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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8d37f81d",
"metadata": {},
"outputs": [
{
"name": "stderr",
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"text": [
"/Users/ivancarevic/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"LSTM training...\n",
"LSTM Epoch 1: Train loss 0.9166 | Validation loss 0.9009\n",
"LSTM Epoch 5: Train loss 0.9075 | Validation loss 0.8981\n",
"LSTM Epoch 10: Train loss 0.9015 | Validation loss 0.8975\n",
"LSTM Epoch 15: Train loss 0.9015 | Validation loss 0.8989\n",
"LSTM Epoch 20: Train loss 0.9000 | Validation loss 0.8957\n",
"LSTM Epoch 25: Train loss 0.8955 | Validation loss 0.8964\n",
"LSTM Epoch 30: Train loss 0.8940 | Validation loss 0.8950\n",
"LSTM Epoch 35: Train loss 0.8903 | Validation loss 0.8952\n",
"LSTM Epoch 40: Train loss 0.8852 | Validation loss 0.8968\n",
"LSTM Epoch 45: Train loss 0.8830 | Validation loss 0.8928\n",
"LSTM Epoch 50: Train loss 0.8804 | Validation loss 0.8953\n",
"\n",
"LSTM Classification report:\n",
" precision recall f1-score support\n",
"\n",
" positive 1.0000 0.0139 0.0274 216\n",
" neutral 0.5880 1.0000 0.7405 431\n",
" negative 1.0000 0.0532 0.1010 94\n",
"\n",
" accuracy 0.5924 741\n",
" macro avg 0.8627 0.3557 0.2897 741\n",
"weighted avg 0.7604 0.5924 0.4515 741\n",
"\n",
"LSTM Confusion matrix:\n",
" [[ 3 213 0]\n",
" [ 0 431 0]\n",
" [ 0 89 5]]\n",
"\n",
"GRU training...\n",
"GRU Epoch 1: Train loss 0.9175 | Validation loss 0.8988\n",
"GRU Epoch 5: Train loss 0.9083 | Validation loss 0.8972\n",
"GRU Epoch 10: Train loss 0.8926 | Validation loss 0.8841\n",
"GRU Epoch 15: Train loss 0.7687 | Validation loss 0.8025\n",
"GRU Epoch 20: Train loss 0.7075 | Validation loss 0.7651\n",
"GRU Epoch 25: Train loss 0.6198 | Validation loss 0.8362\n",
"GRU Epoch 30: Train loss 0.4990 | Validation loss 0.9217\n",
"GRU Epoch 35: Train loss 0.3581 | Validation loss 1.2447\n",
"GRU Epoch 40: Train loss 0.2617 | Validation loss 1.4024\n",
"GRU Epoch 45: Train loss 0.1703 | Validation loss 1.6796\n",
"GRU Epoch 50: Train loss 0.1398 | Validation loss 1.7962\n",
"\n",
"GRU Classification report:\n",
" precision recall f1-score support\n",
"\n",
" positive 0.8818 0.8287 0.8544 216\n",
" neutral 0.8824 0.9397 0.9101 431\n",
" negative 0.8228 0.6915 0.7514 94\n",
"\n",
" accuracy 0.8758 741\n",
" macro avg 0.8623 0.8200 0.8387 741\n",
"weighted avg 0.8746 0.8758 0.8737 741\n",
"\n",
"GRU Confusion matrix:\n",
" [[179 32 5]\n",
" [ 17 405 9]\n",
" [ 7 22 65]]\n",
"\n",
"CNN training...\n",
"CNN Epoch 1: Train loss 0.9127 | Validation loss 0.8888\n",
"CNN Epoch 5: Train loss 0.8167 | Validation loss 0.8061\n",
"CNN Epoch 10: Train loss 0.7162 | Validation loss 0.7756\n",
"CNN Epoch 15: Train loss 0.6233 | Validation loss 0.7725\n",
"CNN Epoch 20: Train loss 0.5443 | Validation loss 0.7931\n",
"CNN Epoch 25: Train loss 0.4808 | Validation loss 0.7701\n",
"CNN Epoch 30: Train loss 0.4096 | Validation loss 0.8236\n",
"CNN Epoch 35: Train loss 0.3591 | Validation loss 0.8460\n",
"CNN Epoch 40: Train loss 0.3101 | Validation loss 0.8653\n",
"CNN Epoch 45: Train loss 0.2801 | Validation loss 0.8993\n",
"CNN Epoch 50: Train loss 0.2406 | Validation loss 0.9453\n",
"\n",
"CNN Classification report:\n",
" precision recall f1-score support\n",
"\n",
" positive 0.9045 0.8333 0.8675 216\n",
" neutral 0.8842 0.9745 0.9272 431\n",
" negative 0.9552 0.6809 0.7950 94\n",
"\n",
" accuracy 0.8961 741\n",
" macro avg 0.9147 0.8296 0.8632 741\n",
"weighted avg 0.8991 0.8961 0.8930 741\n",
"\n",
"CNN Confusion matrix:\n",
" [[180 35 1]\n",
" [ 9 420 2]\n",
" [ 10 20 64]]\n"
]
}
],
"source": [
"# !pip install gensim scikit-learn pandas numpy torch tqdm\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"from sklearn.model_selection import train_test_split\n",
"from collections import Counter\n",
"import gensim\n",
"\n",
"# --- UČITAVANJE I PODJELA PODATAKA ---\n",
"full_df = pd.read_csv('TRAIN.csv')\n",
"test2_df = pd.read_csv('test-2.csv')\n",
"\n",
"def get_text_column(df):\n",
" for col in df.columns:\n",
