Validating:  90%|███████████████████████████▉   | 18/20 [00:02<00:00,  7.65it/s]\u001b[A\n",
      "Epoch 29: 100%|██████████| 120/120 [00:41<00:00,  2.86it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Epoch 29: 100%|██████████| 120/120 [00:42<00:00,  2.85it/s, loss=48.6, v_num=21]\u001b[A\n",
      "Epoch 29: 100%|██████████| 120/120 [00:42<00:00,  2.85it/s, loss=48.6, v_num=21]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "model_CNN = CNN()\n",
    "\n",
    "print(model_CNN)\n",
    "print('CNN : Number of trainable parameters = %d'%(sum(p.numel() for p in model_CNN.parameters() if p.requires_grad)))\n",
    "\n",
    "tb_logger = pl_loggers.TensorBoardLogger(\"logs/\")\n",
    "#Train the model\n",
    "trainer = pl.Trainer(logger=tb_logger,max_epochs=30,enable_progress_bar=True,limit_train_batches=1.0)\n",
    "trainer.fit(model_CNN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(model_CNN,max_epoch=20,path = 'Figures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables reconstruction score : [7.9279046 9.020522  1.3525057]\n",
      "Global reconstruction score : 6.100311279296875\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(model_CNN,idx=25,dataset = Test_dataset,path = 'Figures',type_LM = 'L63',name='CNNL1DW1')\n",
    "\n",
    "r_score = R_score(model_CNN,dataset = Test_dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "3xBXaOz7_gfA"
   ],
   "name": "Copie de notebookPyTorch_InvProb_ModelBased_L63.ipynb",
   "provenance": []
  },
  "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",
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