train_GRU_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "from tqdm import tqdm, trange\n",
    "import yaml\n",
    "import os\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from model import GRU_CNN\n",
    "from dataset import load_NASA\n",
    "\n",
    "# Load the YAML configuration file\n",
    "with open('config.yaml', 'r') as file:\n",
    "    cfg = yaml.safe_load(file)\n",
    "\n",
    "# # Access the variables\n",
    "NUM_CYCLES = cfg['NUM_CYCLES']\n",
    "NUM_PREDS = cfg['NUM_PREDS']\n",
    "FEATURE_DIM1 = cfg['FEATURE_DIM1']\n",
    "FEATURE_DIM2 = cfg['FEATURE_DIM2']\n",
    "NUM_ATTENTION = cfg['NUM_ATTENTION']\n",
    "EPOCHS = cfg['EPOCHS']\n",
    "LEARNING_RATE = cfg['LEARNING_RATE']\n",
    "BATCH_SIZE = cfg['BATCH_SIZE']\n",
    "\n",
    "# Check if GPU is available\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Load data\n",
    "train_dataset, test_dataset = load_NASA(folder='NASA_DATA', num_cycles=1, split_ratio=0.5, scale_data=True)\n",
    "\n",
    "# Train/test split\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_dataloader  = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2000 [00:00<?, ?it/s]c:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:304: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\Convolution.cpp:1032.)\n",
      "  return F.conv1d(input, weight, bias, self.stride,\n",
      "train loss: 0.005191770192511656, test loss: 0.05389964833323445:  50%|█████     | 1006/2000 [17:15<17:02,  1.03s/it]  \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m inputs \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mfloat()\u001b[38;5;241m.\u001b[39mto(device)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     18\u001b[0m outputs \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mfloat()\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m---> 19\u001b[0m predicted_outputs \u001b[38;5;241m=\u001b[39m model(inputs)\n\u001b[0;32m     20\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m     21\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(predicted_outputs, outputs)\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\OneDrive - University of Twente\\Desktop\\SOH\\Battery-SoH-Transformer\\model.py:88\u001b[0m, in \u001b[0;36mGRU_CNN.forward\u001b[1;34m(self, input_stream)\u001b[0m\n\u001b[0;32m     85\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflatten(x1)\n\u001b[0;32m     86\u001b[0m x1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdense1(x1)\n\u001b[1;32m---> 88\u001b[0m _, x2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgru(input_stream\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     89\u001b[0m x2 \u001b[38;5;241m=\u001b[39m x2\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     90\u001b[0m x2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdense2(x2)\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\HeydarianArdakaniA\\AppData\\Local\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\rnn.py:1139\u001b[0m, in \u001b[0;36mGRU.forward\u001b[1;34m(self, input, hx)\u001b[0m\n\u001b[0;32m   1137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_forward_args(\u001b[38;5;28minput\u001b[39m, hx, batch_sizes)\n\u001b[0;32m   1138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1139\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mgru(\u001b[38;5;28minput\u001b[39m, hx, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flat_weights, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_layers,\n\u001b[0;32m   1140\u001b[0m                      \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_first)\n\u001b[0;32m   1141\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1142\u001b[0m     result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mgru(\u001b[38;5;28minput\u001b[39m, batch_sizes, hx, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flat_weights, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias,\n\u001b[0;32m   1143\u001b[0m                      \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_layers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# NN model\n",
    "model = GRU_CNN().to(device)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.L1Loss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
