AE_V3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/t-willi/Simula/blob/main/AE_V3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "8eUpzrSTCodl"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from timeit import default_timer as timer \n",
        "import glob\n",
        "import torch.optim as optim\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "\n",
        "if torch.cuda.is_available()==True:\n",
        "  device=\"cuda:0\"\n",
        "else:\n",
        "  device =\"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Veu-bkI8hXSr"
      },
      "outputs": [],
      "source": [
        "def normalize_df_per_column(df):\n",
        "  for i,k in enumerate(df.columns):\n",
        "    max=df.iloc[:,i].max()\n",
        "    min=df.iloc[:,i].min()\n",
        "    df.iloc[:,i] = df.iloc[:,i].apply(lambda x: ((x-min)/(max-min)))\n",
        "  return df\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_list(l):\n",
        "  max=max(l)\n",
        "  min=min(l)\n",
        "  l=l.apply(lambda x:((x-min)/(max-min)))\n",
        "  return l\n",
        "\n"
      ],
      "metadata": {
        "id": "mGusGEeqpdEQ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "cal55bVVRw1N"
      },
      "outputs": [],
      "source": [
        "# Create custom dataset class to load ECG data into dataset, containing \n",
        "# input tensor with lead 1 and output tensor with desired other leads\n",
        "class Custom_dataset(Dataset):\n",
        "    def __init__(self, data_dir,column=2):\n",
        "      #get all files from directory loaded in all_files list\n",
        "      self.column=column\n",
        "      self.all_files = []\n",
        "      self.files = glob.glob(data_dir + '/*.csv')\n",
        "      for f in self.files:\n",
        "        temp_df = pd.read_csv(f,sep=\" \")\n",
        "        self.all_files.append(temp_df)\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "      return len(self.all_files)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "      Data = []\n",
        "      #turn list of dataframes into Tensor\n",
        "      for f in self.files:\n",
        "        #load_dataframe\n",
        "        temp_df=pd.read_csv(f,sep=\" \")\n",
        "        #load input tensor\n",
        "        temp_list_in=temp_df.iloc[:,1]\n",
        "        temp_list_in=normalize([temp_list_in], norm=\"max\")\n",
        "        temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.float32)\n",
        "        #temp_tensor_in=temp_tensor_in.unsqueeze(0)\n",
        "        #load label Tensor\n",
        "        temp_list_out=temp_df.iloc[:,self.column].values\n",
        "        temp_list_out=normalize([temp_list_out], norm=\"max\")\n",
        "        temp_tensor_out=torch.tensor(temp_list_out,dtype=torch.float32)\n",
        "        #temp_tensor_out=temp_tensor_out.unsqueeze(0)\n",
        "        #combine input and label and output\n",
        "        temp_tensor_pair= temp_tensor_in,temp_tensor_out\n",
        "      return temp_tensor_pair"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(8):\n",
        "  i+=1\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcVwl8zWcRPw",
        "outputId": "bf53c4da-c418-4385-bc6d-6f96604ea9b6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "lNtnRLsjUMIp"
      },
      "outputs": [],
      "source": [
        "#create trainingsets for all 8 conversions\n",
        "#1 to 1\n",
        "datasetlist = []\n",
        "train_dataset1_1 = Custom_dataset(data_dir=\"/content/train\",column=1)\n",
        "datasetlist.append(train_dataset1_1)\n",
        "train_dataset1_2 = Custom_dataset(data_dir=\"/content/train\",column=2)\n",
        "datasetlist.append(train_dataset1_2)\n",
        "train_dataset1_3 = Custom_dataset(data_dir=\"/content/train\",column=3)\n",
        "datasetlist.append(train_dataset1_3)\n",
        "train_dataset1_4 = Custom_dataset(data_dir=\"/content/train\",column=4)\n",
        "datasetlist.append(train_dataset1_4)\n",
        "train_dataset1_5 = Custom_dataset(data_dir=\"/content/train\",column=5)\n",
        "datasetlist.append(train_dataset1_5)\n",
        "train_dataset1_6 = Custom_dataset(data_dir=\"/content/train\",column=6)\n",
        "datasetlist.append(train_dataset1_6)\n",
        "train_dataset1_7 = Custom_dataset(data_dir=\"/content/train\",column=7)\n",
        "datasetlist.append(train_dataset1_7)\n",
        "train_dataset1_8 = Custom_dataset(data_dir=\"/content/train\",column=8)\n",
        "datasetlist.append(train_dataset1_8)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP88T4BDvW0e",
        "outputId": "a5c3483a-bb78-4c94-d1e7-4601a84f6e35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 4999]), torch.float32, torch.Size([1, 4999]), torch.float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "#check for shape of the data in dataset\n",
        "x,y=datasetlist[0][0]\n",
        "x.shape,x.dtype,y.shape,y.dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "sA756IN7LuVK"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "BATCH_SIZE = 10\n",
        "dataloaderlist = []\n",
        "#turn datasets into iterables\n",
        "for data in datasetlist:\n",
        "  train_dataloader = DataLoader(data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True\n",
        "                              )\n",
        "  dataloaderlist.append(train_dataloader)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = next(iter(dataloaderlist[0]))\n",
        "x.shape,x.dtype,y.shape,y.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCXDPwp4fE0c",
        "outputId": "f860a66b-9ceb-435d-fb36-547a99d29931"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([10, 1, 4999]),\n",
              " torch.float32,\n",
              " torch.Size([10, 1, 4999]),\n",
              " torch.float32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "t5WfPAwVIWSB"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Here, we define the autoencoder model.This model is taken from \"https://github.com/L1aoXingyu/pytorch-beginner/blob/master/08-AutoEncoder/simple_autoencoder.py\"\n",
        "\"\"\"\n",
