Book_Genre_Prediction.ipynb

{
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    {
      "cell_type": "markdown",
      "source": [
        "# **Book Genre Classifier: Exploring Various Approaches for Genre Classification**\n",
        "\n",
        " In this exploration, I delve into cutting-edge techniques and traditional methods to tackle the fascinating challenge of classifying book genres.\n",
        "\n",
        "**Section 1: Fine-Tuning Huggingface Transformer LLMs**\n",
        "> In **Part 1a**,  we review the process of fine-tuning Huggingface Transformer Language Models (LLMs) for genre predictions. Moving on to **Part 1b**, I employ the PEFT/LoRA technique, shedding light on its potential in genre classification.\n",
        "\n",
        "**Section 2: Genre Classification with scikit-learn and nltk**\n",
        "> Moving into Section 2, we're using reliable tools like scikit-learn and nltk to understand how to sort things into categories, like different genres. We're checking out how well these tools work and when they're useful. We tried different models, like Support Vector, Naive Bayes, and Random Forest, to see which one works the best. After lots of testing, we picked the one that performed the best. This shows a hands-on way to classify genres effectively.\n",
        "\n",
        "\n",
        "**Why understanding book genres is interesting and important:**\n",
        "\n",
        "It's difficult to figure out what genre a book is based on its title and summary, but it's important for several reasons.\n",
        "\n",
        "* **Recommendation systems:** A good genre classifier can help recommendation systems give readers more personalized book recommendations, which can lead to more reader engagement on digital platforms.\n",
        "* **Literary success:** Understanding the relationship between genres and how they're portrayed in summaries can give us insights into what makes books commercially successful, or where they could be improved.\n",
        "* **Book themes and trends:** Studying book genres can help us better understand the themes and trends in the publishing industry, which can be valuable for cultural and societal studies.\n",
        "\n",
        "Let the journey begin!\n"
      ],
      "metadata": {
        "id": "R2lu2MwFEixk"
      },
      "id": "R2lu2MwFEixk"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Part 1a. Fine-Tuning Huggingface Transformer LLMs**"
      ],
      "metadata": {
        "id": "_VSiIg2wG7JK"
      },
      "id": "_VSiIg2wG7JK"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "pip install -q transformers\n",
        "pip install -q datasets\n",
        "pip install -q peft\n",
        "pip install -q rouge_score\n",
        "pip install -q loralib\n",
        "pip install -q peft\n",
        "pip install -q evaluate\n",
        "pip install -q accelerate"
      ],
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b7093c8-8ace-484a-c3ba-964e79350c8c"
      },
      "id": "i276u_cwE4-h",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.9/7.9 MB 60.8 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 311.2/311.2 kB 18.3 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.8/3.8 MB 108.6 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.3/1.3 MB 61.3 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 520.4/520.4 kB 4.1 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 115.3/115.3 kB 5.5 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 134.8/134.8 kB 10.3 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 174.7/174.7 kB 4.0 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 261.4/261.4 kB 26.8 MB/s eta 0:00:00\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.1/84.1 kB 1.7 MB/s eta 0:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from datasets import Dataset, load_dataset\n",
        "from transformers import AutoTokenizer\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "from transformers import Trainer, TrainingArguments\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import re"
      ],
      "metadata": {
        "id": "5c1Svnc3IdTC"
      },
      "id": "5c1Svnc3IdTC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading and Analyzing Data"
      ],
      "metadata": {
        "id": "p6tX3fpA3SHO"
      },
      "id": "p6tX3fpA3SHO"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!cp /content/drive/MyDrive/Python/Projects/project_1_books_classification/book_classifier/BooksDataSet.csv ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdC0KGeK0pyJ",
        "outputId": "30a11be9-5ca0-4ae6-9a9b-20574606aca6"
      },
      "id": "hdC0KGeK0pyJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('csv', data_files='BooksDataSet.csv', split='train')\n",
        "dataset = dataset.remove_columns(['Unnamed: 0', 'book_id'])\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184,
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        "id": "615PUhbJIW1a",
        "outputId": "690395bb-2efb-4ec1-f68d-9edbcbc3c1f5"
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      "id": "615PUhbJIW1a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a1e5c5f26bc429baa8f26fe2e1ae52c"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['book_name', 'genre', 'summary'],\n",
              "    num_rows: 3000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = max(len(sample['summary']) for sample in dataset)\n",
        "print(f\"The length of the longest text content is: {max_length}\")"
      ],
      "metadata": {
        "id": "ffnh7v27M5mV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36bff2a0-5ec9-443c-baf9-23b69fb7fe2b"
      },
      "id": "ffnh7v27M5mV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length of the longest text content is: 31263\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'nreimers/MiniLM-L3-H384-uncased'\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
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        "id": "CBPXWGTCK_Ao",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "id": "CBPXWGTCK_Ao",
      "execution_count": null,
      "outputs": [
        {
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              "(…)cased/resolve/main/tokenizer_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
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              "(…)M-L3-H384-uncased/resolve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset.set_format('pandas')\n",
        "df = dataset[:]"
      ],
      "metadata": {
        "id": "p92K5lN7QMb6"
      },
      "id": "p92K5lN7QMb6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text_len'] = df['summary'].apply(lambda x: len(x.split(\" \")))\n",
        "sns.boxplot(data=df, x='genre', y='text_len')\n",
        "plt.title(\"Word length per class\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "oLZ5MMp5PbkE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "6c1ac73b-340a-4c03-9bc6-5d6706308123"
      },
      "id": "oLZ5MMp5PbkE",
      "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['text_len'] == df['text_len'].min()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "HLzrgGXeRKLs",
        "outputId": "25f74d28-cd61-456e-d63e-57581dc25900"
      },
      "id": "HLzrgGXeRKLs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    book_name             genre       summary  \\\n",
              "338                      The Caverns of Kalte           Fantasy    ==Receptio   \n",
              "1574  Guardians of Ga'Hoole Book 4: The Siege  Historical novel    ==Receptio   \n",
              "2410                     The Eyes of Darkness            Horror   ==Character   \n",
              "\n",
              "      text_len  \n",
              "338          2  \n",
              "1574         2  \n",
              "2410         2  "
            ],
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              "      <th>338</th>\n",
              "      <td>The Caverns of Kalte</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>==Receptio</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1574</th>\n",
              "      <td>Guardians of Ga'Hoole Book 4: The Siege</td>\n",
              "      <td>Historical novel</td>\n",
              "      <td>==Receptio</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2410</th>\n",
              "      <td>The Eyes of Darkness</td>\n",
              "      <td>Horror</td>\n",
              "      <td>==Character</td>\n",
              "      <td>2</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "        const element = document.querySelector('#df-9bfaa2f8-5cc1-4e54-aa3d-8cbd042b60e4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e5ea5266-4e5d-4b8f-8221-f66bcea75fe4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['text_len'] > 4000].sort_values(by='text_len', ascending=False)"
      ],
      "metadata": {
        "id": "2JFHZmoXR8kM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f5cbd6b3-49a2-42b7-bdfa-0501eff0cd6c"
      },
      "id": "2JFHZmoXR8kM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        book_name             genre  \\\n",
              "2054     El Príncipe de la Niebla            Horror   \n",
              "1277                The Scapegoat     Crime Fiction   \n",
              "1634            The King Must Die  Historical novel   \n",
              "1700       The Great Indian Novel  Historical novel   \n",
              "1871  The Wake of the Lorelei Lee  Historical novel   \n",
              "\n",
              "                                                summary  text_len  \n",
              "2054   Max Carver, son of a watchmaker, has moved wi...      5664  \n",
              "1277   'I left the car by the side of the cathedral,...      5475  \n",
              "1634   The story is told by Theseus, looking back on...      5089  \n",
              "1700   The organization of the sections and chapters...      4289  \n",
              "1871   In the previous book, Rapture of the Deep, Ja...      4026  "
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              "      <th>2054</th>\n",
              "      <td>El Príncipe de la Niebla</td>\n",
              "      <td>Horror</td>\n",
              "      <td>Max Carver, son of a watchmaker, has moved wi...</td>\n",
              "      <td>5664</td>\n",
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              "    <tr>\n",
              "      <th>1277</th>\n",
              "      <td>The Scapegoat</td>\n",
              "      <td>Crime Fiction</td>\n",
              "      <td>'I left the car by the side of the cathedral,...</td>\n",
              "      <td>5475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1634</th>\n",
              "      <td>The King Must Die</td>\n",
              "      <td>Historical novel</td>\n",
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              "      <td>5089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1700</th>\n",
              "      <td>The Great Indian Novel</td>\n",
              "      <td>Historical novel</td>\n",
              "      <td>The organization of the sections and chapters...</td>\n",
              "      <td>4289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1871</th>\n",
              "      <td>The Wake of the Lorelei Lee</td>\n",
              "      <td>Historical novel</td>\n",
              "      <td>In the previous book, Rapture of the Deep, Ja...</td>\n",
              "      <td>4026</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-18cc8383-c36d-44d8-971a-2674eebfe7ad 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-18cc8383-c36d-44d8-971a-2674eebfe7ad');\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",
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              "\n",
              "\n",
              "<div id=\"df-34938077-268b-43df-b214-88b713d38d71\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34938077-268b-43df-b214-88b713d38d71')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34938077-268b-43df-b214-88b713d38d71 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.reset_format()"
      ],
      "metadata": {
        "id": "wZsgd26lTcV4"
      },
      "id": "wZsgd26lTcV4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[338]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAzRvyGeTk4d",
        "outputId": "12631452-6776-4b56-e4f4-60e30872b698"
      },
      "id": "OAzRvyGeTk4d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'book_name': 'The Caverns of Kalte',\n",
              " 'genre': 'Fantasy',\n",
              " 'summary': ' ==Receptio'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## cleaning the text\n",
        "def cleantext(example):\n",
        "\n",
        "    text = example['summary']\n",
        "    # removing the \"\\\" (most of the times '\\' is an escape character)\n",
        "    text = re.sub(\"'\\'\",\"\",text)\n",
        "\n",
        "    # removing special symbols\n",
