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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Locrian24/csc421-project-stress-classification/blob/main/csc421_project_cnn_binary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "casP87SYEop6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8be1dc65-1457-4da6-8332-7f8a6a0084da"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/drive/MyDrive/WESAD_RespiBAN_binary_train.tar .\n",
        "!cp /content/drive/MyDrive/WESAD_RespiBAN_binary_test.tar ."
      ],
      "metadata": {
        "id": "jdVAOs2qEnzc"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install webdataset\n",
        "!pip install pytorch_lightning"
      ],
      "metadata": {
        "id": "Tbwg-qMSBiuc"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "yRUdv_c-G70X"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import webdataset as wds\n",
        "import pytorch_lightning as pl\n",
        "import logging\n",
        "from torchmetrics.functional import accuracy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 0.0001\n",
        "BATCH_SIZE = 40\n",
        "NUM_EPOCHS = 100"
      ],
      "metadata": {
        "id": "S2792NAEFuyz"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_block():\n",
        "  block = nn.Sequential(\n",
        "      nn.Conv1d(in_channels=1, out_channels=8, kernel_size=32, stride=2, padding=1),\n",
        "      nn.ReLU(),\n",
        "      nn.MaxPool1d(4, 4),\n",
        "      nn.Conv1d(in_channels=8, out_channels=16, kernel_size=7, stride=2, padding=1),\n",
        "      nn.ReLU(),\n",
        "      nn.MaxPool1d(4, 4),\n",
        "      nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=2),\n",
        "      nn.ReLU(),\n",
        "      nn.MaxPool1d(2, 2),\n",
        "  )\n",
        "\n",
        "  return block"
      ],
      "metadata": {
        "id": "cKVol97CHBAQ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BinaryStressClassifier(pl.LightningModule):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_block = conv_block()\n",
        "    self.fc1 = nn.Sequential(\n",
        "        nn.Linear(6912, 32),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "    self.fc2 = nn.Sequential(\n",
        "        nn.Linear(32, 16),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "    self.fc3 = nn.Sequential(\n",
        "        nn.Linear(16, 1),\n",
        "        nn.Sigmoid()\n",
        "    )\n",
        "\n",
        "    self.criterion = nn.BCELoss()\n",
        "  \n",
        "  def forward(self, X):\n",
        "    X = X.float()\n",
        "\n",
        "    ECG_x = self.conv_block(X[:,:,0])\n",
        "    EDA_x = self.conv_block(X[:,:,1])\n",
        "    EMG_x = self.conv_block(X[:,:,2])\n",
        "    Temp_x = self.conv_block(X[:,:,3])\n",
        "    Resp_x = self.conv_block(X[:,:,4])\n",
        "    ACC_x_x = self.conv_block(X[:,:,5])\n",
        "    ACC_y_x = self.conv_block(X[:,:,6])\n",
        "    ACC_z_x = self.conv_block(X[:,:,7])\n",
        "\n",
        "    ECG_x = torch.flatten(ECG_x, 1)\n",
        "    EDA_x = torch.flatten(EDA_x, 1)\n",
        "    EMG_x = torch.flatten(EMG_x, 1)\n",
        "    Temp_x = torch.flatten(Temp_x, 1)\n",
        "    Resp_x = torch.flatten(Resp_x, 1)\n",
        "    ACC_x_x = torch.flatten(ACC_x_x, 1)\n",
        "    ACC_y_x = torch.flatten(ACC_y_x, 1)\n",
        "    ACC_z_x = torch.flatten(ACC_z_x, 1)\n",
        "\n",
        "    x = torch.cat((ECG_x,EDA_x,EMG_x,Temp_x,Resp_x,ACC_x_x,ACC_y_x,ACC_z_x), dim=1)\n",
        "\n",
        "    x = self.fc1(x)\n",
        "    x = self.fc2(x)\n",
        "    x = self.fc3(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    dataset = (\n",
        "      wds.WebDataset('WESAD_RespiBAN_binary_train.tar')\n",
        "        .shuffle(4 * BATCH_SIZE)\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 1 if x == 2 else 0 ) # Map labels for binary classification (2 = \"stress\")\n",
        "    )\n",
        "\n",
        "    return torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "\n",
        "  def test_dataloader(self):\n",
        "    dataset = (\n",
        "      wds.WebDataset('WESAD_RespiBAN_binary_test.tar')\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 1 if x == 2 else 0 ) # Map labels for binary classification (2 = \"stress\")\n",
        "    )\n",
        "\n",
        "    return torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    return torch.optim.Adam(self.parameters(), lr=1e-4)\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    labels = labels.unsqueeze(1).float()\n",
