LSTM+BiLSTM+BERT_LIAR_Dataset.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "## Loading Data\n"
      ],
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive  # Import the necessary module for mounting Google Drive\n",
        "drive.mount('/content/drive')  # Mount Google Drive to the specified directory '/content/drive'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the autoreload extension\n",
        "%load_ext autoreload\n",
        "# Set autoreload mode to 2\n",
        "%autoreload 2"
      ],
      "metadata": {
        "id": "mq1xtOT42lQ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from collections import defaultdict\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "7FrV4VF_2mre"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation"
      ],
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        "id": "OogSOwyNRD-A"
      }
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    {
      "cell_type": "code",
      "source": [
        "# Define the file paths for the train, test, and validation datasets\n",
        "train_path = '/content/drive/MyDrive/Deep learning Final Project Notebooks/data/train.tsv'\n",
        "test_path = '/content/drive/MyDrive/Deep learning Final Project Notebooks/data/test.tsv'\n",
        "val_path = '/content/drive/MyDrive/Deep learning Final Project Notebooks/data/valid.tsv'\n",
        "\n",
        "# Read the train dataset using pandas\n",
        "train_df = pd.read_csv(train_path, sep=\"\\t\", header=None)\n",
        "\n",
        "# Read the test dataset using pandas\n",
        "test_df = pd.read_csv(test_path, sep=\"\\t\", header=None)\n",
        "\n",
        "# Read the validation dataset using pandas\n",
        "val_df = pd.read_csv(val_path, sep=\"\\t\", header=None)\n"
      ],
      "metadata": {
        "id": "WhJsgcZg2stH"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "display(train_df)"
      ],
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              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12134.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>We have less Americans working now than in the...</td>\n",
              "      <td>economy,jobs</td>\n",
              "      <td>vicky-hartzler</td>\n",
              "      <td>U.S. Representative</td>\n",
              "      <td>Missouri</td>\n",
              "      <td>republican</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>an interview with ABC17 News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>238.json</td>\n",
              "      <td>pants-fire</td>\n",
              "      <td>When Obama was sworn into office, he DID NOT u...</td>\n",
              "      <td>obama-birth-certificate,religion</td>\n",
              "      <td>chain-email</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>none</td>\n",
              "      <td>11</td>\n",
              "      <td>43</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>105</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7891.json</td>\n",
              "      <td>false</td>\n",
              "      <td>Says Having organizations parading as being so...</td>\n",
              "      <td>campaign-finance,congress,taxes</td>\n",
              "      <td>earl-blumenauer</td>\n",
              "      <td>U.S. representative</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>democrat</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>a U.S. Ways and Means hearing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8169.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>Says nearly half of Oregons children are poor.</td>\n",
              "      <td>poverty</td>\n",
              "      <td>jim-francesconi</td>\n",
              "      <td>Member of the State Board of Higher Education</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>none</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>an opinion article</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>929.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>On attacks by Republicans that various program...</td>\n",
              "      <td>economy,stimulus</td>\n",
              "      <td>barack-obama</td>\n",
              "      <td>President</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>democrat</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>160</td>\n",
              "      <td>163</td>\n",
              "      <td>9</td>\n",
              "      <td>interview with CBS News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1279</th>\n",
              "      <td>3419.json</td>\n",
              "      <td>half-true</td>\n",
              "      <td>For the first time in more than a decade, impo...</td>\n",
              "      <td>energy,oil-spill,trade</td>\n",
              "      <td>barack-obama</td>\n",
              "      <td>President</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>democrat</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>160</td>\n",
              "      <td>163</td>\n",
              "      <td>9</td>\n",
              "      <td>a press conference</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1280</th>\n",
              "      <td>12548.json</td>\n",
              "      <td>mostly-true</td>\n",
              "      <td>Says Donald Trump has bankrupted his companies...</td>\n",
              "      <td>candidates-biography</td>\n",
              "      <td>hillary-clinton</td>\n",
              "      <td>Presidential candidate</td>\n",
              "      <td>New York</td>\n",
              "      <td>democrat</td>\n",
              "      <td>40</td>\n",
              "      <td>29</td>\n",
              "      <td>69</td>\n",
              "      <td>76</td>\n",
              "      <td>7</td>\n",
              "      <td>a speech on the economy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1281</th>\n",
              "      <td>401.json</td>\n",
              "      <td>true</td>\n",
              "      <td>John McCain and George Bush have \"absolutely n...</td>\n",
              "      <td>health-care</td>\n",
              "      <td>campaign-defend-america</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Washington, D.C.</td>\n",
              "      <td>none</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>a television ad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1282</th>\n",
              "      <td>1055.json</td>\n",
              "      <td>false</td>\n",
              "      <td>A new poll shows 62 percent support the presid...</td>\n",
              "      <td>health-care</td>\n",
              "      <td>americans-united-change</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>none</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>an Internet ad.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>9117.json</td>\n",
              "      <td>barely-true</td>\n",
              "      <td>No one claims the report vindicating New Jerse...</td>\n",
              "      <td>candidates-biography,infrastructure</td>\n",
              "      <td>rudy-giuliani</td>\n",
              "      <td>Attorney</td>\n",
              "      <td>New York</td>\n",