        "class ECG_AE_v1(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ECG_AE_v1, self).__init__()\n",
        "        self.AE = nn.Sequential(\n",
        "            nn.Linear(4999, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,10),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(10,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,4999),\n",
        "        )\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.AE(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "model = ECG_AE_v1().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eYaklXRV8FkT"
      },
      "outputs": [],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QD39NrScQxlq"
      },
      "outputs": [],
      "source": [
        "# try: \n",
        "#     import torchinfo\n",
        "# except:\n",
        "#     !pip install torchinfo\n",
        "#     import torchinfo\n",
        "\n",
        "# from torchinfo import summary\n",
        "# summary(model, input_size=[1,4999]) # do a test pass through of an example input size "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_loop(Epochs=1,dataloader=None):\n",
        "  model = ECG_AE_v1().to(device)\n",
        "  from tqdm.auto import tqdm\n",
        "  for epoch in tqdm(range(Epochs)):\n",
        "    print(f\"Epoch:{epoch}\")\n",
        "    train_loss=0\n",
        "    for batch, (X,y) in enumerate(dataloader): \n",
        "      X, y = X.to(device), y.to(device) \n",
        "      model.train()\n",
        "      output=model(X)\n",
        "      loss = criterion(output,y)\n",
        "      train_loss += loss\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "    #average loss per batch\n",
        "    train_loss /= len(train_dataloader)\n",
        "    print(f\"\\nTrain loss: {train_loss:.5f}\")\n",
        "  return model\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "\n"
      ],
      "metadata": {
        "id": "8OnVpS3EQIEw"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_of_models = []\n",
        "for dataloader in dataloaderlist:\n",
        "  model = train_loop(dataloader=dataloader)\n",
        "  list_of_models.append(model)\n",
        "\n",
        "\n",
        "\n"
      ],
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        "id": "OcMF2KA2hU83",
        "outputId": "6e842f39-2110-4ed6-f403-3fba99f97ead"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.02698\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.02740\n"
          ]
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        {
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          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.03267\n"
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          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.04636\n"
          ]
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        {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.05555\n"
          ]
        },
        {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n",
            "\n",
            "Train loss: 0.02848\n"
          ]
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        {
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            "Epoch:0\n",
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            "Epoch:0\n",
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    {
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      "execution_count": null,
      "metadata": {
        "id": "3uMREYqwckwg"
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      "source": [
        "from tqdm.auto import tqdm\n",
        "torch.manual_seed(42)\n",
        "#train and test loop\n",
        "Epochs = 5\n",
        "for epoch in tqdm(range(Epochs)):\n",
        "  print(f\"Epoch:{epoch}\")\n",
        "  train_loss=0\n",
        "  for batch, (X,y) in enumerate(train_dataloader): \n",
        "    X, y = X.to(device), y.to(device) \n",
        "    model.train()\n",
        "    output=model(X)\n",
        "    ##reshaping output from linear to 2d from 40000 to 8 times 5000\n",
        "    #output=torch.reshape(output,(1,1,8,4999))\n",
        "    ######\n",
        "    loss = criterion(output,y)\n",
        "    train_loss += loss\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  #average loss per batch\n",
        "  train_loss /= len(train_dataloader)\n",
        "  #start testing\n",
        "  # test_loss = 0\n",
        "  # model.eval()\n",
        "  # with torch.inference_mode():\n",
        "  #   for X,y in test_dataloader:\n",
        "  #     X, y = X.to(device), y.to(device) \n",
        "  #     test_pred = model(X)\n",
        "  #     ##reshaping output from linear to 2d from 40000 to 8 times 5000\n",
        "  #     #test_pred=torch.reshape(test_pred,(10,1,8,4999))\n",
        "  #     ######\n",
        "  #     test_loss += criterion(test_pred,y)    \n",
        "  #   test_loss /= len(test_dataloader)\n",
        "\n",
        "  #print what is happening\n",
        "  print(f\"\\nTrain loss: {train_loss:.5f}\")\n",
        "    \n",
        "\n",
        "\n"
      ]
    },
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        "input,output = train_dataset[0]\n",
        "input\n",
        "input=input.tolist()\n",
        "data=input[0][0]\n",
        "type(data)\n",
        "plt.plot(data)\n",
        "# plt.title(\"Input ECG lead1\")"
      ]
    },
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      "execution_count": null,
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        "model.to(\"cpu\")\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "  output=model(X)\n",
        "output=output.tolist()\n",
        "data=output[0][0]\n",
        "plt.plot(data)\n",
        "plt.title(\"Output ECG trained for 20epochs on 50 datapoints\")"
      ]
    },
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      "execution_count": null,
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        "model.to(\"cpu\")\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
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        "output"
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