CAT-AGnews2.ipynb

{
 "cells": [
  
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "220814d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-20T07:00:35.456406Z",
     "iopub.status.busy": "2025-01-20T07:00:35.456050Z",
     "iopub.status.idle": "2025-01-20T08:06:06.331064Z",
     "shell.execute_reply": "2025-01-20T08:06:06.330067Z"
    },
    "papermill": {
     "duration": 3930.878786,
     "end_time": "2025-01-20T08:06:06.332528",
     "exception": false,
     "start_time": "2025-01-20T07:00:35.453742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c6ca4252dcf4fd78dbf1041458e1b0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/8.07k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4ca4c2d68dd4ffba2bcd2b11ca1d1bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train-00000-of-00001.parquet:   0%|          | 0.00/18.6M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69318d8e18854b378a5184c9626a0f54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "test-00000-of-00001.parquet:   0%|          | 0.00/1.23M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82e9bb86e5c44d509a5bfd7b8d3ad54f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/120000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08778a18cf344a4bbe055f0fa7fda797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/7600 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a803350a1b9a458eb23a22b4142c677e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/120000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa46988de351479fb6ce5812c63956ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/7600 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 50002\n",
      "Training model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6 Loss: 0.43857914172827817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/6 Loss: 0.2471751975241119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/6 Loss: 0.1916904093296543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/6 Loss: 0.1490779308550584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/6 Loss: 0.11390007378410365\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 938/938 [10:48<00:00,  1.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/6 Loss: 0.0886133187738404\n",
      "Evaluating model...\n",
      "Accuracy: 0.9118\n",
      "F1 Score: 0.9117\n",
      "Precision: 0.9119\n",
      "Recall: 0.9118\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from datasets import load_dataset\n",
    "from collections import Counter\n",
    "import string\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Load the AG News dataset\n",
    "dataset = load_dataset('ag_news')\n",
    "\n",
    "# Filter out any NaN values\n",
    "train_dataset = dataset['train'].filter(lambda x: x['text'] is not None and x['label'] is not None)\n",
    "test_dataset = dataset['test'].filter(lambda x: x['text'] is not None and x['label'] is not None)\n",
    "\n",
    "# Use a subset for training and testing\n",
    "train_dataset = train_dataset.shuffle(seed=42).select(range(120000))\n",
    "test_dataset = test_dataset\n",
    "\n",
    "# Tokenizer: Build Vocabulary\n",
    "def basic_tokenizer(text):\n",
    "    text = text.lower().translate(str.maketrans(\"\", \"\", string.punctuation))\n",
    "    return text.split()\n",
    "\n",
    "def build_vocab(dataset, max_vocab_size=50000):\n",
    "    counter = Counter()\n",
    "    for example in dataset['text']:\n",
    "        tokens = basic_tokenizer(example)\n",
    "        counter.update(tokens)\n",
    "    most_common = counter.most_common(max_vocab_size)\n",
    "    vocab = {word: idx + 2 for idx, (word, _) in enumerate(most_common)}  # Reserve 0 for [PAD], 1 for [UNK]\n",
    "    vocab[\"[PAD]\"] = 0\n",
    "    vocab[\"[UNK]\"] = 1\n",
    "    return vocab\n",
    "\n",
    "vocab = build_vocab(train_dataset, max_vocab_size=50000)\n",
    "vocab_size = len(vocab)\n",
    "print(f\"Vocabulary size: {vocab_size}\")\n",
    "\n",
    "# Custom Tokenizer\n",
    "def custom_tokenizer(text, vocab, max_length=99):\n",
    "    tokens = basic_tokenizer(text)\n",
    "    token_ids = [vocab.get(token, vocab[\"[UNK]\"]) for token in tokens]\n",
    "    if len(token_ids) < max_length:\n",
    "        token_ids += [vocab[\"[PAD]\"]] * (max_length - len(token_ids))  # Padding\n",
    "    else:\n",
    "        token_ids = token_ids[:max_length]  # Truncate\n",
    "    return token_ids\n",
    "\n",
    "# Tokenize Dataset\n",
    "def tokenize_custom_dataset(dataset, vocab, max_length=99):\n",
    "    tokenized_data = []\n",
    "    for example in dataset:\n",
    "        tokenized_data.append({\n",
    "            'input_ids': custom_tokenizer(example['text'], vocab, max_length),\n",
    "            'labels': example['label']\n",
    "        })\n",
    "    return tokenized_data\n",
    "\n",
    "tokenized_train_data = tokenize_custom_dataset(train_dataset, vocab)\n",
    "tokenized_test_data = tokenize_custom_dataset(test_dataset, vocab)\n",
    "\n",
    "# Convert to PyTorch Dataset\n",
    "class CustomDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\n",
    "            'input_ids': torch.tensor(self.data[idx]['input_ids'], dtype=torch.long),\n",
    "            'labels': torch.tensor(self.data[idx]['labels'], dtype=torch.long)\n",
    "        }\n",
    "\n",
    "train_dataset = CustomDataset(tokenized_train_data)\n",
    "test_dataset = CustomDataset(tokenized_test_data)\n",
    "\n",
    "# DataLoader\n",
    "batch_size = 128\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# Cyclic Attention Block with Masked and Hierarchical Attention\n",
    "class CyclicAttentionBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, dropout=0.1, shift=1, mask_window=None):\n",
    "        super(CyclicAttentionBlock, self).__init__()\n",
