multitask_MTL_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Driver Identification and Transport Mode Classification using ResNet50-GRU and BiLSTM MTL model\n",
    "To better understand the code, check out `./prep_files/MultiTaskModel.ipynb`. That notebook explains the thought process behind this code on a sample dataset whilst this goes straight to the point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchsummary import summary\n",
    "from torch.utils.data import random_split, DataLoader\n",
    "\n",
    "from src.engine import MTL_engine\n",
    "from src.model_multitask import MultitaskModel\n",
    "from src.plot import MTL_plot\n",
    "from src.dataset import CombinedDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing class weights for both tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***driver identification***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: [0 1 2 3]\n",
      "Class weights: tensor([0.0153, 0.2646, 0.1717, 0.5485], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "y_train = pd.read_csv('./data/feature_maps_labels/train/metadata.csv')\n",
    "y_train = y_train.iloc[:,1].values\n",
    "print('Classes:', np.unique(y_train))\n",
    "\n",
    "class_weights_dr = torch.tensor(compute_class_weight('balanced', classes=np.unique(y_train), y=y_train), dtype=torch.float32)\n",
    "class_weights_dr = class_weights_dr / class_weights_dr.sum()\n",
    "class_weights_dr = class_weights_dr.to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n",
    "\n",
    "print('Class weights:', class_weights_dr)\n",
    "del y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Driver Id Class labels = [`'Not_driving'`, `'User1'`, `'User2'`, `'User3'`]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***transport mode classification***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: [0. 1. 2. 3. 4. 5. 6. 7.]\n",
      "Class weights: tensor([0.0521, 0.0592, 0.4732, 0.1143, 0.0655, 0.1070, 0.0639, 0.0648],\n",
      "       device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "y_train = pd.read_csv('./data/lstm_features_labels/train/metadata.csv')\n",
    "y_train = y_train.iloc[:,1].values\n",
    "print('Classes:', np.unique(y_train))\n",
    "\n",
    "class_weights_tr = torch.tensor(compute_class_weight('balanced', classes=np.unique(y_train), y=y_train), dtype=torch.float32)\n",
    "class_weights_tr = class_weights_tr / class_weights_tr.sum()\n",
    "class_weights_tr = class_weights_tr.to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n",
    "\n",
    "print('Class weights:', class_weights_tr)\n",
    "del y_train"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class labels for driver identification. Note that label 0 (Null) was removed during preprocessing and `1` was subtracted from all labels so that it labels start from `0`.\n",
    "\n",
    "Transport class labels: [`'Still'`, `'Walking'`, `'Run'`, `'Bike'`, `'Car'`, `'Bus'`, `'Train'`, `'Subway'`]\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. First run with desirable config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_base_dir = './data/lstm_features_labels/'\n",
    "fmap_base_dir = './data/feature_maps_labels/'\n",
    "np.random.seed(42)\n",
    "\n",
    "train_datasets = CombinedDataset(fmap_base_dir, lstm_base_dir, mode='train', \n",
    "                                 rescale=False, augment=True)\n",
    "val_test_datasets = CombinedDataset(fmap_base_dir, lstm_base_dir, mode='valid',\n",
    "                                 rescale=False, augment=True)\n",
    "\n",
