bot_spam_signup_detection_for_newsletter.ipynb

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      "cell_type": "markdown",
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      "source": [
        "# Installing and Importing Dependencies\n",
        "\n",
        "| Requirement | Description |\n",
        "| ----------- | ----------- |\n",
        "| torch | The core PyTorch library for deep learning. |\n",
        "| transformers | Hugging Face Transformers library, providing pre-trained models like BERT. |\n",
        "| AdamW | An optimizer commonly used with Transformers models. |\n",
        "| get_linear_schedule_with_warmup | Creates a learning rate scheduler for optimization. |\n",
        "| sklearn.metrics | Metrics from scikit-learn to evaluate model performance. |\n",
        "| warnings.filterwarnings('ignore') | Suppresses warnings; not ideal for production code, but might be done here for brevity in the example. |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "Qc5FU0v82Avh",
        "outputId": "cb345d02-f994-446d-d72d-2cfa9fffba68"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "uBT2n_h2wuRC"
      },
      "outputs": [],
      "source": [
        "\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertModel, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFGN_FrkgLj2"
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      "source": [
        "# Setting Variables\n",
        "\n",
        "|Variable | Description |\n",
        "|---      |---           |\n",
        "| PRE_TRAINED_MODEL_NAME | Specifies the BERT model to use (cased version, sensitive to capitalization). |\n",
        "| DEVICE | Check for CUDA (GPU) availability; otherwise defaults to CPU. |\n",
        "| TOKENIZER | Loads the BERT tokenizer for text preprocessing. |\n",
        "| EPOCHS | Number of training iterations. |\n",
        "| MAX_LEN | Maximum sequence length for tokenization. |\n",
        "| BATCH_SIZE | Batch size for training. |\n",
        "| INITIAL_TEST_SIZE | Percentage of data to be held out for initial testing |\n",
        "| RANDOM_SEED | For reproducibility |\n",
        "| VALIDATION_SIZE | Percentage of the initial test set to use for validation |"
      ]
    },
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        "outputId": "fffe818f-557e-47bf-9ffb-c7502cc95cf6"
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      "outputs": [
        {
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      "source": [
        "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'\n",
        "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "TOKENIZER = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "EPOCHS=10\n",
        "MAX_LEN=512\n",
        "BATCH_SIZE=16\n",
        "INITIAL_TEST_SIZE = 0.2  \n",
        "RANDOM_SEED = 42         \n",
        "VALIDATION_SIZE = 0.5  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ietiWNbYgR-i"
      },
      "source": [
        "# Reading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "iQ6uw08ox3XY",
        "outputId": "f4c8c76c-990c-4884-ff23-a772342b78c4"
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      "outputs": [
        {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 180,\n  \"fields\": [\n    {\n      \"column\": \"msg\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 180,\n        \"samples\": [\n          \"Amolith amolith@secluded.site\",\n          \"Rijul Rajesh rijulrajtkeey2@gmail.com\",\n          \"Utkarsh utkarsh89000@gmail.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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            "text/plain": [
              "                                                 msg  spam\n",
              "0                        athreya athreyac4@gmail.com     0\n",
              "1  Withdrawing 32 911 Dollars.  G t withdrwl >>> ...     1\n",
              "2                        Carlos cgsaid@ucasal.edu.ar     0\n",
              "3                             Jack dalonng@gmail.com     0\n",
              "4   You got 54 877 US dollars.  Gt withdrwl >>> h...     1"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split as tts\n",
        "\n",
        "# df = pd.read_csv('https://raw.githubusercontent.com/animesharma3/SPAM-SMS-Detection/master/spam_sms_collection.csv')[['name_email', 'bot']]\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/lovestaco/bot-bot-signup-to-newsletter/main/spam_signup_collection.csv')[['name_email', 'bot']]\n",
        "\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Creating the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "78kRG9Qkzi3W"
      },
      "outputs": [],
      "source": [
        "class NewsletterCollectionDataset(Dataset):\n",
        "    \"\"\"\n",
        "    Creates a PyTorch Dataset for bot sign up detection with a BERT tokenizer.\n",
        "\n",
        "    Args:\n",
        "        bot: Labels for each sample (0 or 1).\n",
        "        name_emails:  List of email/name text samples.\n",
        "        tokenizer: BERT tokenizer for preprocessing.\n",
        "        max_len:  Maximum sequence length. \n",
