DNN_LRP.ipynb

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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/monouns/SAI-board/blob/master/DNN_LRP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install captum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUwHH3-QQXir",
        "outputId": "7ce37ecd-1ea1-49d2-c592-b8180641eb0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: captum in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
            "Requirement already satisfied: torch>=1.6 in /usr/local/lib/python3.7/dist-packages (from captum) (1.11.0+cu113)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from captum) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from captum) (3.2.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6->captum) (4.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->captum) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->captum) (1.4.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->captum) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->captum) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->captum) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1yvR5OsqDtj9",
        "outputId": "50276a44-5c55-4621-b1c7-0f10dc749c32"
      },
      "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",
            "/content/drive/My Drive/Colab Notebooks/CNN_LRP\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "%cd /content/drive/My Drive/Colab Notebooks/DNN_LRP"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Pre-Processing"
      ],
      "metadata": {
        "id": "y5gJcN2TQoaE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "ywHYBXprFdBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = pd.read_csv(f'./data/validation_1.csv')\n",
        "data1.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
        "data2 = pd.read_csv(f'./data/validation_2.csv')\n",
        "data2.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
        "data = pd.concat([data1, data2])\n",
        "data.set_index('timestamp', inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "LX7zN-xdFPiQ",
        "outputId": "ca5f3651-306b-4f97-8aa0-1a36c6171338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>31.79738</td>\n",
              "      <td>0.716042</td>\n",
              "      <td>1179.76929</td>\n",
              "      <td>348.03607</td>\n",
              "      <td>1</td>\n",
              "      <td>1018.394040</td>\n",
              "      <td>12.0</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-10 23:59:58</th>\n",
              "      <td>-2.3564</td>\n",
              "      <td>0</td>\n",
              "      <td>12.26196</td>\n",
              "      <td>-0.00101</td>\n",
              "      <td>15.53497</td>\n",
              "      <td>0.2907</td>\n",
              "      <td>12.669310</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>31.78784</td>\n",
              "      <td>0.716042</td>\n",
              "      <td>1152.66187</td>\n",
              "      <td>348.81366</td>\n",
              "      <td>1</td>\n",
              "      <td>1018.394040</td>\n",
              "      <td>12.0</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-10 23:59:59</th>\n",
              "      <td>-2.1541</td>\n",
              "      <td>0</td>\n",
              "      <td>12.26196</td>\n",
              "      <td>-0.00166</td>\n",
              "      <td>15.53497</td>\n",
              "      <td>0.3280</td>\n",
              "      <td>12.669310</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>31.77029</td>\n",
              "      <td>0.716042</td>\n",
              "      <td>1130.65723</td>\n",
              "      <td>349.13916</td>\n",
              "      <td>1</td>\n",
              "      <td>1018.394040</td>\n",
              "      <td>12.0</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-11 00:00:00</th>\n",
              "      <td>-2.0198</td>\n",
              "      <td>0</td>\n",
              "      <td>12.26196</td>\n",
              "      <td>-0.00195</td>\n",
              "      <td>15.53497</td>\n",
              "      <td>0.3280</td>\n",
              "      <td>12.669310</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>31.76915</td>\n",
              "      <td>0.716042</td>\n",
              "      <td>1106.10132</td>\n",
              "      <td>346.82434</td>\n",
              "      <td>1</td>\n",
              "      <td>1018.394040</td>\n",
              "      <td>12.0</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>86400 rows × 87 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa29ab6e-0a70-4522-9954-3bed8986f020')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
