Essay_Scoring_Final.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4A845DKV2aZ",
        "outputId": "17d5caca-9b1e-45db-aac9-125bd827680f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
            "Building wheels for collected packages: sklearn\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=54c71441b866f3a4986e1f501393c683cd69190385df7b8cd7f6ee30d6d7398b\n",
            "  Stored in directory: /root/.cache/pip/wheels/14/25/f7/1cc0956978ae479e75140219088deb7a36f60459df242b1a72\n",
            "Successfully built sklearn\n",
            "Installing collected packages: sklearn\n",
            "Successfully installed sklearn-0.0.post1\n"
          ]
        }
      ],
      "source": [
        "! pip install sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byEuxC8zWmdW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BTp8z6Z-W3-D"
      },
      "outputs": [],
      "source": [
        "dataset_dir = \"../content/\"\n",
        "glove_dir = './glove.6B/'\n",
        "save_dir = './content/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "gEWG_iGsXLnO",
        "outputId": "5434cef5-6df9-4217-83bb-c8ad9ac32aed"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5547052f-8a6c-492a-b420-87044b9402f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>essay_id</th>\n",
              "      <th>essay_set</th>\n",
              "      <th>essay</th>\n",
              "      <th>domain1_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear local newspaper, I think effects computer...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear @CAPS1 @CAPS2, I believe that using compu...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear Local Newspaper, @CAPS1 I have found that...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear @LOCATION1, I know having computers has a...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12971</th>\n",
              "      <td>21626</td>\n",
              "      <td>8</td>\n",
              "      <td>In most stories mothers and daughters are eit...</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12972</th>\n",
              "      <td>21628</td>\n",
              "      <td>8</td>\n",
              "      <td>I never understood the meaning laughter is th...</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12973</th>\n",
              "      <td>21629</td>\n",
              "      <td>8</td>\n",
              "      <td>When you laugh, is @CAPS5 out of habit, or is ...</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12974</th>\n",
              "      <td>21630</td>\n",
              "      <td>8</td>\n",
              "      <td>Trippin' on fen...</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12975</th>\n",
              "      <td>21633</td>\n",
              "      <td>8</td>\n",
              "      <td>Many people believe that laughter can improve...</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12976 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5547052f-8a6c-492a-b420-87044b9402f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5547052f-8a6c-492a-b420-87044b9402f8 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-5547052f-8a6c-492a-b420-87044b9402f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       essay_id  essay_set                                              essay  \\\n",
              "0             1          1  Dear local newspaper, I think effects computer...   \n",
              "1             2          1  Dear @CAPS1 @CAPS2, I believe that using compu...   \n",
              "2             3          1  Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...   \n",
              "3             4          1  Dear Local Newspaper, @CAPS1 I have found that...   \n",
              "4             5          1  Dear @LOCATION1, I know having computers has a...   \n",
              "...         ...        ...                                                ...   \n",
              "12971     21626          8   In most stories mothers and daughters are eit...   \n",
              "12972     21628          8   I never understood the meaning laughter is th...   \n",
              "12973     21629          8  When you laugh, is @CAPS5 out of habit, or is ...   \n",
              "12974     21630          8                                 Trippin' on fen...   \n",
              "12975     21633          8   Many people believe that laughter can improve...   \n",
              "\n",
              "       domain1_score  \n",
              "0                  8  \n",
              "1                  9  \n",
              "2                  7  \n",
              "3                 10  \n",
              "4                  8  \n",
              "...              ...  \n",
              "12971             35  \n",
              "12972             32  \n",
              "12973             40  \n",
              "12974             40  \n",
              "12975             40  \n",
              "\n",
              "[12976 rows x 4 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(os.path.join(dataset_dir, 'training_set_rel3.tsv'), sep='\\t', encoding='ISO-8859-1')\n",
        "y = df['domain1_score']\n",
        "df = df.dropna(axis=1)\n",
        "df = df.drop(columns=['rater1_domain1', 'rater2_domain1'])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jPloL-UaS8J"
      },
      "outputs": [],
      "source": [
        "min_scores = np.array([-1, 2, 1, 0, 0, 0, 0, 0, 0])\n",
        "max_scores = np.array([-1, 12, 6, 3, 3, 4, 4, 30, 60])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "mFJ263JdaXTG",
        "outputId": "f0f846fc-ead4-4379-a4e6-c1e1219fb32c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-da830ac2-cd13-4c60-bc08-ba549a90ddc6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>essay_id</th>\n",
              "      <th>essay_set</th>\n",
              "      <th>essay</th>\n",
              "      <th>domain1_score</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear local newspaper, I think effects computer...</td>\n",