        "    \"\"\"    \n",
        "    def __init__(self, bots, name_emails, tokenizer, max_len):\n",
        "        self.name_emails = name_emails\n",
        "        self.bots = bots\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.name_emails)\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        name_email = str(self.name_emails[i])\n",
        "        bot = self.bots[i]\n",
        "\n",
        "        encoding = self.tokenizer.encode_plus(\n",
        "            name_email,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            truncation=True,\n",
        "            return_token_type_ids=False,\n",
        "            pad_to_max_length=True,\n",
        "            return_attention_mask=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "\n",
        "        return {\n",
        "            'name_email': name_email,\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'bot': torch.tensor(bot, dtype=torch.long)\n",
        "        }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsfQUZPQyPLl",
        "outputId": "50cb18be-45c5-4e85-ac50-5f70e463ad90"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((144, 2), (18, 2), (18, 2))"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Split the data into training and testing sets\n",
        "\n",
        "df_train, df_test = tts(df, \n",
        "                        test_size=INITIAL_TEST_SIZE, \n",
        "                        random_state=RANDOM_SEED\n",
        "                        )\n",
        "\n",
        "# Further split the testing set into a validation set and a final testing set\n",
        "\n",
        "df_val, df_test = tts(df_test, \n",
        "                      test_size=VALIDATION_SIZE, \n",
        "                      random_state=RANDOM_SEED,\n",
        "                      )\n",
        "\n",
        "# Check the shapes of the resulting datasets\n",
        "print(\"Training set shape:\", df_train.shape)\n",
        "print(\"Validation set shape:\", df_val.shape)\n",
        "print(\"Testing set shape:\", df_test.shape)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Splitting data and loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "0RK3hJDCzS73"
      },
      "outputs": [],
      "source": [
        "def create_data_loader(df, tokenizer, max_len, batch_size):\n",
        "    \"\"\"\n",
        "    Creates a PyTorch DataLoader for bot detection.\n",
        "\n",
        "    Args:\n",
        "        df (pandas.DataFrame): The DataFrame containing email/name data and 'bot' labels.\n",
        "        tokenizer: The BERT tokenizer for text preprocessing.\n",
        "        max_len (int): The maximum length for tokenized sequences.\n",
        "        batch_size (int): Number of samples per batch.\n",
        "\n",
        "    Returns:\n",
        "        torch.utils.data.DataLoader: A DataLoader instance for iterating over the dataset.\n",
        "    \"\"\"\n",
        "    \n",
        "    ds = NewsletterCollectionDataset(\n",
        "        bots=df['bot'].to_numpy(),\n",
        "        name_emails=df['name_email'].to_numpy(),\n",
        "        tokenizer=tokenizer,\n",
        "        max_len=max_len\n",
        "    )\n",
        "\n",
        "    return DataLoader(\n",
        "        ds,\n",
        "        batch_size=batch_size,\n",
        "        num_workers=4\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "rSd4b1Mx6OR4"
      },
      "outputs": [],
      "source": [
        "# Creating model data for training, validation, and testing. \n",
        "train_data_loader = create_data_loader(df_train, TOKENIZER, MAX_LEN, BATCH_SIZE)\n",
        "test_data_loader = create_data_loader(df_test, TOKENIZER, MAX_LEN, BATCH_SIZE)\n",
        "val_data_loader = create_data_loader(df_val, TOKENIZER, MAX_LEN, BATCH_SIZE)\n",
        "\n",
        "\n",
        "d = next(iter(train_data_loader)) \n",
        "\n",
        "# Shapes of data in the batch\n",
        "print(\"Input IDs shape:\", d['input_ids'].shape)\n",
        "print(\"Attention Mask shape:\", d['attention_mask'].shape)\n",
        "print(\"Labels (bot) shape:\", d['bot'].shape)\n",
        "\n",
        "# Example of a preprocessed text sample \n",
        "print(\"Example Text:\", d['name_email'][0])  # Assuming the first item is the raw text\n",
        "print(\"Example Input IDs:\", d['input_ids'][0])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDxtdPbJgY5n"
      },
      "source": [
        "# Building the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "YXy7V_kJ8g7H"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class BotClassifier(nn.Module):\n",
        "    \"\"\"\n",
        "    A neural network model for spam classification using a pre-trained BERT model.\n",
        "\n",
        "    Attributes:\n",
        "        n_classes (int): The number of output classes (e.g., 2 for bot vs. human).\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, n_classes):\n",
        "        super(BotClassifier, self).__init__()\n",
        "        self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