        "    text = re.sub(\"[^a-zA-Z]\",\" \",text)\n",
        "\n",
        "    # removing the unnecesary whitespaces\n",
        "    text = ' '.join(text.split())\n",
        "\n",
        "    # convert text to lowercase\n",
        "    text = text.lower()\n",
        "    example['summary'] = text\n",
        "    return example\n",
        "\n",
        "\n",
        "dataset = dataset.map(cleantext)"
      ],
      "metadata": {
        "id": "DiC8-TUrPVnv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6d6a4bd878e14a45923867be1aa164a2",
            "741c2eef5d55432e9420c3f7c23c743b",
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            "d7c2353d3a314741b8e937580ccca8a9",
            "6c5ba5453d0c4c938060e26cdc1e599f",
            "dfd72584922343d5a0ab1646a585f42d",
            "cb95fd844e28468aa85019c2371398c8",
            "5c61c1dfd0d2456d956ec107c3be6596",
            "98b200cef6ab4e2fbc3c3ee1b6c3b574",
            "9d3b04d4d9924528899db9b35582ed1c",
            "b0cf132e5e70403fb04a62afce3f5238"
          ]
        },
        "outputId": "de033d8b-5c68-44ff-fb1b-5f112e31cb5e"
      },
      "id": "DiC8-TUrPVnv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d6a4bd878e14a45923867be1aa164a2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[338]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzF7p3UKUfoM",
        "outputId": "921bbf66-07f6-4259-fb71-b0bc44989a96"
      },
      "id": "DzF7p3UKUfoM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'book_name': 'The Caverns of Kalte',\n",
              " 'genre': 'Fantasy',\n",
              " 'summary': 'receptio'}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_number_of_trainable_model_parameters(model):\n",
        "    trainable_model_params = 0\n",
        "    all_model_params = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_model_params += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_model_params += param.numel()\n",
        "    return f\"trainable model parameters: {trainable_model_params}\\nall model parameters: {all_model_params}\\npercentage of trainable model parameters: {100 * trainable_model_params / all_model_params:.2f}%\""
      ],
      "metadata": {
        "id": "xYBokWK0PCIC"
      },
      "id": "xYBokWK0PCIC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6).to('cuda')\n",
        "# model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "51433df1e6e24bbea3c052aa1ca30d54",
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            "89eb4e0ecee44ab290459085a8350d3c",
            "e9198910d57442398696578fab0463ec",
            "85b1571db5be41858a3aeb38ce6a8d74",
            "e18e1928adb1491da45a326090605674",
            "48c42ec5aec84a4b97f2427c71e5b773",
            "b2beea0780554376a1385537fa4fff60",
            "1b9f97681e5645a68848b94651f59756",
            "7c247772047a432a82e6307340f4f216"
          ]
        },
        "id": "LHJN10JIVAWL",
        "outputId": "25bbd010-fa0f-4e2f-97cc-8fce2c71a0db"
      },
      "id": "LHJN10JIVAWL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/69.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51433df1e6e24bbea3c052aa1ca30d54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at nreimers/MiniLM-L3-H384-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(print_number_of_trainable_model_parameters(model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcoK_g-0Uvhi",
        "outputId": "59ae391b-2bc6-43ce-fd77-8eebe5ed5810"
      },
      "id": "QcoK_g-0Uvhi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable model parameters: 17392134\n",
            "all model parameters: 17392134\n",
            "percentage of trainable model parameters: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(print_number_of_trainable_model_parameters(model.classifier))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YatY--JVFRK",
        "outputId": "dfc49ac0-dc9d-4ea6-aa16-044e52100dc1"
      },
      "id": "0YatY--JVFRK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable model parameters: 2310\n",
            "all model parameters: 2310\n",
            "percentage of trainable model parameters: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = dict(zip(df['genre'].unique(), list(range(6))))\n",
        "mapping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8YSbfp_cLV8",
        "outputId": "03051ce2-0aa8-4a99-be0c-449bf21b78d9"
      },
      "id": "G8YSbfp_cLV8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Fantasy': 0,\n",
              " 'Science Fiction': 1,\n",
              " 'Crime Fiction': 2,\n",
              " 'Historical novel': 3,\n",
              " 'Horror': 4,\n",
              " 'Thriller': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['labels'] = [mapping.get(item) for item in df['genre']]"
      ],
      "metadata": {
        "id": "xXRpQXMVsIkk"
      },
      "id": "xXRpQXMVsIkk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.add_column('labels', df['labels'])"
      ],
      "metadata": {
        "id": "U61pv9p3upXj"
      },
      "id": "U61pv9p3upXj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = 2000\n",
        "dataset['genre'][index], dataset['labels'][index]"
      ],
      "metadata": {
        "id": "hSbBnxTxux37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e172a36-69d8-4f2e-dc99-505b6723e0b4"
      },
      "id": "hSbBnxTxux37",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Horror', 4)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splits = dataset.train_test_split(test_size=0.2, seed=42)\n",
        "train_ds = splits['train']\n",
        "test_ds = splits['test']\n",
        "\n",
        "train_ds, test_ds"
      ],
      "metadata": {
        "id": "26RCIjHgK66K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1f0f5f8-7aa8-44cb-9d37-acc1cf239e32"
      },
      "id": "26RCIjHgK66K",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['book_name', 'genre', 'summary', 'labels'],\n",
              "     num_rows: 2400\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['book_name', 'genre', 'summary', 'labels'],\n",
              "     num_rows: 600\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "J2DgjfHKVeFq"
      },
      "id": "J2DgjfHKVeFq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(example):\n",
        "    example['input_ids'] = tokenizer(example['summary'], padding=True, truncation=True).input_ids\n",
        "    example['attention_mask'] = tokenizer(example['summary'], padding=True, truncation=True).attention_mask\n",
        "    # example['labels'] = torch.tensor(example['labels'])\n",
        "    return example\n",
        "\n",
        "train_tokenized = train_ds.map(tokenize_function, batched=True)\n",
        "test_tokenized = test_ds.map(tokenize_function, batched=True)\n",
        "train_tokenized, test_tokenized"
      ],
      "metadata": {
        "id": "bRGI-cOBbRSy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224,
          "referenced_widgets": [
            "4cc50ab9c3864e378dbc340a8e2cf8f0",
            "cb450b15d8254c66a14c1ed39a3becaa",
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            "86997c4dd11b4b87ac9413125c250f04",
            "f33ed2fe950a47b3b463f416adc7f822",
            "907657493def4c2daf8c9789aa857e88",
            "7f5fd1b3e82946f2bd5e110d788e23a0",
            "ad1e45d2647147e0ad8d504321c328bd"
          ]
        },
        "outputId": "8db371b9-801a-430d-95d9-2ddf4f61c658"
      },
      "id": "bRGI-cOBbRSy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cc50ab9c3864e378dbc340a8e2cf8f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a1eaa6fcee84d23906c3c5f7f91de76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['book_name', 'genre', 'summary', 'labels', 'input_ids', 'attention_mask'],\n",
              "     num_rows: 2400\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['book_name', 'genre', 'summary', 'labels', 'input_ids', 'attention_mask'],\n",
              "     num_rows: 600\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_tokenized = train_tokenized.remove_columns(['book_name', 'genre', 'summary'])\n",
        "test_tokenized = test_tokenized.remove_columns(['book_name', 'genre', 'summary'])\n",
        "train_tokenized, test_tokenized"
      ],
      "metadata": {
        "id": "6EZGevzH_LgB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84e73793-ca03-4ad3-bf33-f8867c905762"
      },
      "id": "6EZGevzH_LgB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['labels', 'input_ids', 'attention_mask'],\n",
              "     num_rows: 2400\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['labels', 'input_ids', 'attention_mask'],\n",
              "     num_rows: 600\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_tokenized['input_ids'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEDzhA-khCcf",
        "outputId": "608b9f09-101c-4db2-feb3-74af048e8a77"
      },
      "id": "fEDzhA-khCcf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
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              " 0,\n",
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              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
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              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "length = []\n",
        "for value in train_tokenized['input_ids']:\n",
        "    length.append(len)"
      ],
      "metadata": {
        "id": "5PSATgzSgrF8"
      },
      "id": "5PSATgzSgrF8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    pred, labels = p\n",
        "    pred = np.argmax(pred, axis=1)\n",
        "    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "    recall = recall_score(y_true=labels, y_pred=pred, pos_label='positive', average='weighted')\n",
        "    precision = precision_score(y_true=labels, y_pred=pred, pos_label='positive', average='weighted')\n",
        "    f1 = f1_score(y_true=labels, y_pred=pred, pos_label='positive', average='weighted')\n",
        "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
      ],
      "metadata": {
        "id": "WNMru2-6BVVc"
      },
      "id": "WNMru2-6BVVc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training"
      ],
      "metadata": {
        "id": "8dYbgK9_PTtH"
      },
      "id": "8dYbgK9_PTtH"
    },
    {
      "cell_type": "code",
      "source": [
        "rm -rf MiniLM-L3-H384-uncased-fine-tuned-book-genres/"
      ],
      "metadata": {
        "id": "QC3oBzLPZNM8"
      },
      "id": "QC3oBzLPZNM8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name = model_name.split(\"/\")[-1]\n",
        "model_names = f\"{name}-fine-tuned-book-genres\"\n",
        "args = TrainingArguments(\n",
        "    output_dir=model_names,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=500,\n",
        "    auto_find_batch_size=True,\n",
        "    num_train_epochs=10,\n",
        "    seed=0,\n",
        "    load_best_model_at_end=True,\n",
        "    )"
      ],
      "metadata": {
        "id": "_m4YHw4kBvMV"
      },
      "id": "_m4YHw4kBvMV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_tokenized,\n",
        "    eval_dataset=test_tokenized,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "ERg3Y_XWB7P4"
      },
      "id": "ERg3Y_XWB7P4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del trainer"
      ],
      "metadata": {
        "id": "Ir5w943dYgMN"
      },
      "id": "Ir5w943dYgMN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "IQzGBJd2CITT",
        "outputId": "406f9b6d-94f8-4607-8d88-11e1e5e71dc0"
      },
      "id": "IQzGBJd2CITT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3000/3000 05:09, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.320700</td>\n",
              "      <td>1.125524</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.637810</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.620853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.711600</td>\n",
              "      <td>1.314119</td>\n",
              "      <td>0.595000</td>\n",
              "      <td>0.627780</td>\n",
              "      <td>0.595000</td>\n",
              "      <td>0.592605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.399400</td>\n",
              "      <td>1.394762</td>\n",
              "      <td>0.641667</td>\n",
              "      <td>0.652630</td>\n",
              "      <td>0.641667</td>\n",
              "      <td>0.635831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.196000</td>\n",
              "      <td>1.587612</td>\n",
              "      <td>0.671667</td>\n",
              "      <td>0.679835</td>\n",
              "      <td>0.671667</td>\n",
              "      <td>0.667128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.105100</td>\n",
              "      <td>1.795240</td>\n",
              "      <td>0.663333</td>\n",
              "      <td>0.671375</td>\n",
              "      <td>0.663333</td>\n",
