RNN_LSTM_Model_Book_Genre_Final.ipynb

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    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "g2C_Z6q-YCep"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "csv_path = \"/content/train_text_clean.csv\"  # Replace with the correct path\n",
        "dataset = pd.read_csv(csv_path)"
      ],
      "metadata": {
        "id": "lGtQdJgAcIcJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words_list = np.load(\"wordsList.npy\", allow_pickle=True)\n",
        "word_vectors = np.load(\"wordVectors.npy\")\n",
        "print(f\"Words List Size: {len(words_list)}\")\n",
        "print(f\"Word Vectors Shape: {word_vectors.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdO5U82qsiQW",
        "outputId": "b72c9008-0d21-44ef-b5cd-3d22aa934c93"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Words List Size: 400000\n",
            "Word Vectors Shape: (400000, 300)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_list = np.load(\"wordsList.npy\", allow_pickle=True).tolist()  # Use allow_pickle to ensure compatibility\n",
        "word_vectors = np.load(\"wordVectors.npy\")"
      ],
      "metadata": {
        "id": "HqmPtfxScM8c"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Parameters\n",
        "max_seq_length = 20  # Maximum sequence length\n",
        "embedding_dim = word_vectors.shape[1]  # Dimension of word vectors"
      ],
      "metadata": {
        "id": "WPG2kUyMycFQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper function: Convert text to indices\n",
        "def text_to_indices(text, words_list, max_seq_length):\n",
        "    tokens = text.strip(\"[]\").replace(\"'\", \"\").split(\", \")\n",
        "    indices = [words_list.index(word) if word in words_list else 0 for word in tokens]\n",
        "    if len(indices) > max_seq_length:\n",
        "        indices = indices[:max_seq_length]\n",
        "    else:\n",
        "        indices.extend([0] * (max_seq_length - len(indices)))\n",
        "    return indices"
      ],
      "metadata": {
        "id": "jm5W5paQyfJ9"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the dataset\n",
        "dataset['text_indices'] = dataset['title_author'].apply(lambda x: text_to_indices(x, words_list, max_seq_length))"
      ],
      "metadata": {
        "id": "Ihzd95LWyhzh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split dataset into train and validation sets\n",
        "train_data, val_data = train_test_split(dataset, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "bKeSfSpT4_vq"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom Dataset class\n",
        "class BookDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.texts = torch.tensor(data['text_indices'].tolist(), dtype=torch.long)\n",
        "        self.labels = torch.tensor(data['category id'].tolist(), dtype=torch.long)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.texts[idx], self.labels[idx]"
      ],
      "metadata": {
        "id": "fNZe_Xay0oi_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare train data loaders\n",
        "train_dataset = BookDataset(train_data)\n",
        "val_dataset = BookDataset(val_data)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "OQPjhCJR0ucQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare test data loaders\n",
        "test_csv_path = \"test_text_clean.csv\"  # Replace with your test dataset path\n",
        "test_dataset = pd.read_csv(test_csv_path)\n",
        "test_dataset['text_indices'] = test_dataset['title_author'].apply(lambda x: text_to_indices(x, words_list, max_seq_length))\n",
        "test_dataset = BookDataset(test_dataset)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "12JrlrtP5vVl"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define RNN-LSTM model\n",
        "class RNNLSTM(nn.Module):\n",
        "    def __init__(self, embedding_matrix, hidden_dim, output_dim):\n",
        "        super(RNNLSTM, self).__init__()\n",
        "        self.embedding = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))\n",
        "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=2, batch_first=True)\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        embedded = self.embedding(x)\n",
        "        embedded = self.dropout(embedded)\n",
        "        _, (hidden, _) = self.lstm(embedded)\n",
        "        hidden = self.dropout(hidden[-1])\n",
        "        output = self.fc(hidden)\n",
        "        return output"
      ],
      "metadata": {
        "id": "fqVGXPBs0v1t"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model parameters\n",
        "hidden_dim = 256\n",
        "output_dim = len(dataset['category id'].unique())  # Number of categories\n",
        "model = RNNLSTM(word_vectors, hidden_dim, output_dim)\n",
        "\n",
        "# Training setup\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.0005, weight_decay=1e-5)"
      ],
      "metadata": {
        "id": "PMXMOJUZ0zV3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_losses = []\n",
        "validation_losses = []\n",
        "testing_losses = []\n",
        "epochs = 25\n",
        "best_val_accuracy = 0\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    # Training phase\n",
        "    model.train()\n",
        "    epoch_loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for texts, labels in train_dataloader:\n",
        "        optimizer.zero_grad()\n",
        "        predictions = model(texts)\n",
        "        loss = criterion(predictions, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        epoch_loss += loss.item()\n",
        "        _, predicted = torch.max(predictions, 1)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "        total += labels.size(0)\n",
        "\n",
        "    train_accuracy = correct / total\n",
        "    avg_train_loss = epoch_loss / len(train_dataloader)  # Average loss for the epoch\n",
