03a - LSTM_Model_Chest_Device.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/santteegt/om-fol-timeseries/blob/master/LSTM_Model_Chest_device.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xsvTkUtynjfO"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data.dataloader import DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "zWa2GDwbVEpS",
    "outputId": "d861a83b-2f31-40ce-964f-2f13352126c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch-lr-finder in /usr/local/lib/python3.6/dist-packages (0.2.0)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch-lr-finder) (1.18.5)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from torch-lr-finder) (3.2.2)\n",
      "Requirement already satisfied: torch>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from torch-lr-finder) (1.5.1+cu101)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from torch-lr-finder) (20.4)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from torch-lr-finder) (4.41.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch-lr-finder) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch-lr-finder) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch-lr-finder) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->torch-lr-finder) (0.10.0)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=0.4.1->torch-lr-finder) (0.16.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->torch-lr-finder) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install torch-lr-finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "xaZHaL8rpWKQ",
    "outputId": "8a19a9f0-3c1f-40d8-d6eb-b360ac79e7b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1_56HAZc2XSXQCAR4iZODq9VAoyq5CvAs\n",
      "To: /content/all_subjects.csv\n",
      "172MB [00:01, 154MB/s]\n"
     ]
    }
   ],
   "source": [
    "# !wget https://github.com/santteegt/om-fol-timeseries/archive/master.zip -O om-fol-timeseries.zip && unzip om-fol-timeseries.zip && rm om-fol-timeseries.zip\n",
    "# !wget https://github.com/santteegt/om-fol-timeseries/raw/master/segmented_data/WESAD_segmented.zip && unzip -d data WESAD_segmented.zip && rm WESAD_segmented.zip\n",
    "# from google.colab import files\n",
    "# uploaded = files.upload()\n",
    "\n",
    "!gdown --id 1_56HAZc2XSXQCAR4iZODq9VAoyq5CvAs  # segmented dataset on my Google drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "hL3XC4qKpa4W",
    "outputId": "76f017d8-0f79-4601-8381-d3af0d43dfc8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 167928\n",
      "-rw-r--r-- 1 root root 171953449 Jul  6 12:35 all_subjects.csv\n",
      "drwxr-xr-x 1 root root      4096 Jun 26 16:26 sample_data\n"
     ]
    }
   ],
   "source": [
    " !ls -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vkjh0j6rsaUu"
   },
   "source": [
    "## Load Subject Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NQQm1-SIpdGB"
   },
   "outputs": [],
   "source": [
    "# BASE_PATH = './data'\n",
    "# [subject for subject in os.listdir(BASE_PATH) if subject.endswith('.feather')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eJ0PTSZts2eI",
    "outputId": "4f232abf-9696-4c02-dade-fee7928c818c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121813, 79)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('all_subjects.csv', index_col=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kn05bY8QPvb-"
   },
   "source": [
    "This should be done directly in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LnejF7Kd2172"
   },
   "outputs": [],
   "source": [
    "df.label = df.label - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eBFzzX0sIeIg"
   },
   "source": [
    "## Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zq0UvOYj60q6"
   },
   "outputs": [],
   "source": [
    "class WESADDataset(Dataset):\n",
    "    #Constructor is mandatory\n",
    "    def __init__(self, dataframe, transform=None):\n",
    "        # normalizer = StandardScaler()\n",
    "        # Since different subjects have different responses on the signal values, min-max normalisation was conducted across different signals to normalize them to the same scale\n",
    "        normalizer = MinMaxScaler()\n",
    "        self.dataframe = dataframe.drop(columns=['subject','label'])\n",
    "        # self.X = self.dataframe.astype(np.float32).to_numpy()\n",
    "        # Normalize features\n",
    "        self.X = normalizer.fit_transform(self.dataframe.astype(np.float32))\n",
    "        self.labels = dataframe['label']\n",
    "        self.transform = transform # e.g. torch.Tensor\n",
    "    \n",