              "      <td>8</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear @CAPS1 @CAPS2, I believe that using compu...</td>\n",
              "      <td>9</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...</td>\n",
              "      <td>7</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear Local Newspaper, @CAPS1 I have found that...</td>\n",
              "      <td>10</td>\n",
              "      <td>80.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Dear @LOCATION1, I know having computers has a...</td>\n",
              "      <td>8</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12971</th>\n",
              "      <td>21626</td>\n",
              "      <td>8</td>\n",
              "      <td>In most stories mothers and daughters are eit...</td>\n",
              "      <td>35</td>\n",
              "      <td>58.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12972</th>\n",
              "      <td>21628</td>\n",
              "      <td>8</td>\n",
              "      <td>I never understood the meaning laughter is th...</td>\n",
              "      <td>32</td>\n",
              "      <td>53.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12973</th>\n",
              "      <td>21629</td>\n",
              "      <td>8</td>\n",
              "      <td>When you laugh, is @CAPS5 out of habit, or is ...</td>\n",
              "      <td>40</td>\n",
              "      <td>66.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12974</th>\n",
              "      <td>21630</td>\n",
              "      <td>8</td>\n",
              "      <td>Trippin' on fen...</td>\n",
              "      <td>40</td>\n",
              "      <td>66.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12975</th>\n",
              "      <td>21633</td>\n",
              "      <td>8</td>\n",
              "      <td>Many people believe that laughter can improve...</td>\n",
              "      <td>40</td>\n",
              "      <td>66.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12976 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da830ac2-cd13-4c60-bc08-ba549a90ddc6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da830ac2-cd13-4c60-bc08-ba549a90ddc6 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-da830ac2-cd13-4c60-bc08-ba549a90ddc6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       essay_id  essay_set                                              essay  \\\n",
              "0             1          1  Dear local newspaper, I think effects computer...   \n",
              "1             2          1  Dear @CAPS1 @CAPS2, I believe that using compu...   \n",
              "2             3          1  Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...   \n",
              "3             4          1  Dear Local Newspaper, @CAPS1 I have found that...   \n",
              "4             5          1  Dear @LOCATION1, I know having computers has a...   \n",
              "...         ...        ...                                                ...   \n",
              "12971     21626          8   In most stories mothers and daughters are eit...   \n",
              "12972     21628          8   I never understood the meaning laughter is th...   \n",
              "12973     21629          8  When you laugh, is @CAPS5 out of habit, or is ...   \n",
              "12974     21630          8                                 Trippin' on fen...   \n",
              "12975     21633          8   Many people believe that laughter can improve...   \n",
              "\n",
              "       domain1_score      score  \n",
              "0                  8  60.000000  \n",
              "1                  9  70.000000  \n",
              "2                  7  50.000000  \n",
              "3                 10  80.000000  \n",
              "4                  8  60.000000  \n",
              "...              ...        ...  \n",
              "12971             35  58.333333  \n",
              "12972             32  53.333333  \n",
              "12973             40  66.666667  \n",
              "12974             40  66.666667  \n",
              "12975             40  66.666667  \n",
              "\n",
              "[12976 rows x 5 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "old_minimum = min_scores[df['essay_set']]\n",
        "old_maximum = max_scores[df['essay_set']]\n",
        "old_range = old_maximum - old_minimum\n",
        "new_minimum = 0\n",
        "new_maximum = 100\n",
        "new_range = (new_maximum - new_minimum)  \n",
        "df['score'] = (((df['domain1_score'] - old_minimum) * new_range) / old_range) + new_minimum\n",
        "\n",
        "y = np.round(df['score'])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6khzQT6ayJA",
        "outputId": "660d49cc-2c70-4376-f1fd-08512138f58e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        60.0\n",
              "1        70.0\n",
              "2        50.0\n",
              "3        80.0\n",
              "4        60.0\n",
              "         ... \n",
              "12971    58.0\n",
              "12972    53.0\n",
              "12973    67.0\n",
              "12974    67.0\n",
              "12975    67.0\n",
              "Name: score, Length: 12976, dtype: float64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1D3y8x9a0sB",
        "outputId": "2bb9588e-5f15-410e-d73b-e40493823774"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from gensim.models import Word2Vec\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "\n",
        "def esai_ke_listkata(essay_v, remove_stopwords):\n",
        "    essay_v = re.sub(\"[^a-zA-Z]\", \" \", essay_v)\n",
        "    kata = essay_v.lower().split()\n",
        "    if remove_stopwords:\n",
        "        stops = set(stopwords.words(\"english\"))\n",
        "        kata = [w for w in kata if not w in stops]\n",
        "    return (kata)\n",
        "\n",
        "def esai_ke_kalimat(essay_v, remove_stopwords):\n",
        "    tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
        "    raw_sentences = tokenizer.tokenize(essay_v.strip())\n",