    "# Training loop\n",
    "best_epoch = 0\n",
    "best_loss = float('inf')\n",
    "Loss_log = []\n",
    "os.makedirs('models', exist_ok=True)\n",
    "model.train()\n",
    "t_range = trange(EPOCHS)\n",
    "for epoch in t_range:\n",
    "    train_losses = []\n",
    "    for inputs, outputs in train_dataloader:\n",
    "        inputs = inputs.float().to(device).squeeze(1).permute(0,2,1)\n",
    "        outputs = outputs.float().to(device)\n",
    "        predicted_outputs = model(inputs)\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(predicted_outputs, outputs)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_losses.append(loss.item())\n",
    "    test_losses = []\n",
    "    for inputs, outputs in test_dataloader:\n",
    "        inputs = inputs.float().to(device).squeeze(1).permute(0,2,1)\n",
    "        outputs = outputs.float().to(device)\n",
    "        with torch.no_grad():\n",
    "            predicted_outputs = model(inputs)\n",
    "            test_loss = criterion(predicted_outputs, outputs)\n",
    "            test_losses.append(test_loss.item())\n",
    "    Loss_log.append([np.mean(train_losses),np.mean(test_losses)])\n",
    "    # Print the loss for monitoring after each epoch\n",
    "    t_range.set_description(f\"train loss: {np.mean(train_losses)}, test loss: {np.mean(test_losses)}\")\n",
    "    t_range.refresh()\n",
    "    # Check if the current loss is the best so far\n",
    "    if np.mean(test_losses) < best_loss:\n",
    "        best_epoch = epoch\n",
    "        best_loss = np.mean(test_losses)\n",
    "        torch.save(model, f'models/trained_model_{best_loss:.6f}_{best_epoch}.pt')\n",
    "Loss_log = np.array(Loss_log)\n",
    "np.save(f'models/training_log_{NUM_CYCLES}_{NUM_PREDS}.npy', Loss_log)\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(Loss_log[0,:best_epoch])\n",
    "plt.plot(Loss_log[1,:best_epoch])\n",
    "plt.legend([\"Train Loss\",\"Test Loss\"])\n",
    "plt.grid(\"on\")\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HeydarianArdakaniA\\AppData\\Local\\Temp\\ipykernel_7284\\270819026.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model = torch.load(f'models/trained_model_0.037717_595.pt')\n"
     ]
    }
   ],
   "source": [
    "model = torch.load(f'models/trained_model_0.037717_595.pt')\n",
    "# Train/test split\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_dataloader  = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "error_data_MAE = []\n",
    "error_data_MSE = []\n",
    "error_data_RMSE = []\n",
    "\n",
    "criterion1 = nn.L1Loss()\n",
    "criterion2 = nn.MSELoss()\n",
    "\n",
    "for inputs, outputs in test_dataloader:\n",
    "    inputs = inputs.float().to(device).squeeze(1).permute(0,2,1)\n",
    "    outputs = outputs.float().to(device)\n",
    "    with torch.no_grad():\n",
    "        predicted_outputs = model(inputs)\n",
    "        MAE_L = criterion1(predicted_outputs, outputs)\n",
    "        MSE_L = criterion2(predicted_outputs, outputs)\n",
    "        error_data_MAE.append(MAE_L.item())\n",
    "        error_data_MSE.append(MSE_L.item())\n",
    "        error_data_RMSE.append(np.sqrt(MSE_L.item()))\n",
    "\n",
    "error_data_MAE = np.array(error_data_MAE)\n",
    "error_data_MSE = np.array(error_data_MSE)\n",
    "error_data_RMSE = np.array(error_data_RMSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average - MSE:0.004138, RMSE:0.060561, MAE:0.037229, MAX_ERR:0.059465\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average - MSE:{np.mean(error_data_MSE):.6f}, RMSE:{np.mean(error_data_RMSE):.6f}, MAE:{np.mean(error_data_MAE):.6f}, MAX_ERR:{np.max(error_data_MAE):.6f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "charge_data = pd.read_csv('NASA_DATA'+'/charge_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B0005', 'B0006', 'B0007', 'B0018', 'B0025', 'B0027', 'B0028',\n",
       "       'B0029', 'B0030', 'B0031', 'B0032', 'B0034', 'B0036', 'B0045',\n",
       "       'B0049', 'B0051', 'B0053', 'B0054', 'B0055', 'B0056'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(charge_data['Battery_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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