              "      <td>republican</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>comments on NBC's \"Meet the Press\"</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1284 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a398908-276a-4a92-9577-7740541dbbce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "_1SajwUwRQ8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def data_cleaning(dataset):\n",
        "    # Dropping unwanted columns\n",
        "    dataset = dataset.drop(labels=[0, 8, 9, 10, 11, 12], axis=1)\n",
        "    \n",
        "    # Renaming column 2 as 'title'\n",
        "    dataset[\"title\"] = dataset[2]\n",
        "    \n",
        "    # Dropping metadata columns, as they have been merged into a single column\n",
        "    dataset = dataset.drop([2, 3, 4, 5, 6, 7, 13], axis=1)\n",
        "    \n",
        "    # Dropping rows with null values\n",
        "    dataset.dropna()\n",
        "    \n",
        "    # Creating a new column called 'label' with 1 for 'true' and 'mostly-true' values, else 0 (1=real, 0=fake)\n",
        "    dataset['label'] = [1 if x == \"true\" or x == \"mostly-true\" else 0 for x in dataset[1]]\n",
        "    \n",
        "    # Dropping the original label column\n",
        "    dataset = dataset.drop(labels=[1], axis=1)\n",
        "    \n",
        "    return dataset\n"
      ],
      "metadata": {
        "id": "HKH2Sz5Y3nY9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = data_cleaning(train_df)\n",
        "test_df = data_cleaning(test_df)\n",
        "val_df = data_cleaning(val_df)"
      ],
      "metadata": {
        "id": "3puTOW6_5i1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "EoRbiNmx6Ft8",
        "outputId": "f0b18ac1-cdfa-48a2-9d96-7ba70e363c17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                                                     [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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      "cell_type": "code",
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        "display(val_df)"
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          "height": 424
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                  title  label\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-23828c78-5996-4502-88e7-24d14405da18');\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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    {
      "cell_type": "code",
      "source": [
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Resetting the index of train_df, val_df, and test_df after dropping rows\n",
        "# The drop=True parameter ensures that the old index is not added as a new column\n",
        "\n",
        "print('trainset size:', train_df.shape)  # Print the size of the trainset (number of rows and columns)\n",
        "print('valset size:', val_df.shape)  # Print the size of the valset (number of rows and columns)\n",
        "print('testset size:', test_df.shape)  # Print the size of the testset (number of rows and columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTDRnGgUEW-_",
        "outputId": "bf02ad06-90f8-474c-a108-a5b1426bc40a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainset size: (10240, 2)\n",
            "valset size: (1284, 2)\n",
            "testset size: (1267, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.to_csv('train.tsv', sep='\\t', index=False)\n",
        "val_df.to_csv('val.tsv', sep='\\t', index=False)\n",
        "test_df.to_csv('test.tsv', sep='\\t', index=False)"
      ],
      "metadata": {
        "id": "KnynoIm9CyWg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([train_df, val_df, test_df])\n",
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qGDu_jOT9bxL",
        "outputId": "ddef7ef5-0a97-49c4-f335-c8d7d37f39c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "                                                  title  label\n",
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              "4     The economic turnaround started at the end of ...      0\n",
              "...                                                 ...    ...\n",
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              "      <th>0</th>\n",
              "      <td>Says the Annies List political group supports ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>When did the decline of coal start? It started...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Health care reform legislation is likely to ma...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The economic turnaround started at the end of ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1262</th>\n",
              "      <td>Says his budget provides the highest state fun...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1263</th>\n",
              "      <td>Ive been here almost every day.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>In the early 1980s, Sen. Edward Kennedy secret...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>Says an EPA permit languished under Strickland...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>Says the governor is going around the state ta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12791 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96c388f2-4332-44e1-80c2-b36ac34f4949')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-96c388f2-4332-44e1-80c2-b36ac34f4949 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-96c388f2-4332-44e1-80c2-b36ac34f4949');\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Removing stopwords"
      ],
      "metadata": {
        "id": "J7UVZYc2RcDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "# Download stopwords from NLTK\n",
        "nltk.download(\"stopwords\")\n",
        "\n",
        "# Import stopwords from NLTK corpus\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Get the list of English stopwords\n",
        "stop_words = stopwords.words('english')\n",
        "\n",
        "# Add additional stopwords to the list\n",
        "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxB_H1AJ9eZh",
        "outputId": "39c96298-a6cb-4a06-dffb-0faccd201ef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(text):\n",
        "    result = []\n",
        "    for token in gensim.utils.simple_preprocess(text):\n",
        "      # Check if the token is not a stopword, has a length greater than 3, and is not in the additional stop_words\n",
        "      if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3 and token not in stop_words:\n",
        "        result.append(token)\n",
        "    return result"
      ],
      "metadata": {
        "id": "OJTmjQq19fxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim\n",
        "\n",
        "# Applying The Function To The Dataframe\n",
        "df['clean'] = df['title'].apply(preprocess)"
      ],
      "metadata": {
        "id": "CtrHnThM9h3y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_of_words = []  # Initialize an empty list to store individual words\n",
        "for i in df.clean:  # Iterate over each entry in the 'clean' column of the DataFrame\n",
        "    for j in i:  # Iterate over each word in the entry\n",
        "        list_of_words.append(j)  # Append the word to the list_of_words\n",
        "\n",
        "total_words = len(list(set(list_of_words)))  # Count the total number of unique words\n",