        "        self.drop = nn.Dropout(p=0.3)\n",
        "        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask):\n",
        "        \"\"\"\n",
        "        Defines the forward pass through the spam classification model.\n",
        "\n",
        "        Args:\n",
        "            input_ids (torch.Tensor): Tokenized input sequences.\n",
        "            attention_mask (torch.Tensor): Attention mask indicating real vs. padded tokens.\n",
        "\n",
        "        Returns:\n",
        "            torch.Tensor: The model's output logits (un normalized class probabilities).\n",
        "        \"\"\"\n",
        "\n",
        "        pooled_output = self.bert(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask\n",
        "        )[1]\n",
        "        output = self.drop(pooled_output)\n",
        "        return self.out(output)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "_1uiooAZPru3"
      },
      "outputs": [],
      "source": [
        "model = BotClassifier(n_classes=2)\n",
        "model = model.to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IlXMoLageQl"
      },
      "source": [
        "# Loss, Optimizer and Scheduler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "06B3UYiEP2N8"
      },
      "outputs": [],
      "source": [
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHGjC_Wlgphj"
      },
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "7sad0YiJRVjH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def train(\n",
        "    model,\n",
        "    loss_fn,\n",
        "    optimizer,\n",
        "    scheduler,\n",
        "    device,\n",
        "    data_loader,\n",
        "    n_examples\n",
        "):\n",
        "    \"\"\"\n",
        "    Trains a model for a single epoch on a specified dataset.\n",
        "\n",
        "    This function performs a single training loop over a provided PyTorch DataLoader. \n",
        "    For each batch, it calculates the loss, updates the model's weights (backpropagation), \n",
        "    and tracks training accuracy and loss.\n",
        "\n",
        "    Args:\n",
        "        model (nn.Module): The PyTorch model to train.\n",
        "        loss_fn (nn.Module): The loss function for calculating error.\n",
        "        optimizer (torch.optim.Optimizer): The optimizer used for updating model parameters.\n",
        "        scheduler: A learning rate scheduler to adjust learning rate during training.\n",
        "        device (torch.device): The device where the model and data should be loaded ('cpu' or 'cuda')\n",
        "        data_loader (torch.utils.data.DataLoader): A DataLoader providing batches of training data.\n",
        "        n_examples (int): The total number of training examples in the dataset. \n",
        "\n",
        "    Returns:\n",
        "        tuple: A tuple containing:\n",
        "            * train_acc (float): Training accuracy for the epoch.\n",
        "            * train_loss (float): Average training loss for the epoch.\n",
        "    \"\"\"\n",
        "\n",
        "    model = model.train()  # Sets the model to training mode \n",
        "\n",
        "    losses = []\n",
        "    correct_predictions = 0\n",
        "\n",
        "    for d in data_loader:\n",
        "        # Data preparation\n",
        "        input_ids = d['input_ids'].to(device) \n",
        "        attention_mask = d['attention_mask'].to(device) \n",
        "        targets = d['bot'].to(device) \n",
        "\n",
        "        # Forward pass \n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "\n",
        "        # Loss calculation\n",
        "        loss = loss_fn(outputs, targets)\n",
        "\n",
        "        # Accuracy calculation\n",
        "        _, preds = torch.max(outputs, dim=1)\n",
        "        correct_predictions += torch.sum(preds == targets)\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # Back propagation \n",
        "        loss.backward()\n",
        "        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)  # Gradient clipping\n",
        "        \n",
        "        # Optimization\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        optimizer.zero_grad() \n",
        "\n",
        "    train_acc = correct_predictions.double() / n_examples\n",
        "    train_loss = np.mean(losses)\n",
        "\n",
        "    return train_acc, train_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXYnwR0KguQ-"
      },
      "source": [
        "# Validating the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "G9PtqPEFenkA"
      },
      "outputs": [],
      "source": [
        "def evaluate_model(\n",
        "    model,\n",
        "    loss_fn,\n",
        "    device,\n",
        "    data_loader,\n",
        "    n_examples\n",
        "):\n",
        "    \"\"\"\n",
        "    Evaluates the performance of a model on a given dataset.\n",
        "\n",
        "    This function calculates the accuracy and loss of the provided model on a DataLoader, \n",