        "    kalimat = []\n",
        "    for raw_sentence in raw_sentences:\n",
        "        if len(raw_sentence) > 0:\n",
        "            kalimat.append(esai_ke_listkata(raw_sentence, remove_stopwords))\n",
        "    return kalimat\n",
        "\n",
        "def makeFeatureVec(kata, model, num_features):\n",
        "    featureVec = np.zeros((num_features,),dtype=\"float32\")\n",
        "    num_words = 0.\n",
        "    for word in kata:\n",
        "        if word in model:\n",
        "            num_words += 1\n",
        "            featureVec = np.add(featureVec, model[word])       \n",
        "    featureVec = np.divide(featureVec,num_words)\n",
        "    return featureVec\n",
        "\n",
        "def getAvgFeatureVecs(essays, model, num_features):\n",
        "    counter = 0\n",
        "    essayFeatureVecs = np.zeros((len(essays),num_features),dtype=\"float32\")\n",
        "    for essay in essays:\n",
        "        essayFeatureVecs[counter] = makeFeatureVec(essay, model, num_features)\n",
        "        counter = counter + 1\n",
        "    return essayFeatureVecs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bkwgiwlHc_Hh"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Lambda, Flatten\n",
        "from tensorflow.keras.models import Sequential, load_model, model_from_config\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "def getmodel():\n",
        "    model = Sequential()\n",
        "    model.add(LSTM(200, dropout=0.4, recurrent_dropout=0.4, input_shape=[1, 50], return_sequences=True))\n",
        "    model.add(LSTM(128, recurrent_dropout=0.4))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(1, activation='relu'))\n",
        "\n",
        "    model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae'])\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQ9G3-nihTkE",
        "outputId": "6374bfc7-6412-4d74-a7c6-f19e2c01f6ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--------Fold 1--------\n",
            "\n",
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_22 (LSTM)              (None, 1, 200)            200800    \n",
            "                                                                 \n",
            " lstm_23 (LSTM)              (None, 128)               168448    \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,377\n",
            "Trainable params: 369,377\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "163/163 [==============================] - 9s 19ms/step - loss: 2193.6431 - mae: 41.1059\n",
            "Epoch 2/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 1079.9158 - mae: 27.1662\n",
            "Epoch 3/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 644.9995 - mae: 20.5937\n",
            "Epoch 4/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 577.5239 - mae: 19.0965\n",
            "Epoch 5/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 573.4106 - mae: 19.0267\n",
            "Epoch 6/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 567.7161 - mae: 18.9409\n",
            "Epoch 7/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 544.0055 - mae: 18.4909\n",
            "Epoch 8/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 512.0529 - mae: 17.8847\n",
            "Epoch 9/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 494.8783 - mae: 17.4913\n",
            "Epoch 10/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 486.5512 - mae: 17.2771\n",
            "Epoch 11/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 483.0687 - mae: 17.2133\n",
            "Epoch 12/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 482.4286 - mae: 17.2027\n",
            "Epoch 13/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 476.6512 - mae: 17.0954\n",
            "Epoch 14/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 471.7585 - mae: 17.0134\n",
            "Epoch 15/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 468.4319 - mae: 16.9470\n",
            "Epoch 16/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 470.6828 - mae: 16.9898\n",
            "Epoch 17/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 463.9258 - mae: 16.8592\n",
            "Epoch 18/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 461.0988 - mae: 16.8439\n",
            "Epoch 19/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 463.4893 - mae: 16.9062\n",
            "Epoch 20/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 458.8770 - mae: 16.7844\n",
            "Epoch 21/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 451.8540 - mae: 16.5659\n",
            "Epoch 22/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 457.0997 - mae: 16.7076\n",
            "Epoch 23/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 449.9200 - mae: 16.5767\n",
            "Epoch 24/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 442.9373 - mae: 16.4933\n",
            "Epoch 25/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 447.0022 - mae: 16.4955\n",
            "Epoch 26/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 443.5378 - mae: 16.5325\n",
            "Epoch 27/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 440.2490 - mae: 16.3781\n",
            "Epoch 28/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 436.8882 - mae: 16.2706\n",
            "Epoch 29/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 439.2525 - mae: 16.3714\n",
            "Epoch 30/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 426.8224 - mae: 16.1521\n",
            "Epoch 31/50\n",
            "163/163 [==============================] - 5s 28ms/step - loss: 435.1463 - mae: 16.3435\n",
            "Epoch 32/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 425.8763 - mae: 16.1565\n",
            "Epoch 33/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 434.3308 - mae: 16.2918\n",
            "Epoch 34/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 423.8667 - mae: 16.0195\n",
            "Epoch 35/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 422.7482 - mae: 15.9667\n",