              "      <td>0.658718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.076800</td>\n",
              "      <td>1.871006</td>\n",
              "      <td>0.668333</td>\n",
              "      <td>0.675197</td>\n",
              "      <td>0.668333</td>\n",
              "      <td>0.663739</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3000, training_loss=0.46825604502360024, metrics={'train_runtime': 309.3756, 'train_samples_per_second': 77.576, 'train_steps_per_second': 9.697, 'total_flos': 403609927680000.0, 'train_loss': 0.46825604502360024, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res = trainer.evaluate()\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "5b6n1NFxCKnu",
        "outputId": "1201f5fc-f953-43fd-8a9d-addfe51b5173"
      },
      "id": "5b6n1NFxCKnu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='75' max='75' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [75/75 00:02]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.1255240440368652,\n",
              " 'eval_accuracy': 0.625,\n",
              " 'eval_precision': 0.6378104519193544,\n",
              " 'eval_recall': 0.625,\n",
              " 'eval_f1': 0.620853316909507,\n",
              " 'eval_runtime': 2.3575,\n",
              " 'eval_samples_per_second': 254.51,\n",
              " 'eval_steps_per_second': 31.814,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Part 1b. Leveraging PEFT/LoRA Technique**"
      ],
      "metadata": {
        "id": "jQJr-gbmOyra"
      },
      "id": "jQJr-gbmOyra"
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model, TaskType\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=64, # Rank\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query\", \"value\"],\n",
        "    lora_dropout=0.1,\n",
        "    bias=\"lora_only\",\n",
        "    modules_to_save=[\"decode_head\"],\n",
        ")"
      ],
      "metadata": {
        "id": "5S6jOEuhOUAr"
      },
      "id": "5S6jOEuhOUAr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=6).to('cuda')\n",
        "lora_model = get_peft_model(peft_model,\n",
        "                            lora_config)\n",
        "print(print_number_of_trainable_model_parameters(lora_model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r62k6eHpO6WN",
        "outputId": "b056b2c6-9f20-4778-db3b-e0443464d87b"
      },
      "id": "r62k6eHpO6WN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at nreimers/MiniLM-L3-H384-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable model parameters: 297216\n",
            "all model parameters: 17687046\n",
            "percentage of trainable model parameters: 1.68%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "peft_name = model_name.split(\"/\")[-1]\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=f\"{peft_name}-book-genre-lora\",\n",
        "    learning_rate=1e-4,\n",
        "    num_train_epochs=20,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    eval_steps=500,\n",
        "    remove_unused_columns=False,\n",
        "    label_names=[\"labels\"],\n",
        "    seed=42,\n",
        ")"
      ],
      "metadata": {
        "id": "gchXBZegAyCl"
      },
      "id": "gchXBZegAyCl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_trainer = Trainer(\n",
        "    model=lora_model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_tokenized,\n",
        "    eval_dataset=test_tokenized,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "IXXIfESHBq71"
      },
      "id": "IXXIfESHBq71",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KGbjnWbXBq1o",
        "outputId": "5921cdad-fdd5-4275-dc3c-b8b317b7ecf5"
      },
      "id": "KGbjnWbXBq1o",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6000' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6000/6000 08:41, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.790275</td>\n",
              "      <td>0.151667</td>\n",
              "      <td>0.046165</td>\n",
              "      <td>0.151667</td>\n",
              "      <td>0.058710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.788200</td>\n",
              "      <td>1.741070</td>\n",
              "      <td>0.331667</td>\n",
              "      <td>0.159632</td>\n",
              "      <td>0.331667</td>\n",
              "      <td>0.208515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.788200</td>\n",
              "      <td>1.689164</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.176065</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.205977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.713200</td>\n",
              "      <td>1.665932</td>\n",
              "      <td>0.366667</td>\n",
              "      <td>0.361371</td>\n",
              "      <td>0.366667</td>\n",
              "      <td>0.249046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.660600</td>\n",
              "      <td>1.648124</td>\n",
              "      <td>0.386667</td>\n",
              "      <td>0.395596</td>\n",
              "      <td>0.386667</td>\n",
              "      <td>0.275935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.660600</td>\n",
              "      <td>1.636864</td>\n",
              "      <td>0.398333</td>\n",
              "      <td>0.374073</td>\n",
              "      <td>0.398333</td>\n",
              "      <td>0.287368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.634400</td>\n",
              "      <td>1.629392</td>\n",
              "      <td>0.438333</td>\n",
              "      <td>0.476923</td>\n",
              "      <td>0.438333</td>\n",
              "      <td>0.365019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.634400</td>\n",
              "      <td>1.618014</td>\n",
              "      <td>0.465000</td>\n",
              "      <td>0.554980</td>\n",
              "      <td>0.465000</td>\n",
              "      <td>0.396049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.615900</td>\n",
              "      <td>1.612758</td>\n",
              "      <td>0.471667</td>\n",
              "      <td>0.519771</td>\n",
              "      <td>0.471667</td>\n",
              "      <td>0.411347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.603000</td>\n",
              "      <td>1.605588</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.541842</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.425840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.603000</td>\n",
              "      <td>1.602313</td>\n",
              "      <td>0.490000</td>\n",
              "      <td>0.527949</td>\n",
              "      <td>0.490000</td>\n",
              "      <td>0.436017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.593700</td>\n",
              "      <td>1.598461</td>\n",
              "      <td>0.495000</td>\n",
              "      <td>0.540813</td>\n",
              "      <td>0.495000</td>\n",
              "      <td>0.441879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.593700</td>\n",
              "      <td>1.595294</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.532179</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.453248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.585600</td>\n",
              "      <td>1.594521</td>\n",
              "      <td>0.505000</td>\n",
              "      <td>0.525197</td>\n",
              "      <td>0.505000</td>\n",
              "      <td>0.456908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.580800</td>\n",
              "      <td>1.593098</td>\n",
              "      <td>0.503333</td>\n",
              "      <td>0.521614</td>\n",
              "      <td>0.503333</td>\n",
              "      <td>0.457089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.580800</td>\n",
              "      <td>1.591641</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.530847</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.465781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.576800</td>\n",
              "      <td>1.590370</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.538999</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.470749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.576800</td>\n",
              "      <td>1.589854</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.537320</td>\n",
              "      <td>0.516667</td>\n",
              "      <td>0.470381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.576100</td>\n",
              "      <td>1.589440</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.533903</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.468734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.575700</td>\n",
              "      <td>1.589420</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.532405</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.469075</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is 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:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is 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:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is 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:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is 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:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6000, training_loss=1.6253465474446616, metrics={'train_runtime': 521.1825, 'train_samples_per_second': 92.098, 'train_steps_per_second': 11.512, 'total_flos': 850706399232000.0, 'train_loss': 1.6253465474446616, 'epoch': 20.0})"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lora_res= lora_trainer.evaluate()\n",
        "lora_res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
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      "outputs": [
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 'positive') is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.\n",
            "  warnings.warn(\n"
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            "text/plain": [
              "{'eval_loss': 1.5894196033477783,\n",
              " 'eval_accuracy': 0.515,\n",
              " 'eval_precision': 0.5324050992104428,\n",
              " 'eval_recall': 0.515,\n",
              " 'eval_f1': 0.46907524323508937,\n",
              " 'eval_runtime': 2.512,\n",
              " 'eval_samples_per_second': 238.849,\n",
              " 'eval_steps_per_second': 29.856,\n",
              " 'epoch': 20.0}"
            ]
          },
          "metadata": {},
          "execution_count": 100
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "JYNt-g-GBA7V"
      },
      "id": "JYNt-g-GBA7V"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Genre Classification with scikit-learn and nltk**"
      ],
      "metadata": {
        "id": "fdDn3zOFFFEX"
      },
      "id": "fdDn3zOFFFEX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "classified-minutes",
      "metadata": {
        "id": "classified-minutes"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import nltk\n",
        "from collections import Counter\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB,MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "foreign-concert",
      "metadata": {
        "id": "foreign-concert",
        "outputId": "ec50a872-44b6-4b12-83a5-86bfb0fdb58e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        {
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            "text/plain": [
              "   Unnamed: 0   book_id                  book_name    genre  \\\n",
              "0           0   3248537          Drowned Wednesday  Fantasy   \n",
              "1           1  27796919              The Lost Hero  Fantasy   \n",
              "2           2   3910776  The Eyes of the Overworld  Fantasy   \n",
              "3           3   5969644            Magic's Promise  Fantasy   \n",
              "4           4   3173445             Taran Wanderer  Fantasy   \n",
              "\n",
              "                                             summary  \n",
              "0   Drowned Wednesday is the first Trustee among ...  \n",
              "1   As the book opens, Jason awakens on a school ...  \n",
              "2   Cugel is easily persuaded by the merchant Fia...  \n",
              "3   The book opens with Herald-Mage Vanyel return...  \n",
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          "metadata": {},
          "execution_count": 39
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      ],
      "source": [
        "# Loading Dataset\n",
        "books = pd.read_csv('BooksDataSet.csv')\n",
        "books.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cooperative-portsmouth",
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            "text/plain": [
              "    book_id                  book_name    genre  \\\n",
              "0   3248537          Drowned Wednesday  Fantasy   \n",
              "1  27796919              The Lost Hero  Fantasy   \n",