" if col.lower() in ['sentence', 'text']:\n",
" return col\n",
" raise ValueError(\"Nema stupca 'Sentence' ili 'Text'!\")\n",
"\n",
"text_col = get_text_column(full_df)\n",
"\n",
"# Stratified split: 95% train, 5% valid\n",
"train_df, valid_df = train_test_split(full_df, test_size=0.05, stratify=full_df['Label'], random_state=42)\n",
"\n",
"# --- TOKENIZACIJA I VOKABULAR ---\n",
"def tokenize(text):\n",
" return text.lower().split()\n",
"\n",
"counter = Counter()\n",
"for text in train_df[text_col]:\n",
" counter.update(tokenize(text))\n",
"vocab = {word: idx+2 for idx, (word, _) in enumerate(counter.most_common())}\n",
"vocab['<unk>'] = 0\n",
"vocab['<pad>'] = 1\n",
"\n",
"# --- EMBEDDING ---\n",
"from gensim.models.fasttext import load_facebook_model\n",
"\n",
"embedding_path = 'cc.hr.300.bin'\n",
"ft_model = load_facebook_model(embedding_path)\n",
"embeddings = ft_model.wv \n",
"\n",
"embedding_dim = embeddings.vector_size\n",
"embedding_matrix = np.zeros((len(vocab), embedding_dim))\n",
"for word, idx in vocab.items():\n",
" if word in embeddings:\n",
" embedding_matrix[idx] = embeddings[word]\n",
" else:\n",
" embedding_matrix[idx] = np.random.normal(scale=0.6, size=(embedding_dim, ))\n",
"\n",
"# --- DATASET ---\n",
"class TextDataset(Dataset):\n",
" def __init__(self, df, text_col, vocab, max_len=50):\n",
" self.texts = df[text_col].tolist()\n",
" self.labels = df['Label'].tolist()\n",
" self.vocab = vocab\n",
" self.max_len = max_len\n",
" def __len__(self):\n",
" return len(self.texts)\n",
" def __getitem__(self, idx):\n",
" tokens = tokenize(self.texts[idx])\n",
" ids = [self.vocab.get(token, self.vocab['<unk>']) for token in tokens][:self.max_len]\n",
" ids += [self.vocab['<pad>']] * (self.max_len - len(ids))\n",
" return torch.tensor(ids), torch.tensor(self.labels[idx])\n",
"\n",
"max_len = 50\n",
"batch_size = 32\n",
"train_ds = TextDataset(train_df, text_col, vocab, max_len)\n",
"valid_ds = TextDataset(valid_df, text_col, vocab, max_len)\n",
"test2_text_col = get_text_column(test2_df)\n",
"test2_ds = TextDataset(test2_df, test2_text_col, vocab, max_len)\n",
"\n",
"train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
"valid_dl = DataLoader(valid_ds, batch_size=batch_size)\n",
"test2_dl = DataLoader(test2_ds, batch_size=batch_size)\n",
"\n",
"# --- MODELI ---\n",
"class LSTMClassifier(nn.Module):\n",
" def __init__(self, embedding_matrix, hidden_dim=256, num_classes=3, dropout=0.8):\n",
" super().__init__()\n",
" num_embeddings, embedding_dim = embedding_matrix.shape\n",
" self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n",
" self.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))\n",
" self.embedding.weight.requires_grad = False\n",
" self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.fc = nn.Linear(hidden_dim, num_classes)\n",
" def forward(self, x):\n",
" x = self.embedding(x)\n",
" _, (hidden, _) = self.lstm(x)\n",
" out = self.dropout(hidden[-1])\n",
" return self.fc(out)\n",
"\n",
"class GRUClassifier(nn.Module):\n",
" def __init__(self, embedding_matrix, hidden_dim=256, num_classes=3, dropout=0.8):\n",
" super().__init__()\n",
" num_embeddings, embedding_dim = embedding_matrix.shape\n",
" self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n",
" self.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))\n",
" self.embedding.weight.requires_grad = False\n",
" self.gru = nn.GRU(embedding_dim, hidden_dim, batch_first=True)\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.fc = nn.Linear(hidden_dim, num_classes)\n",
" def forward(self, x):\n",
" x = self.embedding(x)\n",
" _, hidden = self.gru(x)\n",
" out = self.dropout(hidden[-1])\n",
" return self.fc(out)\n",
"\n",
"class CNNClassifier(nn.Module):\n",
" def __init__(self, embedding_matrix, num_filters=128, kernel_sizes=[3,4,5], num_classes=3, dropout=0.8):\n",
" super().__init__()\n",
" num_embeddings, embedding_dim = embedding_matrix.shape\n",
" self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n",
" self.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))\n",
" self.embedding.weight.requires_grad = False\n",