            "Epoch 36/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 419.2896 - mae: 15.9776\n",
            "Epoch 37/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 417.4524 - mae: 15.8691\n",
            "Epoch 38/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 416.0201 - mae: 15.9733\n",
            "Epoch 39/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 412.4163 - mae: 15.8816\n",
            "Epoch 40/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 409.4613 - mae: 15.7254\n",
            "Epoch 41/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 406.9291 - mae: 15.7192\n",
            "Epoch 42/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 403.4451 - mae: 15.6753\n",
            "Epoch 43/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 403.5229 - mae: 15.6546\n",
            "Epoch 44/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 395.2178 - mae: 15.4822\n",
            "Epoch 45/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 402.4785 - mae: 15.5350\n",
            "Epoch 46/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 391.8058 - mae: 15.4358\n",
            "Epoch 47/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 391.6432 - mae: 15.3594\n",
            "Epoch 48/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 394.4912 - mae: 15.4809\n",
            "Epoch 49/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 390.7077 - mae: 15.4647\n",
            "Epoch 50/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 390.2584 - mae: 15.4676\n",
            "82/82 [==============================] - 1s 3ms/step\n",
            "Skor Kappa: 0.5710696448290304\n",
            "\n",
            "--------Fold 2--------\n",
            "\n",
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_24 (LSTM)              (None, 1, 200)            200800    \n",
            "                                                                 \n",
            " lstm_25 (LSTM)              (None, 128)               168448    \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,377\n",
            "Trainable params: 369,377\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "163/163 [==============================] - 8s 20ms/step - loss: 2123.8728 - mae: 40.3104\n",
            "Epoch 2/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 1031.0927 - mae: 26.4981\n",
            "Epoch 3/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 633.1159 - mae: 20.2434\n",
            "Epoch 4/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 572.9017 - mae: 19.0116\n",
            "Epoch 5/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 572.0527 - mae: 18.9711\n",
            "Epoch 6/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 556.1992 - mae: 18.7306\n",
            "Epoch 7/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 538.9484 - mae: 18.3923\n",
            "Epoch 8/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 517.9456 - mae: 17.9249\n",
            "Epoch 9/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 498.4277 - mae: 17.6094\n",
            "Epoch 10/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 482.8015 - mae: 17.2781\n",
            "Epoch 11/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 478.7705 - mae: 17.0453\n",
            "Epoch 12/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 469.5655 - mae: 16.9629\n",
            "Epoch 13/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 467.9453 - mae: 16.8934\n",
            "Epoch 14/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 472.3663 - mae: 17.0296\n",
            "Epoch 15/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 468.3096 - mae: 17.0326\n",
            "Epoch 16/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 455.1342 - mae: 16.7237\n",
            "Epoch 17/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 459.2344 - mae: 16.7640\n",
            "Epoch 18/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 456.1637 - mae: 16.7075\n",
            "Epoch 19/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 457.7342 - mae: 16.7478\n",
            "Epoch 20/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 455.2830 - mae: 16.7236\n",
            "Epoch 21/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 449.8177 - mae: 16.6460\n",
            "Epoch 22/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 450.3957 - mae: 16.6441\n",
            "Epoch 23/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 442.1094 - mae: 16.4454\n",
            "Epoch 24/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 435.2888 - mae: 16.3757\n",
            "Epoch 25/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 432.1003 - mae: 16.2750\n",
            "Epoch 26/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 440.2217 - mae: 16.4442\n",
            "Epoch 27/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 436.0229 - mae: 16.3625\n",
            "Epoch 28/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 432.0837 - mae: 16.2374\n",
            "Epoch 29/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 433.4893 - mae: 16.3055\n",
            "Epoch 30/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 432.5625 - mae: 16.2449\n",
            "Epoch 31/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 426.1918 - mae: 16.1561\n",
            "Epoch 32/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 422.5554 - mae: 16.0843\n",
            "Epoch 33/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 421.3407 - mae: 16.1442\n",
            "Epoch 34/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 412.6387 - mae: 15.8838\n",
            "Epoch 35/50\n",
            "163/163 [==============================] - 4s 24ms/step - loss: 417.9456 - mae: 15.9604\n",
            "Epoch 36/50\n",
            "163/163 [==============================] - 4s 24ms/step - loss: 418.2792 - mae: 15.9181\n",
            "Epoch 37/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 404.8391 - mae: 15.7272\n",