              "2   3910776  The Eyes of the Overworld  Fantasy   \n",
              "3   5969644            Magic's Promise  Fantasy   \n",
              "4   3173445             Taran Wanderer  Fantasy   \n",
              "\n",
              "                                             summary  \n",
              "0   Drowned Wednesday is the first Trustee among ...  \n",
              "1   As the book opens, Jason awakens on a school ...  \n",
              "2   Cugel is easily persuaded by the merchant Fia...  \n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f397dcc1-649e-482d-8efd-cc8696c4f63e 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-f397dcc1-649e-482d-8efd-cc8696c4f63e');\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",
              "\n",
              "\n",
              "<div id=\"df-67268229-e0b6-409b-8a56-94578d7cada2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67268229-e0b6-409b-8a56-94578d7cada2')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-67268229-e0b6-409b-8a56-94578d7cada2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Removing the 'Unnamed : 0' column\n",
        "\n",
        "books = books.drop('Unnamed: 0', axis=1)\n",
        "books.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "liquid-athletics",
      "metadata": {
        "id": "liquid-athletics",
        "outputId": "32fd5d4c-a2bd-40ec-b3a4-b820f806f807",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   book_id    3000 non-null   int64 \n",
            " 1   book_name  3000 non-null   object\n",
            " 2   genre      3000 non-null   object\n",
            " 3   summary    3000 non-null   object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 93.9+ KB\n"
          ]
        }
      ],
      "source": [
        "# Summary of the DataFrame's structure and information about its columns.\n",
        "books.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "compliant-height",
      "metadata": {
        "id": "compliant-height",
        "outputId": "f08cee5a-61c3-492c-939b-9f798e503ce8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing the distribution of 'genre' in the 'books' DataFrame.\n",
        "\n",
        "sn.countplot(x=books['genre'], hue=books['genre'], palette='plasma')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "inclusive-motel",
      "metadata": {
        "id": "inclusive-motel",
        "outputId": "af08b0b8-9d37-4d78-e269-5cea8bc33855",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Drowned Wednesday is the first Trustee among the Morrow Days who is on Arthur\\'s side and wishes the Will to be fulfilled. She appears as a leviathan/whale and suffers from Gluttony. The book begins when Leaf is visiting Arthur and they are discussing the invitation that Drowned Wednesday sent him. Arthur had been admitted to hospital because of the damage done to his leg when he attempted to enter Tuesday\\'s Treasure Tower. Suddenly, the hospital room becomes flooded with water as the two are transported to the Border Sea of the House. Leaf is snatched away by a large ship with green sails, known as the Flying Mantis, while Arthur remains in his bed. When the Medallion given him by the immortal called the Mariner apparently fails to summon help, Arthur is without hope. Eventually, a buoy marking the pirate Elishar Feverfew\\'s treasure floats toward him. As soon as Arthur opens it, his hand is marked with a bloody red colour. Arthur now has the Red Hand, by which Feverfew marks whoever has found his treasure, so that he can identify them later. Not long after, a scavenging ship called the Moth rescues Arthur. On board, Arthur (going by the name of Arth) is introduced to Sunscorch, the First Mate, and to Captain Catapillow. Their journey brings them through the Line of Storms and into the Border Sea, where they are later pursued by Feverfew\\'s ghostly ship, the Shiver. The damage inflicted on the Moth is serious; therefore Sunscorch commands an Upper House Sorcerer, Dr. Scamandros, to open a transfer portal to elsewhere in the Secondary Realms. Scamandros claims that Arthur is carrying something that interfered with his magic, and tells Sunscorch to throw him overboard. As a last resort, Arthur shows them the Mariner\\'s Medallion, which stops Scamandros saying that they must get rid of Arthur. After going through the transfer portal (with Arthur\\'s help), the ship is grounded on a beach. When Arthur wished to learn what happened to Leaf, Dr. Scamandros applies his sorcery to make it possible. She is revealed to be aboard the ship Flying Mantis. Arthur joins Catapillow for supper, later to reveal his identity. At first, Propaganda issued by Dame Primus (Arthur\\'s Steward) makes them skeptical of this, but they eventually become convinced. A few days later, Wednesday\\'s Dawn takes Arthur to meet Wednesday for her \\'luncheon of seventeen removes\\'. As they approach, Wednesday shrinks into her human form to meet Arthur. During their lunch, Wednesday tells Arthur that after Part 3 of the Will has been released, she will surrender the Third Key to Arthur. Arthur is then taken by Wednesday\\'s Dawn to a place called the triangle in search of his friend Leaf. He learns that Leaf has been forced to work on the Mantis, but is otherwise intact. Arthur later makes a deal with the Raised Rats, a group of anthropomorphic rats brought to the House by the Piper, to take him to Feverfew\\'s hideout, which they believe is inside a miniature world located within Drowned Wednesday\\'s stomach. On the Raised Rats\\' ship, Arthur opens a gift from Dr. Scamandros, which proves to be a golden transfer watch. With this, he communicates with and rescues Dr. Scamandros. He then uses a scrying mirror Dr. Scamandros gave him to watch Leaf. A rat watches him during the scry, and later saves him from a battle with Feverfew. Later, the Rats bring Arthur onto their submarine, where he meets with Suzy Turquiose Blue. In contrast to Suzy\\'s former cockney attitude, she has assumed a more \"ladylike and proper\" demeanor on the orders of Dame Primus. Only when they are no longer on the Border Sea, but under it, does she resume her customary ways of speech and dress. They are, with navigational difficulty, able to enter the stomach of Lady Wednesday and the worldlet therein. There, Arthur and Suzy, disguised as rats, find escaped slaves, professed followers of the Carp. These exiles take them to the Carp, who is the third part of the Will. They are halted in their attempt to escape by Feverfew, who proposes that each of them will try to kill the other by means of one strike only. Arthur fails his first try, then dodges Feverfew and severs his head. Leaf, who is Feverfew\\'s prisoner, then kicks it into a mud puddle containing Nothing, which consumes it. Upon his death, the worldlet begins to collapse. Via the Moth, Arthur and all his friends (with the exception of the reluctant Catapillow) are able to escape. Lady Wednesday recovers from her gluttony, then dies as a result of being poisoned by the worldlet, which had opened a void to Nothing. Arthur, now Duke of the Border Sea, appoints Sunscorch as his Noon and Scamandros as his Dusk. fr:Mercredi sous les flots th:พุธเพชฌฆาต'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Before cleaning the text, let's see how is the summary\n",
        "books['summary'].iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LQ6wmwGxEgOt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b81cbad-3291-4b95-fc27-80d72de7be49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-ebc4459bf443>:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  books['summary'] = books['summary'].str.replace(r'\\\\', '')\n"
          ]
        }
      ],
      "source": [
        "books['summary'] = books['summary'].str.replace(r'\\\\', '')"
      ],
      "id": "LQ6wmwGxEgOt"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y2NJPqwEEgOu",
        "outputId": "60cd36cf-3841-4d05-f79f-9bfd4c38b421",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Drowned Wednesday is the first Trustee among the Morrow Days who is on Arthur\\'s side and wishes the Will to be fulfilled. She appears as a leviathan/whale and suffers from Gluttony. The book begins when Leaf is visiting Arthur and they are discussing the invitation that Drowned Wednesday sent him. Arthur had been admitted to hospital because of the damage done to his leg when he attempted to enter Tuesday\\'s Treasure Tower. Suddenly, the hospital room becomes flooded with water as the two are transported to the Border Sea of the House. Leaf is snatched away by a large ship with green sails, known as the Flying Mantis, while Arthur remains in his bed. When the Medallion given him by the immortal called the Mariner apparently fails to summon help, Arthur is without hope. Eventually, a buoy marking the pirate Elishar Feverfew\\'s treasure floats toward him. As soon as Arthur opens it, his hand is marked with a bloody red colour. Arthur now has the Red Hand, by which Feverfew marks whoever has found his treasure, so that he can identify them later. Not long after, a scavenging ship called the Moth rescues Arthur. On board, Arthur (going by the name of Arth) is introduced to Sunscorch, the First Mate, and to Captain Catapillow. Their journey brings them through the Line of Storms and into the Border Sea, where they are later pursued by Feverfew\\'s ghostly ship, the Shiver. The damage inflicted on the Moth is serious; therefore Sunscorch commands an Upper House Sorcerer, Dr. Scamandros, to open a transfer portal to elsewhere in the Secondary Realms. Scamandros claims that Arthur is carrying something that interfered with his magic, and tells Sunscorch to throw him overboard. As a last resort, Arthur shows them the Mariner\\'s Medallion, which stops Scamandros saying that they must get rid of Arthur. After going through the transfer portal (with Arthur\\'s help), the ship is grounded on a beach. When Arthur wished to learn what happened to Leaf, Dr. Scamandros applies his sorcery to make it possible. She is revealed to be aboard the ship Flying Mantis. Arthur joins Catapillow for supper, later to reveal his identity. At first, Propaganda issued by Dame Primus (Arthur\\'s Steward) makes them skeptical of this, but they eventually become convinced. A few days later, Wednesday\\'s Dawn takes Arthur to meet Wednesday for her \\'luncheon of seventeen removes\\'. As they approach, Wednesday shrinks into her human form to meet Arthur. During their lunch, Wednesday tells Arthur that after Part 3 of the Will has been released, she will surrender the Third Key to Arthur. Arthur is then taken by Wednesday\\'s Dawn to a place called the triangle in search of his friend Leaf. He learns that Leaf has been forced to work on the Mantis, but is otherwise intact. Arthur later makes a deal with the Raised Rats, a group of anthropomorphic rats brought to the House by the Piper, to take him to Feverfew\\'s hideout, which they believe is inside a miniature world located within Drowned Wednesday\\'s stomach. On the Raised Rats\\' ship, Arthur opens a gift from Dr. Scamandros, which proves to be a golden transfer watch. With this, he communicates with and rescues Dr. Scamandros. He then uses a scrying mirror Dr. Scamandros gave him to watch Leaf. A rat watches him during the scry, and later saves him from a battle with Feverfew. Later, the Rats bring Arthur onto their submarine, where he meets with Suzy Turquiose Blue. In contrast to Suzy\\'s former cockney attitude, she has assumed a more \"ladylike and proper\" demeanor on the orders of Dame Primus. Only when they are no longer on the Border Sea, but under it, does she resume her customary ways of speech and dress. They are, with navigational difficulty, able to enter the stomach of Lady Wednesday and the worldlet therein. There, Arthur and Suzy, disguised as rats, find escaped slaves, professed followers of the Carp. These exiles take them to the Carp, who is the third part of the Will. They are halted in their attempt to escape by Feverfew, who proposes that each of them will try to kill the other by means of one strike only. Arthur fails his first try, then dodges Feverfew and severs his head. Leaf, who is Feverfew\\'s prisoner, then kicks it into a mud puddle containing Nothing, which consumes it. Upon his death, the worldlet begins to collapse. Via the Moth, Arthur and all his friends (with the exception of the reluctant Catapillow) are able to escape. Lady Wednesday recovers from her gluttony, then dies as a result of being poisoned by the worldlet, which had opened a void to Nothing. Arthur, now Duke of the Border Sea, appoints Sunscorch as his Noon and Scamandros as his Dusk. fr:Mercredi sous les flots th:พุธเพชฌฆาต'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "books['summary'].iloc[0]"
      ],
      "id": "y2NJPqwEEgOu"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nh1nEPQFEgOv"
      },
      "source": [
        "'\\\\' is an escape character however let's clean it."