        "display(total_words)  # Display the total number of unique words"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "J18pVvi99mtK",
        "outputId": "7bf2661b-3df0-4996-ee21-fefb3e117e34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "11977"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk import word_tokenize"
      ],
      "metadata": {
        "id": "A8V3kwYnA_qD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### using tokenizer to create Tokens"
      ],
      "metadata": {
        "id": "FS7pJlPMRve1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Creating a Tokenizer object\n",
        "tokenizer = Tokenizer(num_words=total_words)\n",
        "\n",
        "# Fitting the Tokenizer on the 'title' column of the train_df\n",
        "tokenizer.fit_on_texts(train_df['title'])\n",
        "\n",
        "# Converting the 'title' column of train_df, val_df, and test_df into sequences of tokenized words\n",
        "train_sequences = tokenizer.texts_to_sequences(train_df['title'])\n",
        "val_sequences = tokenizer.texts_to_sequences(val_df['title'])\n",
        "test_sequences = tokenizer.texts_to_sequences(test_df['title'])\n"
      ],
      "metadata": {
        "id": "EHyK90aaBLtd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Adding Padding to train_sequences\n",
        "padded_train = pad_sequences(train_sequences, maxlen=20, padding='post', truncating='post')\n",
        "\n",
        "# Adding Padding to val_sequences\n",
        "padded_val = pad_sequences(val_sequences, maxlen=20, padding='post', truncating='post')\n",
        "\n",
        "# Adding Padding to test_sequences\n",
        "padded_test = pad_sequences(test_sequences, maxlen=20, padding='post', truncating='post')\n"
      ],
      "metadata": {
        "id": "X5-9VhcfBMQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **BERT**"
      ],
      "metadata": {
        "id": "8LQgpZGrBOZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "\n",
        "import torch\n",
        "from transformers import BertTokenizer"
      ],
      "metadata": {
        "id": "jqFWj6AXBQ23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae520b8f-d85f-433c-9720-7266a92d6dc3"
      },
      "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",
            "Collecting transformers\n",
            "  Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m112.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m103.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Processing Data for BERT model"
      ],
      "metadata": {
        "id": "EoqLD0VXR043"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class FakeNewsDataset(Dataset):\n",
        "    def __init__(self, mode, tokenizer):\n",
        "        assert mode in ['train', 'test']\n",
        "        self.mode = mode\n",
        "        self.df = pd.read_csv(mode + '.tsv', sep='\\t').fillna(\"\")\n",
        "        self.len = len(self.df)\n",
        "        self.tokenizer = tokenizer  # BERT tokenizer\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        # Get the statement and label from the dataframe\n",
        "        statement, label = self.df.iloc[idx, :].values\n",
        "        \n",
        "        # Convert the label to a tensor\n",
        "        label_tensor = torch.tensor(label)\n",
        "        \n",
        "        # Tokenize the statement\n",
        "        word_pieces = ['[CLS]']\n",
        "        statement = self.tokenizer.tokenize(statement)\n",
        "        word_pieces += statement + ['[SEP]']\n",
        "        len_st = len(word_pieces)\n",
        "\n",
        "        # Convert the tokens to token IDs\n",
        "        ids = self.tokenizer.convert_tokens_to_ids(word_pieces)\n",
        "        tokens_tensor = torch.tensor(ids)\n",
        "        \n",
        "        # Create segments tensor with all 0s\n",
        "        segments_tensor = torch.tensor([0] * len_st, dtype=torch.long)\n",
        "        \n",
        "        return (tokens_tensor, segments_tensor, label_tensor)\n",
        "      \n",
        "    def __len__(self):\n",
        "        return self.len\n",
        "    \n",
        "# Initialize Datasets for Transformation\n",
        "trainset = FakeNewsDataset('train', tokenizer=tokenizer)\n",
        "testset = FakeNewsDataset('test', tokenizer=tokenizer)\n",
        "\n",
        "print('trainset size:', trainset.__len__())\n",
        "print('testset size:', testset.__len__())\n"
      ],
      "metadata": {
        "id": "o1xbDS4bBXVJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0736fc8-5572-43f5-a4eb-2c1374ec32c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainset size: 10240\n",
            "testset size: 1267\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(trainset.df[\"title\"])\n",
        "display(trainset.df[\"label\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "ynGFEkDulY8u",
        "outputId": "a449ba9c-2ac8-4a3c-8e59-cff23351e84b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0        Says the Annies List political group supports ...\n",
              "1        When did the decline of coal start? It started...\n",
              "2        Hillary Clinton agrees with John McCain \"by vo...\n",
              "3        Health care reform legislation is likely to ma...\n",
              "4        The economic turnaround started at the end of ...\n",
              "                               ...                        \n",
              "10235    There are a larger number of shark attacks in ...\n",
              "10236    Democrats have now become the party of the [At...\n",
              "10237    Says an alternative to Social Security that op...\n",
              "10238    On lifting the U.S. Cuban embargo and allowing...\n",
              "10239    The Department of Veterans Affairs has a manua...\n",
              "Name: title, Length: 10240, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        0\n",
              "2        1\n",
              "3        0\n",
              "4        0\n",
              "        ..\n",
              "10235    1\n",
              "10236    1\n",
              "10237    0\n",
              "10238    0\n",
              "10239    0\n",
              "Name: label, Length: 10240, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titles = trainset.df[\"title\"]\n",
        "labels = trainset.df[\"label\"]\n"
      ],
      "metadata": {
        "id": "p3ueRkaYC-Vl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#Split data into training and testing dataset\n",
        "#We teach our model based on the training set (including the labels)\n",
        "#We take the labels away to test our model with the testing set\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(titles, labels, test_size=0.2, random_state=1000)"
      ],
      "metadata": {
        "id": "xmCAjLSrDTpJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-text\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Zj79M4n_oFe",
        "outputId": "738ad6ad-4f95-4bbf-d940-5793ccdb4c66"
      },
      "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",
            "Collecting tensorflow-text\n",
            "  Downloading tensorflow_text-2.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB)\n",
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            "Installing collected packages: tensorflow-text\n",