    "    def to_torchtensor(self):            \n",
    "        self.dataframe = torch.from_numpy(self.dataframe)\n",
    "        self.labels = torch.from_numpy(self.labels)\n",
    "    \n",
    "    def __len__(self):\n",
    "        #Mandatory\n",
    "        '''Returns:\n",
    "                Length [int]: Length of Dataset/batches\n",
    "        '''\n",
    "        return self.dataframe.shape[0]\n",
    "\n",
    "    def __getitem__(self, idx): \n",
    "        #Mandatory \n",
    "        \n",
    "        '''Returns:\n",
    "                    Data [Torch Tensor]: \n",
    "                    Target [ Torch Tensor]:\n",
    "        '''\n",
    "        sample = self.X[idx]\n",
    "        # sample = self.X[idx].reshape(1, -1)\n",
    "        target = self.labels[idx]\n",
    "                \n",
    "        if self.transform:\n",
    "            sample = self.transform(sample)\n",
    "\n",
    "        return sample, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yQZ-fX0SIj5L"
   },
   "outputs": [],
   "source": [
    "def get_data_loader(subject, train_batch_size=128, val_batch_size=5):\n",
    "\n",
    "    train = WESADDataset(df[df['subject'] != subject].reset_index(drop=True))\n",
    "    test = WESADDataset(df[df['subject'] == subject].reset_index(drop=True))\n",
    "\n",
    "    train_dl = torch.utils.data.DataLoader(train, batch_size=train_batch_size, shuffle=True, pin_memory=True, num_workers=4)\n",
    "    val_dl = torch.utils.data.DataLoader(test, batch_size=val_batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "    \n",
    "    return train_dl, val_dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PLPy0--FptDt"
   },
   "outputs": [],
   "source": [
    "def get_default_device():\n",
    "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
    "    if torch.cuda.is_available():\n",
    "        return torch.device('cuda')\n",
    "    else:\n",
    "        return torch.device('cpu')\n",
    "\n",
    "def to_device(data, device):\n",
    "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
    "    if isinstance(data, (list,tuple)):\n",
    "        return [to_device(x, device) for x in data]\n",
    "    return data.to(device, non_blocking=True)\n",
    "\n",
    "\n",
    "class DeviceDataLoader():\n",
    "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
    "    def __init__(self, dl, device):\n",
    "        self.dl = dl\n",
    "        self.device = device\n",
    "        \n",
    "    def __iter__(self):\n",
    "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
    "        for b in self.dl: \n",
    "            yield to_device(b, self.device)\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"Number of batches\"\"\"\n",
    "        return len(self.dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mVvM8CGVAzRC"
   },
   "source": [
    "## Methods & utils for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zsinJ1Z-7BCU"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def accuracy(outputs, labels):\n",
    "    _, preds = torch.max(outputs, dim=1)\n",
    "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
    "\n",
    "def f1(output, label, threshold=0.5, beta=1):\n",
    "    probs = torch.argmax(output.data.to('cpu'), dim=1)\n",
    "    preds = label.data.to('cpu')\n",
    "    return f1_score(preds, probs, average='macro')\n",
    "\n",
    "def plot_scores(history):\n",
    "    t_acc = [x['train_acc'] for x in history]\n",
    "    plt.plot(t_acc, '-x', label='Train Acc')\n",
    "\n",
    "    accuracies = [x['val_acc'] for x in history]\n",
    "    f1 = [x['val_f1'] for x in history]\n",
    "    plt.plot(accuracies, '-x', label='Valid Acc')\n",
    "    plt.plot(f1, '-x', label='Val F1')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('Score')\n",
    "    plt.title('Score vs. No. of epochs')\n",
    "    plt.legend();\n",
    "\n",
    "def plot_losses(history):\n",
    "    train_losses = [x.get('train_loss') for x in history]\n",
    "    val_losses = [x['val_loss'] for x in history]\n",
    "    plt.plot(train_losses, '-bx')\n",
    "    plt.plot(val_losses, '-rx')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('loss')\n",
    "    plt.legend(['Training', 'Validation'])\n",
    "    plt.title('Loss vs. No. of epochs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3bKM-vZh6WDN"
   },
   "outputs": [],
   "source": [
    "class BaseModel(nn.Module):\n",
    "    def training_step(self, batch):\n",
    "        data, labels = batch \n",