      ],
      "id": "Nh1nEPQFEgOv"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "gothic-quick",
      "metadata": {
        "id": "gothic-quick",
        "outputId": "14b29153-1b16-4bd7-c4cf-d486333dd128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drowned wednesday is the first trustee among the morrow days who is on arthur s side and wishes the will to be fulfilled she appears as a leviathan whale and suffers from gluttony the book begins when leaf is visiting arthur and they are discussing the invitation that drowned wednesday sent him arthur had been admitted to hospital because of the damage done to his leg when he attempted to enter tuesday s treasure tower suddenly the hospital room becomes flooded with water as the two are transported to the border sea of the house leaf is snatched away by a large ship with green sails known as the flying mantis while arthur remains in his bed when the medallion given him by the immortal called the mariner apparently fails to summon help arthur is without hope eventually a buoy marking the pirate elishar feverfew s treasure floats toward him as soon as arthur opens it his hand is marked with a bloody red colour arthur now has the red hand by which feverfew marks whoever has found his treasure so that he can identify them later not long after a scavenging ship called the moth rescues arthur on board arthur going by the name of arth is introduced to sunscorch the first mate and to captain catapillow their journey brings them through the line of storms and into the border sea where they are later pursued by feverfew s ghostly ship the shiver the damage inflicted on the moth is serious therefore sunscorch commands an upper house sorcerer dr scamandros to open a transfer portal to elsewhere in the secondary realms scamandros claims that arthur is carrying something that interfered with his magic and tells sunscorch to throw him overboard as a last resort arthur shows them the mariner s medallion which stops scamandros saying that they must get rid of arthur after going through the transfer portal with arthur s help the ship is grounded on a beach when arthur wished to learn what happened to leaf dr scamandros applies his sorcery to make it possible she is revealed to be aboard the ship flying mantis arthur joins catapillow for supper later to reveal his identity at first propaganda issued by dame primus arthur s steward makes them skeptical of this but they eventually become convinced a few days later wednesday s dawn takes arthur to meet wednesday for her luncheon of seventeen removes as they approach wednesday shrinks into her human form to meet arthur during their lunch wednesday tells arthur that after part of the will has been released she will surrender the third key to arthur arthur is then taken by wednesday s dawn to a place called the triangle in search of his friend leaf he learns that leaf has been forced to work on the mantis but is otherwise intact arthur later makes a deal with the raised rats a group of anthropomorphic rats brought to the house by the piper to take him to feverfew s hideout which they believe is inside a miniature world located within drowned wednesday s stomach on the raised rats ship arthur opens a gift from dr scamandros which proves to be a golden transfer watch with this he communicates with and rescues dr scamandros he then uses a scrying mirror dr scamandros gave him to watch leaf a rat watches him during the scry and later saves him from a battle with feverfew later the rats bring arthur onto their submarine where he meets with suzy turquiose blue in contrast to suzy s former cockney attitude she has assumed a more ladylike and proper demeanor on the orders of dame primus only when they are no longer on the border sea but under it does she resume her customary ways of speech and dress they are with navigational difficulty able to enter the stomach of lady wednesday and the worldlet therein there arthur and suzy disguised as rats find escaped slaves professed followers of the carp these exiles take them to the carp who is the third part of the will they are halted in their attempt to escape by feverfew who proposes that each of them will try to kill the other by means of one strike only arthur fails his first try then dodges feverfew and severs his head leaf who is feverfew s prisoner then kicks it into a mud puddle containing nothing which consumes it upon his death the worldlet begins to collapse via the moth arthur and all his friends with the exception of the reluctant catapillow are able to escape lady wednesday recovers from her gluttony then dies as a result of being poisoned by the worldlet which had opened a void to nothing arthur now duke of the border sea appoints sunscorch as his noon and scamandros as his dusk fr mercredi sous les flots th'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "## cleaning the text\n",
        "\n",
        "def cleantext(text):\n",
        "\n",
        "    # removing the \"\\\" (most of the times '\\' is an escape character)\n",
        "    text = re.sub(\"'\\'\",\"\",text)\n",
        "\n",
        "    # removing special symbols\n",
        "    text = re.sub(\"[^a-zA-Z]\",\" \",text)\n",
        "\n",
        "    # removing the unnecesary whitespaces\n",
        "    text = ' '.join(text.split())\n",
        "\n",
        "    # convert text to lowercase\n",
        "    text = text.lower()\n",
        "\n",
        "    return text\n",
        "\n",
        "\n",
        "books['summary'] = books['summary'].apply(lambda x:cleantext(x))\n",
        "books['summary'].iloc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "searching-startup",
      "metadata": {
        "id": "searching-startup"
      },
      "source": [
        "#### Plotting the most Frequent Words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GfcX23tGEgO3",
        "outputId": "d2a7b9e7-a41a-4011-cadf-936d241de1da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FreqDist({'the': 54, 'to': 34, 'arthur': 28, 'a': 22, 'of': 20, 'is': 16, 'and': 15, 'his': 14, 's': 13, 'with': 12, ...})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "text = ['drowned wednesday is the first trustee among the morrow days', 'who is on arthur s side and wishes the will to be fulfilled she appears as a leviathan whale and suffers from gluttony the book begins when leaf is visiting arthur and they are discussing the invitation that drowned wednesday sent him arthur had been admitted to hospital because of the damage done to his leg when he attempted to enter tuesday s treasure tower suddenly the hospital room becomes flooded with water as the two are transported to the border sea of the house leaf is snatched away by a large ship with green sails known as the flying mantis while arthur remains in his bed when the medallion given him by the immortal called the mariner apparently fails to summon help arthur is without hope eventually a buoy marking the pirate elishar feverfew s treasure floats toward him as soon as arthur opens it his hand is marked with a bloody red colour arthur now has the red hand by which feverfew marks whoever has found his treasure so that he can identify them later not long after a scavenging ship called the moth rescues arthur on board arthur going by the name of arth is introduced to sunscorch the first mate and to captain catapillow their journey brings them through the line of storms and into the border sea where they are later pursued by feverfew s ghostly ship the shiver the damage inflicted on the moth is serious therefore sunscorch commands an upper house sorcerer dr scamandros to open a transfer portal to elsewhere in the secondary realms scamandros claims that arthur is carrying something that interfered with his magic and tells sunscorch to throw him overboard as a last resort arthur shows them the mariner s medallion which stops scamandros saying that they must get rid of arthur after going through the transfer portal with arthur s help the ship is grounded on a beach when arthur wished to learn what happened to leaf dr scamandros applies his sorcery to make it possible she is revealed to be aboard the ship flying mantis arthur joins catapillow for supper later to reveal his identity at first propaganda issued by dame primus arthur s steward makes them skeptical of this but they eventually become convinced a few days later wednesday s dawn takes arthur to meet wednesday for her luncheon of seventeen removes as they approach wednesday shrinks into her human form to meet arthur during their lunch wednesday tells arthur that after part of the will has been released she will surrender the third key to arthur arthur is then taken by wednesday s dawn to a place called the triangle in search of his friend leaf he learns that leaf has been forced to work on the mantis but is otherwise intact arthur later makes a deal with the raised rats a group of anthropomorphic rats brought to the house by the piper to take him to feverfew s hideout which they believe is inside a miniature world located within drowned wednesday s stomach on the raised rats ship arthur opens a gift from dr scamandros which proves to be a golden transfer watch with this he communicates with and rescues dr scamandros he then uses a scrying mirror dr scamandros gave him to watch leaf a rat watches him during the scry and later saves him from a battle with feverfew later the rats bring arthur onto their submarine where he meets with suzy turquiose blue in contrast to suzy s former cockney attitude she has assumed a more ladylike and proper demeanor on the orders of dame primus only when they are no longer on the border sea but under it does she resume her customary ways of speech and dress they are with navigational difficulty able to enter the stomach of lady wednesday and the worldlet therein there arthur and suzy disguised as rats find escaped slaves professed followers of the carp these exiles take them to the carp who is the third part of the will they are halted in their attempt to escape by feverfew who proposes that each of them will try to kill the other by means of one strike only arthur fails his first try then dodges feverfew and severs his head leaf who is feverfew s prisoner then kicks it into a mud puddle containing nothing which consumes it upon his death the worldlet begins to collapse via the moth arthur and all his friends with the exception of the reluctant catapillow are able to escape lady wednesday recovers from her gluttony then dies as a result of being poisoned by the worldlet which had opened a void to nothing arthur now duke of the border sea appoints sunscorch as his noon and scamandros as his dusk fr mercredi sous les flots th']\n",
        "nltk.FreqDist(' '.join([char for char in text]).split())"
      ],
      "id": "GfcX23tGEgO3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "beautiful-junction",
      "metadata": {
        "scrolled": true,
        "id": "beautiful-junction",
        "outputId": "41a2dfdc-2cf7-4e8a-85c5-b0e47aa9c275",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def showmostfrequentwords(text,no_of_words):\n",
        "\n",
        "    allwords = ' '.join([char for char in text])\n",
        "    allwords = allwords.split()\n",
        "    fdist = nltk.FreqDist(allwords)\n",
        "\n",
        "    wordsdf = pd.DataFrame({'word':list(fdist.keys()),'count':list(fdist.values())})\n",
        "\n",
        "    df = wordsdf.nlargest(columns=\"count\",n = no_of_words)\n",
        "\n",
        "    plt.figure(figsize=(7,5))\n",
        "    ax = sn.barplot(data=df,x = 'count',y = 'word')\n",
        "    ax.set(ylabel = 'Word')\n",
        "    plt.show()\n",
        "\n",
        "    return wordsdf\n",
        "\n",
        "\n",
        "# 25 most frequent words\n",
        "\n",
        "wordsdf = showmostfrequentwords(books['summary'],25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "confident-mentor",
      "metadata": {
        "id": "confident-mentor",
        "outputId": "61f464b3-8eb5-4008-ffb6-f451f889a1f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d379e1cb670>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_aba8f_row0_col1 {\n",
              "  background-color: #f0f921;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_aba8f_row1_col1 {\n",
              "  background-color: #b02991;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row2_col1 {\n",
              "  background-color: #a62098;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row3_col1 {\n",
              "  background-color: #8004a8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row4_col1 {\n",
              "  background-color: #7a02a8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row5_col1 {\n",
              "  background-color: #3e049c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row6_col1 {\n",
              "  background-color: #38049a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row7_col1 {\n",
              "  background-color: #130789;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row8_col1 {\n",
              "  background-color: #100788;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_aba8f_row9_col1 {\n",
              "  background-color: #0d0887;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_aba8f\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_aba8f_level0_col0\" class=\"col_heading level0 col0\" >word</th>\n",
              "      <th id=\"T_aba8f_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
              "      <td id=\"T_aba8f_row0_col0\" class=\"data row0 col0\" >the</td>\n",
              "      <td id=\"T_aba8f_row0_col1\" class=\"data row0 col1\" >99808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row1\" class=\"row_heading level0 row1\" >17</th>\n",
              "      <td id=\"T_aba8f_row1_col0\" class=\"data row1 col0\" >to</td>\n",
              "      <td id=\"T_aba8f_row1_col1\" class=\"data row1 col1\" >49665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
              "      <td id=\"T_aba8f_row2_col0\" class=\"data row2 col0\" >and</td>\n",