            "Successfully installed tensorflow-text-2.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating BERT Model"
      ],
      "metadata": {
        "id": "x-PZKvwYR6sN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Check if GPU is available and set the device accordingly\n",
        "if tf.test.gpu_device_name():\n",
        "    device = tf.device('/GPU:0')\n",
        "    print('Using GPU')\n",
        "else:\n",
        "    device = tf.device('/CPU:0')\n",
        "    print('Using CPU')\n",
        "\n",
        "with device:\n",
        "    # Use the BERT preprocesser and BERT encoder from TensorFlow Hub\n",
        "    bert_preprocess = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n",
        "    bert_encoder = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4')\n",
        "\n",
        "    # Define the input layer\n",
        "    input_layer = tf.keras.layers.Input(shape=(), dtype=tf.string, name='news')\n",
        "\n",
        "    # Preprocess the input using the BERT preprocesser\n",
        "    processed = bert_preprocess(input_layer)\n",
        "\n",
        "    # Encode the preprocessed input using the BERT encoder\n",
        "    output = bert_encoder(processed)\n",
        "\n",
        "    # Apply dropout to the pooled output from BERT\n",
        "    layer = tf.keras.layers.Dropout(0.2, name='dropout')(output['pooled_output'])\n",
        "\n",
        "    # Add a fully connected layer with 2000 neurons and ReLU activation\n",
        "    layer = tf.keras.layers.Dense(2000, activation='relu', name='hidden')(layer)\n",
        "    layer = tf.keras.layers.ReLU()(layer)\n",
        "\n",
        "    # Add another fully connected layer with 2500 neurons and ReLU activation\n",
        "    layer = tf.keras.layers.Dense(2500, activation='relu', name='hidden2')(layer)\n",
        "\n",
        "    # Apply ReLU activation to the layer\n",
        "    layer = tf.keras.layers.ReLU()(layer)\n",
        "\n",
        "    # Add the output layer with sigmoid activation for binary classification\n",
        "    layer = tf.keras.layers.Dense(1, activation='sigmoid', name='output')(layer)\n",
        "\n",
        "    # Create the model with input and output layers\n",
        "    model = tf.keras.Model(inputs=[input_layer], outputs=[layer])\n"
      ],
      "metadata": {
        "id": "S9ozdVwJ_o3R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1160a42d-87f0-4f36-b4be-b5ebec8c6b42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using GPU\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L74ToBLI22xZ",
        "outputId": "b1f503d4-a031-450a-dfd2-988cad71eb6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " news (InputLayer)              [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       {'input_type_ids':   0           ['news[0][0]']                   \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_word_ids':                                                \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_mask': (Non                                               \n",
            "                                e, 128)}                                                          \n",
            "                                                                                                  \n",
            " keras_layer_1 (KerasLayer)     {'encoder_outputs':  109482241   ['keras_layer[0][0]',            \n",
            "                                 [(None, 128, 768),               'keras_layer[0][1]',            \n",
            "                                 (None, 128, 768),                'keras_layer[0][2]']            \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768)],                                               \n",
            "                                 'pooled_output': (                                               \n",
            "                                None, 768),                                                       \n",
            "                                 'default': (None,                                                \n",
            "                                768),                                                             \n",
            "                                 'sequence_output':                                               \n",
            "                                 (None, 128, 768)}                                                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 768)          0           ['keras_layer_1[0][13]']         \n",
            "                                                                                                  \n",
            " hidden (Dense)                 (None, 2000)         1538000     ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " re_lu (ReLU)                   (None, 2000)         0           ['hidden[0][0]']                 \n",
            "                                                                                                  \n",
            " hidden2 (Dense)                (None, 2500)         5002500     ['re_lu[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_1 (ReLU)                 (None, 2500)         0           ['hidden2[0][0]']                \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 1)            2501        ['re_lu_1[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 116,025,242\n",
            "Trainable params: 6,543,001\n",
            "Non-trainable params: 109,482,241\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "73Xrs0TJR_6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "epochs = 5\n",
        "train_loss = []\n",
        "train_accuracy = []\n",
        "test_loss = []\n",
        "test_accuracy = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    # Train model for one epoch\n",
        "    history = model.fit(x_train, y_train, epochs=1, verbose=1)\n",
        "    \n",
        "    # Get train loss and accuracy\n",
        "    train_loss.append(history.history['loss'][0])\n",
        "    train_accuracy.append(history.history['accuracy'][0])\n",
        "    \n",
        "    # Evaluate model on test data\n",
        "    loss, accuracy = model.evaluate(x_test, y_test)\n",
        "    test_loss.append(loss)\n",
        "    test_accuracy.append(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FM0ysNR0_tBR",
        "outputId": "35e6aa6a-67e4-41f6-e7d4-dbd57b2b7baa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256/256 [==============================] - 120s 379ms/step - loss: 0.6960 - accuracy: 0.6376\n",
            "64/64 [==============================] - 25s 373ms/step - loss: 0.6507 - accuracy: 0.6377\n",
            "256/256 [==============================] - 96s 375ms/step - loss: 0.6480 - accuracy: 0.6467\n",
            "64/64 [==============================] - 24s 374ms/step - loss: 0.6539 - accuracy: 0.6372\n",
            "256/256 [==============================] - 98s 384ms/step - loss: 0.6490 - accuracy: 0.6454\n",
            "64/64 [==============================] - 24s 381ms/step - loss: 0.6478 - accuracy: 0.6372\n",
            "256/256 [==============================] - 96s 374ms/step - loss: 0.6443 - accuracy: 0.6466\n",
