Add a new method: Naive CNN

scripts/train_cnn.py

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+import numpy as np
+from sawaw import SAWAWEntry, SentimentResult
+from pathlib import Path
+import torch
+from loguru import logger
+from tqdm import tqdm
+
+from methods.tokenizer import to_vec
+from methods.model import SentimentAspectCNN
+# Load the data from semeval dataset
+path = Path("./data/restaurant_train.raw")
+content = path.read_text()
+
+def parse_content(content: str):
+    '''I 'm partial to the $T$ .
+    Gnocchi
+    1'''
+    lines = content.split("\n")
+    entries = []
+    for i in range(0, len(lines), 3):
+        if i + 2 >= len(lines):
+            break
+        sentence, aspect_word, sentiment = lines[i], lines[i+1], lines[i+2]
+        sentence_replaced = sentence.replace("$T$", aspect_word)
+        entries.append(SAWAWEntry(sentence_replaced, [aspect_word], [SentimentResult(int(sentiment)+1)]))
+    return entries
+
+entries = parse_content(content)
+logger.info("Loaded {} entries from {}", len(entries), path)
+
+# Load the tokenizer
+max_len = 80
+data_vectors, sentiment_gts = [], []
+for entry in tqdm(entries):
+    data_vector, sentiment_gt = to_vec(entry, max_len=max_len, should_return_sentiment=True) # shape: (num_of_aspect_words, 80, 26); (num_of_aspect_words, )
+    data_vectors.append(data_vector)
+    sentiment_gts.append(sentiment_gt)
+
+data_vectors = torch.cat(data_vectors, dim=0)
+sentiment_gts = torch.Tensor(sentiment_gts).unsqueeze(1)  # shape: (num_of_aspect_words, 1)
+
+# Train the model
+embedding_dim = 26  # 25 for word embeddings + 1 for aspect indicator
+num_filters = 88
+filter_sizes = [3, 4, 3]
+output_dim = 1
+dropout = 0.2
+
+model = SentimentAspectCNN(embedding_dim, num_filters, filter_sizes, output_dim, dropout)
+model.train()
+optimizer = torch.optim.Adam(model.parameters())
+criterion = torch.nn.BCELoss()
+
+batch_size = 16
+from torch.utils.data import TensorDataset, DataLoader
+dataset = TensorDataset(data_vectors, sentiment_gts)
+dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+try:
+    epochs = 100
+    for epoch in range(epochs):
+        epoch_loss = 0
+        for batch in tqdm(dataloader):
+            data_vectors, sentiment_gts = batch
+            optimizer.zero_grad()
+            outputs = model(data_vectors)
+            loss = criterion(outputs, sentiment_gts)
+            loss.backward()
+            optimizer.step()
+            epoch_loss += loss.item()
+        logger.info("Epoch {}: loss={}", epoch, epoch_loss)
+        
+except KeyboardInterrupt:
+    logger.info("Training stopped by user")
+# Save the model
+torch.save(model.state_dict(), "./data/model.pt")
+logger.info("Model saved to {}", "./data/model.pt")
+
+# Test the model to find the best threshold
+model.eval()
+
+for threshold in np.arange(0.1, 1, 0.1):
+    logger.info("Testing with threshold={}", threshold)
+    num_correct = 0
+    num_total = 0
+    for batch in tqdm(dataloader):
+        data_vectors, sentiment_gts = batch
+        outputs = model(data_vectors)
+        outputs = outputs > threshold
+        num_correct += torch.sum(outputs == sentiment_gts).item()
+        num_total += len(sentiment_gts)
+    logger.info("Accuracy: {}", num_correct / num_total)