              "      <td id=\"T_aba8f_row2_col1\" class=\"data row2 col1\" >47183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row3\" class=\"row_heading level0 row3\" >46</th>\n",
              "      <td id=\"T_aba8f_row3_col0\" class=\"data row3 col0\" >of</td>\n",
              "      <td id=\"T_aba8f_row3_col1\" class=\"data row3 col1\" >38003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row4\" class=\"row_heading level0 row4\" >23</th>\n",
              "      <td id=\"T_aba8f_row4_col0\" class=\"data row4 col0\" >a</td>\n",
              "      <td id=\"T_aba8f_row4_col1\" class=\"data row4 col1\" >36749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row5\" class=\"row_heading level0 row5\" >80</th>\n",
              "      <td id=\"T_aba8f_row5_col0\" class=\"data row5 col0\" >in</td>\n",
              "      <td id=\"T_aba8f_row5_col1\" class=\"data row5 col1\" >24496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row6\" class=\"row_heading level0 row6\" >2</th>\n",
              "      <td id=\"T_aba8f_row6_col0\" class=\"data row6 col0\" >is</td>\n",
              "      <td id=\"T_aba8f_row6_col1\" class=\"data row6 col1\" >23358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row7\" class=\"row_heading level0 row7\" >49</th>\n",
              "      <td id=\"T_aba8f_row7_col0\" class=\"data row7 col0\" >his</td>\n",
              "      <td id=\"T_aba8f_row7_col1\" class=\"data row7 col1\" >17464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row8\" class=\"row_heading level0 row8\" >51</th>\n",
              "      <td id=\"T_aba8f_row8_col0\" class=\"data row8 col0\" >he</td>\n",
              "      <td id=\"T_aba8f_row8_col1\" class=\"data row8 col1\" >17210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_aba8f_level0_row9\" class=\"row_heading level0 row9\" >12</th>\n",
              "      <td id=\"T_aba8f_row9_col0\" class=\"data row9 col0\" >s</td>\n",
              "      <td id=\"T_aba8f_row9_col1\" class=\"data row9 col1\" >16709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "wordsdf.sort_values('count',ascending=False).head(10).style.background_gradient(cmap='plasma')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "contrary-doctrine",
      "metadata": {
        "id": "contrary-doctrine"
      },
      "source": [
        "### Removing the stopwords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "convinced-rebound",
      "metadata": {
        "collapsed": true,
        "id": "convinced-rebound",
        "outputId": "d51a8316-6537-4b69-d8fe-700c3c0e6ab0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'book opens jason awakens school bus unable remember anything past sitting next piper mclean leo valdez call name say girlfriend best friend respectively three part class field trip grand canyon arrive classmate dylan turns venti storm spirit attacks trio trip leader coach gleeson hedge ensuing fight jason surprises everyone including one coins turns sword uses battle storm spirits coach hedge reveals satyr fight taken captive fleeing spirit battle flying chariot arrives rescue trio one people annabeth upset discovers missing boyfriend percy jackson expected annabeth seeking percy told vision goddess hera look guy one shoe turns jason shoe destroyed fight jason piper leo told demigods taken back camp half blood meet greek demigod children like leo revealed son hephaestus piper daughter aphrodite jason son zeus though hera tells champion jason later discovers full brother zeus demigod daughter thalia grace hunter artemis shortly arrive three given quest rescue hera captured set soon discover enemies working orders gaea overthrow gods quest encounter thalia hunters looking percy thalia jason reunite first since jason captured age two way aeolus castle jason leo piper become separated thalia promises meet wolf house last place thalia seen jason meeting nearly apprehended aeolus gaea orders trio manage escape thanks mellie aeolus former assistant end san francisco thanks result dream piper aphrodite landing san francisco trio rush mt diablo fight giant enceladus kidnapped piper father manage kill giant save piper father rush wolf house free hera although heroes hunters save hera king giants porphyrion rises fully disappears hole earth jason memory starts returning remembers hero roman counterpart camp half blood somewhere near san francisco son jupiter zeus roman aspect realizes hera also known juno switched percy jackson roman camp memory life hopes two camps would ultimately work together fight giants defeat goddess gaea'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# removing the stopwords\n",
        "def removestopwords(text):\n",
        "\n",
        "    removedstopword = [word for word in text.split() if word not in stop_words]\n",
        "    return ' '.join(removedstopword)\n",
        "\n",
        "books['summary'] = books['summary'].apply(lambda x:removestopwords(x))\n",
        "books['summary'].iloc[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "efficient-purple",
      "metadata": {
        "id": "efficient-purple"
      },
      "source": [
        "## Lemmatizing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "valued-gather",
      "metadata": {
        "id": "valued-gather",
        "outputId": "f2f2cf07-f214-4e6a-d165-0cedecc0c890",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "lemma=WordNetLemmatizer()\n",
        "\n",
        "def lematizing(sentence):\n",
        "    stemSentence = \"\"\n",
        "    for word in sentence.split():\n",
        "        stem = lemma.lemmatize(word)\n",
        "        stemSentence += stem\n",
        "        stemSentence += \" \"\n",
        "    stemSentence = stemSentence.strip()\n",
        "    return stemSentence\n",
        "\n",
        "\n",
        "books['summary'] = books['summary'].apply(lambda x: lematizing(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qdaxhsp6EgO-",
        "outputId": "1d9608e7-60c7-4ab2-9e3a-00bccaec1c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drowned wednesday first trustee among morrow day arthur side wish fulfilled appears leviathan whale suffers gluttony book begin leaf visiting arthur discussing invitation drowned wednesday sent arthur admitted hospital damage done leg attempted enter tuesday treasure tower suddenly hospital room becomes flooded water two transported border sea house leaf snatched away large ship green sail known flying mantis arthur remains bed medallion given immortal called mariner apparently fails summon help arthur without hope eventually buoy marking pirate elishar feverfew treasure float toward soon arthur open hand marked bloody red colour arthur red hand feverfew mark whoever found treasure identify later long scavenging ship called moth rescue arthur board arthur going name arth introduced sunscorch first mate captain catapillow journey brings line storm border sea later pursued feverfew ghostly ship shiver damage inflicted moth serious therefore sunscorch command upper house sorcerer dr scamandros open transfer portal elsewhere secondary realm scamandros claim arthur carrying something interfered magic tell sunscorch throw overboard last resort arthur show mariner medallion stop scamandros saying must get rid arthur going transfer portal arthur help ship grounded beach arthur wished learn happened leaf dr scamandros applies sorcery make possible revealed aboard ship flying mantis arthur join catapillow supper later reveal identity first propaganda issued dame primus arthur steward make skeptical eventually become convinced day later wednesday dawn take arthur meet wednesday luncheon seventeen remove approach wednesday shrink human form meet arthur lunch wednesday tell arthur part released surrender third key arthur arthur taken wednesday dawn place called triangle search friend leaf learns leaf forced work mantis otherwise intact arthur later make deal raised rat group anthropomorphic rat brought house piper take feverfew hideout believe inside miniature world located within drowned wednesday stomach raised rat ship arthur open gift dr scamandros prof golden transfer watch communicates rescue dr scamandros us scrying mirror dr scamandros gave watch leaf rat watch scry later save battle feverfew later rat bring arthur onto submarine meet suzy turquiose blue contrast suzy former cockney attitude assumed ladylike proper demeanor order dame primus longer border sea resume customary way speech dress navigational difficulty able enter stomach lady wednesday worldlet therein arthur suzy disguised rat find escaped slave professed follower carp exile take carp third part halted attempt escape feverfew proposes try kill mean one strike arthur fails first try dodge feverfew severs head leaf feverfew prisoner kick mud puddle containing nothing consumes upon death worldlet begin collapse via moth arthur friend exception reluctant catapillow able escape lady wednesday recovers gluttony dy result poisoned worldlet opened void nothing arthur duke border sea appoints sunscorch noon scamandros dusk fr mercredi sou le flots th'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "books['summary'][0]"
      ],
      "id": "qdaxhsp6EgO-"
    },
    {
      "cell_type": "markdown",
      "id": "freelance-property",
      "metadata": {
        "id": "freelance-property"
      },
      "source": [
        "## Stemming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "excellent-acoustic",
      "metadata": {
        "id": "excellent-acoustic",
        "outputId": "0afebe34-5456-49ff-8cb8-c1b30d2b8d89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drown wednesday first truste among morrow day arthur side wish fulfil appear leviathan whale suffer gluttoni book begin leaf visit arthur discuss invit drown wednesday sent arthur admit hospit damag done leg attempt enter tuesday treasur tower suddenli hospit room becom flood water two transport border sea hous leaf snatch away larg ship green sail known fli manti arthur remain bed medallion given immort call marin appar fail summon help arthur without hope eventu buoy mark pirat elishar feverfew treasur float toward soon arthur open hand mark bloodi red colour arthur red hand feverfew mark whoever found treasur identifi later long scaveng ship call moth rescu arthur board arthur go name arth introduc sunscorch first mate captain catapillow journey bring line storm border sea later pursu feverfew ghostli ship shiver damag inflict moth seriou therefor sunscorch command upper hous sorcer dr scamandro open transfer portal elsewher secondari realm scamandro claim arthur carri someth interf magic tell sunscorch throw overboard last resort arthur show marin medallion stop scamandro say must get rid arthur go transfer portal arthur help ship ground beach arthur wish learn happen leaf dr scamandro appli sorceri make possibl reveal aboard ship fli manti arthur join catapillow supper later reveal ident first propaganda issu dame primu arthur steward make skeptic eventu becom convinc day later wednesday dawn take arthur meet wednesday luncheon seventeen remov approach wednesday shrink human form meet arthur lunch wednesday tell arthur part releas surrend third key arthur arthur taken wednesday dawn place call triangl search friend leaf learn leaf forc work manti otherwis intact arthur later make deal rais rat group anthropomorph rat brought hous piper take feverfew hideout believ insid miniatur world locat within drown wednesday stomach rais rat ship arthur open gift dr scamandro prof golden transfer watch commun rescu dr scamandro us scri mirror dr scamandro gave watch leaf rat watch scri later save battl feverfew later rat bring arthur onto submarin meet suzi turquios blue contrast suzi former cockney attitud assum ladylik proper demeanor order dame primu longer border sea resum customari way speech dress navig difficulti abl enter stomach ladi wednesday worldlet therein arthur suzi disguis rat find escap slave profess follow carp exil take carp third part halt attempt escap feverfew propos tri kill mean one strike arthur fail first tri dodg feverfew sever head leaf feverfew prison kick mud puddl contain noth consum upon death worldlet begin collaps via moth arthur friend except reluct catapillow abl escap ladi wednesday recov gluttoni dy result poison worldlet open void noth arthur duke border sea appoint sunscorch noon scamandro dusk fr mercredi sou le flot th'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "stemmer = PorterStemmer()\n",
        "\n",
        "def stemming(sentence):\n",
        "\n",
        "    stemmed_sentence = \"\"\n",
        "    for word in sentence.split():\n",
        "        stem = stemmer.stem(word)\n",
        "        stemmed_sentence+=stem\n",
        "        stemmed_sentence+=\" \"\n",
        "\n",
        "    stemmed_sentence = stemmed_sentence.strip()\n",
        "    return stemmed_sentence\n",
        "\n",
        "books['summary'] = books['summary'].apply(lambda text:stemming(text))\n",
        "books['summary'].iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "little-morrison",
      "metadata": {
        "id": "little-morrison",
        "outputId": "c3f21610-e12e-436d-8f1e-98e72fd3d138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualising Frequent Words\n",
        "\n",
        "freq_df = showmostfrequentwords(books['summary'],25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "simple-waters",
      "metadata": {
        "id": "simple-waters",
        "outputId": "c4ae160e-8760-4d93-d341-a97aa8d9259c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d377eb95900>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_fc327_row0_col1 {\n",
              "  background-color: #f0f921;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_fc327_row1_col1 {\n",
              "  background-color: #fdab33;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_fc327_row2_col1 {\n",
              "  background-color: #f58b47;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row3_col1 {\n",
              "  background-color: #a01a9c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row4_col1 {\n",
              "  background-color: #7801a8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row5_col1 {\n",