        "    training_losses.append(avg_train_loss)  # Append training loss for the epoch\n",
        "\n",
        "    # Validation phase\n",
        "    model.eval()\n",
        "    val_loss = 0\n",
        "    val_correct = 0\n",
        "    val_total = 0\n",
        "    with torch.no_grad():\n",
        "        for texts, labels in val_dataloader:\n",
        "            predictions = model(texts)\n",
        "            loss = criterion(predictions, labels)  # Compute validation loss\n",
        "            val_loss += loss.item()\n",
        "            _, predicted = torch.max(predictions, 1)\n",
        "            val_correct += (predicted == labels).sum().item()\n",
        "            val_total += labels.size(0)\n",
        "\n",
        "    val_accuracy = val_correct / val_total\n",
        "    avg_val_loss = val_loss / len(val_dataloader)  # Average validation loss for the epoch\n",
        "    validation_losses.append(avg_val_loss)  # Append validation loss for the epoch\n",
        "\n",
        "    # Testing phase\n",
        "    test_loss = 0\n",
        "    test_correct = 0\n",
        "    test_total = 0\n",
        "    with torch.no_grad():\n",
        "        for texts, labels in test_dataloader:\n",
        "            predictions = model(texts)\n",
        "            loss = criterion(predictions, labels)  # Compute testing loss\n",
        "            test_loss += loss.item()\n",
        "            _, predicted = torch.max(predictions, 1)\n",
        "            test_correct += (predicted == labels).sum().item()\n",
        "            test_total += labels.size(0)\n",
        "\n",
        "    test_accuracy = test_correct / test_total\n",
        "    avg_test_loss = test_loss / len(test_dataloader)  # Average testing loss for the epoch\n",
        "    testing_losses.append(avg_test_loss)  # Append testing loss for the epoch\n",
        "\n",
        "    print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {avg_train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, \"\n",
        "          f\"Validation Loss: {avg_val_loss:.4f}, Validation Accuracy: {val_accuracy:.4f}, \"\n",
        "          f\"Test Loss: {avg_test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n",
        "\n",
        "    # Early stopping\n",
        "    if val_accuracy > best_val_accuracy:\n",
        "        best_val_accuracy = val_accuracy\n",
        "        torch.save(model.state_dict(), \"best_model.pth\")\n",
        "        print(\"Best model saved.\")\n",
        "    else:\n",
        "        print(\"Early stopping triggered.\")\n",
        "        break\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Osglk2903CS",
        "outputId": "fe00fb29-3944-41f9-d629-a1bf6f3a2cf6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25, Train Loss: 3.0441, Train Accuracy: 0.1131, Validation Loss: 2.4878, Validation Accuracy: 0.2585, Test Loss: 2.4898, Test Accuracy: 0.2618\n",
            "Best model saved.\n",
            "Epoch 2/25, Train Loss: 2.3140, Train Accuracy: 0.3263, Validation Loss: 2.0332, Validation Accuracy: 0.4184, Test Loss: 2.0425, Test Accuracy: 0.4095\n",
            "Best model saved.\n",
            "Epoch 3/25, Train Loss: 2.0424, Train Accuracy: 0.4208, Validation Loss: 1.8735, Validation Accuracy: 0.4689, Test Loss: 1.9003, Test Accuracy: 0.4596\n",
            "Best model saved.\n",
            "Epoch 4/25, Train Loss: 1.9050, Train Accuracy: 0.4649, Validation Loss: 1.8133, Validation Accuracy: 0.4907, Test Loss: 1.8442, Test Accuracy: 0.4816\n",
            "Best model saved.\n",
            "Epoch 5/25, Train Loss: 1.8074, Train Accuracy: 0.4914, Validation Loss: 1.7482, Validation Accuracy: 0.5101, Test Loss: 1.7666, Test Accuracy: 0.5059\n",
            "Best model saved.\n",
            "Epoch 6/25, Train Loss: 1.7376, Train Accuracy: 0.5114, Validation Loss: 1.7006, Validation Accuracy: 0.5208, Test Loss: 1.7239, Test Accuracy: 0.5178\n",
            "Best model saved.\n",
            "Epoch 7/25, Train Loss: 1.6799, Train Accuracy: 0.5289, Validation Loss: 1.6673, Validation Accuracy: 0.5323, Test Loss: 1.6946, Test Accuracy: 0.5261\n",
            "Best model saved.\n",
            "Epoch 8/25, Train Loss: 1.6180, Train Accuracy: 0.5467, Validation Loss: 1.6439, Validation Accuracy: 0.5408, Test Loss: 1.6738, Test Accuracy: 0.5321\n",
            "Best model saved.\n",
            "Epoch 9/25, Train Loss: 1.5729, Train Accuracy: 0.5568, Validation Loss: 1.6335, Validation Accuracy: 0.5422, Test Loss: 1.6688, Test Accuracy: 0.5333\n",
            "Best model saved.\n",
            "Epoch 10/25, Train Loss: 1.5291, Train Accuracy: 0.5667, Validation Loss: 1.6160, Validation Accuracy: 0.5512, Test Loss: 1.6555, Test Accuracy: 0.5392\n",
            "Best model saved.\n",
            "Epoch 11/25, Train Loss: 1.4860, Train Accuracy: 0.5803, Validation Loss: 1.6210, Validation Accuracy: 0.5512, Test Loss: 1.6532, Test Accuracy: 0.5408\n",
            "Early stopping triggered.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generate epochs list based on the length of losses lists\n",
        "epochs = list(range(1, len(training_losses) + 1))\n",
        "\n",
        "# Plot training, validation, and testing losses\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(epochs, training_losses, label='Training Loss', marker='o')\n",
        "if validation_losses:  # Only include if you have validation loss\n",
        "    plt.plot(epochs, validation_losses, label='Validation Loss', marker='o')\n",
        "if testing_losses:  # Only include if you have testing loss\n",
        "    plt.plot(epochs, testing_losses, label='Testing Loss', marker='o')\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training, Validation, and Testing Loss Over Epochs')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "ElqcILYGV29-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "a04b0fd7-87d1-472a-80c2-c3f0083e3855"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}