BERT Model finetuning.ipynb

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   },
   "source": [
    "# Importing libraries"
   ]
  },
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     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n",
    "\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from transformers import BertTokenizerFast, BertModel, AdamW\n",
    "\n",
    "import time\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
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   "source": [
    "# This cell is to read the data and create a balanced dataset if needed\n",
    "\n",
    "# size = 100\n",
    "df = pd.read_csv('/kaggle/input/combined-dataset/data.csv', usecols=['text', 'generated'], )\n",
    "# df_0 = df[df['generated'] == 0].sample(n=size//2, random_state=42)\n",
    "# df_1 = df[df['generated'] == 1].sample(n=size//2, random_state=42)\n",
    "# df = pd.concat([df_0, df_1]).reset_index(drop=True)"
   ]
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   "source": [
    "def tokenize_and_encode(tokenizer, comments, labels, max_length=128): \n",
    "    input_ids = [] \n",
    "    attention_masks = [] \n",
    "\n",
    "    for comment in comments: \n",
    "        encoded_dict = tokenizer.encode_plus( \n",
    "            comment,  \n",
    "            add_special_tokens=True, \n",
    "            max_length=max_length, \n",
    "            pad_to_max_length=True, \n",
    "            return_attention_mask=True, \n",
    "            return_tensors='pt'\n",
    "        ) \n",
    "\n",
    "        input_ids.append(encoded_dict['input_ids']) \n",
    "        attention_masks.append(encoded_dict['attention_mask']) \n",
    "\n",
    "    input_ids = torch.cat(input_ids, dim=0) \n",
    "    attention_masks = torch.cat(attention_masks, dim=0) \n",
    "\n",
    "    labels = torch.tensor(labels, dtype=torch.float32) \n",
    "    \n",
    "    return input_ids, attention_masks, labels \n",
    "\n"
   ]
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    "train_texts, val_texts, train_labels, val_labels = train_test_split(df.text.tolist(), df.generated.tolist(), \\\n",
    "                                                  test_size=0.3, random_state=42, \\\n",
    "                                                  stratify=df.generated.tolist())"
   ]
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      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n",
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2870: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "MAX_LEN = 400\n",
    "BATCH_SIZE = 32\n",
    "NUM_EPOCHS = 10\n",
    "\n",
    "tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased', do_lower_case=True)\n",
    "\n",
    "train_input_ids, train_attention_masks, train_labels = tokenize_and_encode( \n",
    "    tokenizer, \n",
    "    train_texts, \n",
    "    train_labels,\n",
    "    max_length = MAX_LEN\n",
    ") \n",
    "\n",
    "val_input_ids, val_attention_masks, val_labels = tokenize_and_encode( \n",
    "    tokenizer, \n",
    "    val_texts, \n",
    "    val_labels,\n",
    "    max_length = MAX_LEN\n",
    ") "
   ]
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   "outputs": [],
   "source": [
    "train_dataset = TensorDataset(train_input_ids, train_attention_masks, train_labels) \n",
    "val_dataset = TensorDataset(val_input_ids, val_attention_masks, val_labels) \n"
   ]
  },
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   "execution_count": 7,
   "id": "a58da38c",
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   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833664d5",
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    "tags": []
   },
   "source": [
    "# Model Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12954429",
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   "outputs": [],
   "source": [
    "# MLP archetecture is [768, 512, 256, 128, 2]\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim1, hidden_dim2, hidden_dim3, output_dim):\n",