              "  background-color: #6c00a8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row6_col1 {\n",
              "  background-color: #280592;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row7_col1 {\n",
              "  background-color: #240691;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row8_col1 {\n",
              "  background-color: #16078a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_fc327_row9_col1 {\n",
              "  background-color: #0d0887;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_fc327\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_fc327_level0_col0\" class=\"col_heading level0 col0\" >word</th>\n",
              "      <th id=\"T_fc327_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row0\" class=\"row_heading level0 row0\" >242</th>\n",
              "      <td id=\"T_fc327_row0_col0\" class=\"data row0 col0\" >one</td>\n",
              "      <td id=\"T_fc327_row0_col1\" class=\"data row0 col1\" >3858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row1\" class=\"row_heading level0 row1\" >230</th>\n",
              "      <td id=\"T_fc327_row1_col0\" class=\"data row1 col0\" >find</td>\n",
              "      <td id=\"T_fc327_row1_col1\" class=\"data row1 col1\" >3533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row2\" class=\"row_heading level0 row2\" >240</th>\n",
              "      <td id=\"T_fc327_row2_col0\" class=\"data row2 col0\" >kill</td>\n",
              "      <td id=\"T_fc327_row2_col1\" class=\"data row2 col1\" >3380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row3\" class=\"row_heading level0 row3\" >153</th>\n",
              "      <td id=\"T_fc327_row3_col0\" class=\"data row3 col0\" >take</td>\n",
              "      <td id=\"T_fc327_row3_col1\" class=\"data row3 col1\" >2743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row4\" class=\"row_heading level0 row4\" >1143</th>\n",
              "      <td id=\"T_fc327_row4_col0\" class=\"data row4 col0\" >time</td>\n",
              "      <td id=\"T_fc327_row4_col1\" class=\"data row4 col1\" >2563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row5\" class=\"row_heading level0 row5\" >88</th>\n",
              "      <td id=\"T_fc327_row5_col0\" class=\"data row5 col0\" >go</td>\n",
              "      <td id=\"T_fc327_row5_col1\" class=\"data row5 col1\" >2509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row6\" class=\"row_heading level0 row6\" >38</th>\n",
              "      <td id=\"T_fc327_row6_col0\" class=\"data row6 col0\" >two</td>\n",
              "      <td id=\"T_fc327_row6_col1\" class=\"data row6 col1\" >2235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row7\" class=\"row_heading level0 row7\" >418</th>\n",
              "      <td id=\"T_fc327_row7_col0\" class=\"data row7 col0\" >life</td>\n",
              "      <td id=\"T_fc327_row7_col1\" class=\"data row7 col1\" >2224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row8\" class=\"row_heading level0 row8\" >415</th>\n",
              "      <td id=\"T_fc327_row8_col0\" class=\"data row8 col0\" >also</td>\n",
              "      <td id=\"T_fc327_row8_col1\" class=\"data row8 col1\" >2179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_fc327_level0_row9\" class=\"row_heading level0 row9\" >407</th>\n",
              "      <td id=\"T_fc327_row9_col0\" class=\"data row9 col0\" >return</td>\n",
              "      <td id=\"T_fc327_row9_col1\" class=\"data row9 col1\" >2158</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "freq_df.sort_values('count',ascending=False).head(10).style.background_gradient(cmap = 'plasma')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acute-membrane",
      "metadata": {
        "id": "acute-membrane"
      },
      "source": [
        "##  Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cultural-compiler",
      "metadata": {
        "id": "cultural-compiler",
        "outputId": "4be9ff10-1aae-4dda-f69c-9e513a4a109a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Fantasy': 0, 'Science Fiction': 1, 'Crime Fiction': 2, 'Historical novel': 3, 'Horror': 4, 'Thriller': 5}\n"
          ]
        }
      ],
      "source": [
        "books_list = list(books['genre'].unique())\n",
        "encode = [i for i in range(len(books_list))]\n",
        "mapper = dict(zip(books_list,encode))\n",
        "print(mapper)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "abroad-liberia",
      "metadata": {
        "id": "abroad-liberia",
        "outputId": "13598dec-5166-420f-e3d9-858d79a85f41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "books['genre'] = books['genre'].map(mapper)\n",
        "books['genre'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "mediterranean-owner",
      "metadata": {
        "id": "mediterranean-owner"
      },
      "source": [
        "## Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "municipal-farmer",
      "metadata": {
        "scrolled": true,
        "id": "municipal-farmer",
        "outputId": "fd323acf-6664-4a68-eb06-3bf899d0aaa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<3000x1000 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 245415 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "## count vectorizer\n",
        "\n",
        "count_vec = CountVectorizer(max_df=0.90,\n",
        "                            min_df=0.0,\n",
        "                            max_features=1000,\n",
        "                            stop_words='english')\n",
        "\n",
        "bagofword_vec = count_vec.fit_transform(books['summary'])\n",
        "bagofword_vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "federal-glance",
      "metadata": {
        "id": "federal-glance",
        "outputId": "60a0d2f9-4fb5-4f4b-a07b-0c160266c881",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2400, 1000), (600, 1000))"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(bagofword_vec,\n",
        "                                                    books['genre'],\n",
        "                                                    test_size=0.2)\n",
        "X_train.shape,X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mlh65843EgPE"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ],
      "id": "mlh65843EgPE"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "handled-rolling",
      "metadata": {
        "id": "handled-rolling",
        "outputId": "8b3e8273-8e85-409e-eb76-0491c69a097f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5733333333333334\n"
          ]
        }
      ],
      "source": [
        "svc = SVC()\n",
        "svc.fit(X_train,y_train)\n",
        "svccpred = svc.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test,svccpred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "trained-peeing",
      "metadata": {
        "id": "trained-peeing",
        "outputId": "979e69a8-6007-4c63-9f84-b38d777fa6dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6416666666666667\n"
          ]
        }
      ],
      "source": [
        "mb = MultinomialNB()\n",
        "mb.fit(X_train,y_train)\n",
        "mbpred = mb.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test,mbpred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "medium-peeing",
      "metadata": {
        "id": "medium-peeing",
        "outputId": "35de113d-441a-4547-9481-893394102aad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6116666666666667\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train,y_train)\n",
        "print(metrics.accuracy_score(y_test,rf.predict(X_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "theoretical-indicator",
      "metadata": {
        "id": "theoretical-indicator"
      },
      "source": [
        "## Part 2 Model Building\n",
        "\n",
        " + Changing from Countvectorizer to TFDIF vectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JEtaceLnEgPG",
        "outputId": "6c84994f-e3b0-42d5-f14d-b263186517f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0   book_id                  book_name    genre  \\\n",
              "0           0   3248537          Drowned Wednesday  Fantasy   \n",
              "1           1  27796919              The Lost Hero  Fantasy   \n",
              "2           2   3910776  The Eyes of the Overworld  Fantasy   \n",
              "3           3   5969644            Magic's Promise  Fantasy   \n",
              "4           4   3173445             Taran Wanderer  Fantasy   \n",
              "\n",
              "                                             summary  \n",
              "0   Drowned Wednesday is the first Trustee among ...  \n",
              "1   As the book opens, Jason awakens on a school ...  \n",
              "2   Cugel is easily persuaded by the merchant Fia...  \n",
              "3   The book opens with Herald-Mage Vanyel return...  \n",
              "4   Taran and Gurgi have returned to Caer Dallben...  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-fec85850-1115-47e3-9bb5-d0d18ba6b3dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-fec85850-1115-47e3-9bb5-d0d18ba6b3dc');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69ab39af-26fc-422f-a495-e420ec9d92e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Loading Dataset\n",
        "books_new = pd.read_csv('BooksDataSet.csv')\n",
        "books_new.head()"
      ],
      "id": "JEtaceLnEgPG"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "comparative-shuttle",
      "metadata": {
        "id": "comparative-shuttle",
        "outputId": "74d49352-a89e-4b02-a254-00ef9fa926b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2400,), (600,))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "#Labeling each 'genre' with a unique number\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y=le.fit_transform(books['genre'])\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(books['summary'],\n",
        "                                                 y,\n",
        "                                                 test_size=0.2,\n",
        "                                                 random_state=42)\n",
        "\n",
        "X_train.shape,X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "connected-coach",
      "metadata": {
        "id": "connected-coach"
      },
      "outputs": [],
      "source": [
        "#Performing tf-idf\n",
        "\n",
        "tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=10000)\n",
        "xtrain_tfidf = tfidf_vectorizer.fit_transform(X_train.values.astype('U'))\n",
        "xtest_tfidf = tfidf_vectorizer.transform(X_test.values.astype('U'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "swedish-generator",
      "metadata": {
        "id": "swedish-generator",
        "outputId": "5094e043-d855-427b-d67d-00df758cd869",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7216666666666667\n"
          ]
        }
      ],
      "source": [
        "svc = SVC()\n",
        "svc.fit(xtrain_tfidf,y_train)\n",
        "svccpred = svc.predict(xtest_tfidf)\n",
        "print(metrics.accuracy_score(y_test,svccpred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "intended-framing",
      "metadata": {
        "id": "intended-framing",
        "outputId": "e539fb6b-031a-466b-b8c5-edb3118b2eaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7133333333333334\n"
          ]
        }
      ],
      "source": [
        "mb = MultinomialNB()\n",
        "mb.fit(xtrain_tfidf,y_train)\n",
        "mbpred = mb.predict(xtest_tfidf)\n",
        "print(metrics.accuracy_score(y_test,mbpred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ULsbzHpjEgPJ",
        "outputId": "40a163eb-1c09-49f0-b583-d391f61c6996",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6283333333333333\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier()\n",
        "rf.fit(xtrain_tfidf,y_train)\n",
        "rfpred = rf.predict(xtest_tfidf)\n",
        "print(metrics.accuracy_score(y_test,rfpred))"
      ],
      "id": "ULsbzHpjEgPJ"
    },
    {
      "cell_type": "markdown",
      "id": "ultimate-worcester",
      "metadata": {
        "id": "ultimate-worcester"
      },
      "source": [
        "## Testing the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "shared-naples",
      "metadata": {
        "id": "shared-naples",
        "outputId": "59e99555-5fe2-45b4-e4ff-fcf94b3ddfe8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       [3]\n",
              "1       [0]\n",
              "2       [0]\n",
              "3       [0]\n",
              "4       [0]\n",
              "       ... \n",
              "2995    [5]\n",
              "2996    [5]\n",
              "2997    [5]\n",
              "2998    [5]\n",
              "2999    [2]\n",
              "Name: summary, Length: 3000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "def test(text,model):\n",
        "\n",
        "    text = cleantext(text)\n",
        "    text = removestopwords(text)\n",
        "    text = lematizing(text)\n",
        "    text = stemming(text)\n",
        "\n",
        "    text_vector = tfidf_vectorizer.transform([text])\n",
        "    predicted = model.predict(text_vector)\n",
        "    return predicted\n",
        "\n",
        "\n",
        "ans = books['summary'].apply(lambda text:test(text,mb))\n",
        "\n",
        "# printing the\n",
        "# print(list(mapper.keys())[list(mapper.values()).index(ans)])\n",
        "\n",
        "ans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "challenging-texture",
      "metadata": {
        "id": "challenging-texture"
      },
      "outputs": [],
      "source": [
        "predicted_genres = []\n",
        "for i in range(len(ans)):\n",
        "\n",
        "    index_val = ans[i][0]\n",
        "    predicted_genres.append(list(mapper.keys())[list(mapper.values()).index(index_val)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "outstanding-current",
      "metadata": {
        "id": "outstanding-current",
        "outputId": "236601ea-5b1a-4cdd-f2a5-e8f8823bc981",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Fantasy': 0,\n",
              " 'Science Fiction': 1,\n",
              " 'Crime Fiction': 2,\n",