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              "          document.querySelector('#df-aa29ab6e-0a70-4522-9954-3bed8986f020 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aa29ab6e-0a70-4522-9954-3bed8986f020');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data[['attack']]\n",
        "df['filter'] = df['attack']==1\n",
        "df_attack = df.loc[df['filter']==True]\n",
        "\n",
        "attack_timeline = []\n",
        "attack_start = df_attack.index[0]\n",
        "flag = True\n",
        "\n",
        "cnt = len(df.loc[:attack_start])\n",
        "while True:\n",
        "  if df.iloc[cnt]['filter']==False and flag==True:\n",
        "    attack_end = df.index[cnt]\n",
        "    attack_timeline.append([attack_start, attack_end])\n",
        "    flag = False\n",
        "  elif df.iloc[cnt]['filter']==True and flag==False:\n",
        "    attack_start = df.index[cnt]\n",
        "    flag = True\n",
        "  cnt += 1\n",
        "  if cnt == len(df):\n",
        "    break\n",
        "print(attack_timeline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oIrAXagGSps",
        "outputId": "a323e23f-63c2-491c-f5a5-60fcc410067a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['2021-07-10 05:41:22', '2021-07-10 05:44:33'], ['2021-07-10 07:19:12', '2021-07-10 07:20:07'], ['2021-07-10 11:25:13', '2021-07-10 11:27:20'], ['2021-07-10 15:39:12', '2021-07-10 15:40:07'], ['2021-07-10 16:42:17', '2021-07-10 16:47:14'], ['2021-07-10 19:21:16', '2021-07-10 19:22:48'], ['2021-07-10 22:35:20', '2021-07-10 22:36:28']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for start, end in attack_timeline:\n",
        "  print(len(df.loc[:end]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnRIvcvFIYBo",
        "outputId": "e1dd3b39-bde3-4feb-a72a-5080140c136e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20673\n",
            "26407\n",
            "41240\n",
            "56407\n",
            "60434\n",
            "69768\n",
            "81388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "train = data.iloc[:60000, :-1]\n",
        "train_y = data.iloc[:60000, -1]\n",
        "valid = data.iloc[60000:70000, :-1]\n",
        "valid_y = data.iloc[60000:70000, -1]\n",
        "test = data.iloc[70000:, :-1]\n",
        "test_y = data.iloc[70000:, -1]\n",
        "\n",
        "sc.fit(train.values)\n",
        "train_sc = pd.DataFrame(sc.transform(train.values), columns=train.columns)\n",
        "valid_sc = pd.DataFrame(sc.transform(valid.values), columns=valid.columns)\n",
        "test_sc = pd.DataFrame(sc.transform(test.values), columns = test.columns)"
      ],
      "metadata": {
        "id": "P4ctDDyhFcUu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Pytorch DNN model"
      ],
      "metadata": {
        "id": "lsvOhIvpQrig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import Dataset\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from sklearn.utils import class_weight\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "SEED = 10\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "np.random.seed(SEED)"
      ],
      "metadata": {
        "id": "Z82Lf8KqI2CY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window = 1\n",
        "batch_size = 256\n",
        "num_workers = 4\n",
        "pin_memory = True\n",
        "device='cuda'\n",
        "lr = 1e-4\n",
        "num_epoch=5"
      ],
      "metadata": {
        "id": "rNiI-0iSKbok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDataset(Dataset):\n",
        "    def __init__(self, data, y, window):\n",
        "        self.x = torch.Tensor(data)\n",
        "        self.y = torch.Tensor(y)\n",
        "        self.window = window\n",
        "        self.shape = self.__getshape__()\n",
        "        self.size = self.__getsize__()\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        x = self.x[index: index+self.window, :].T\n",
        "        y = self.y[index+self.window]\n",
        "        return x, y\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.x)-self.window\n",
        "    \n",
        "    def __getshape__(self):\n",
        "        return (self.__len__(), *self.__getitem__(0)[0].shape)\n",
        "    \n",
        "    def __getsize__(self):\n",
        "        return (self.__len__())"
      ],
      "metadata": {
        "id": "rzeF7Z_WJ12F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = MyDataset(data = train_sc.values,\n",
        "                          y = train_y.values,\n",
        "                         window = window,\n",
        "                         )\n",
        "valid_dataset = MyDataset(data = valid_sc.values,\n",