            "Epoch 38/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 406.5072 - mae: 15.7311\n",
            "Epoch 39/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 410.4274 - mae: 15.7872\n",
            "Epoch 40/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 408.3684 - mae: 15.7780\n",
            "Epoch 41/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 407.6619 - mae: 15.7674\n",
            "Epoch 42/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 400.4628 - mae: 15.5619\n",
            "Epoch 43/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 399.8668 - mae: 15.5900\n",
            "Epoch 44/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 392.9648 - mae: 15.4435\n",
            "Epoch 45/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 400.2265 - mae: 15.6259\n",
            "Epoch 46/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 389.4682 - mae: 15.4075\n",
            "Epoch 47/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 392.4613 - mae: 15.4791\n",
            "Epoch 48/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 391.1505 - mae: 15.3562\n",
            "Epoch 49/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 386.8630 - mae: 15.3266\n",
            "Epoch 50/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 391.0515 - mae: 15.4444\n",
            "82/82 [==============================] - 1s 3ms/step\n",
            "Skor Kappa: 0.5556768714250664\n",
            "\n",
            "--------Fold 3--------\n",
            "\n",
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_26 (LSTM)              (None, 1, 200)            200800    \n",
            "                                                                 \n",
            " lstm_27 (LSTM)              (None, 128)               168448    \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,377\n",
            "Trainable params: 369,377\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "163/163 [==============================] - 9s 26ms/step - loss: 2115.7373 - mae: 40.1997\n",
            "Epoch 2/50\n",
            "163/163 [==============================] - 4s 25ms/step - loss: 1020.9732 - mae: 26.4063\n",
            "Epoch 3/50\n",
            "163/163 [==============================] - 4s 25ms/step - loss: 635.3137 - mae: 20.3444\n",
            "Epoch 4/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 577.8510 - mae: 19.1261\n",
            "Epoch 5/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 578.2784 - mae: 19.1342\n",
            "Epoch 6/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 572.6600 - mae: 18.9863\n",
            "Epoch 7/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 554.8354 - mae: 18.6667\n",
            "Epoch 8/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 537.9128 - mae: 18.3662\n",
            "Epoch 9/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 515.2159 - mae: 17.9384\n",
            "Epoch 10/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 500.5962 - mae: 17.6044\n",
            "Epoch 11/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 488.9598 - mae: 17.3463\n",
            "Epoch 12/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 486.5754 - mae: 17.2606\n",
            "Epoch 13/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 477.0579 - mae: 17.0981\n",
            "Epoch 14/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 473.9813 - mae: 17.0822\n",
            "Epoch 15/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 472.4556 - mae: 16.9716\n",
            "Epoch 16/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 472.3658 - mae: 17.0225\n",
            "Epoch 17/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 469.8858 - mae: 17.0166\n",
            "Epoch 18/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 467.3504 - mae: 16.9878\n",
            "Epoch 19/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 468.6619 - mae: 16.9432\n",
            "Epoch 20/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 462.4024 - mae: 16.8432\n",
            "Epoch 21/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 459.2703 - mae: 16.8529\n",
            "Epoch 22/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 453.3047 - mae: 16.6981\n",
            "Epoch 23/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 450.0681 - mae: 16.5747\n",
            "Epoch 24/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 457.3571 - mae: 16.6966\n",
            "Epoch 25/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 453.5377 - mae: 16.7290\n",
            "Epoch 26/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 448.1685 - mae: 16.5297\n",
            "Epoch 27/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 446.2638 - mae: 16.5327\n",
            "Epoch 28/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 435.9145 - mae: 16.3147\n",
            "Epoch 29/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 442.0298 - mae: 16.4444\n",
            "Epoch 30/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 437.9319 - mae: 16.3527\n",
            "Epoch 31/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 436.3393 - mae: 16.3246\n",
            "Epoch 32/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 431.9803 - mae: 16.2610\n",
            "Epoch 33/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 423.6072 - mae: 16.1347\n",
            "Epoch 34/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 427.1180 - mae: 16.1535\n",
            "Epoch 35/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 421.2147 - mae: 16.0570\n",
            "Epoch 36/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 420.1302 - mae: 15.9663\n",
            "Epoch 37/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 419.3214 - mae: 15.9927\n",
            "Epoch 38/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 415.3254 - mae: 15.9171\n",
            "Epoch 39/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 420.2362 - mae: 15.9684\n",
            "Epoch 40/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 414.2898 - mae: 15.9102\n",