    "        out = self(data)                  # Generate predictions\n",
    "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
    "        return loss\n",
    "    \n",
    "    def validation_step(self, batch):\n",
    "        data, labels = batch \n",
    "        out = self(data)                    # Generate predictions\n",
    "        # print('out', out.shape)\n",
    "        loss = F.cross_entropy(out, labels)   # Calculate loss\n",
    "        # print('loss', loss.shape)\n",
    "        out = F.softmax(out, dim=1)           # Apply Softmax\n",
    "        acc = accuracy(out, labels)           # Calculate accuracy\n",
    "        f1_ = f1(out, labels)                  # Calculate F1\n",
    "        return {'val_loss': loss.detach(), 'val_acc': acc, 'val_f1': f1_}\n",
    "        \n",
    "    def validation_epoch_end(self, outputs):\n",
    "        batch_losses = [x['val_loss'] for x in outputs]\n",
    "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
    "        batch_accs = [x['val_acc'] for x in outputs]\n",
    "        batch_f1 = [x['val_f1'] for x in outputs]\n",
    "        # print('batch_f1', len(batch_f1))\n",
    "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
    "        epoch_f1 = np.mean(np.array(batch_f1))         # Combine F1 scores\n",
    "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item(), 'val_f1': epoch_f1.item()}\n",
    "    \n",
    "    def epoch_end(self, epoch, result):\n",
    "        print(\"Epoch [{}], train_loss: {:.4f}, train_acc: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}, val_f1: {:.4f}\".format(\n",
    "            epoch, result['train_loss'], result['train_acc'], result['val_loss'], result['val_acc'], result['val_f1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ewGiwm2H6DOg"
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def evaluate(model, val_loader):\n",
    "    model.eval()\n",
    "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
    "    return model.validation_epoch_end(outputs)\n",
    "\n",
    "def fit(epochs, lr, model, train_loader, val_loader, optimizer):\n",
    "    history = []\n",
    "    for epoch in range(epochs):\n",
    "        # Training Phase \n",
    "        model.train()\n",
    "        train_accs = []\n",
    "        train_losses = []\n",
    "        for batch in tqdm(train_loader):\n",
    "            loss = model.training_step(batch)\n",
    "            train_losses.append(loss)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            data, labels = batch\n",
    "            train_out = F.softmax(model(data), dim=1)\n",
    "            train_accs.append(accuracy(train_out, labels))\n",
    "\n",
    "\n",
    "        # Validation phase\n",
    "        result = evaluate(model, val_loader)\n",
    "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
    "        result['train_acc'] = torch.stack(train_accs).mean().item()\n",
    "        history.append(result)\n",
    "\n",
    "        # if epoch < 5 or (epoch + 1) % 5 == 0:\n",
    "        model.epoch_end(epoch, result)\n",
    "            \n",
    "    return history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sbWcJEn7IEAo"
   },
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fDiM6gn4jdN"
   },
   "outputs": [],
   "source": [
    "class WesadFeedForward(BaseModel):\n",
    "    def __init__(self, input_dim, output_dim=3):\n",
    "        super().__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(input_dim, 128),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 256),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.Linear(128, output_dim),\n",
    "            #nn.Dropout(0.5),\n",
    "            # nn.Softmax(dim=1)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.network(x)\n",
    "\n",
    "\n",
    "class WesadLSTM(BaseModel):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim=3, lstm_layers=1):\n",
    "        super().__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.classes = output_dim\n",
    "        # self.linear1 = nn.Linear(input_dim, input_dim)\n",
    "        # self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers=lstm_layers, batch_first=True, dropout=0.2)\n",
    "        self.lstm = nn.LSTM(input_size=input_dim, hidden_size=input_dim, num_layers=lstm_layers, dropout=0.2)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        xb = x.view(-1, 1, self.input_dim)\n",
    "        # print('x', xb.shape)\n",
    "        # out = self.linear1(x)\n",
    "        # out = x.view(x.shape, -1)\n",
    "        # print(out.shape)\n",