        "                          y = valid_y.values,\n",
        "                         window = window,)\n",
        "test_dataset = MyDataset(data = test_sc.values,\n",
        "                          y = test_y.values,\n",
        "                         window = window,)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          drop_last=False,\n",
        "                                          shuffle=True,\n",
        "                                          num_workers=num_workers,\n",
        "                                          pin_memory=pin_memory)\n",
        "valid_loader = torch.utils.data.DataLoader(valid_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          drop_last=False,\n",
        "                                          shuffle=False,\n",
        "                                          num_workers=num_workers,\n",
        "                                          pin_memory=pin_memory)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          drop_last=False,\n",
        "                                          shuffle=False,\n",
        "                                          num_workers=num_workers,\n",
        "                                          pin_memory=pin_memory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccUViFwxKH5G",
        "outputId": "04f1537f-c77b-41e4-bda4-ca2d1a75853a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DNN(nn.Module):\n",
        "    def __init__(self, in_features):\n",
        "        super(DNN, self).__init__()\n",
        "        self.hidden = 4\n",
        "        \n",
        "        self.fc1 = nn.Linear(in_features*window, self.hidden)      \n",
        "        self.fc2 = nn.Linear(self.hidden, 1)\n",
        "        \n",
        "        self.relu = nn.Tanh()\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = x.view(x.size(0), -1)\n",
        "        out = self.relu(self.dropout(self.fc1(out)))\n",
        "        out = self.fc2(out)\n",
        "        \n",
        "        return out\n",
        "    \n",
        "model = DNN(in_features = len(train_sc.columns)).to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "\n",
        "weight1 = class_weight.compute_class_weight(class_weight = \"balanced\" , \n",
        "                     classes=np.unique(train_y.values), \n",
        "                     y = train_y.values)\n",
        "weight1 = torch.as_tensor(np.unique(weight1)[1]).to(device)\n",
        "criterion = nn.BCEWithLogitsLoss(pos_weight = weight1)"
      ],
      "metadata": {
        "id": "NMEnK-gDMsmi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_list = []  \n",
        "valid_loss_list = []\n",
        "valid_acc_list=[]\n",
        "for epoch in range(num_epoch):\n",
        "        model.train()\n",
        "        total_step = len(train_loader)\n",
        "        total, total_ce_loss, total_dtw_loss = 0, 0, 0\n",
        "       \n",
        "        for x,y in tqdm(train_loader, leave=False):\n",
        "            x, y = x.cuda(), y.cuda()\n",
        "            logits = model(x)\n",
        "\n",
        "            ce_loss = criterion(logits.squeeze(), y.squeeze())\n",
        "            optimizer.zero_grad()\n",
        "            ce_loss.backward(retain_graph=True)\n",
        "            optimizer.step()\n",
        "\n",
        "            with torch.no_grad():\n",
        "                total_ce_loss += ce_loss.item() * x.size(0)\n",
        "                total += x.size(0)\n",
        "            \n",
        "        train_loss = total_ce_loss / total\n",
        "        train_loss_list.append(train_loss)\n",
        "       \n",
        "        predictions = []\n",
        "        answers = []\n",
        "        with torch.no_grad():\n",
        "            model.eval()\n",
        "            correct, test_total = 0, 0\n",
        "            total_step = len(valid_loader)\n",
        "            total, total_ce_loss, total_dtw_loss = 0, 0, 0\n",
        "            for x,y in tqdm(valid_loader, leave=False):\n",
        "                x, y = x.cuda(), y.cuda()\n",
        "                answers.extend(y.detach().cpu().numpy())\n",
        "                \n",
        "                logits = model(x)\n",
        "                _, predicted = torch.max(nn.Sigmoid()(logits), 1)\n",
        "                predictions.extend(predicted.detach().cpu().numpy())\n",
        "                \n",
        "                test_total += x.size(0)\n",
        "                correct += (predicted == y.squeeze()).sum().item()\n",
        "                \n",