            "Epoch 41/50\n",
            "163/163 [==============================] - 4s 26ms/step - loss: 405.9092 - mae: 15.7267\n",
            "Epoch 42/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 404.7411 - mae: 15.7012\n",
            "Epoch 43/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 405.2408 - mae: 15.7221\n",
            "Epoch 44/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 400.9873 - mae: 15.6253\n",
            "Epoch 45/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 396.4969 - mae: 15.5728\n",
            "Epoch 46/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 396.3601 - mae: 15.5304\n",
            "Epoch 47/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 396.8645 - mae: 15.5385\n",
            "Epoch 48/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 396.7242 - mae: 15.5900\n",
            "Epoch 49/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 400.7334 - mae: 15.5782\n",
            "Epoch 50/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 399.5910 - mae: 15.5768\n",
            "82/82 [==============================] - 1s 3ms/step\n",
            "Skor Kappa: 0.5692868192684646\n",
            "\n",
            "--------Fold 4--------\n",
            "\n",
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_28 (LSTM)              (None, 1, 200)            200800    \n",
            "                                                                 \n",
            " lstm_29 (LSTM)              (None, 128)               168448    \n",
            "                                                                 \n",
            " dropout_14 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,377\n",
            "Trainable params: 369,377\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "163/163 [==============================] - 9s 19ms/step - loss: 2204.9312 - mae: 41.2176\n",
            "Epoch 2/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 1084.4869 - mae: 27.2792\n",
            "Epoch 3/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 651.4299 - mae: 20.6426\n",
            "Epoch 4/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 580.1991 - mae: 19.1787\n",
            "Epoch 5/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 575.6976 - mae: 19.0719\n",
            "Epoch 6/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 575.3519 - mae: 19.0362\n",
            "Epoch 7/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 561.7444 - mae: 18.8299\n",
            "Epoch 8/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 539.7850 - mae: 18.3601\n",
            "Epoch 9/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 524.3340 - mae: 18.0234\n",
            "Epoch 10/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 498.3247 - mae: 17.4568\n",
            "Epoch 11/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 490.6072 - mae: 17.3417\n",
            "Epoch 12/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 479.5172 - mae: 17.0882\n",
            "Epoch 13/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 484.6547 - mae: 17.2608\n",
            "Epoch 14/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 477.7400 - mae: 17.0663\n",
            "Epoch 15/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 472.4740 - mae: 17.0028\n",
            "Epoch 16/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 471.8318 - mae: 16.9774\n",
            "Epoch 17/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 464.7086 - mae: 16.8546\n",
            "Epoch 18/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 461.8828 - mae: 16.8400\n",
            "Epoch 19/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 459.9379 - mae: 16.8108\n",
            "Epoch 20/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 463.5518 - mae: 16.7977\n",
            "Epoch 21/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 459.6269 - mae: 16.7325\n",
            "Epoch 22/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 449.4110 - mae: 16.5874\n",
            "Epoch 23/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 449.3268 - mae: 16.5483\n",
            "Epoch 24/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 450.9249 - mae: 16.6086\n",
            "Epoch 25/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 443.6025 - mae: 16.4654\n",
            "Epoch 26/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 447.4173 - mae: 16.5913\n",
            "Epoch 27/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 441.8333 - mae: 16.3902\n",
            "Epoch 28/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 435.4816 - mae: 16.2543\n",
            "Epoch 29/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 436.9333 - mae: 16.3128\n",
            "Epoch 30/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 431.3989 - mae: 16.2392\n",
            "Epoch 31/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 432.6129 - mae: 16.2986\n",
            "Epoch 32/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 424.9627 - mae: 16.1183\n",
            "Epoch 33/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 424.9071 - mae: 16.1020\n",
            "Epoch 34/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 420.3071 - mae: 16.0074\n",
            "Epoch 35/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 423.4649 - mae: 16.0564\n",
            "Epoch 36/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 415.2849 - mae: 15.8980\n",
            "Epoch 37/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 411.3788 - mae: 15.8162\n",
            "Epoch 38/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 415.8417 - mae: 15.9344\n",
            "Epoch 39/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 410.4581 - mae: 15.7851\n",
            "Epoch 40/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 408.8441 - mae: 15.6882\n",
            "Epoch 41/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 406.0512 - mae: 15.7480\n",
            "Epoch 42/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 402.1830 - mae: 15.6346\n",