            "64/64 [==============================] - 24s 381ms/step - loss: 0.6456 - accuracy: 0.6372\n",
            "256/256 [==============================] - 95s 372ms/step - loss: 0.6440 - accuracy: 0.6466\n",
            "64/64 [==============================] - 24s 381ms/step - loss: 0.6445 - accuracy: 0.6372\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Displaying Network architecture"
      ],
      "metadata": {
        "id": "YDn6y7nwSCYO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='model_bert.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "XJq5WDv0i3De",
        "outputId": "5891483f-d0c3-49f9-c387-fba5e84c7772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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2dvPtTHbs2CGn01kqGzBLgoyMjECHAAAAAABArho3biyXy6V58+bp/PnzRbIPvh+XXvHx8Ro3bpzGjRunixcvBjqcPGVkZGjZsmVKTU1Vz549Ax2Oh7Fjx6pZs2YaNGiQpMDEWpJqMT/nIEtS3EWNmis81NzthdooPLfWRpZLly7pnXfekcPhUIsWLQIUHQAAAACgpLotmzD37Nkjh8OR558/X+5HjBihffv26YcfflC/fv30+uuvl/iYAQAAAACAf6KiojR//nx99913ioqK0rx58wIdUp5K6zmG0hr3yJEj1a1bN/Xs2VMXLlwIdDi52rBhg5YsWaLVq1fL5XIFOhy3KVOmaPv27Vq1apWCg4MlldxY86MgOV1U5yALK75AouYKjporWTldWKiNgsupNiRpzZo1Kl++vFasWKEpU6aoYcOGAYwSAAAAAFAScTvyfBo9erTefPNN1axZU7NmzVLHjh0DHZLfAnU78ldffVVvv/22rl27prp162rSpElKSEgo1hh8RT0AAAAAAIpKafp+jLz985//1Pr16zVhwoRAh1KqLF++XLt379bw4cMVFBQUkBhKYi36cg6yJMZdnKg5/1Bztz9qwz8loTYAAAAAACWft347mjDvYIFqwixNqAcAAAAAAAAAAAAAAAAAgLd+u9vyduQAAAAAAAAAAAAAAAAAAABFjSZMAAAAAAAAAAAAAAAAAAAAP9CECQAAAAAAAAAAAAAAAAAA4AeaMAEAAAAAAAAAAAAAAAAAAPxQ1tsD3bp1K844EABff/21JJ7r3Bw5ckQScwQAAAAAAAAAAAAAAAAAd7Kvv/5aDzzwQLbl/BImAAAAAAAAAAAAAAAAAACAH7z+EubixYuLMw4EQNavO/Jce/fhhx+qR48ezBEAAAAAAAAAAAAAAAAA3MG83U2ZX8IEAAAAAAAAAAAAAAAAAADwA02YAAAAAAAAAAAAAAAAAAAAfqAJEwAAAAAAAAAAAAAAAAAAwA80YQIAAAAAAAAAAAAAAAAAAPiBJkwAAAAAAAAAAAAAAAAAAAA/FHkT5pIlSxQbGyuHwyGHw6HatWtr/vz57sc3btyomjVryuFwqFq1apozZ05Rh+RTnNWqVVPv3r0DEgtuHzNnzlSNGjXkcDg0b948SdL+/fvlcDj0yiuvBDg6FIfCeL7Xrl2rkSNHZnud6tOnT7Z1f/3rXys8PFxBQUFq3Lixtm7dWpDwi1RmZqamTp2q1q1bZ3ts3LhxatSokSIiIhQSEqL4+HgNHz5cFy9e9FjvH//4h1q2bKnw8HDVqVNH/fr10/Hjx/Mdy0MPPeSe11v/ypcv714vPT1db7zxhuLj41WuXDlVqFBBTZo00YEDB3yKacWKFZo4caIyMjLyHaOUdz71799f4eHhcjgc2r59u8/jXr16VYMHD1a1atXkcrm0Zs0av+K72eTJkxUdHS2Hw6F3333X63rUSHa3Sz6WFP7WBW7w93keOHCgR+7m9Ln6dqvdLLy/FRy5QW54Q26QG96QG+SGN+QGueENuUFueENukBvekBvkhjfkBrnhDblBbnhDbpAb3pAbt39uLFu2zGOflStXzlf8gbo25+t137zcSdfsuAaeXaDrT5LWrFkjh8OhKlWqaPTo0YV/kHaL5ORky2FxgcXFxVlkZGS25ZmZmda/f3/77W9/a5mZmYW+3/zyFuftKCEhwRISEgIdRolWkHo4dOiQlSlTxrp3724pKSnu5devX7fg4GB79913CytMlGAFfb7HjBljHTt29MihuLg4q1SpkkmylStXZttm9erV9sQTT/gdc3H44YcfrE2bNibJ7r333myPt2vXzmbNmmVnzpyxlJQUS05OtuDgYHv00Ufd6yxatMgk2cSJE+38+fO2bds2i42NtWbNmll6enq+4mnXrp1JyvGvQ4cO7vU6d+5sDRs2tK+//trS09Pt2LFj1qlTJ9u5c6fPMU2bNs3atWtn586dy/e8+ZJPSUlJJsm2bdvm87jjx4+3Bg0a2Llz5+yvf/2rLV68ON+x5WTfvn0myd555x2v61Aj2d1O+VhS+FMXuMHf53nAgAFWsWJFW716te3du9euXLni8fjtWLtmvL/5+3pyM3KD3PCG3CA3vCE3yA1vyA1ywxtyg9zwhtwgN7whN8gNb8gNcsMbcoPc8IbcIDe8ITfujNzIzMy0I0eO2KZNm+w3v/mNVapUKV/xB/LanC/XfX1xp1yz4xp4doGuvyxpaWn2xz/+0STZV1995dexeOu3C2gTZkZGhvXr189efPHFEtGAaUYTZnFJS0uzVq1alfixC1IPa9euNUn2ySefZHssPj7e1q1bV9Dw4Kfizj9/n+8333zTGjRoYJcvX/ZYHhcXZ3//+9+tTJkyVrNmTTt//rzH4yX9zXX79u3WpUsXW7hwoTVr1izHD9yPP/64Xb9+3WNZ9+7dTZIdOnTIzMzat29vNWrU8Hj/mDlzpkmyzZs35yumDh06eHyAyTJgwAD3c5eUlGQOh8N27NjhdRxfYxo0aJC1atUq318MzPLOJ38+uLZs2dKefvrpfMeSl5w+jFMjebud8rGkKI1f6ErSZyV/nucBAwZYzZo1c3zsdq1d3t8K9npiRm6QG96RG+SGN+QGueENuUFueENukBvekBvkhjfkBrnhDblBbnhDbpAb3pAb5IY35MadmRuDBw/OdxOmWeCuzd0OTZj0iQRWSaq/nTt3miSbN2+eX8fird+uyG9H7k1mZqaee+45uVwuzZ49Ww6HI1ChIADmz5+vkydPlrqx8yM9PV2S5HQ6sz1Wv359xcfHF9m+Dx48qMuXLxfZ+KVdceefP8/3jz/+qD//+c/6y1/+kmMOtW7dWkOGDNHRo0dL/K2Ab3XvvfdqyZIl6tWrl0JCQnJcZ+XKlQoKCvJYlvVz7GlpaZKkw4cPq3r16h7vH7Vr15Z0owbyY82aNQoPD/dYdvjwYX333Xd6+OGHJUnvvPOOmjdvrqZNm3odx9eYxo4dq+3bt2vatGn5ilPKO5/8eT89cuSIgoOD872dP6iRvN1O+VhSlMbPmSXps1JhPs+3c+3y/law1xNyg9zwhtwgN7whN8gNb8gNcsMbcoPc8IbcIDe8ITfIDW/IDXLDG3KD3PCG3CA3vCE3yI38Ki3X5rwJ5DU7+kQCqyTVX9bcZvV1FZaANGFmZmaqb9++ioyM1MyZM3NcJyMjQ2PGjFFMTIxCQ0N1zz33KDk5WZL01ltvyeVyKTw8XCdPntTQoUNVs2ZN7d27V1988YUaNWqkyMhIOZ1ONW3aVJ999pl73I0bN+oXv/iFXC6XIiIi1LRpU6WkpOT7GHLbT//+/d33rY+Li9O2bdskSf369ZPL5VJkZKRWrFjh9zEGkplpypQpuvvuuxUSEqKoqCg9+eST2rNnjyRp0KBBKleunKpVq+be5ve//73CwsLkcDh0+vRpDRkyREOHDtX+/fvlcDgUHx+v6dOny+l0Kjo6WgMHDlT16tXldDrVunVrbdmypUBjSzeKOSIiQuPHjy/G2fJu1apViomJkZR7TuaWI9KN52PSpElq0KCBypUrpwoVKqhRo0aqV6+e9u7d69Oc5bWf2bNnKywsTC6XS8uXL9djjz2miIgI1apVS0lJSR7H9cEHH6hFixZyOp0KCwtT3bp19frrr/t0LL4orfl38/Ptay5Onz5dZqZOnTp5XScxMVENGjTQvHnztHbtWr/nzdfnuDCew4I4evSoQkNDVa9ePUlSbGxstg8yx48fdz9WUBMmTNDgwYMlSdeuXdPXX3+tZs2a5bqNrzFFRUWpXbt2mjZtmswsX3HdnE9ZrwENGzZUSEiIIiMjNWzYMI/1c3vePv/8c8XHx+vnn3/W3/72NzkcDpUvX15S7u9zvr623Ioa8V9pyMfieC/J67nKWqcgdVHQz2Cl9b0qt88j/tSoN3da7fqC97cbyI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI0byI3syI3cZV2z8eW6keT7tbW81smJr/0suV2z80VpvfbGNfC8lbb6y9OtP41Z1Lcjv379uvXq1cuCg4Nt7969Xtd/5ZVXLCQkxD766CM7d+6cvfrqq1amTBn75ptvzMxs1KhRJskGDx5sM2bMsC5dutj3339vixcvtrFjx9rZs2ftzJkz9sADD7h/wvfixYsWERFhEydOtMuXL9vx48etS5cudurUqWxx5iW3/ZiZde3a1YKCguzo0aMe2z39/9m78+io6ruP458hJJnsi7JE9l1ZFFEeIcIjuNQqxaqAAaFP44IoRdYqAi4giwQs4VChFVR6LC4QUbBVrI9aRB/RqoBiVGQRCEISFslCgtm+zx+cDAzJJJNJYCbwfp0z55S7zff+5nPvneZ+vXPnnfbmm2/Wah/rii8/R/74449bSEiI/f3vf7ejR4/a119/bT169LALL7zQMjMzzcxs+PDh1qRJE7f15s+fb5JcYz1o0CBr166d2zKjRo2yiIgI+/bbb+348eOWnp5uPXv2tKioKNejpn3d9j//+U+LioqyJ598skb7W5vj4cUXX6z2EdjVZbK6jMyZM8ccDofNmzfPjhw5YgUFBa7H7JY/vtmbMfM2i++//77l5ORYdna29e3b1yIiIqyoqMjMzFJTU02SPfXUU3b48GE7cuSIPfvsszZ8+HCv3sMb9TV/p/I2i23btrXOnTtXOq9du3b2448/mpnZJ598Yg0aNLDWrVtbfn6+mVV8zLQ34+bNZ1wXn+HprrrqqkofPX+6Y8eOWVRUlI0dO9Y1bf369RYcHGyLFi2y3Nxc++abb+ySSy6xG2+80ed6yu3bt886d+5spaWlZmb2448/miTr3r279evXz5o2bWqhoaF28cUX2zPPPON61HVNapoyZUqtH7U+bdo0czgc9qc//cl+/vlnKygosMWLF7tt15vPrUmTJvb73//ebdvVXee8OR4qeyw9x0jN1Zc8no1ribefVW2Pi9p8B6uP1ypvviOX8/YY9fRz5OfLscv1rebnE7LhjmycRDbckY2TyIY7snES2XBHNk4iG+7Ixklkwx3ZOIlsuCMbJ5ENd2TjJLLhjmycRDbckY2TyIa78ykbvv4c+am8uW/kTS68Waay+77e3G+r7p6dN+rjvbfTcQ+8In8efxkZGSbJZs+e7VPtnvrtzmoTZlRUlA0bNsx69OhhkqxLly6uMJyqsLDQwsPDbejQoa5pBQUFFhoaaqNHjzazk0EoLCys8n3nzJljkiw7O9u++eYbk2T//Oc/q6zTmybMqt7HzOy9994zSTZr1izXMjk5OdahQwcrKSmp0330VU2bMAsKCiwyMtKtZjOz//znPybJdbKozcnt9LH//PPPTZLNmDGjVtv2lS/HQ1lZmX3//ffWvXt369ChQ5WfX1WZrC4jx44ds9jYWLv++uvd1nvllVdq1ITpaxbLL447duywoqIii42Ntf79+7u9T0lJiS1cuNCr96jO+ZS//Px8czgcNnDgwErnn3pxNTObNGmSSbIxY8aYmfvF1dtxq+4zrovPsDLefuGeNm2adezY0XJzc92mP/rooybJ9WrevLllZGT4XE+5MWPGuH2B3Lp1q0myG264wf7v//7PDh8+bEePHrVHHnnEJNmKFStqXNMLL7xgkuzFF1/0qcaCggILDw+3G264wW36qecAbz+3ypowT3f6de5MNWF643w6RszqRx7PxrXEm8+qro4LX7+D1ddrlTffkWuqsibM8+nY5fpWs/MJ2aiIbJxANioiGyeQjYrIxglkoyKycQLZqIhsnEA2KiIbJ5CNisjGCWSjIrJxAtmoiGycQDYqIhsnkI2Kzqds1FUTZlX3jby9t+ZNdk6/71tdVry5Z+eN+nrvzRfn0znBzL/HX1lZmV199dXWunVr27Rpk6sR1Fue+u3O6s+RFxQU6JprrtGXX36p2267Tenp6br33nsrLLdt2zYVFBSoa9eurmlhYWFq2rRptY+7PV1wcLCkE49Hbdu2rRo3bqwRI0Zo+vTp2r17d632x9P7SNK1116rjh076oUXXnA91vTVV1/V0KFDFRQUVKf7eLakp6crPz9fV155pdv0nj17KiQkxO2RxnXlyiuvVHh4eMCOSWX+9a9/6eKLL9aRI0f0z3/+U06n0+OyVWWyuoxs375dR48e1fXXX1+ren3NYkhIiCSpuLhYX3/9tY4ePaobb7zRbZmgoCCNGzeuTvJ+PuUvOztbZqbw8HCvlp81a5Y6deqkxYsX6+OPP3abV5txO/Uz9uc56/XXX9eqVav0r3/9S1FRUa7p06ZN09KlS/X+++8rPz9fu3btUmJionr37q2MjAyf32///v168803lZyc7JoWGhoqSerSpYsSExMVHx+vmJgYzZgxQzExMVq6dGmNayr/fLOysnyqc8eOHSooKNB1113ncZkzdT31t/PpGKkveTwb1xJvPquzfVycrr5eq87kd+RTnU/Hrje4vp1ENtyRjZPIhjuycRLZcEc2TiIb7sjGSWTDHdk4iWy4IxsnkQ13ZOMksuGObJxENtyRjZPIhjuycRLZcEc26sap9428yYWv2akuK97cs/NGfb335ovz6Zzg7+PP4XDo9ddfV1RUlHr06KEXXnjB53051VltwoyMjNSoUaMkScuXL1fbtm316quvKjU11W25Y8eOSZIeffRRORwO12vPnj0qKCio8j3eeust9evXT40aNVJoaKgefvhh17ywsDB98MEH6tOnj2bPnq22bdtq6NChKiwsrPG+VPU+0okP7P7779euXbv0/vvvS5JefPFF3XPPPbXeR385evSopBOf4+liY2OVl5d3Rt43NDRUBw8ePCPbPhNuvPFGbdq0SSEhIbr11ltVUlLicdmqMlldRg4cOCBJatSoUa3qrYss5ubmSjqRgzP1HudT/o4fP+56b284nU4tX75cDodDd999t9s5ra7GzV/nrFdffVVz587V+vXr1bp1a9f0AwcOKCUlRffdd5+uvfZaRUREqE2bNlq2bJn279+v+fPn+/yeKSkpGjlypFsDdUJCgiTp0KFDbsuGhISoVatW2rlzZ41rCgsLk3Ty866pffv2Sar6HHCmrqf+dj4dI/Ulj2fjWuLNZ3Wmj4vq1NdrVV1+R67K+XTsVofrmzuycRLZcEc2TiIb7sjGSWTDHdk4iWy4IxsnkQ13ZOMksuGObJxENtyRjZPIhjuycRLZcEc2TiIb7sjGSWSjbpXfN/ImF75mp7qseHPPzhv19d6bL86nc0IgHH/Dhg1TVlaWNmzYoLvuusvnfTnVWW3CPFVMTIzS0tJcjR0bNmxwzSs/CFNTU2UnfjLd9dq4caPHbe7du1e33XabmjZtqs8++0w5OTlKSUlxW6ZLly76xz/+of3792vy5MlauXKlnn76aa9q3rBhg1JTU716H0lKTk6W0+nUc889p23btik6OlqtWrWq1T76U3lTRGUH49GjR9W8efM6f8/i4uIztu0zxeFw6PLLL9ekSZP03XffafPmzVUu7ymT1WXkwgsvlHTy5OmrusjiRRddJKniybAu3+N8yl/5haAmTxzs3bu3Jk6cqO3bt2vmzJmu6XU1bv44Z/35z3/WihUr9MEHH7gyVm779u0qLS2tMD06Olrx8fFKT0/36T0zMzP18ssva/To0W7TIyMj1aFDB3377bcV1ikpKVFMTEyNayoqKpJ08vOuqfIvJL/88ovHZc7k9dSfzpdjpD7l8WxcS7z5rM7kceGN+nytqs13ZG+dL8dudbi+VUQ2TiAbFZGNE8hGRWTjBLJREdk4gWxURDZOIBsVkY0TyEZFZOMEslER2TiBbFRENk4gGxWRjRPIRkVk4wSyUbdOvW/kTS58zU51WfHmnp036vO9t5o6X84JgXD8ZWVl6YMPPtDIkSPVt29fBQUF+bQvp/NbE6Yk9ejRQ6mpqSopKdEdd9yh/fv3S5JatGghp9OpLVu21Gh7W7duVXFxsUaPHq22bdvK6XTK4XC45u/fv9/1YTVq1EhPPfWUevToUekHWJkvv/xSERER1b5Pubi4OCUlJWnNmjV6+umnNXLkSNc8X/fRn7p27arIyEh98cUXbtM/++wzFRUV6YorrpAkNWzYUMXFxXXynuvXr5eZqVevXnW+7TOtvOE2JyfH4zJVZbK6jLRv316hoaH69NNPq6yjujGriyy2bt1a8fHxevfdd8/Ye5xP+WvcuLEcDkeV2anMzJkzdfHFF7s1/no7btU5m+csM9PkyZO1detWrVmzptL/OqP8S0H5E2HL5eXl6ciRI2rRooVP752SkqIRI0YoPj6+wrykpCRt3rxZu3btck0rKCjQnj171K1btxrXVP75NmnSxKdau3btqgYNGujDDz/0uMyZup5K/j0fny/HSH3K49m4lnjzWZ3J48Ib9fVaVdvvyN46X45dT7i+eUY2yIYnZINseEI2yIYnZINseEI2yIYnZINseEI2yIYnZINseEI2yIYnZINseEI2yMaZcOp9I2/vrfmSneqy4s09O2/U13tvvjhfzgmBcPyVN6c2a9as9jt0KjvNypUrrZLJtdauXTuLiYmpdN6wYcNMkiUmJlpRUZGZmT3wwAMWEhJiixcvtpycHCspKbGMjAzbv3+/mZlNmzbNJFlhYaFrO19//bVJsscee8wKCwvthx9+sMGDB5skO3DggH3++efWt29f++677+yXX36xTZs22QUXXGALFy6sss6ioiLLzMy0W265xVasWFHt+5zq008/NUnWtGlTKy4udn18W+oAACAASURBVJvnyz7WpcGDB9vgwYNrtM4TTzxhwcHB9ve//91ycnLs66+/tssvv9wSEhIsPz/fzMxmzpxpkuyNN96woqIiy87OtjFjxpgkO3jwoJmZjRw50sLCwuzHH3+03NxcKyoqslGjRllUVJQdOXLEiouL7auvvrLOnTtby5Yt7fjx47Xa9ttvv21RUVE2a9asGu1vbY6HdevWmST73//9X4/LVJfJ6jJy3333WVBQkD377LOWm5trx44ds7lz55ok27x5s5l5N2a+ZHHZsmUmyb777jszM3v66adNkj344IO2b98+Ky0ttdzcXEtPT/fqPbxRX/N3Km+z2K5dO+vevbvHeT/++GOl8zZu3GhBQUH229/+tkbj5s1nXN1nmJSUZI0bN7Yvv/yyyn071VVXXWWXXXaZ27RvvvnGJHl8zZ8/38rKyqx///7WtGlT+/DDD62goMD27t1rw4YNswYNGtiGDRtqXFNmZqZFR0fbnj17Kp1/5MgRa926tfXt29f27Nljhw4dsjFjxliDBg1s8+bNXtdUbvr06SbJtmzZ4vP4DRkyxIKCguy5556znJwc++qrr6x///5u5wBvjr0mTZrY73//e9e/vbnOeXM8bN++3STZX/7yF9e2OUbO3TyejWuJN59VXRwXtfkOVh+vVZ988km135HLeXuMjho1ypo1a1Zh+rl+7Jbj+lbz8wnZIBuekA2y4QnZIBuekA2y4QnZIBuekA2y4QnZIBuekA2y4QnZIBuekA2y4QnZOP+yUW7cuHF2wQUXuP7ty7h6c9/Im1x4s0xl932ry4o39+y8UR/vvXEPvHKBcvxVluea8NRvd8abMF9//XVr166d60TZvHlzmzp1qtsyeXl51qlTJ5NkjRs3tueff95++eUXmzx5srVs2dIaNmxojRo1skGDBll6erqlpKRYWFiYSbIWLVrY3//+d9e2Jk+ebPHx8RYbG2tDhgyxZ555xiRZu3bt7KOPPrLExESLi4uzoKAgu+iii2zatGlWUlJSoU5Pr9dff73a99m7d6/b/l1++eU2ZcqUCmPj6z7WFV+aMMvKymz+/PnWoUMHCw4Otri4OLvtttts27ZtrmUOHz5s/fv3N6fTaW3atLEHH3zQHnroIZNk7du3t71799qmTZusVatWFhYWZn369LHMzEw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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting confusion matrix, and Accuracy"
      ],
      "metadata": {
        "id": "6ekzV16fSIRG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, classification_report\n",
        "\n",
        "# Get predictions from the model\n",
        "predictions = model.predict(x_test)\n",
        "\n",
        "# Convert predictions to binary values\n",
        "binary_predictions = (predictions > 0.5).astype(int)\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "# The labels argument specifies the order of the classes in the matrix\n",
        "# The normalize argument is set to 'pred' to normalize the matrix by the predicted column\n",
        "cm = confusion_matrix(y_test, binary_predictions, labels=[1, 0], normalize='pred')\n",
        "print(cm)\n",
        "\n",
        "# Display the confusion matrix\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Real', 'Fake'])\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "\n",
        "# Calculate the accuracy\n",
        "accuracy = accuracy_score(binary_predictions, y_test)\n",
        "print('Acc:', accuracy)\n",
        "\n",
        "# Generate the classification report\n",
        "report = classification_report(y_test, binary_predictions)\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "i_AFnlFrsdYk",
        "outputId": "f16a3021-d6c8-4b43-a210-8aff00d5981e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64/64 [==============================] - 24s 367ms/step\n",
            "[[0.         0.36279297]\n",
            " [0.         0.63720703]]\n",
            "Acc: 0.63720703125\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      1.00      0.78      1305\n",
            "           1       0.00      0.00      0.00       743\n",
            "\n",
            "    accuracy                           0.64      2048\n",
            "   macro avg       0.32      0.50      0.39      2048\n",
            "weighted avg       0.41      0.64      0.50      2048\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accuracy"
      ],
      "metadata": {
        "id": "jy0BVzAxUNRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the accuracy\n",
        "accuracy = accuracy_score(binary_predictions, y_test)\n",
        "print('Accuracy Achieved:', accuracy)"
      ],
      "metadata": {
        "id": "z3DRKphNSzWb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e042be24-5667-4285-eedb-2e77f6770a2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Achieved: 0.63720703125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting train vs test loss and train and test accuracy"
      ],
      "metadata": {
        "id": "N7Sh2-5eS-gv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the train and test loss\n",
        "plt.figure()\n",
        "plt.plot(train_loss, label='Train Loss')\n",
        "plt.plot(test_loss, label='Test Loss')\n",
        "plt.title('Train vs. Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.ylim(0, 1)\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot the train and test accuracy\n",
        "plt.figure()\n",
        "plt.plot(train_accuracy, label='Train Accuracy')\n",
        "plt.plot(test_accuracy, label='Test Accuracy')\n",
        "plt.title('Train vs. Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "GGqVd1fBu5-G",
        "outputId": "c056a74a-7eb8-4750-edd9-fea73a08ac03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LSTM**"
      ],
      "metadata": {
        "id": "0NfUBtByCERB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating LSTM Model"
      ],
      "metadata": {
        "id": "K4qsnN_TTQnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Embedding, Input, LSTM, Conv1D, MaxPool1D, Bidirectional, Dropout, BatchNormalization\n",
        "\n",
        "# Set the number of embedding vector features\n",
        "embedding_vector_features = 40\n",
        "\n",
        "# Create a Sequential model\n",
        "model = Sequential()\n",