        "                ce_loss = criterion(logits.squeeze(), y.squeeze())\n",
        "                total_ce_loss += ce_loss.item() * x.size(0)\n",
        "                total += x.size(0)\n",
        "            \n",
        "            valid_loss_list.append(total_ce_loss/total)\n",
        "            valid_acc_list.append(correct/test_total)\n",
        "        print(valid_loss_list[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
            "191f32af6e224c44b89ea70d722e6fd5",
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            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
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          "name": "stdout",
          "text": [
            "2.8275115117929928\n"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8811843679754574\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = []\n",
        "answers = []\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    correct, test_total = 0, 0\n",
        "    total_step = len(test_loader)\n",
        "    total, total_ce_loss, total_dtw_loss = 0, 0, 0\n",
        "    for x,y in tqdm(valid_loader, leave=False):\n",
        "        x, y = x.cuda(), y.cuda()\n",
        "        answers.extend(y.detach().cpu().numpy())\n",
        "        \n",
        "        logits = model(x)\n",
        "        _, predicted = torch.max(nn.Sigmoid()(logits), 1)\n",
        "        predictions.extend(predicted.detach().cpu().numpy())\n",
        "        \n",
        "        test_total += x.size(0)\n",
        "        correct += (predicted == y.squeeze()).sum().item()\n",
        "        \n",
        "        ce_loss = criterion(logits.squeeze(), y.squeeze())\n",
        "        total_ce_loss += ce_loss.item() * x.size(0)\n",
        "        total += x.size(0)\n",
        "print(\"Loss\", total_ce_loss/total, \"ACC\", correct/test_total)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92,
          "referenced_widgets": [
            "b154dc281c51408fa1b1a3f86772d928",
            "3e6fc2a1e7b141bca7faf17e9318386f",
            "f1ecf35129714daab3a24760b1a2a8dd",
            "a6236a59707d4f74a56a96420a98c870",
            "99c1e8705f994a2894adc3c9f423bf9d",
            "9618a3772dc34149921d346caf438e62",
            "ea6b2abd5ccc455ba1d26cb489057877",
            "5a68f9fc00df4a92a2130fd2a355e061",
            "e99073bb992f470a8be224d6a8be7b5d",
            "487cb153fb4a4f83bb292cee2285cab4",
            "5681b332d25d4db98e715d3c48bf7ee3"
          ]
        },
        "id": "oEjIOy6AOyOl",
        "outputId": "339be315-4981-41f1-a13e-e661dbfd44a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b154dc281c51408fa1b1a3f86772d928"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss 2.8811843679754574 ACC 0.9610961096109611\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Captum LRP"
      ],
      "metadata": {
        "id": "k-Kw2aBEQufH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from captum.attr import LRP"
      ],
      "metadata": {
        "id": "wYBiCCBgQPfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lrp = LRP(model)"
      ],
      "metadata": {
        "id": "TfPO26ePQWAk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = []\n",
        "answers = []\n",
        "relevance = []\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    correct, test_total = 0, 0\n",
        "    total_step = len(test_loader)\n",
        "    total, total_ce_loss, total_dtw_loss = 0, 0, 0\n",
        "    for x,y in tqdm(valid_loader, leave=False):\n",
        "        x, y = x.cuda(), y.cuda()\n",
        "        answers.extend(y.detach().cpu().numpy())\n",
        "        \n",
        "        logits = model(x)\n",
        "        _, predicted = torch.max(nn.Sigmoid()(logits), 1)\n",
        "        predictions.extend(predicted.detach().cpu().numpy())\n",
        "        \n",
        "        test_total += x.size(0)\n",
        "        correct += (predicted == y.squeeze()).sum().item()\n",
        "        \n",
        "        ce_loss = criterion(logits.squeeze(), y.squeeze())\n",
        "        total_ce_loss += ce_loss.item() * x.size(0)\n",
        "        total += x.size(0)\n",
        "\n",
        "        input_relevance_values = lrp.attribute(x)\n",
        "        relevance.append(input_relevance_values.detach().cpu().numpy())\n",
        "    relevance = np.vstack(relevance).squeeze()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110,
          "referenced_widgets": [
            "466d1f7f67674acc89637ef916c04495",
            "caafe47fa2534a6db58cc9149d594c56",
            "2b0d801a6b7d4b0cb2ac117c6b0399fe",
            "a494d9a8519d48cda66a83e5d869a457",