              " 'Historical novel': 3,\n",
              " 'Horror': 4,\n",
              " 'Thriller': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "mapper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "stable-publisher",
      "metadata": {
        "scrolled": true,
        "id": "stable-publisher",
        "outputId": "c1e8faf4-ebea-468c-f80e-ddf47adaa356",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Fantasy',\n",
              " 1: 'Science Fiction',\n",
              " 2: 'Crime Fiction',\n",
              " 3: 'Historical novel',\n",
              " 4: 'Horror',\n",
              " 5: 'Thriller'}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "## mapping the training genre as well\n",
        "newmap = dict([(value,key) for key,value in mapper.items()])\n",
        "newmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "quick-illinois",
      "metadata": {
        "id": "quick-illinois",
        "outputId": "3f750a90-83c2-4fd2-b171-5636e49620d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'Fantasy', 1: 'Science Fiction', 2: 'Crime Fiction', 3: 'Historical novel', 4: 'Horror', 5: 'Thriller'}\n"
          ]
        }
      ],
      "source": [
        "print(newmap)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "local-protection",
      "metadata": {
        "id": "local-protection",
        "outputId": "5d21e9cc-479c-4a45-f323-4dd39b4f7e00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    book_id                  book_name  genre  \\\n",
              "0   3248537          Drowned Wednesday      0   \n",
              "1  27796919              The Lost Hero      0   \n",
              "2   3910776  The Eyes of the Overworld      0   \n",
              "3   5969644            Magic's Promise      0   \n",
              "4   3173445             Taran Wanderer      0   \n",
              "\n",
              "                                             summary Actual Genre  \\\n",
              "0  drown wednesday first truste among morrow day ...      Fantasy   \n",
              "1  book open jason awaken school bu unabl rememb ...      Fantasy   \n",
              "2  cugel easili persuad merchant fianosth attempt...      Fantasy   \n",
              "3  book open herald mage vanyel return countri va...      Fantasy   \n",
              "4  taran gurgi return caer dallben follow event t...      Fantasy   \n",
              "\n",
              "    Predicted_genre  \n",
              "0  Historical novel  \n",
              "1           Fantasy  \n",
              "2           Fantasy  \n",
              "3           Fantasy  \n",
              "4           Fantasy  "
            ],
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-56d03a4c-9985-46fb-bbef-42de0f9f7295 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-56d03a4c-9985-46fb-bbef-42de0f9f7295');\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef9658b5-c3f5-4f93-a967-6bbb7ea153f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "books['Actual Genre'] = books['genre'].map(newmap)\n",
        "books['Predicted_genre'] = np.array(predicted_genres)\n",
        "books.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "sound-milwaukee",
      "metadata": {
        "scrolled": true,
        "id": "sound-milwaukee",
        "outputId": "9bc9ce46-ddd6-434e-abe0-c75fb8975f55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      book_name  \\\n",
              "0             Drowned Wednesday   \n",
              "1                 The Lost Hero   \n",
              "2     The Eyes of the Overworld   \n",
              "3               Magic's Promise   \n",
              "4                Taran Wanderer   \n",
              "...                         ...   \n",
              "2995                White Death   \n",
              "2996          Venus with Pistol   \n",
              "2997                 Blackwater   \n",
              "2998   The Rainbow and the Rose   \n",
              "2999                     Chiefs   \n",
              "\n",
              "                                                summary Actual Genre  \\\n",
              "0     drown wednesday first truste among morrow day ...      Fantasy   \n",
              "1     book open jason awaken school bu unabl rememb ...      Fantasy   \n",
              "2     cugel easili persuad merchant fianosth attempt...      Fantasy   \n",
              "3     book open herald mage vanyel return countri va...      Fantasy   \n",
              "4     taran gurgi return caer dallben follow event t...      Fantasy   \n",
              "...                                                 ...          ...   \n",
              "2995  novel numa file kurt austin adventur novel mai...     Thriller   \n",
              "2996  gilbert kemp dealer special antiqu gun london ...     Thriller   \n",
              "2997  know deep davey alway live shadow older brothe...     Thriller   \n",
              "2998  stori concern life johnni pasco retir commerci...     Thriller   \n",
              "2999  first chief henri lee novel open grow town del...     Thriller   \n",
              "\n",
              "       Predicted_genre  \n",
              "0     Historical novel  \n",
              "1              Fantasy  \n",
              "2              Fantasy  \n",
              "3              Fantasy  \n",
              "4              Fantasy  \n",
              "...                ...  \n",
              "2995          Thriller  \n",
              "2996          Thriller  \n",
              "2997          Thriller  \n",
              "2998          Thriller  \n",
              "2999     Crime Fiction  \n",
              "\n",
              "[3000 rows x 4 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>book_name</th>\n",
              "      <th>summary</th>\n",
              "      <th>Actual Genre</th>\n",
              "      <th>Predicted_genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drowned Wednesday</td>\n",
              "      <td>drown wednesday first truste among morrow day ...</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Historical novel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Lost Hero</td>\n",
              "      <td>book open jason awaken school bu unabl rememb ...</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Eyes of the Overworld</td>\n",
              "      <td>cugel easili persuad merchant fianosth attempt...</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Magic's Promise</td>\n",
              "      <td>book open herald mage vanyel return countri va...</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Taran Wanderer</td>\n",
              "      <td>taran gurgi return caer dallben follow event t...</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Fantasy</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>2995</th>\n",
              "      <td>White Death</td>\n",
              "      <td>novel numa file kurt austin adventur novel mai...</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>Venus with Pistol</td>\n",
              "      <td>gilbert kemp dealer special antiqu gun london ...</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>Blackwater</td>\n",
              "      <td>know deep davey alway live shadow older brothe...</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>The Rainbow and the Rose</td>\n",
              "      <td>stori concern life johnni pasco retir commerci...</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>Chiefs</td>\n",
              "      <td>first chief henri lee novel open grow town del...</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>Crime Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 4 columns</p>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "books = books[['book_name','summary','Actual Genre','Predicted_genre']]\n",
        "books"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "unauthorized-librarian",
      "metadata": {
        "id": "unauthorized-librarian",
        "outputId": "9df7f1a4-32f8-45c9-dc0b-49ad83d2cf44",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Fantasy': 500,\n",
              " 'Science Fiction': 500,\n",
              " 'Crime Fiction': 500,\n",
              " 'Historical novel': 500,\n",
              " 'Horror': 500,\n",
              " 'Thriller': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "dict(Counter(books['Actual Genre'].values))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cooperative-kansas",
      "metadata": {
        "id": "cooperative-kansas",
        "outputId": "5f7a6ca7-b8dc-4f06-824f-23ac08469eb8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Historical novel': 527,\n",
              " 'Fantasy': 418,\n",
              " 'Science Fiction': 547,\n",
              " 'Thriller': 486,\n",
              " 'Horror': 500,\n",
              " 'Crime Fiction': 522}"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "dict(Counter(books['Predicted_genre'].values))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "organizational-kruger",
      "metadata": {
        "id": "organizational-kruger",
        "outputId": "04ff924f-7b61-4fe8-f87c-892f0a5c6e47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sn.countplot(x = books['Predicted_genre'])\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nonprofit-zimbabwe",
      "metadata": {
        "id": "nonprofit-zimbabwe",
        "outputId": "383c6f96-9876-4693-93c6-23ab67d069a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sn.countplot(x = books['Actual Genre'])\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fluid-constraint",
      "metadata": {
        "id": "fluid-constraint"
      },
      "outputs": [],
      "source": [
        "# saving the model\n",
        "import pickle\n",
        "with open('bookgenremodel2.pkl','wb') as fp:\n",
        "    pickle.dump(mb,fp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "distant-danger",
      "metadata": {
        "id": "distant-danger",
        "outputId": "2bbb4ae4-55b6-4cff-ee61-22369831c6f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drown wednesday first truste among morrow day arthur side wish fulfil appear leviathan whale suffer gluttoni book begin leaf visit arthur discuss invit drown wednesday sent arthur admit hospit damag done leg attempt enter tuesday treasur tower suddenli hospit room becom flood water two transport border sea hous leaf snatch away larg ship green sail known fli manti arthur remain bed medallion given immort call marin appar fail summon help arthur without hope eventu buoy mark pirat elishar feverfew treasur float toward soon arthur open hand mark bloodi red colour arthur red hand feverfew mark whoever found treasur identifi later long scaveng ship call moth rescu arthur board arthur go name arth introduc sunscorch first mate captain catapillow journey bring line storm border sea later pursu feverfew ghostli ship shiver damag inflict moth seriou therefor sunscorch command upper hous sorcer dr scamandro open transfer portal elsewher secondari realm scamandro claim arthur carri someth interf magic tell sunscorch throw overboard last resort arthur show marin medallion stop scamandro say must get rid arthur go transfer portal arthur help ship ground beach arthur wish learn happen leaf dr scamandro appli sorceri make possibl reveal aboard ship fli manti arthur join catapillow supper later reveal ident first propaganda issu dame primu arthur steward make skeptic eventu becom convinc day later wednesday dawn take arthur meet wednesday luncheon seventeen remov approach wednesday shrink human form meet arthur lunch wednesday tell arthur part releas surrend third key arthur arthur taken wednesday dawn place call triangl search friend leaf learn leaf forc work manti otherwis intact arthur later make deal rais rat group anthropomorph rat brought hous piper take feverfew hideout believ insid miniatur world locat within drown wednesday stomach rais rat ship arthur open gift dr scamandro prof golden transfer watch commun rescu dr scamandro us scri mirror dr scamandro gave watch leaf rat watch scri later save battl feverfew later rat bring arthur onto submarin meet suzi turquios blue contrast suzi former cockney attitud assum ladylik proper demeanor order dame primu longer border sea resum customari way speech dress navig difficulti abl enter stomach ladi wednesday worldlet therein arthur suzi disguis rat find escap slave profess follow carp exil take carp third part halt attempt escap feverfew propos tri kill mean one strike arthur fail first tri dodg feverfew sever head leaf feverfew prison kick mud puddl contain noth consum upon death worldlet begin collaps via moth arthur friend except reluct catapillow abl escap ladi wednesday recov gluttoni dy result poison worldlet open void noth arthur duke border sea appoint sunscorch noon scamandro dusk fr mercredi sou le flot th'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "books['summary'].iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cordless-candy",
      "metadata": {
        "id": "cordless-candy",
        "outputId": "770fd690-210e-4916-9c42-42cca2bcb8c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TfidfVectorizer(max_df=0.8, max_features=10000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(max_df=0.8, max_features=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_df=0.8, max_features=10000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "tfidf_vectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "extra-check",
      "metadata": {
        "id": "extra-check"
      },
      "outputs": [],
      "source": [
        "with open('tfdifvector2.pkl','wb') as fp:\n",
        "    pickle.dump(tfidf_vectorizer,fp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "accredited-vintage",
      "metadata": {
        "id": "accredited-vintage",
        "outputId": "ffc89345-a27d-43d2-b5ce-da90ee8e2fb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to ./...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "wordnet_down = nltk.download('wordnet',download_dir='./')\n",
        "wordnet_down"
      ]
    },
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