        "\n",
        "# Add an Embedding layer\n",
        "# The input dimension is set to total_words, and the output dimension is set to embedding_vector_features\n",
        "# The input_length is set to 20 to match the length of the padded sequences\n",
        "model.add(Embedding(total_words, embedding_vector_features, input_length=20))\n",
        "\n",
        "# Add a Dropout layer for regularization\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "# Add a LSTM layer with 500 units and return sequences\n",
        "model.add(LSTM(500, return_sequences=True))\n",
        "\n",
        "# Add a Dropout layer for regularization\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Add another LSTM layer with 500 units\n",
        "model.add(LSTM(500))\n",
        "\n",
        "# Add a Dropout layer for regularization\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "# Add a Dense layer with 1 unit and sigmoid activation for binary classification\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Print the model summary\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDqf4p1MCF97",
        "outputId": "4d2befbf-4040-4366-beba-3161f7fe3518"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 20, 40)            479080    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 20, 40)            0         \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 20, 500)           1082000   \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 20, 500)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 500)               2002000   \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 500)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 501       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,563,581\n",
            "Trainable params: 3,563,581\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "FUDHYtZWTT_a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.asarray(train_df['label'])\n",
        "y_test = np.asarray(test_df['label'])\n",
        "\n",
        "# Compile the model with the specified optimizer, loss function, and metrics\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Set the number of epochs\n",
        "epochs = 5\n",
        "\n",
        "# Initialize empty lists to store train and test metrics\n",
        "train_loss = []\n",
        "train_accuracy = []\n",
        "test_loss = []\n",
        "test_accuracy = []\n",
        "\n",
        "# Iterate over the specified number of epochs\n",
        "for epoch in range(epochs):\n",
        "    # Train the model for one epoch\n",
        "    history = model.fit(padded_train, y_train, epochs=1, verbose=1)\n",
        "    \n",
        "    # Get the train loss and accuracy for the current epoch\n",
        "    train_loss.append(history.history['loss'][0])\n",
        "    train_accuracy.append(history.history['accuracy'][0])\n",
        "    \n",
        "    # Evaluate the model on the test data\n",
        "    loss, accuracy = model.evaluate(padded_test, y_test)\n",
        "    test_loss.append(loss)\n",
        "    test_accuracy.append(accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9DVWbDYCIMP",
        "outputId": "313ead2a-5174-4f88-a296-a61fe120ff50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "320/320 [==============================] - 23s 57ms/step - loss: 0.6463 - accuracy: 0.6459\n",
            "40/40 [==============================] - 1s 5ms/step - loss: 0.6403 - accuracy: 0.6496\n",
            "320/320 [==============================] - 7s 22ms/step - loss: 0.5785 - accuracy: 0.6999\n",
            "40/40 [==============================] - 0s 7ms/step - loss: 0.6348 - accuracy: 0.6614\n",
            "320/320 [==============================] - 4s 14ms/step - loss: 0.4824 - accuracy: 0.7750\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 0.6576 - accuracy: 0.6456\n",
            "320/320 [==============================] - 4s 14ms/step - loss: 0.3793 - accuracy: 0.8380\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 0.7716 - accuracy: 0.6290\n",
            "320/320 [==============================] - 4s 11ms/step - loss: 0.3014 - accuracy: 0.8766\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 0.8198 - accuracy: 0.6433\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Network Architecture"
      ],
      "metadata": {
        "id": "ngr1k2tGTpBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='model_lstm.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "id": "tBuPfWhW1ReB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "5795de76-0415-43ae-c6ae-559150bc2dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting confusion matrix and accuracy"
      ],
      "metadata": {
        "id": "1fm6tmghTXvP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions on the test data\n",
        "prediction = (model.predict(padded_test) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Calculating the accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "y_test = np.asarray(test_df['label'])\n",
        "accuracy = accuracy_score(list(y_test), prediction)\n",
        "\n",
        "print(\"Accuracy: \", accuracy)\n",
        "\n",
        "# Calculating the confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(list(y_test), prediction)\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(6, 6))\n",
        "sns.heatmap(cm, cmap=\"Blues\", linecolor='black', annot=True)\n",
        "\n",
        "# Generating the classification report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, prediction))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "FhJlKXq4CIVV",
        "outputId": "e919a5c9-c676-47c5-a728-d1fd26ceb247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 1s 4ms/step\n",
            "Accuracy:  0.6432517758484609\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.77      0.74       818\n",
            "           1       0.50      0.41      0.45       449\n",
            "\n",
            "    accuracy                           0.64      1267\n",
            "   macro avg       0.60      0.59      0.59      1267\n",
            "weighted avg       0.63      0.64      0.63      1267\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accuracy"
      ],
      "metadata": {
        "id": "iDGtAbBCURVP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the accuracy\n",
        "accuracy = accuracy_score(list(y_test), prediction)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "id": "M9yuewjbURVP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db765f7d-2051-4181-d612-39d761aec1d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.6432517758484609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting train vs test loss and train vs test accuracy"
      ],
      "metadata": {
        "id": "Wyc43yutTcQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the train and test loss\n",
        "plt.figure()\n",
        "plt.plot(train_loss, label='Train Loss')\n",
        "plt.plot(test_loss, label='Test Loss')\n",
        "plt.title('Train vs. Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.ylim(0, 1) \n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot the train and test accuracy\n",
        "plt.figure()\n",
        "plt.plot(train_accuracy, label='Train Accuracy')\n",
        "plt.plot(test_accuracy, label='Test Accuracy')\n",