            "Epoch 43/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 401.4043 - mae: 15.6231\n",
            "Epoch 44/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 398.7498 - mae: 15.6263\n",
            "Epoch 45/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 397.8452 - mae: 15.5444\n",
            "Epoch 46/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 399.3855 - mae: 15.5801\n",
            "Epoch 47/50\n",
            "163/163 [==============================] - 4s 26ms/step - loss: 395.9115 - mae: 15.4832\n",
            "Epoch 48/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 396.2753 - mae: 15.5250\n",
            "Epoch 49/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 391.9512 - mae: 15.4571\n",
            "Epoch 50/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 388.5607 - mae: 15.3593\n",
            "82/82 [==============================] - 1s 3ms/step\n",
            "Skor Kappa: 0.5559932205560765\n",
            "\n",
            "--------Fold 5--------\n",
            "\n",
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_30 (LSTM)              (None, 1, 200)            200800    \n",
            "                                                                 \n",
            " lstm_31 (LSTM)              (None, 128)               168448    \n",
            "                                                                 \n",
            " dropout_15 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 369,377\n",
            "Trainable params: 369,377\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "163/163 [==============================] - 8s 20ms/step - loss: 2203.6428 - mae: 41.1398\n",
            "Epoch 2/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 1077.2457 - mae: 27.1693\n",
            "Epoch 3/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 653.7339 - mae: 20.6684\n",
            "Epoch 4/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 580.3811 - mae: 19.1639\n",
            "Epoch 5/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 581.6893 - mae: 19.1203\n",
            "Epoch 6/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 576.2004 - mae: 19.0245\n",
            "Epoch 7/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 551.6563 - mae: 18.6016\n",
            "Epoch 8/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 530.9775 - mae: 18.1912\n",
            "Epoch 9/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 502.3727 - mae: 17.6051\n",
            "Epoch 10/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 489.6959 - mae: 17.3481\n",
            "Epoch 11/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 483.6079 - mae: 17.2083\n",
            "Epoch 12/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 482.9630 - mae: 17.1593\n",
            "Epoch 13/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 473.4652 - mae: 17.0139\n",
            "Epoch 14/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 474.6620 - mae: 17.0175\n",
            "Epoch 15/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 471.7585 - mae: 16.9942\n",
            "Epoch 16/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 466.2674 - mae: 16.8826\n",
            "Epoch 17/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 470.9147 - mae: 17.0255\n",
            "Epoch 18/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 472.1697 - mae: 16.9850\n",
            "Epoch 19/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 461.7584 - mae: 16.8331\n",
            "Epoch 20/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 461.6536 - mae: 16.8708\n",
            "Epoch 21/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 462.8845 - mae: 16.8988\n",
            "Epoch 22/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 456.5146 - mae: 16.6555\n",
            "Epoch 23/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 458.9179 - mae: 16.7502\n",
            "Epoch 24/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 450.4321 - mae: 16.6130\n",
            "Epoch 25/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 450.7580 - mae: 16.5979\n",
            "Epoch 26/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 444.6206 - mae: 16.4520\n",
            "Epoch 27/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 444.1992 - mae: 16.4239\n",
            "Epoch 28/50\n",
            "163/163 [==============================] - 3s 19ms/step - loss: 444.9943 - mae: 16.5614\n",
            "Epoch 29/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 442.8845 - mae: 16.4403\n",
            "Epoch 30/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 439.2228 - mae: 16.3261\n",
            "Epoch 31/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 432.3595 - mae: 16.2482\n",
            "Epoch 32/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 438.1409 - mae: 16.3728\n",
            "Epoch 33/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 431.6827 - mae: 16.2231\n",
            "Epoch 34/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 429.0719 - mae: 16.1035\n",
            "Epoch 35/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 427.5035 - mae: 16.1756\n",
            "Epoch 36/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 428.5825 - mae: 16.0905\n",
            "Epoch 37/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 425.5840 - mae: 16.0844\n",
            "Epoch 38/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 420.8114 - mae: 15.9704\n",
            "Epoch 39/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 417.7406 - mae: 15.9492\n",
            "Epoch 40/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 417.6902 - mae: 15.9564\n",
            "Epoch 41/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 415.8571 - mae: 15.8925\n",
            "Epoch 42/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 405.1563 - mae: 15.6824\n",
            "Epoch 43/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 402.8758 - mae: 15.6599\n",
