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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_multiclass.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": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3rc-lT68O-2",
        "outputId": "e32619e9-e28b-4fea-fbf5-20c591b222a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/drive/MyDrive/WESAD_RespiBAN_multiclass_train.tar .\n",
        "!cp /content/drive/MyDrive/WESAD_RespiBAN_multiclass_test.tar ."
      ],
      "metadata": {
        "id": "aDPPR46h8UiN"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install webdataset\n",
        "!pip install pytorch_lightning"
      ],
      "metadata": {
        "id": "n3geqxjC8Uw0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "sstRAS7P8YZi"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 0.0001\n",
        "BATCH_SIZE = 40\n",
        "NUM_EPOCHS = 100"
      ],
      "metadata": {
        "id": "ClPP6gHiQvz7"
      },
      "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": "yQi4yfw48dDf"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MulticlassClassifier(pl.LightningModule):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    self.lr = 1e-3\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, 3),\n",
        "        nn.Softmax(1)\n",
        "    )\n",
        "\n",
        "    self.criterion = nn.CrossEntropyLoss()\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_multiclass_train.tar')\n",
        "        .shuffle(4 * BATCH_SIZE)\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 0 if x == 1 else 1 if x == 2 else 2 )\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_multiclass_test.tar')\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 0 if x == 1 else 1 if x == 2 else 2 )\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=self.lr)\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    labels = labels.squeeze()\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 = MulticlassClassifier()\n"
      ],
      "metadata": {
        "id": "jaL97HENe3XZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = pl.Trainer(max_epochs=NUM_EPOCHS, accelerator=\"auto\", auto_lr_find=True)\n",
        "logging.getLogger(\"lightning\").setLevel(logging.WARNING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ol-xRZExL2yt",
        "outputId": "d6d67340-58f9-4df9-ca92-514ab85fd149"
      },
      "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": 292,
          "referenced_widgets": [
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            "793af30644ce443393281f91fee280b2",
            "d240bb14a8b24d02b97a8eaedbf63e23"
          ]
        },
        "id": "ym-UE3EHL-9N",
        "outputId": "4a4dba1a-b57b-4763-8f51-b20888071cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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       | 51    \n",
            "4 | criterion  | CrossEntropyLoss | 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": "471cfc7e833a43a49e90f4b7d6cfc27a"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_checkpoint(\"multiclass_cnn.ckpt\")"
      ],
      "metadata": {
        "id": "yunNnGeMMAR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.test(classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "9fe84e1b1aa4466aaff0ab0d373e66d8",
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            "ae6e9673eec14205865d6c428d045e42",
            "cd679f403b4a482980e2d1e3bdc3ef84"
          ]
        },
        "id": "WGC9lfBnTneT",
        "outputId": "01da4a27-ac2f-4358-8fa8-0759529e1fa6"
      },
      "execution_count": null,
      "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": "9fe84e1b1aa4466aaff0ab0d373e66d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "       Test metric             DataLoader 0\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "        accuracy:            0.966050922870636\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'accuracy:': 0.966050922870636}]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification without Accelerometer data"
      ],
      "metadata": {
        "id": "7AMmMKqPjcEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MulticlassNoAccClassifier(pl.LightningModule):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    self.lr = 1e-4\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, 3),\n",
        "        nn.Softmax(1)\n",
        "    )\n",
        "\n",
        "    self.criterion = nn.CrossEntropyLoss()\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_multiclass_train.tar')\n",
        "        .shuffle(4 * BATCH_SIZE)\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 0 if x == 1 else 1 if x == 2 else 2 )\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_multiclass_test.tar')\n",
        "        .decode()\n",
        "        .to_tuple(\"input.npy\", \"label.cls\")\n",
        "        .map_tuple(transforms.ToTensor(), lambda x: 0 if x == 1 else 1 if x == 2 else 2 )\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=self.lr)\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\n",
        "    inputs, labels = batch\n",
        "    outputs = self.forward(inputs)\n",
        "    labels = labels.squeeze()\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 = MulticlassNoAccClassifier()"
      ],
      "metadata": {
        "id": "o4SK-UY_jebH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_acc_trainer = pl.Trainer(max_epochs=NUM_EPOCHS, accelerator=\"auto\", auto_lr_find=True)\n",
        "logging.getLogger(\"lightning\").setLevel(logging.WARNING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRbEM07ljnOH",
        "outputId": "6e2901e3-3d31-4bc5-ae39-7090652947c9"
      },
      "execution_count": 21,
      "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": 292,
          "referenced_widgets": [
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        "id": "7pM_5iMzjpWf",
        "outputId": "36e64c48-076b-44dc-b4f8-50d1a5dbdd5f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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       | 51    \n",
            "4 | criterion  | CrossEntropyLoss | 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]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_acc_trainer.test(no_acc_classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "9da3db49340445af8b3db2ffa13ccf66",
            "256a60b7a2af4e43af5ce355a63637b5",
            "eeaafda43c3e435cacb13af85fedb820",
            "e083665d147748348afbd38d2ba8115b",
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        },
        "id": "iQw7mF2UmCSY",
        "outputId": "c0b94bcb-f172-403c-f1ae-f89d5c26cef9"
      },
      "execution_count": 23,
      "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": "9da3db49340445af8b3db2ffa13ccf66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "       Test metric             DataLoader 0\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "        accuracy:           0.7383924126625061\n",
            "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'accuracy:': 0.7383924126625061}]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}