    "        # super helps in initializing the classes in the correct order when inheritance on it's own\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1  = nn.Linear(input_dim, hidden_dim1)\n",
    "        self.fc2  = nn.Linear(hidden_dim1, hidden_dim2)\n",
    "        self.fc3  = nn.Linear(hidden_dim2, hidden_dim3)\n",
    "        self.fc4  = nn.Linear(hidden_dim3, output_dim)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        self.batchnorm1 = nn.BatchNorm1d(hidden_dim1)\n",
    "        self.batchnorm2 = nn.BatchNorm1d(hidden_dim2)\n",
    "        self.batchnorm3 = nn.BatchNorm1d(hidden_dim3)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.batchnorm1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.batchnorm2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = self.batchnorm3(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        return x\n",
    "            \n",
    "# This class passes the input into BERT model and then through the MLP layer\n",
    "class CombinedModel(nn.Module):\n",
    "    def __init__(self, bert_model, mlp):\n",
    "        super(CombinedModel, self).__init__()\n",
    "        self.bert = bert_model\n",
    "        self.mlp = mlp\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        outputs = self.bert(input_ids, attention_mask=attention_mask)\n",
    "        last_hidden_state = outputs.last_hidden_state[:, 0, :]\n",
    "        logits = self.mlp(last_hidden_state)\n",
    "        return logits"
   ]
  },
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   "source": [
    "bert_model = BertModel.from_pretrained('bert-base-uncased')\n",
    "mlp = MLP(768, 512, 256, 128, 2)\n",
    "model = CombinedModel(bert_model, mlp).to(device)\n",
    "\n",
    "# AdamW  is a more efficient variant of Adam with sigmoid layer already present\n",
    "# which means in our model, we don't 1 neuron in our final layer but 2\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)\n",
    "# loss function\n",
    "loss_fn = nn.CrossEntropyLoss()\n"
   ]
  },
  {
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   "id": "2a1b24df",
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   "source": [
    "###########   Code for freezing BERT layers (computationally expensive)  ############\n",
    "###########   add after initializing the bert model                      ############\n",
    "#\n",
    "# num_layers = len(bert_model.encoder.layer)  \n",
    "# for i in range(0, num_layers):\n",
    "#     if i >= num_layers - 0:\n",
    "#         for param in bert_model.encoder.layer[i].parameters():\n",
    "#             param.requires_grad = True\n",
    "#     else:\n",
    "#         for param in bert_model.encoder.layer[i].parameters():/\n",
    "#             param.requires_grad = False\n"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
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   "source": [
    "'''\n",
    "    This function is used to train the model\n",
    "    \n",
    "'''\n",
    "def train(model, train_loader, optimizer, loss_fn):\n",
    "    # sets the model in training mode\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    for batch in tqdm(train_loader):\n",
    "        # move the data in batch to device\n",
    "        input_ids, attention_mask, labels = [t.to(device) for t in batch]\n",
    "        # clear the gradients of all optimized variables\n",
    "        optimizer.zero_grad()\n",
    "        # forward pass\n",
    "        outputs = model(input_ids, attention_mask=attention_mask) \n",
    "        # calculate the loss and then they are sent to device\n",
    "        # (datatype of the loss should be same as the outputs)\n",
    "        loss = loss_fn(outputs.to(device), labels.type(torch.LongTensor).to(device))\n",
    "        # compute the gradients of all optimized variables based on the loss\n",
    "        loss.backward()\n",