            "Epoch 44/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 404.3392 - mae: 15.6459\n",
            "Epoch 45/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 404.5534 - mae: 15.7397\n",
            "Epoch 46/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 401.7948 - mae: 15.6135\n",
            "Epoch 47/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 402.8774 - mae: 15.6633\n",
            "Epoch 48/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 397.4134 - mae: 15.5591\n",
            "Epoch 49/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 390.6430 - mae: 15.3804\n",
            "Epoch 50/50\n",
            "163/163 [==============================] - 3s 20ms/step - loss: 401.6960 - mae: 15.6866\n",
            "82/82 [==============================] - 1s 3ms/step\n",
            "Skor Kappa: 0.5527646402417449\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "import pickle\n",
        "\n",
        "corpus = []\n",
        "for essay in df['essay']:\n",
        "    corpus.append(esai_ke_listkata(essay, True))\n",
        "\n",
        "embedding_dict={}\n",
        "with open('/content/glove.6B.50d.txt','r') as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        vectors = np.asarray(values[1:],'float32')\n",
        "        embedding_dict[word] = vectors\n",
        "\n",
        "\n",
        "cv = KFold(n_splits=5, shuffle=True)\n",
        "results = []\n",
        "y_pred_list = []\n",
        "\n",
        "count = 1\n",
        "for traincv, testcv in cv.split(df):\n",
        "    \n",
        "    print(\"\\n--------Fold {}--------\\n\".format(count))\n",
        "    X_test, X_train, y_test, y_train = df.iloc[testcv], df.iloc[traincv], y.iloc[testcv], y.iloc[traincv]\n",
        "    train_essays = X_train['essay']\n",
        "    test_essays = X_test['essay']\n",
        "    \n",
        "    sentences = []\n",
        "    \n",
        "    for essay in train_essays:\n",
        "        sentences += esai_ke_listkata(essay, remove_stopwords = True)\n",
        "\n",
        "    num_features = 50\n",
        "\n",
        "    model = embedding_dict\n",
        "\n",
        "    clean_train_essays = []\n",
        "    for essay_v in train_essays:\n",
        "        clean_train_essays.append(esai_ke_listkata(essay_v, remove_stopwords=True))\n",
        "    trainDataVecs = getAvgFeatureVecs(clean_train_essays, model, num_features)\n",
        "    \n",
        "    clean_test_essays = []\n",
        "    for essay_v in test_essays:\n",
        "        clean_test_essays.append(esai_ke_listkata( essay_v, remove_stopwords=True ))\n",
        "    testDataVecs = getAvgFeatureVecs( clean_test_essays, model, num_features )\n",
        "    \n",
        "    trainDataVecs = np.array(trainDataVecs)\n",
        "    testDataVecs = np.array(testDataVecs)\n",
        "    \n",
        "    trainDataVecs = np.reshape(trainDataVecs, (trainDataVecs.shape[0], 1, trainDataVecs.shape[1]))\n",
        "    testDataVecs = np.reshape(testDataVecs, (testDataVecs.shape[0], 1, testDataVecs.shape[1]))\n",
        "    \n",
        "    lstm_model = getmodel()\n",
        "    lstm_model.fit(trainDataVecs, y_train, batch_size=64, epochs=50)\n",
        "    y_pred = lstm_model.predict(testDataVecs)\n",
        "\n",
        "    if count == 5:\n",
        "         lstm_model.save('./content/model_lstm.h5')\n",
        "         with open('saved_dictionary.pkl', 'wb') as f:\n",
        "            pickle.dump(embedding_dict, f)         \n",
        "         with open('saved_dictionary.pkl', 'rb') as f:\n",
        "            loaded_dict = pickle.load(f)\n",
        "\n",
        "    y_pred = np.round(y_pred)\n",
        "    \n",
        "    result = cohen_kappa_score(y_test.values,y_pred,weights='quadratic')\n",
        "    print(\"Skor Kappa: {}\".format(result))\n",
        "    results.append(result)\n",
        "\n",
        "    count += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0CDUEFoiFUy",
        "outputId": "f1eb3c68-938b-4fef-cf29-7504291ff78b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rata-rata skor kappa:  0.5425\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"Rata-rata skor kappa: \", np.around(np.array(results).mean(),decimals=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHk4z3vWp_2x",
        "outputId": "bc560b04-97ed-4a6c-b778-d5561125e19b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "[[28.]]\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "from gensim.test.utils import datapath\n",
        "\n",
        "contoh_konten = \"\"\"\n",
        "        I aegre waf the evansmant ov tnachnolage. The evansmant ov tnachnolige is being to halp fined a kohar froi alnsas. Tnanchnolage waf ont ot we wod not go to the moon. Tnachnologe evans as we maech at. The people are in tnacholege to the frchr fror the good ov live. Famas invanyor ues tnacholage leki lena orde dvanse and his fling mashine. Tnachologe is the grat\n",
        "    \"\"\" \n",
        "\n",
        "content = contoh_konten\n",
        "    \n",
        "if len(content) > 20:\n",
        "    num_features = 50\n",
        "    clean_test_essays = []\n",
        "    clean_test_essays.append(esai_ke_listkata( content, remove_stopwords=True ))\n",
        "    testDataVecs = getAvgFeatureVecs( clean_test_essays, model, num_features )\n",
        "    testDataVecs = np.array(testDataVecs)\n",
        "    testDataVecs = np.reshape(testDataVecs, (testDataVecs.shape[0], 1, testDataVecs.shape[1]))\n",
        "\n",
        "    prediksi = lstm_model.predict(testDataVecs)\n",
        "\n",
        "    if math.isnan(prediksi):\n",
        "        prediksi = 0\n",
        "    else:\n",
        "      prediksi = np.round(prediksi)\n",
        "\n",
        "    if prediksi < 0:\n",
        "        prediksi = 0\n",
        "else:\n",
        "    prediksi = 0\n",
        "    \n",
        "print(prediksi)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "14958d3aee5f1cad06795f787e54b96185c25fb40dfec723a5be941f3a531b8c"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}