        "    without updating the model's weights (since this is for evaluation, not training).\n",
        "\n",
        "    Args:\n",
        "        model (nn.Module): The PyTorch model to evaluate.\n",
        "        loss_fn (nn.Module): The loss function to calculate error.\n",
        "        device (torch.device): The device to use for computation ('cpu' or 'cuda').\n",
        "        data_loader (torch.utils.data.DataLoader): A DataLoader providing batches of evaluation data.\n",
        "        n_examples (int): The total number of evaluation examples.\n",
        "\n",
        "    Returns:\n",
        "        tuple: A tuple containing:\n",
        "            * eval_acc (float): The evaluation accuracy for the dataset.\n",
        "            * eval_loss (float): The average evaluation loss for the dataset.\n",
        "    \"\"\"\n",
        "\n",
        "    model = model.eval()  # Sets the model to evaluation mode (disables dropout, etc.)\n",
        "\n",
        "    losses = []\n",
        "    correct_predictions = 0\n",
        "\n",
        "    with torch.no_grad():  # Disables gradient calculations for efficiency\n",
        "        for d in data_loader:\n",
        "            input_ids = d['input_ids'].to(device)\n",
        "            attention_mask = d['attention_mask'].to(device)\n",
        "            targets = d['bot'].to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "            loss = loss_fn(outputs, targets)\n",
        "\n",
        "            _, preds = torch.max(outputs, dim=1)\n",
        "            correct_predictions += torch.sum(preds == targets)\n",
        "            losses.append(loss.item())\n",
        "\n",
        "    eval_acc = correct_predictions.double() / n_examples\n",
        "    eval_loss = np.mean(losses)\n",
        "\n",
        "    return eval_acc, eval_loss \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tABNoeFUg1iW"
      },
      "source": [
        "# Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gN6kZep2aAsY",
        "outputId": "a8735440-3fdc-4ec1-e35d-fdc0bf05f966"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "----------\n",
            "Train loss 0.4524265229701996 accuracy 0.8125\n",
            "Validation loss 0.13861329853534698 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 2/10\n",
            "----------\n",
            "Train loss 0.13377878804587656 accuracy 0.9722222222222222\n",
            "Validation loss 0.01685995887964964 accuracy 1.0\n",
            "\n",
            "Epoch 3/10\n",
            "----------\n",
            "Train loss 0.13878986306695473 accuracy 0.9583333333333333\n",
            "Validation loss 0.6645699292421341 accuracy 0.7777777777777777\n",
            "\n",
            "Epoch 4/10\n",
            "----------\n",
            "Train loss 0.1918790644267574 accuracy 0.9444444444444444\n",
            "Validation loss 0.18694046174641699 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 5/10\n",
            "----------\n",
            "Train loss 0.018076400777014594 accuracy 0.9930555555555555\n",
            "Validation loss 0.11930578161263838 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 6/10\n",
            "----------\n",
            "Train loss 0.0007173250616890275 accuracy 1.0\n",
            "Validation loss 0.0005039466195739806 accuracy 1.0\n",
            "\n",
            "Epoch 7/10\n",
            "----------\n",
            "Train loss 0.001489059077963854 accuracy 1.0\n",
            "Validation loss 0.1403410683851689 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 8/10\n",
            "----------\n",
            "Train loss 0.0003758141473453078 accuracy 1.0\n",
            "Validation loss 0.21394181363575626 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 9/10\n",
            "----------\n",
            "Train loss 0.00032681469262267154 accuracy 1.0\n",
            "Validation loss 0.22577022331824992 accuracy 0.9444444444444444\n",
            "\n",
            "Epoch 10/10\n",
            "----------\n",
            "Train loss 0.0003239141628404872 accuracy 1.0\n",
            "Validation loss 0.22768274320696946 accuracy 0.9444444444444444\n",
            "\n",
            "CPU times: user 1min 30s, sys: 46.3 s, total: 2min 16s\n",
            "Wall time: 2min 27s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from collections import defaultdict \n",
        "history = defaultdict(list)\n",
        "\n",
        "\n",
        "def start_training():\n",
        "    best_accuracy = 0\n",
        "\n",
        "    for epoch in range(EPOCHS):\n",
        "        print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
        "        print('-' * 10)\n",
        "\n",
        "        train_acc, train_loss = train(\n",
        "        model,\n",
        "        loss_fn,\n",
        "        optimizer,\n",
        "        scheduler,\n",
        "        DEVICE,\n",
        "        train_data_loader,\n",
        "        len(df_train)\n",
        "    )\n",
        "\n",
        "        print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "        val_acc, val_loss = evaluate_model(\n",