    "        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm = nn.LayerNorm(embed_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.gate = nn.Linear(embed_dim, 1)  # Gating mechanism\n",
    "        self.shift = shift\n",
    "        self.mask_window = mask_window\n",
    "\n",
    "    def forward(self, x):\n",
    "        attn_output, _ = self.attention(x, x, x)\n",
    "        x = self.norm(x + attn_output)\n",
    "\n",
    "        # Cyclic shifts\n",
    "        cyclic_term_pos = torch.roll(attn_output, shifts=self.shift, dims=0)\n",
    "        cyclic_term_neg = torch.roll(attn_output, shifts=-self.shift, dims=0)\n",
    "\n",
    "        # Apply masking if specified\n",
    "        if self.mask_window is not None:\n",
    "            mask = torch.zeros_like(attn_output, device=attn_output.device)\n",
    "            mask[:, :, :self.mask_window] = 1  # Enable only within the mask window\n",
    "            cyclic_term_pos = cyclic_term_pos * mask\n",
    "            cyclic_term_neg = cyclic_term_neg * mask\n",
    "\n",
    "        # Gating mechanism\n",
    "        g_pos = torch.sigmoid(self.gate(attn_output))\n",
    "        g_neg = torch.sigmoid(self.gate(attn_output))\n",
    "        x = self.norm(x + g_pos * cyclic_term_pos + g_neg * cyclic_term_neg)\n",
    "\n",
    "        return self.dropout(x)\n",
    "\n",
    "# Transformer Block with Cyclic Attention\n",
    "class CombinedTransformerBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1, shift=1, mask_window=None):\n",
    "        super(CombinedTransformerBlock, self).__init__()\n",
    "        self.attention1 = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm1 = nn.LayerNorm(embed_dim)\n",
    "        self.cyclic_attention = CyclicAttentionBlock(embed_dim, num_heads, dropout, shift, mask_window)\n",
    "        self.attention2 = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm2 = nn.LayerNorm(embed_dim)\n",
    "        self.ff = nn.Sequential(\n",
    "            nn.Linear(embed_dim, ff_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ff_dim, embed_dim)\n",
    "        )\n",
    "        self.norm3 = nn.LayerNorm(embed_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        attn_output, _ = self.attention1(x, x, x)\n",
    "        x = self.norm1(x + attn_output)\n",
    "        x = self.cyclic_attention(x)\n",
    "        attn_output, _ = self.attention2(x, x, x)\n",
    "        x = self.norm2(x + attn_output)\n",
    "        ff_output = self.ff(x)\n",
    "        x = self.norm3(x + self.dropout(ff_output))\n",
    "        return x\n",
    "\n",
    "# Custom Transformer Model\n",
    "class CustomTransformer(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_classes, max_length, num_layers):\n",
    "        super(CustomTransformer, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_dim)\n",
    "        self.layers = nn.ModuleList([\n",
    "            CombinedTransformerBlock(\n",
    "                embed_dim, \n",
    "                num_heads, \n",
    "                ff_dim, \n",
    "                shift=(i + 1),  # Hierarchical shifts (1 for first layer, 2 for second, etc.)\n",
    "                mask_window=max_length // (i + 2)  # Smaller mask window for deeper layers\n",
    "            )\n",
    "            for i in range(num_layers)\n",
    "        ])\n",
    "        self.global_pooling = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Linear(embed_dim, num_classes)\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        x = self.embedding(input_ids)\n",
    "        x = x.permute(1, 0, 2)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "        x = x.permute(1, 2, 0)\n",
    "        x = self.global_pooling(x).squeeze(-1)\n",
    "        logits = self.fc(x)\n",
    "        return logits\n",
    "\n",
    "# Initialize custom Transformer model\n",
    "embed_dim = 1024\n",
    "num_heads = 8\n",
    "ff_dim = 2048\n",
    "num_classes = 4\n",
    "max_length = 99\n",
    "num_layers = 3\n",
    "epochs = 6\n",
    "\n",
    "model = CustomTransformer(vocab_size, embed_dim, num_heads, ff_dim, num_classes, max_length, num_layers).to(device)\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\n",
    "\n",
    "# Training\n",
    "print(\"Training model...\")\n",
    "model.train()\n",
    "for epoch in range(epochs):\n",
    "    total_loss = 0\n",
    "    for batch in tqdm(train_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        optimizer.zero_grad()\n",
    "        logits = model(input_ids)\n",
    "        loss = F.cross_entropy(logits, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "    print(f\"Epoch {epoch + 1}/{epochs} Loss: {total_loss / len(train_loader)}\")\n",
    "\n",
    "# Evaluation\n",
    "print(\"Evaluating model...\")\n",
    "model.eval()\n",
    "all_logits = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in test_loader:\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        logits = model(input_ids)\n",
    "        all_logits.append(logits)\n",
    "        all_labels.append(labels)\n",
    "\n",
    "all_logits = torch.cat(all_logits)\n",
    "all_labels = torch.cat(all_labels)\n",
    "\n",
    "predictions = torch.argmax(all_logits, dim=1).cpu().numpy()\n",
    "true_labels = all_labels.cpu().numpy()\n",
    "\n",
    "accuracy = accuracy_score(true_labels, predictions)\n",
    "f1 = f1_score(true_labels, predictions, average='weighted')\n",
    "precision = precision_score(true_labels, predictions, average='weighted')\n",
    "recall = recall_score(true_labels, predictions, average='weighted')\n",
    "\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(true_labels, predictions)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n"
   ]
  }
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