        "    loss = self.criterion(outputs, labels)\n",
        "\n",
        "    return dict(loss = loss)\n",
        "\n",
        "  def test_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    acc = accuracy(outputs, labels)\n",
        "\n",
        "    self.log(\"accuracy:\", acc, prog_bar=True)\n",
        "\n",
        "classifier = BinaryStressClassifier()\n"
      ],
      "metadata": {
        "id": "jaL97HENe3XZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = pl.Trainer(max_epochs=NUM_EPOCHS, accelerator=\"auto\")\n",
        "logging.getLogger(\"lightning\").setLevel(logging.WARNING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcFuQKQ23ton",
        "outputId": "63a47e14-8eeb-4710-bac0-0a21593d4167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.fit(classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379,
          "referenced_widgets": [
            "b8c64cd376f54b71bd36f010c6bb5161",
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            "953ed1e2b4a74aca834488c92ff55fd4"
          ]
        },
        "id": "NHqAVtAYgVCI",
        "outputId": "9175d198-5b85-429c-c35c-dc5898939885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "Missing logger folder: /content/lightning_logs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type       | Params\n",
            "------------------------------------------\n",
            "0 | conv_block | Sequential | 2.7 K \n",
            "1 | fc1        | Sequential | 221 K \n",
            "2 | fc2        | Sequential | 528   \n",
            "3 | fc3        | Sequential | 17    \n",
            "4 | criterion  | BCELoss    | 0     \n",
            "------------------------------------------\n",
            "224 K     Trainable params\n",
            "0         Non-trainable params\n",
            "224 K     Total params\n",
            "0.898     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8c64cd376f54b71bd36f010c6bb5161"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trainer.save_checkpoint(\"cnn_1.ckpt\")"
      ],
      "metadata": {
        "id": "SkhwMXWO3Cvr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model = BinaryStressClassifier.load_from_checkpoint(\"./cnn_1.ckpt\")"
      ],
      "metadata": {
        "id": "FFZO5yQT6HXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_trainer = pl.Trainer(accelerator=\"gpu\", devices=1)\n",
        "trainer.test(classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295,
          "referenced_widgets": [
            "511a8f91987143e6ab93ac135fc0fe5f",
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            "efad39bb53da49aabc6f3b41d103c965"
          ]
        },
        "id": "6ejX7IKR3F2X",
        "outputId": "de575f81-6d34-4f10-aeb8-cf52ce523349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/utilities.py:94: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
            "  category=PossibleUserWarning,\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "511a8f91987143e6ab93ac135fc0fe5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "       Test metric             DataLoader 0\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "        accuracy:           0.9498031735420227\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'accuracy:': 0.9498031735420227}]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Binary classification with no accelerometer data"
      ],
      "metadata": {
        "id": "npVDjr13bopB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BinaryStressNoAccClassifier(pl.LightningModule):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv_block = conv_block()\n",
        "    self.fc1 = nn.Sequential(\n",
        "        nn.Linear(4320, 32),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "    self.fc2 = nn.Sequential(\n",
        "        nn.Linear(32, 16),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "    self.fc3 = nn.Sequential(\n",
        "        nn.Linear(16, 1),\n",
        "        nn.Sigmoid()\n",
        "    )\n",
        "\n",
        "    self.criterion = nn.BCELoss()\n",
        "  \n",
        "  def forward(self, X):\n",
        "    X = X.float()\n",
        "\n",
        "    ECG_x = self.conv_block(X[:,:,0])\n",
        "    EDA_x = self.conv_block(X[:,:,1])\n",
        "    EMG_x = self.conv_block(X[:,:,2])\n",
        "    Temp_x = self.conv_block(X[:,:,3])\n",
        "    Resp_x = self.conv_block(X[:,:,4])\n",
        "\n",
        "    ECG_x = torch.flatten(ECG_x, 1)\n",
        "    EDA_x = torch.flatten(EDA_x, 1)\n",