        "plt.title('Train vs. Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vXGPodRT1Y37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "49478f2e-b617-44a4-ed24-d50086b4445c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating BiLSTM Model"
      ],
      "metadata": {
        "id": "gbWxXDtBTf18"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the model using Bidirectional LSTM\n",
        "embedding_vector_features = 40\n",
        "model = Sequential()\n",
        "\n",
        "# Embedding layer\n",
        "model.add(Embedding(total_words, embedding_vector_features, input_length=20))\n",
        "\n",
        "# Dropout layer\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "# Bidirectional LSTM layer with return_sequences=True to get sequences from both directions\n",
        "model.add(Bidirectional(LSTM(500, return_sequences=True)))\n",
        "\n",
        "# Dropout layer\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Bidirectional LSTM layer\n",
        "model.add(Bidirectional(LSTM(500)))\n",
        "\n",
        "# Dropout layer\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "# Output dense layer with sigmoid activation for binary classification\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDCsZJWsCQBl",
        "outputId": "54ea21ba-9d72-4af9-fcc5-e906d03bd74a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 20, 40)            479080    \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 20, 40)            0         \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 20, 1000)         2164000   \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 20, 1000)          0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 1000)             6004000   \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 1000)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 1001      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,648,081\n",
            "Trainable params: 8,648,081\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training the model"
      ],
      "metadata": {
        "id": "yUdkQwCSTiR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.asarray(train_df['label'])\n",
        "y_test = np.asarray(test_df['label'])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "epochs = 5\n",
        "train_loss = []\n",
        "train_accuracy = []\n",
        "test_loss = []\n",
        "test_accuracy = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    # Train model for one epoch\n",
        "    history = model.fit(padded_train, y_train, epochs=1, verbose=1)\n",
        "    \n",
        "    # Get train loss and accuracy\n",
        "    train_loss.append(history.history['loss'][0])\n",
        "    train_accuracy.append(history.history['accuracy'][0])\n",
        "    \n",
        "    # Evaluate model on test data\n",
        "    loss, accuracy = model.evaluate(padded_test, y_test)\n",
        "    test_loss.append(loss)\n",
        "    test_accuracy.append(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lf9AULMWCRxl",
        "outputId": "5719a4d8-9aec-40d0-afbd-2d872d2c8dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "320/320 [==============================] - 27s 61ms/step - loss: 0.6472 - accuracy: 0.6438\n",
            "40/40 [==============================] - 2s 11ms/step - loss: 0.6185 - accuracy: 0.6590\n",
            "320/320 [==============================] - 7s 23ms/step - loss: 0.5803 - accuracy: 0.6955\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.6150 - accuracy: 0.6519\n",
            "320/320 [==============================] - 7s 23ms/step - loss: 0.4660 - accuracy: 0.7827\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.6963 - accuracy: 0.6527\n",
            "320/320 [==============================] - 7s 20ms/step - loss: 0.3488 - accuracy: 0.8525\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.7947 - accuracy: 0.6109\n",
            "320/320 [==============================] - 7s 22ms/step - loss: 0.2536 - accuracy: 0.8985\n",
            "40/40 [==============================] - 0s 9ms/step - loss: 0.9517 - accuracy: 0.6204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Network Architecture"
      ],
      "metadata": {
        "id": "IPFKYptnTlaq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='model_bilstm.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "id": "xxIUu5UH1iKj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "e8ecd59d-0f74-40f2-bf2c-07f5c12c66fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting confusion matrix and accuracy"
      ],
      "metadata": {
        "id": "IVUitFAuTs02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions\n",
        "prediction = (model.predict(padded_test) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Getting the accuracy\n",
        "y_test = np.asarray(test_df['label'])\n",
        "accuracy = accuracy_score(list(y_test), prediction)\n",
        "print(\"Accuracy: \", accuracy)\n",
        "\n",
        "# Getting the confusion matrix\n",
        "cm = confusion_matrix(list(y_test), prediction)\n",
        "plt.figure(figsize=(6, 6))\n",
        "sns.heatmap(cm, cmap=\"Blues\", linecolor='black', annot=True)\n",
        "\n",
        "# Printing the classification report\n",
        "print(classification_report(y_test, prediction))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "eTjyw9Z5CXGn",
        "outputId": "a522ce1b-1dd9-49ff-a1fb-1081305828c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 2s 10ms/step\n",
            "Accuracy:  0.6203630623520127\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.76      0.72       818\n",
            "           1       0.46      0.36      0.40       449\n",
            "\n",
            "    accuracy                           0.62      1267\n",
            "   macro avg       0.57      0.56      0.56      1267\n",
            "weighted avg       0.60      0.62      0.61      1267\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accuracy"
      ],
      "metadata": {
        "id": "-4MqBh50UUXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the accuracy\n",
        "accuracy = accuracy_score(list(y_test), prediction)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "id": "yoJ4ExhkUUXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53db2194-b1cc-4534-ba42-40d3b4e5e7ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.6203630623520127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting train vs test loss and train vs test accuracy"
      ],
      "metadata": {
        "id": "Q2ctaw4NTwUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the train and test loss\n",
        "plt.figure()\n",
        "plt.plot(train_loss, label='Train Loss')\n",
        "plt.plot(test_loss, label='Test Loss')\n",
        "plt.title('Train vs. Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.ylim(0, 1) \n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot the train and test accuracy\n",
        "plt.figure()\n",
        "plt.plot(train_accuracy, label='Train Accuracy')\n",
        "plt.plot(test_accuracy, label='Test Accuracy')\n",
        "plt.title('Train vs. Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Dd27Ri8p1l-I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "f91fc03d-8541-41cc-b04b-54315af148a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}