            "67631bd0d7cb4b7b9a4d42c0037f083d",
            "08b1c96249574d618d4f000ebbe3f09d",
            "394a983a9ae04bbab81fc75eab5e9697",
            "d7d621e494e2474ca0e5f57723ffb61f",
            "f54ca1113fa14c869097fcbc231c62b4",
            "f9dd0be813a3497390270ed80df5ab17",
            "76bf959c73804056970b25c17659baac"
          ]
        },
        "id": "8yd01bJmQwUl",
        "outputId": "7707d7e0-33e6-42d7-ee8d-0c82bfcb185e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "466d1f7f67674acc89637ef916c04495"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "/usr/local/lib/python3.7/dist-packages/captum/_utils/gradient.py:59: UserWarning: Input Tensor 0 did not already require gradients, required_grads has been set automatically.\n",
            "  \"required_grads has been set automatically.\" % index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "relevance.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ql8EpAoPQ_0X",
        "outputId": "511f8f1f-38f0-4c27-8ed1-bb33f78cbe7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9999, 86)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rel_test = pd.DataFrame(np.abs(relevance).mean(axis=0), index=test_sc.columns, columns=['ABS_MEAN'])"
      ],
      "metadata": {
        "id": "uez4IF4bTEdm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rel_test = rel_test.sort_values(by='ABS_MEAN', ascending=False)\n",
        "top_ranking = rel_test.index[:20]"
      ],
      "metadata": {
        "id": "UrrTkfXHTXQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def draw_heatmap_2d(rel):\n",
        "    mean_train = np.abs(rel).mean(axis=0)\n",
        "    print(mean_train.shape)\n",
        "    \n",
        "    maximum = mean_train.max()\n",
        "    minimum  = mean_train.min()\n",
        "    norm_train = (mean_train-minimum)/(maximum-minimum)\n",
        "    print(norm_train.shape)\n",
        "    \n",
        "    fig, ax = plt.subplots(1,1, dpi=100)\n",
        "    sns.heatmap(norm_train.reshape(-1,1), vmin=0, vmax=1, ax=ax, cbar=False, cmap='Blues')\n",
        "    ax.set_xlabel('Time')\n",
        "    ax.set_ylabel('Sensors')\n",
        "    \n",
        "    fig.suptitle('Absolute Relevances')"
      ],
      "metadata": {
        "id": "AkxgiFgoTYUH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_heatmap_2d(relevance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "sZLgj3f-T1MO",
        "outputId": "4973c1c4-cd9d-45c6-d2e4-e291deff0b59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(86,)\n",
            "(86,)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_ranking"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZIj8h3sUHT3",
        "outputId": "45f5a166-0d44-4db3-a1ae-4944586c903b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['C03', 'C66', 'C54', 'C57', 'C70', 'C73', 'C04', 'C47', 'C81', 'C25',\n",
              "       'C17', 'C65', 'C77', 'C76', 'C86', 'C72', 'C68', 'C28', 'C75', 'C59'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rel_test = rel_test / rel_test.sum()\n",
        "rel_test.reset_index(inplace=True)\n",
        "rel_test.rename(columns={\"index\": \"Sensors\"}, inplace=True)\n",
        "rel_test.to_csv('./data/ranking.csv',index=0)"
      ],
      "metadata": {
        "id": "bJqWuWzLU792"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rel_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6GAp4fPSWjtV",
        "outputId": "7da18e39-686a-4318-f0fc-c5c21b860d8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sensors  ABS_MEAN\n",
              "0      C03  0.119028\n",
              "1      C66  0.094645\n",
              "2      C54  0.046290\n",
              "3      C57  0.034967\n",
              "4      C70  0.030734\n",
              "..     ...       ...\n",
              "81     C52  0.000000\n",
              "82     C39  0.000000\n",
              "83     C49  0.000000\n",
              "84     C02  0.000000\n",
              "85     C55  0.000000\n",
              "\n",
              "[86 rows x 2 columns]"
            ],
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              "      <th>Sensors</th>\n",
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              "      <th>4</th>\n",
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              "      <th>85</th>\n",
              "      <td>C55</td>\n",
              "      <td>0.000000</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a2a98e20-5e4d-4c5a-8db0-8bb7881ce7d4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a2a98e20-5e4d-4c5a-8db0-8bb7881ce7d4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "swMKwQEhYWyZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}