    "        # update the parameters based on the calculated gradients\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss\n",
    "        train_correct += (torch.max(outputs.data, dim=1)[1] == labels).sum().item()\n",
    "        train_total += len(labels)\n",
    "        \n",
    "    \n",
    "    train_loss = train_loss / train_total\n",
    "    train_accuracy = 100 * train_correct / train_total\n",
    "    return train_loss, train_accuracy\n",
    "\n",
    "def validation(model, val_loader, loss_fn):\n",
    "    # set the model in evaluation mode\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    y_pred = []\n",
    "    y_true = []\n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(val_loader):\n",
    "            # move the data in batch to device\n",
    "            input_ids, attention_mask, labels = [t.to(device) for t in batch]\n",
    "            # forward pass\n",
    "            outputs = model(input_ids, attention_mask=attention_mask)\n",
    "            # calculate the loss\n",
    "            loss = loss_fn(outputs.to(device), labels.type(torch.LongTensor).to(device))\n",
    "            \n",
    "            val_loss += loss\n",
    "            val_correct += (torch.max(outputs.data, dim=1)[1] == labels).sum().item()\n",
    "            val_total += len(labels)\n",
    "            # get predicted labels assuming class 1 probability\n",
    "            y_pred.extend(torch.softmax(outputs.data, dim=1)[:, 1].cpu().numpy())  \n",
    "            y_true.extend(labels.cpu().numpy())\n",
    "            \n",
    "            val_correct += (torch.max(outputs.data, dim=1)[1] == labels).sum().item()\n",
    "            val_total += len(labels)\n",
    "    \n",
    "    y_pred_label = [int(num >= 0.5) for num in y_pred]\n",
    "    roc_auc = roc_auc_score(y_true, y_pred_label)\n",
    "    precision = precision_score(y_true, y_pred_label)\n",
    "    recall = recall_score(y_true, y_pred_label)\n",
    "    f1 = f1_score(y_true, y_pred_label)\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_pred) \n",
    "    val_loss = val_loss / val_total\n",
    "    val_accuracy = 100 * val_correct / val_total\n",
    "    return val_loss, val_accuracy, roc_auc, precision, recall, f1, fpr, tpr\n",
    "\n",
    "\n"
   ]
  },
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       "  0%|          | 0/10 [00:00<?, ?it/s]"
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     "text": [
      "Epoch 1\n"
     ]
    },
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       "  0%|          | 0/1012 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0024, Train Acc: 98.5450, Val Loss: 0.0013, Val Acc: 97.1241\n",
      "AOC ROC: 0.9763, Precision: 0.9315, Recall: 0.9973, F1: 0.9633, \n",
      "\n",
      "\n",
      "Epoch 2\n"
     ]
    },
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       "  0%|          | 0/1012 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0008, Train Acc: 99.5768, Val Loss: 0.0006, Val Acc: 98.9188\n",
      "AOC ROC: 0.9907, Precision: 0.9750, Recall: 0.9970, F1: 0.9859, \n",
      "\n",
      "\n",
      "Epoch 3\n"
     ]
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     "output_type": "stream",
     "text": [
      "Train Loss: 0.0005, Train Acc: 99.7683, Val Loss: 0.0006, Val Acc: 98.7675\n",
      "AOC ROC: 0.9897, Precision: 0.9704, Recall: 0.9979, F1: 0.9839, \n",
      "\n",
      "\n",
      "Epoch 4\n"
     ]
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     "text": [
      "Train Loss: 0.0003, Train Acc: 99.7776, Val Loss: 0.0004, Val Acc: 99.1062\n",
      "AOC ROC: 0.9922, Precision: 0.9798, Recall: 0.9970, F1: 0.9883, \n",
      "\n",
      "\n",
      "Epoch 5\n"
     ]
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     "text": [
      "Train Loss: 0.0002, Train Acc: 99.8301, Val Loss: 0.0002, Val Acc: 99.6540\n",
      "AOC ROC: 0.9965, Precision: 0.9947, Recall: 0.9962, F1: 0.9954, \n",
      "\n",
      "\n",
      "Epoch 6\n"
     ]
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     "text": [