    "valid_datasets, test_datasets = random_split(val_test_datasets, [0.5, 0.5])\n",
    "\n",
    "batch_size = 128\n",
    "train_dl = DataLoader(train_datasets, batch_size, shuffle=True, num_workers=4)\n",
    "valid_dl = DataLoader(valid_datasets, batch_size, shuffle=True, num_workers=4)\n",
    "test_dl = DataLoader(test_datasets, batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46240, 5782, 5781)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_datasets), len(valid_datasets), len(test_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion_driver = nn.CrossEntropyLoss(weight=class_weights_dr)\n",
    "criterion_transport = nn.CrossEntropyLoss(weight=class_weights_tr)\n",
    "model = MultitaskModel(input_size=6, hidden_size=1024, num_layers=1, dropout=0.6409051706390204)\n",
    "optimizer = optim.AdamW(model.parameters(), lr=0.0005392288692948486, weight_decay=0.0422480267454383)\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.13618183112843046, patience=10)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = MTL_engine(model, optimizer, scheduler, criterion_driver, criterion_transport, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "validation loss < training loss because of dropout. [*StackExchange*](https://stats.stackexchange.com/questions/187335/validation-error-less-than-training-error)\n",
    "\n",
    "Train time: 345mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Train Loss: 2.7143, Train Transport Acc: 27.64%, Train Driver Acc: 51.85%, Val Loss: 2.1116, Val Transport Acc: 38.69%, Val Driver Acc: 66.81%\n",
      "Epoch [2/50], Train Loss: 2.0490, Train Transport Acc: 43.05%, Train Driver Acc: 68.43%, Val Loss: 1.6409, Val Transport Acc: 52.58%, Val Driver Acc: 73.97%\n",
      "Epoch [3/50], Train Loss: 1.7351, Train Transport Acc: 52.69%, Train Driver Acc: 70.83%, Val Loss: 1.4788, Val Transport Acc: 57.83%, Val Driver Acc: 67.80%\n",
      "Epoch [4/50], Train Loss: 1.5387, Train Transport Acc: 58.01%, Train Driver Acc: 72.16%, Val Loss: 1.2877, Val Transport Acc: 63.89%, Val Driver Acc: 72.52%\n",
      "Epoch [5/50], Train Loss: 1.3948, Train Transport Acc: 61.95%, Train Driver Acc: 72.98%, Val Loss: 1.1734, Val Transport Acc: 67.95%, Val Driver Acc: 74.61%\n",
      "Epoch [6/50], Train Loss: 1.2866, Train Transport Acc: 64.31%, Train Driver Acc: 74.71%, Val Loss: 1.1312, Val Transport Acc: 69.16%, Val Driver Acc: 78.68%\n",
      "Epoch [7/50], Train Loss: 1.2004, Train Transport Acc: 66.67%, Train Driver Acc: 76.41%, Val Loss: 1.1102, Val Transport Acc: 69.61%, Val Driver Acc: 80.23%\n",
      "Epoch [8/50], Train Loss: 1.1451, Train Transport Acc: 68.04%, Train Driver Acc: 77.97%, Val Loss: 1.0059, Val Transport Acc: 71.57%, Val Driver Acc: 80.63%\n",
      "Epoch [9/50], Train Loss: 1.0660, Train Transport Acc: 69.52%, Train Driver Acc: 79.50%, Val Loss: 0.9822, Val Transport Acc: 72.19%, Val Driver Acc: 82.48%\n",
      "Epoch [10/50], Train Loss: 1.0104, Train Transport Acc: 71.20%, Train Driver Acc: 80.40%, Val Loss: 0.9002, Val Transport Acc: 74.92%, Val Driver Acc: 79.30%\n",
      "Epoch [11/50], Train Loss: 0.9609, Train Transport Acc: 72.29%, Train Driver Acc: 81.44%, Val Loss: 0.9466, Val Transport Acc: 74.80%, Val Driver Acc: 78.64%\n",
      "Epoch [12/50], Train Loss: 0.9364, Train Transport Acc: 73.27%, Train Driver Acc: 81.74%, Val Loss: 0.8854, Val Transport Acc: 75.46%, Val Driver Acc: 80.77%\n",
      "Epoch [13/50], Train Loss: 0.8804, Train Transport Acc: 74.36%, Train Driver Acc: 83.42%, Val Loss: 0.8335, Val Transport Acc: 77.46%, Val Driver Acc: 86.27%\n",
