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