      "Train Loss: 0.0002, Train Acc: 99.8486, Val Loss: 0.0003, Val Acc: 99.5531\n",
      "AOC ROC: 0.9955, Precision: 0.9930, Recall: 0.9952, F1: 0.9941, \n",
      "\n",
      "\n",
      "Epoch 7\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0002, Train Acc: 99.8455, Val Loss: 0.0002, Val Acc: 99.5603\n",
      "AOC ROC: 0.9943, Precision: 0.9996, Recall: 0.9888, F1: 0.9942, \n",
      "\n",
      "\n",
      "Epoch 8\n"
     ]
    },
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       "  0%|          | 0/1012 [00:00<?, ?it/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0001, Train Acc: 99.9413, Val Loss: 0.0002, Val Acc: 99.7477\n",
      "AOC ROC: 0.9970, Precision: 0.9983, Recall: 0.9950, F1: 0.9967, \n",
      "\n",
      "\n",
      "Epoch 9\n"
     ]
    },
    {
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       "  0%|          | 0/1012 [00:00<?, ?it/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0000, Train Acc: 99.9846, Val Loss: 0.0002, Val Acc: 99.7405\n",
      "AOC ROC: 0.9973, Precision: 0.9962, Recall: 0.9970, F1: 0.9966, \n",
      "\n",
      "\n",
      "Epoch 10\n"
     ]
    },
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       "  0%|          | 0/1012 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.0002, Train Acc: 99.8764, Val Loss: 0.0002, Val Acc: 99.6612\n",
      "AOC ROC: 0.9966, Precision: 0.9943, Recall: 0.9968, F1: 0.9955, \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# lists to store metrics\n",
    "Tr_loss, Tr_acc, Vl_loss, Vl_acc = [], [], [], []\n",
    "Vl_roc_auc, Vl_precision, Vl_recall, Vl_f1 = [], [], [], []\n",
    "Vl_fpr, Vl_tpr = [], []\n",
    "\n",
    "for epoch in tqdm(range(NUM_EPOCHS)):\n",
    "    print(f\"Epoch {epoch+1}\")\n",
    "\n",
    "    # train the model\n",
    "    train_loss, train_acc = train(model, train_loader, optimizer, loss_fn)\n",
    "\n",
    "    # validate the model\n",
    "    val_loss, val_acc, roc_auc, precision, recall, f1, fpr, tpr = validation(model, val_loader, loss_fn)\n",
    "    \n",
    "    # all the metrics is stored in lists for further analysis\n",
    "    Tr_loss.append(train_loss.item())\n",
    "    Tr_acc.append(train_acc)\n",
    "    Vl_loss.append(val_loss.item())\n",
    "    Vl_acc.append(val_acc)\n",
    "    Vl_roc_auc.append(roc_auc)\n",
    "    Vl_precision.append(precision)\n",
    "    Vl_recall.append(recall)\n",
    "    Vl_f1.append(f1)\n",
    "    Vl_tpr.append(tpr)\n",
    "    Vl_fpr.append(fpr)\n",
    "\n",
    "    print(f\"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")\n",
    "    print(f\"AOC ROC: {roc_auc:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}, \")\n",
    "    print(\"\\n\")\n"
   ]
  },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss vs epoch\n",
    "\n",
    "plt.plot(range(1, NUM_EPOCHS+1), Tr_loss, label=\"Training Loss\")\n",
    "plt.plot(range(1, NUM_EPOCHS+1), Vl_loss, label=\"Validation Loss\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4d38452",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T21:17:03.151010Z",
     "iopub.status.busy": "2024-09-28T21:17:03.150149Z",
     "iopub.status.idle": "2024-09-28T21:17:03.407814Z",
     "shell.execute_reply": "2024-09-28T21:17:03.406970Z"
    },
    "papermill": {
     "duration": 0.327239,
     "end_time": "2024-09-28T21:17:03.409760",
     "exception": false,
     "start_time": "2024-09-28T21:17:03.082521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy vs epoch\n",
    "\n",
    "plt.plot(range(1, NUM_EPOCHS+1), Tr_acc, label=\"Training Accuracy\")\n",
    "plt.plot(range(1, NUM_EPOCHS+1), Vl_acc, label=\"Validation Accuracy\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d59f770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T21:17:03.545017Z",
     "iopub.status.busy": "2024-09-28T21:17:03.544696Z",
     "iopub.status.idle": "2024-09-28T21:17:03.748018Z",
     "shell.execute_reply": "2024-09-28T21:17:03.747047Z"
    },
    "papermill": {
     "duration": 0.272728,