        "        model,\n",
        "        loss_fn,\n",
        "        DEVICE,\n",
        "        val_data_loader,\n",
        "        len(df_val)\n",
        "    )\n",
        "\n",
        "        print(f'Validation loss {val_loss} accuracy {val_acc}')\n",
        "        print()\n",
        "\n",
        "        history['train_acc'].append(train_acc)\n",
        "        history['train_loss'].append(train_loss)\n",
        "        history['val_acc'].append(val_acc)\n",
        "        history['val_loss'].append(val_loss)\n",
        "\n",
        "        if val_acc > best_accuracy:\n",
        "            torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "            best_accuracy = val_acc\n",
        "\n",
        "start_training()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4Yoe_oX40bi"
      },
      "source": [
        "# Evaluating the Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xs-xwmWnv0Zz",
        "outputId": "d3b2dbca-3978-4293-ee70-eef7f2b1a0b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333333"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_acc, _ = evaluate_model(\n",
        "    model,\n",
        "    loss_fn,\n",
        "    DEVICE,\n",
        "    test_data_loader,\n",
        "    len(df_test)\n",
        ")\n",
        "test_acc.item()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "JStEr4A9xI0K",
        "outputId": "cfea494d-ab8e-4a65-c786-878c306bd443"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming history['train_acc'] is a list of tensors\n",
        "train_acc_tensors = history['train_acc']\n",
        "val_acc_tensors = history['val_acc']\n",
        "\n",
        "# Convert each tensor to CPU and then to NumPy array\n",
        "train_acc_np = [tensor.cpu().numpy() for tensor in train_acc_tensors]\n",
        "val_acc_np = [tensor.cpu().numpy() for tensor in val_acc_tensors]\n",
        "\n",
        "def get_accuracy():\n",
        "    # Plot using NumPy arrays\n",
        "    plt.plot(train_acc_np, label='train accuracy')\n",
        "    plt.plot(val_acc_np, label='validation accuracy') \n",
        "\n",
        "    plt.title('Training history')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend()\n",
        "    plt.ylim([0.75, 1.1])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "jxfjEVbVzhef"
      },
      "outputs": [],
      "source": [
        "def get_predictions(\n",
        "    model, data_loader\n",
        "):\n",
        "    model = model.eval()\n",
        "\n",
        "    msgs = []\n",
        "    predictions = []\n",
        "    predictions_probs = []\n",
        "    real_values = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for d in data_loader:\n",
        "            name_email = d['name_email']\n",
        "            input_ids = d['input_ids'].to(DEVICE)\n",
        "            attention_masks = d['attention_mask'].to(DEVICE)\n",
        "            bot = d['bot'].to(DEVICE)\n",
        "\n",
        "            outputs = model(\n",
        "                input_ids=input_ids,\n",
        "                attention_mask=attention_masks\n",
        "            )\n",
        "\n",
        "            _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "            probs = torch.nn.functional.softmax(outputs, dim=1)\n",
        "\n",
        "            msgs.extend(name_email)\n",
        "            predictions.extend(preds)\n",
        "            predictions_probs.extend(probs)\n",
        "            real_values.extend(bot)\n",
        "    predictions = torch.stack(predictions).cpu()\n",
        "    predictions_probs = torch.stack(predictions_probs).cpu()\n",
        "    real_values = torch.stack(real_values).cpu()\n",
        "    return msgs, predictions, predictions_probs, real_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "qCxXj-3F258K"
      },
      "outputs": [],
      "source": [
        "y_msgs, y_pred, y_pred_probs, y_test = get_predictions(\n",
        "  model,\n",
        "  test_data_loader\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gegGnUJ5tzp"
      },
      "source": [
        "# Creating Final Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "Ck66KbYq3Ggq"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "def create_classification_report(Y_test, Y_pred):\n",
        "    print('--------Classification Report---------\\n')\n",
        "    accuracy = accuracy_score(Y_test, Y_pred)\n",
        "    f1 = f1_score(Y_test, Y_pred)\n",
        "    precision = precision_score(Y_test, Y_pred)\n",
        "    recall = recall_score(Y_test, Y_pred)\n",
        "    roc_auc = roc_auc_score(Y_test, Y_pred)\n",
        "    metrices = [accuracy, f1, precision, recall, roc_auc]\n",
        "    scores = pd.DataFrame(pd.Series(metrices).values, index=['accuracy', 'f1-score', 'precision', 'recall', 'roc auc score'], columns=['score'])\n",
        "    print(scores)\n",
        "    print('\\n--------Plotting Confusion Matrix---------')\n",
        "    sns.heatmap(confusion_matrix(Y_test, Y_pred), annot=True, cmap='RdYlGn_r', annot_kws={'size': 16})\n",