      "Epoch [14/50], Train Loss: 0.8489, Train Transport Acc: 75.30%, Train Driver Acc: 83.75%, Val Loss: 0.8980, Val Transport Acc: 76.44%, Val Driver Acc: 84.38%\n",
      "Epoch [15/50], Train Loss: 0.8183, Train Transport Acc: 76.10%, Train Driver Acc: 84.13%, Val Loss: 0.8660, Val Transport Acc: 76.93%, Val Driver Acc: 83.00%\n",
      "Epoch [16/50], Train Loss: 0.7695, Train Transport Acc: 76.76%, Train Driver Acc: 85.83%, Val Loss: 0.7451, Val Transport Acc: 78.88%, Val Driver Acc: 85.71%\n",
      "Epoch [17/50], Train Loss: 0.7320, Train Transport Acc: 77.83%, Train Driver Acc: 86.62%, Val Loss: 0.8066, Val Transport Acc: 78.38%, Val Driver Acc: 85.70%\n",
      "Epoch [18/50], Train Loss: 0.7170, Train Transport Acc: 78.35%, Train Driver Acc: 86.87%, Val Loss: 0.7696, Val Transport Acc: 80.46%, Val Driver Acc: 85.89%\n",
      "Epoch [19/50], Train Loss: 0.6804, Train Transport Acc: 79.08%, Train Driver Acc: 87.68%, Val Loss: 0.7641, Val Transport Acc: 80.44%, Val Driver Acc: 86.80%\n",
      "Epoch [20/50], Train Loss: 0.6722, Train Transport Acc: 79.71%, Train Driver Acc: 87.55%, Val Loss: 0.7614, Val Transport Acc: 80.13%, Val Driver Acc: 87.34%\n",
      "Epoch [21/50], Train Loss: 0.6424, Train Transport Acc: 80.41%, Train Driver Acc: 88.78%, Val Loss: 0.7602, Val Transport Acc: 80.63%, Val Driver Acc: 87.43%\n",
      "Epoch [22/50], Train Loss: 0.6269, Train Transport Acc: 80.88%, Train Driver Acc: 89.11%, Val Loss: 0.7262, Val Transport Acc: 81.41%, Val Driver Acc: 87.72%\n",
      "Epoch [23/50], Train Loss: 0.5971, Train Transport Acc: 81.62%, Train Driver Acc: 89.81%, Val Loss: 0.7147, Val Transport Acc: 82.24%, Val Driver Acc: 88.83%\n",
      "Epoch [24/50], Train Loss: 0.5676, Train Transport Acc: 82.43%, Train Driver Acc: 90.24%, Val Loss: 0.7203, Val Transport Acc: 82.74%, Val Driver Acc: 90.82%\n",
      "Epoch [25/50], Train Loss: 0.5534, Train Transport Acc: 82.83%, Train Driver Acc: 90.73%, Val Loss: 0.7438, Val Transport Acc: 81.70%, Val Driver Acc: 91.01%\n",
      "Epoch [26/50], Train Loss: 0.5459, Train Transport Acc: 83.05%, Train Driver Acc: 90.59%, Val Loss: 0.7191, Val Transport Acc: 82.51%, Val Driver Acc: 89.76%\n",
      "Epoch [27/50], Train Loss: 0.5160, Train Transport Acc: 83.85%, Train Driver Acc: 91.46%, Val Loss: 0.7096, Val Transport Acc: 82.39%, Val Driver Acc: 91.14%\n",
      "Epoch [28/50], Train Loss: 0.4953, Train Transport Acc: 84.36%, Train Driver Acc: 92.07%, Val Loss: 0.7100, Val Transport Acc: 83.95%, Val Driver Acc: 91.47%\n",
      "Epoch [29/50], Train Loss: 0.4891, Train Transport Acc: 84.87%, Train Driver Acc: 92.05%, Val Loss: 0.8191, Val Transport Acc: 83.83%, Val Driver Acc: 91.66%\n",
      "Epoch [30/50], Train Loss: 0.4657, Train Transport Acc: 85.36%, Train Driver Acc: 92.42%, Val Loss: 0.7340, Val Transport Acc: 83.43%, Val Driver Acc: 90.51%\n",
      "Epoch [31/50], Train Loss: 0.4521, Train Transport Acc: 85.85%, Train Driver Acc: 92.73%, Val Loss: 0.6471, Val Transport Acc: 83.73%, Val Driver Acc: 91.21%\n",
      "Epoch [32/50], Train Loss: 0.4445, Train Transport Acc: 86.08%, Train Driver Acc: 92.92%, Val Loss: 0.6705, Val Transport Acc: 84.71%, Val Driver Acc: 90.83%\n",
      "Epoch [33/50], Train Loss: 0.4320, Train Transport Acc: 86.45%, Train Driver Acc: 93.13%, Val Loss: 0.6709, Val Transport Acc: 84.00%, Val Driver Acc: 92.20%\n",
      "Epoch [34/50], Train Loss: 0.4102, Train Transport Acc: 86.90%, Train Driver Acc: 93.83%, Val Loss: 0.7288, Val Transport Acc: 84.73%, Val Driver Acc: 91.85%\n",
      "Epoch [35/50], Train Loss: 0.4019, Train Transport Acc: 87.71%, Train Driver Acc: 93.47%, Val Loss: 0.6969, Val Transport Acc: 84.71%, Val Driver Acc: 93.48%\n",