    "        # out = self.dropout(out)\n",
    "        # lstm_out, (ht, ct) = self.lstm(out)\n",
    "        # out = self.fc(ht[-1])\n",
    "        lstm_out, _ = self.lstm(xb)\n",
    "        # print(lstm_out.view(-1, self.hidden_dim).shape)\n",
    "        out = self.fc(lstm_out.view(-1, self.hidden_dim))\n",
    "        # out = self.dropout(out)\n",
    "        # out = self.softmax(out)\n",
    "        # out = F.softmax(out, dim=1)\n",
    "        return out.view(-1, self.classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RqmrXK4O_tCX"
   },
   "source": [
    "## General Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UBeX47-b_rZP"
   },
   "outputs": [],
   "source": [
    "subjects = df['subject'].unique()\n",
    "subjects.sort()\n",
    "device = get_default_device()\n",
    "\n",
    "train_batch_size = 25\n",
    "val_batch_size = 5\n",
    "input_dim = df.drop(columns=['subject', 'label']).shape[1]\n",
    "output_dim = 3\n",
    "lstm_layers = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "65FyNs92F5eX"
   },
   "outputs": [],
   "source": [
    "# lrs = [0.5, 0.1, 0.01, 0.001]\n",
    "# epochs = [20, 20, 20,  20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366,
     "referenced_widgets": [
      "8186f93a5a464d85a3d7f4fdb2ea201c",
      "0a0a35b218864226abf2fabe92eda3f8",
      "dbfc3bc7a90a4afcb3cc61b34055b76e",
      "1098c4b2731a43ed8cc3a1fedce650b0",
      "db059b494cfd4f28bfba373838df51d9",
      "de583eaa10064427a50bf7b368b7eea5",
      "32675c9f1e1643f89bb394b3e3f1d073",
      "97bd077435b04572a51ae26348b1c099"
     ]
    },
    "colab_type": "code",
    "id": "8Nxfyv_GYUw2",
    "outputId": "3e026ee3-73e0-43bd-eb88-a3870a0f0767"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8186f93a5a464d85a3d7f4fdb2ea201c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping early, the loss has diverged\n",
      "Learning rate search finished. See the graph with {finder_name}.plot()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fd22a9cf8>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch_lr_finder import LRFinder\n",
    "\n",
    "max_lr = 1e-5\n",
    "# grad_clip = 0.001\n",
    "# weight_decay = 1e-4\n",
    "opt_func = torch.optim.Adam\n",
    "criterion = F.cross_entropy\n",
    "train_dl, val_dl = get_data_loader(subject='S2', train_batch_size=train_batch_size, val_batch_size=val_batch_size)\n",
    "\n",
    "# model = WesadFeedForward(input_dim, output_dim)\n",
    "model = WesadLSTM(input_dim=input_dim, hidden_dim=input_dim, output_dim=output_dim, lstm_layers=lstm_layers)\n",
    "# optimizer = opt_func(model.parameters(), lr=max_lr, weight_decay=weight_decay)\n",
    "optimizer = opt_func(model.parameters(), lr=max_lr)\n",
    "lr_finder = LRFinder(model, optimizer, criterion, device=\"cuda\")\n",
    "lr_finder.range_test(train_dl, end_lr=10000, num_iter=1000)\n",
    "lr_finder.plot() # to inspect the loss-learning rate graph\n",
    "# lr_finder.reset() # to reset the model and optimizer to their initial state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tMur3N7FIBS0"
   },
   "source": [
    "## Running models - LOSO CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "P_qs92RNGITw",
    "outputId": "7d16960d-3100-4f7e-f99f-4aa635e52e96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSO S10\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "lr = 1e-4\n",
    "\n",
    "models = []\n",
    "histories = []\n",
    "val_histories = []\n",
    "for subject in subjects:\n",
    "    history = []\n",
    "    print('LOSO', subject)\n",
    "    train_dl, val_dl = get_data_loader(subject, train_batch_size=train_batch_size, val_batch_size=val_batch_size)\n",
    "    train_ddl = DeviceDataLoader(train_dl, device)\n",
    "    val_ddl = DeviceDataLoader(val_dl, device)\n",
    "    # model = to_device(WesadFeedForward(input_dim, output_dim), device)\n",
    "    model = to_device(WesadLSTM(input_dim=input_dim, hidden_dim=input_dim, \n",
    "                                output_dim=output_dim, lstm_layers=lstm_layers), device)\n",
    "\n",
    "    # optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "    # history += fit(epochs, lr, model, train_ddl, val_ddl, optimizer)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "4LfpiXbjEQTQ",
    "outputId": "3a119247-5177-466d-ef24-0f677a46904d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data.shape: torch.Size([25, 77])\n",
      "out[0] tensor([-0.1038,  0.0907, -0.0787], device='cuda:0', grad_fn=<SelectBackward>)\n",