" self.convs = nn.ModuleList([\n",
" nn.Conv2d(1, num_filters, (k, embedding_dim)) for k in kernel_sizes\n",
" ])\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.fc = nn.Linear(num_filters * len(kernel_sizes), num_classes)\n",
" def forward(self, x):\n",
" x = self.embedding(x)\n",
" x = x.unsqueeze(1)\n",
" x = [torch.relu(conv(x)).squeeze(3) for conv in self.convs]\n",
" x = [torch.max(pool, dim=2)[0] for pool in x]\n",
" x = torch.cat(x, dim=1)\n",
" x = self.dropout(x)\n",
" return self.fc(x)\n",
"\n",
"# --- TRENING I VALIDACIJA ---\n",
"def train_epoch(model, dataloader, optimizer, criterion, device):\n",
" model.train()\n",
" total_loss = 0\n",
" for x, y in dataloader:\n",
" x, y = x.to(device), y.to(device)\n",
" optimizer.zero_grad()\n",
" logits = model(x)\n",
" loss = criterion(logits, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
" total_loss += loss.item()\n",
" return total_loss / len(dataloader)\n",
"\n",
"def eval_model(model, dataloader, device, criterion=None, return_loss=False):\n",
" model.eval()\n",
" preds, targets = [], []\n",
" total_loss = 0\n",
" with torch.no_grad():\n",
" for x, y in dataloader:\n",
" x, y = x.to(device), y.to(device)\n",
" logits = model(x)\n",
" if criterion and return_loss:\n",
" loss = criterion(logits, y)\n",
" total_loss += loss.item()\n",
" pred = logits.argmax(1).cpu().numpy()\n",
" preds.extend(pred)\n",
" targets.extend(y.cpu().numpy())\n",
" if return_loss and criterion:\n",
" return np.array(preds), np.array(targets), total_loss / len(dataloader)\n",
" return np.array(preds), np.array(targets)\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"def run_training(model_class, name, epochs=50, dropout=0.8, lr=5e-4):\n",
" print(f\"\\n{name} training...\")\n",
" model = model_class(embedding_matrix, dropout=dropout).to(device)\n",
" optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
" criterion = nn.CrossEntropyLoss()\n",
" for epoch in range(epochs):\n",
" train_loss = train_epoch(model, train_dl, optimizer, criterion, device)\n",
" _, _, val_loss = eval_model(model, valid_dl, device, criterion, return_loss=True)\n",
" if (epoch+1) % 5 == 0 or epoch == 0:\n",
" print(f\"{name} Epoch {epoch+1}: Train loss {train_loss:.4f} | Validation loss {val_loss:.4f}\")\n",
" preds, targets = eval_model(model, test2_dl, device)\n",
" report = classification_report(targets, preds, digits=4, output_dict=True, target_names=[\"positive\", \"neutral\", \"negative\"])\n",
" matrix = confusion_matrix(targets, preds)\n",
" print(f\"\\n{name} Classification report:\\n\", classification_report(targets, preds, digits=4, target_names=[\"positive\", \"neutral\", \"negative\"]))\n",
" print(f\"{name} Confusion matrix:\\n\", matrix)\n",
" return {\n",
" 'precision': report['macro avg']['precision'],\n",
" 'recall': report['macro avg']['recall'],\n",
" 'f1': report['macro avg']['f1-score'],\n",
" 'accuracy': report['accuracy'],\n",
" 'confusion_matrix': matrix.tolist(),\n",
" 'full_report': classification_report(targets, preds, digits=4, target_names=[\"positive\", \"neutral\", \"negative\"])\n",
" }\n",
"\n",
"# --- POKRETANJE ---\n",
"lstm_results = run_training(LSTMClassifier, \"LSTM\", epochs=50, dropout=0.8, lr=5e-4)\n",
"gru_results = run_training(GRUClassifier, \"GRU\", epochs=50, dropout=0.8, lr=5e-4)\n",
"cnn_results = run_training(CNNClassifier, \"CNN\", epochs=50, dropout=0.8, lr=5e-4)\n",
"\n",
"# --- SPREMANJE ---\n",
"with open('results.md', 'w', encoding='utf-8') as f:\n",
" for model_name, results in [('LSTM', lstm_results), ('GRU', gru_results), ('CNN', cnn_results)]:\n",
" f.write(f\"## {model_name}\\n\\n\")\n",
" f.write(f\"- Precision: {results['precision']:.4f}\\n\")\n",
" f.write(f\"- Recall: {results['recall']:.4f}\\n\")\n",
" f.write(f\"- F1: {results['f1']:.4f}\\n\")\n",
" f.write(f\"- Accuracy: {results['accuracy']:.4f}\\n\")\n",
" f.write(f\"- Confusion matrix: {results['confusion_matrix']}\\n\\n\")\n",
" f.write(f\"Full classification report:\\n{results['full_report']}\\n\\n\")\n",
"\n"
]
}
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