     "end_time": "2024-09-28T21:17:03.750082",
     "exception": false,
     "start_time": "2024-09-28T21:17:03.477354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculating the Comfusion Matrix\n",
    "def calculate_tp_tn_fp_fn(accuracy, precision, recall, total_entries):\n",
    "    tp = np.round((1-accuracy) * total_entries / (1/precision + 1/recall -2))\n",
    "    fn = np.round((1/recall - 1) *tp)\n",
    "    fp = np.round((1/precision - 1) *tp)\n",
    "    tn = np.round(total_entries - tp - fp - fn)\n",
    "    return tp, fn, fp, tn\n",
    "array = np.array(calculate_tp_tn_fp_fn(Vl_acc[-1]/100, \\\n",
    "                                        Vl_precision[-1],\\\n",
    "                                        Vl_recall[-1], \\\n",
    "                                        len(val_texts)))\n",
    "matrix = np.array([[array[0], array[1]], [array[2], array[3]]])\n",
    "\n",
    "plt.matshow(matrix * 1/len(val_texts), cmap=plt.cm.Blues, )\n",
    "\n",
    "for i in range(matrix.shape[0]):\n",
    "    for j in range(matrix.shape[1]):\n",
    "        plt.text(x=j, y=i,s=matrix[i, j], va='center', ha='center', size='xx-large')\n",
    "plt.title('Confusion matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06a7db8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T21:17:03.888009Z",
     "iopub.status.busy": "2024-09-28T21:17:03.887330Z",
     "iopub.status.idle": "2024-09-28T21:17:04.201009Z",
     "shell.execute_reply": "2024-09-28T21:17:04.200080Z"
    },
    "papermill": {
     "duration": 0.384118,
     "end_time": "2024-09-28T21:17:04.202985",
     "exception": false,
     "start_time": "2024-09-28T21:17:03.818867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7a31cc3df1c0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.4f)' % roc_auc)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.grid()\n",
    "plt.legend(loc=\"lower right\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae532efd",
   "metadata": {
    "papermill": {
     "duration": 0.06815,
     "end_time": "2024-09-28T21:17:04.339375",
     "exception": false,
     "start_time": "2024-09-28T21:17:04.271225",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Saving Model details and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e1d7bf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T21:17:04.477144Z",
     "iopub.status.busy": "2024-09-28T21:17:04.476766Z",
     "iopub.status.idle": "2024-09-28T21:17:06.336194Z",
     "shell.execute_reply": "2024-09-28T21:17:06.335370Z"
    },
    "papermill": {
     "duration": 1.931576,
     "end_time": "2024-09-28T21:17:06.338870",
     "exception": false,
     "start_time": "2024-09-28T21:17:04.407294",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "checkpoint = {\n",
    "    'epoch': epoch,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'train_loss': Tr_loss,\n",
    "    'train_acc': Tr_acc,\n",
    "    'val_loss': Vl_loss,\n",
    "    'val_acc': Vl_acc,\n",
    "    'val_roc_auc': Vl_roc_auc,\n",
    "    'val_precision': Vl_precision,\n",
    "    'val_recall': Vl_recall,\n",
    "    'val_f1': Vl_f1,\n",
    "    'val_tpr': Vl_tpr,\n",
    "    'val_fpr': Vl_fpr,\n",
    "\n",
    "}\n",
    "torch.save(checkpoint, 'checkpoint.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f5c2df4",
   "metadata": {
    "papermill": {
     "duration": 0.06775,
     "end_time": "2024-09-28T21:17:06.475349",
     "exception": false,
     "start_time": "2024-09-28T21:17:06.407599",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 5508230,
     "sourceId": 9124188,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 5557716,
     "sourceId": 9193345,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30776,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 14737.236128,
   "end_time": "2024-09-28T21:17:08.477778",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-09-28T17:11:31.241650",
   "version": "2.5.0"
  },
  "widgets": {
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