        "    return scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "RtuPlZdj6u6m"
      },
      "outputs": [],
      "source": [
        "def count_parameters(model):\n",
        "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "def get_model_info():\n",
        "\n",
        "    model = BotClassifier(n_classes=2)\n",
        "    model = model.to(DEVICE)\n",
        "    num_parameters = count_parameters(model)\n",
        "\n",
        "    if num_parameters >= 10**9:\n",
        "        num_parameters_billions = num_parameters / 10**9\n",
        "        print(f\"The model has approximately {num_parameters_billions:.2f} billion trainable parameters\")\n",
        "    elif num_parameters >= 10**6:\n",
        "        num_parameters_millions = num_parameters / 10**6\n",
        "        print(f\"The model has approximately {num_parameters_millions:.2f} million trainable parameters\")\n",
        "    else:\n",
        "        print(f\"The model has {num_parameters} trainable parameters\")\n",
        "\n",
        "    import os\n",
        "    model_path = 'best_model_state.bin'\n",
        "    file_size_bytes = os.path.getsize(model_path)\n",
        "    file_size_mb = file_size_bytes / (1024 * 1024)\n",
        "    print(f\"The model file size is approximately {file_size_mb:.2f} MB\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "fOUdUgGM37r4",
        "outputId": "7072bc3a-d171-48a3-e2e4-c699ba6761dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------Classification Report---------\n",
            "\n",
            "                  score\n",
            "accuracy       0.833333\n",
            "f1-score       0.888889\n",
            "precision      1.000000\n",
            "recall         0.800000\n",
            "roc auc score  0.900000\n",
            "\n",
            "--------Plotting Confusion Matrix---------\n"
          ]
        },
        {
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              "summary": "{\n  \"name\": \"create_classification_report(y_test, y_pred)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07641666161799732,\n        \"min\": 0.8,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.888888888888889,\n          0.9,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "text/plain": [
              "                  score\n",
              "accuracy       0.833333\n",
              "f1-score       0.888889\n",
              "precision      1.000000\n",
              "recall         0.800000\n",
              "roc auc score  0.900000"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "create_classification_report(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "g1HTV1g_7zTj",
        "outputId": "38fecd36-fcc5-4f5d-f691-4b1fba74b095"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "get_accuracy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "G_yenye14IKI",
        "outputId": "1e8bc1a6-3f18-4302-a49a-7ea9f2d969d2"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'tokenizer' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-3d5b406fd9f6>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mdata_to_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0memail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m encoding = tokenizer.encode_plus(\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mdata_to_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0madd_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'tokenizer' is not defined"
          ]
        }
      ],
      "source": [
        "# Step 1: Prepare the data\n",
        "\n",
        "email = \"rishic2013@gmail.com\"\n",
        "name = \"Rishi C\"\n",
        "\n",
        "# email = \"lama2@hexmos.com\"\n",
        "# name = \"Lama2.  G t 12 withdrwl >>> \"\n",
        "\n",
        "data_to_test = name + email\n",
        "def test_with_single_data(data_to_test):\n",
        "    encoding = TOKENIZER.encode_plus(\n",
        "    data_to_test,\n",
        "    add_special_tokens=True,\n",
        "    max_length=MAX_LEN,\n",
        "    truncation=True,\n",
        "    return_token_type_ids=False,\n",
        "    pad_to_max_length=True,\n",
        "    return_attention_mask=True,\n",
        "    return_tensors=\"pt\",\n",
        ")\n",
        "\n",
        "# Move the tensors to the device where the model is\n",
        "    input_ids = encoding[\"input_ids\"].to(DEVICE)\n",
        "    attention_mask = encoding[\"attention_mask\"].to(DEVICE)\n",
        "\n",
        "# Step 2: Predict\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        prob = torch.nn.functional.softmax(outputs, dim=1)\n",
        "\n",
        "# Step 3: Interpret the prediction\n",
        "    prediction = torch.argmax(prob, dim=1).item()\n",
        "    if prediction == 1:\n",
        "        print(\"The signup is likely from a bot. \")\n",
        "    else:\n",
        "        print(\"The signup is likely from a human.\")\n",
        "\n",
        "test_with_single_data(data_to_test)\n",
        "\n",
        "get_model_info()\n"
      ]
    }
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