      "Epoch [36/50], Train Loss: 0.3764, Train Transport Acc: 87.98%, Train Driver Acc: 94.46%, Val Loss: 0.7400, Val Transport Acc: 84.37%, Val Driver Acc: 93.83%\n",
      "Epoch [37/50], Train Loss: 0.3738, Train Transport Acc: 88.32%, Train Driver Acc: 94.47%, Val Loss: 0.7110, Val Transport Acc: 85.04%, Val Driver Acc: 94.03%\n",
      "Epoch [38/50], Train Loss: 0.3610, Train Transport Acc: 88.57%, Train Driver Acc: 94.52%, Val Loss: 0.6917, Val Transport Acc: 85.42%, Val Driver Acc: 92.68%\n",
      "Epoch [39/50], Train Loss: 0.3583, Train Transport Acc: 88.88%, Train Driver Acc: 94.70%, Val Loss: 0.6836, Val Transport Acc: 85.09%, Val Driver Acc: 92.72%\n",
      "Epoch [40/50], Train Loss: 0.3444, Train Transport Acc: 89.22%, Train Driver Acc: 94.90%, Val Loss: 0.6742, Val Transport Acc: 85.90%, Val Driver Acc: 93.22%\n",
      "Epoch [41/50], Train Loss: 0.3371, Train Transport Acc: 89.48%, Train Driver Acc: 95.07%, Val Loss: 0.6753, Val Transport Acc: 85.77%, Val Driver Acc: 93.22%\n",
      "Epoch [42/50], Train Loss: 0.3248, Train Transport Acc: 89.63%, Train Driver Acc: 95.35%, Val Loss: 0.7666, Val Transport Acc: 83.95%, Val Driver Acc: 92.58%\n",
      "Epoch [43/50], Train Loss: 0.2471, Train Transport Acc: 92.03%, Train Driver Acc: 96.34%, Val Loss: 0.6672, Val Transport Acc: 87.36%, Val Driver Acc: 94.62%\n",
      "Epoch [44/50], Train Loss: 0.2067, Train Transport Acc: 93.28%, Train Driver Acc: 96.95%, Val Loss: 0.6661, Val Transport Acc: 87.67%, Val Driver Acc: 95.00%\n",
      "Epoch [45/50], Train Loss: 0.1923, Train Transport Acc: 93.61%, Train Driver Acc: 97.47%, Val Loss: 0.7185, Val Transport Acc: 87.56%, Val Driver Acc: 95.09%\n",
      "Epoch [46/50], Train Loss: 0.1823, Train Transport Acc: 94.07%, Train Driver Acc: 97.64%, Val Loss: 0.7173, Val Transport Acc: 88.20%, Val Driver Acc: 95.45%\n",
      "Epoch [47/50], Train Loss: 0.1729, Train Transport Acc: 94.42%, Train Driver Acc: 97.78%, Val Loss: 0.7504, Val Transport Acc: 88.01%, Val Driver Acc: 95.68%\n",
      "Epoch [48/50], Train Loss: 0.1638, Train Transport Acc: 94.50%, Train Driver Acc: 98.01%, Val Loss: 0.7154, Val Transport Acc: 88.19%, Val Driver Acc: 95.26%\n",
      "Epoch [49/50], Train Loss: 0.1556, Train Transport Acc: 94.90%, Train Driver Acc: 98.06%, Val Loss: 0.7506, Val Transport Acc: 88.00%, Val Driver Acc: 95.61%\n",
      "Epoch [50/50], Train Loss: 0.1464, Train Transport Acc: 95.07%, Train Driver Acc: 98.15%, Val Loss: 0.7743, Val Transport Acc: 88.03%, Val Driver Acc: 95.73%\n"
     ]
    }
   ],
   "source": [
    "firstRun_hist = engine.train_validation(train_dl, valid_dl, epochs=50, alpha=1, beta=1,\n",
    "                                        save_path='./model_checkpoint/run1_MTL_model_chkpt.pth')\n",
    "\n",
    "np.save('./model_checkpoint/run1_MTL_model_history.npy', np.array(firstRun_hist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MTL_plot().plot_history(firstRun_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Test Loss: 0.7316, Test Transport Accuracy: 88.57%, Test Driver Accuracy: 95.78%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7316179813250251, 88.56599204289915, 95.77927694170558)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine.test(test_dl, criterion_transport, criterion_driver, alpha=1, beta=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next calculate precision, recall and f1 score. As well as generalization score using the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch-gpu",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}