      "softmax(out[0]): tensor([0.3086, 0.3749, 0.3165], device='cuda:0', grad_fn=<SelectBackward>)\n",
      "tensor(1, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "def try_batch(dl):\n",
    "    model.eval()\n",
    "    for data, labels in dl:\n",
    "        # print(data)\n",
    "        # print(labels)\n",
    "        print('data.shape:', data.shape)\n",
    "        out = model(data)\n",
    "        print('out[0]', out[0])\n",
    "        print('softmax(out[0]):', F.softmax(out, dim=1)[0])\n",
    "        _, p = torch.max(F.softmax(out, dim=1), dim=1)\n",
    "        print(p[0])\n",
    "        break\n",
    "\n",
    "try_batch(train_ddl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "Nc7XHp4RGK5J",
    "outputId": "c206f7ff-8376-4135-9068-cf4c775c3ca8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'val_acc': 0.31704410910606384,\n",
       "  'val_f1': 0.3168419036267665,\n",
       "  'val_loss': 1.1131939888000488}]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = [evaluate(model, val_ddl)]\n",
    "history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ti_YihcVJ7BI"
   },
   "outputs": [],
   "source": [
    "epochs = 5\n",
    "lr = 5e-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347,
     "referenced_widgets": [
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      "43540220a99d43fa9434c6641c983779"
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    },
    "colab_type": "code",
    "id": "1i9617j0DOuG",
    "outputId": "5687c477-d1f3-4161-b73b-8415b9106973"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7df734534de34697af24898a2f195b9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4537.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch [0], train_loss: 0.4630, train_acc: 0.8153, val_loss: 0.3250, val_acc: 0.8965, val_f1: 0.8817\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d819f1d1baea4290aec116d61e358adf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4537.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch [1], train_loss: 0.1342, train_acc: 0.9572, val_loss: 0.5303, val_acc: 0.8629, val_f1: 0.8468\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d344f2b329634552ad3577c978f2dd52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4537.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch [2], train_loss: 0.0932, train_acc: 0.9718, val_loss: 0.6836, val_acc: 0.8338, val_f1: 0.8255\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df4136bbe3d7481cb7c2ae651d1cd0d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4537.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch [3], train_loss: 0.0777, train_acc: 0.9771, val_loss: 0.4298, val_acc: 0.8890, val_f1: 0.8769\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad112087ff3b4cc583a60a610891b1de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4537.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch [4], train_loss: 0.0615, train_acc: 0.9816, val_loss: 0.4548, val_acc: 0.8970, val_f1: 0.8842\n"
     ]
    }
   ],
   "source": [
    "history += fit(epochs, lr, model, train_ddl, val_ddl, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "wFZKOpppLwXf",
    "outputId": "b6250572-eb23-437c-bb7a-f9fa1964bd54"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(history[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "0sBIoVu-gqTL",
    "outputId": "5eacf1c8-9aee-4bbc-e079-4a14ec228550"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_losses(history[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lsmpvyb7sb2-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VZ5hbzj0O8AJ"
   },
   "source": [
    "TODOs\n",
    "\n",
    "* Use [scorch](https://github.com/skorch-dev/skorch) for hyperparameter tuning\n",
    "* Apply SMOTE oversampling technique to tackle class imbalance and naturally adapt it as a data augmentation\n",
    "* a model combining a CNN to automatically extracts local features and RNN to extract temporal dependencies\n",
    "* apply a scalogram to the entire signal in order to get an equivalent image dataset\n",
    "    * *The scalogram is basically a visible time-frequency illustration of a signal using wavelet transform.*\n",
    "* Experiment with fusion schemes to apply different signals for Affect Recognition\n",
    "\n",
    "Related papers:\n",
    "\n",
    "* Psychological Stress Detection Using Deep Convolutional Neural Networks\n",
    "* An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2HHMPQMgPCUH"
   },
   "outputs": [],
   "source": []
  }
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