        "    EMG_x = torch.flatten(EMG_x, 1)\n",
        "    Temp_x = torch.flatten(Temp_x, 1)\n",
        "    Resp_x = torch.flatten(Resp_x, 1)\n",
        "\n",
        "    x = torch.cat((ECG_x,EDA_x,EMG_x,Temp_x,Resp_x), dim=1)\n",
        "\n",
        "    x = self.fc1(x)\n",
        "    x = self.fc2(x)\n",
        "    x = self.fc3(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "  def train_dataloader(self):\n",
        "    dataset = (\n",
        "      wds.WebDataset('WESAD_RespiBAN_binary_train.tar')\n",
        "        .shuffle(4 * BATCH_SIZE)\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 1 if x == 2 else 0 ) # Map labels for binary classification (2 = \"stress\")\n",
        "    )\n",
        "\n",
        "    return torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "\n",
        "  def test_dataloader(self):\n",
        "    dataset = (\n",
        "      wds.WebDataset('WESAD_RespiBAN_binary_test.tar')\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 1 if x == 2 else 0 ) # Map labels for binary classification (2 = \"stress\")\n",
        "    )\n",
        "\n",
        "    return torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE)\n",
        "\n",
        "  def configure_optimizers(self):\n",
        "    return torch.optim.Adam(self.parameters(), lr=1e-4)\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    labels = labels.unsqueeze(1).float()\n",
        "    loss = self.criterion(outputs, labels)\n",
        "\n",
        "    return dict(loss = loss)\n",
        "\n",
        "  def test_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    acc = accuracy(outputs, labels)\n",
        "\n",
        "    self.log(\"accuracy:\", acc, prog_bar=True)\n",
        "\n",
        "no_acc_classifier = BinaryStressNoAccClassifier()"
      ],
      "metadata": {
        "id": "VMTdK0dVbs5K"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_acc_trainer = pl.Trainer(max_epochs=NUM_EPOCHS, accelerator=\"auto\")\n",
        "logging.getLogger(\"lightning\").setLevel(logging.WARNING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msp44jvRcAFK",
        "outputId": "fb8aaf30-4083-453c-f093-39632584d867"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_acc_trainer.fit(no_acc_classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309,
          "referenced_widgets": [
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        },
        "id": "qDhM3ueZcDQS",
        "outputId": "7566648d-2de0-4457-a653-6c5d06fbcba0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Missing logger folder: /content/lightning_logs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type       | Params\n",
            "------------------------------------------\n",
            "0 | conv_block | Sequential | 2.7 K \n",
            "1 | fc1        | Sequential | 138 K \n",
            "2 | fc2        | Sequential | 528   \n",
            "3 | fc3        | Sequential | 17    \n",
            "4 | criterion  | BCELoss    | 0     \n",
            "------------------------------------------\n",
            "141 K     Trainable params\n",
            "0         Non-trainable params\n",
            "141 K     Total params\n",
            "0.566     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ac4578f000c4c238b21c354f94ac52b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_acc_trainer.test(no_acc_classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "348d0a75e86b471495c6b520b6aaafbe",
            "c155760845b843a2bebf48aa55226af9",
            "5d16c786cee3492f94fb912af7e462a1",
            "1445db8ebd094688bbf5fbe5d394352e",
            "80f88172ad944a58b94f017119f70242",
            "b89570d446f84a16a4896cc361c588fc",
            "d222f1fccfa546f7944e0b34f6a7b490",
            "57f3bd06031d450b8d63227a47682e3b",
            "da35ef8342904e419a2e8cad3ee21c47",
            "06248d4470e74b9e9a7d0f3f460b1f38",
            "d528c14421e84c19a60b7e12bdd27d81"
          ]
        },
        "id": "gG4GctQ1i0LM",
        "outputId": "d139304e-68ba-4759-b14b-fa7138c39a63"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Testing: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "348d0a75e86b471495c6b520b6aaafbe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "       Test metric             DataLoader 0\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "        accuracy:           0.9340550899505615\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'accuracy:': 0.9340550899505615}]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}