File size: 6,035 Bytes
fd13bdd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from gensim.models.fasttext import load_facebook_vectors
from sklearn.metrics import classification_report
from collections import defaultdict
train_df = pd.read_csv("Train-1.tsv", sep="\t")
test_df = pd.read_csv("Test-1.tsv", sep="\t")
train_sentences, train_labels = train_df['Sentence'].values, train_df['Label'].values
test_sentences, test_labels = test_df['Sentence'].values, test_df['Label'].values
def tokenize(text):
return text.lower().split()
word_to_idx = {}
idx = 2
word_to_idx['<PAD>'] = 0
word_to_idx['<UNK>'] = 1
def build_vocab(sentences):
global idx
for sentence in sentences:
for word in tokenize(sentence):
if word not in word_to_idx:
word_to_idx[word] = idx
idx += 1
build_vocab(train_sentences)
fasttext_model = load_facebook_vectors("FastText.bin")
embedding_dim = 300
embedding_matrix = np.zeros((len(word_to_idx), embedding_dim))
for word, i in word_to_idx.items():
if word in fasttext_model:
embedding_matrix[i] = fasttext_model[word]
else:
embedding_matrix[i] = np.random.normal(scale=0.6, size=(embedding_dim,))
def encode_sentence(sentence, max_len=100):
tokens = tokenize(sentence)
ids = [word_to_idx.get(w, word_to_idx['<UNK>']) for w in tokens[:max_len]]
if len(ids) < max_len:
ids += [word_to_idx['<PAD>']] * (max_len - len(ids))
return ids
class ReviewDataset(Dataset):
def __init__(self, sentences, labels):
self.sentences = [encode_sentence(s) for s in sentences]
self.labels = torch.tensor(labels, dtype=torch.long)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return torch.tensor(self.sentences[idx]), self.labels[idx]
train_dataset = ReviewDataset(train_sentences, train_labels)
test_dataset = ReviewDataset(test_sentences, test_labels)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32)
class SentimentLSTM(nn.Module):
def __init__(self, embedding_matrix, hidden_dim=128, output_dim=3):
super().__init__()
vocab_size, embedding_dim = embedding_matrix.shape
self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix), freeze=False)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.embedding(x)
_, (hidden, _) = self.lstm(x)
out = self.dropout(hidden[-1])
return self.fc(out)
class SentimentGRU(nn.Module):
def __init__(self, embedding_matrix, hidden_dim=128, output_dim=3):
super().__init__()
vocab_size, embedding_dim = embedding_matrix.shape
self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix), freeze=False)
self.gru = nn.GRU(embedding_dim, hidden_dim, batch_first=True)
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.embedding(x)
_, hidden = self.gru(x)
out = self.dropout(hidden[-1])
return self.fc(out)
class SentimentCNN(nn.Module):
def __init__(self, embedding_matrix, output_dim=3, filter_sizes=[3, 4, 5], num_filters=100):
super().__init__()
vocab_size, embedding_dim = embedding_matrix.shape
self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix), freeze=False)
self.convs = nn.ModuleList([
nn.Conv2d(1, num_filters, (fs, embedding_dim)) for fs in filter_sizes
])
self.fc = nn.Linear(num_filters * len(filter_sizes), output_dim)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.embedding(x).unsqueeze(1) # Add channel dimension
x = [torch.relu(conv(x)).squeeze(3) for conv in self.convs]
x = [torch.max(i, dim=2)[0] for i in x]
x = torch.cat(x, dim=1)
x = self.dropout(x)
return self.fc(x)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train_model(model, train_loader, epochs=5, lr=1e-3):
model.to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
for epoch in range(epochs):
model.train()
total_loss = 0
for x_batch, y_batch in train_loader:
x_batch, y_batch = x_batch.to(device), y_batch.to(device)
optimizer.zero_grad()
preds = model(x_batch)
loss = loss_fn(preds, y_batch)
loss.backward()
optimizer.step()
total_loss += loss.item()
print(f"Epoch {epoch+1}, Loss: {total_loss:.4f}")
def evaluate_model(model, test_loader):
model.eval()
all_preds = []
all_labels = []
with torch.no_grad():
for x_batch, y_batch in test_loader:
x_batch = x_batch.to(device)
preds = model(x_batch)
pred_labels = torch.argmax(preds, dim=1).cpu().numpy()
all_preds.extend(pred_labels)
all_labels.extend(y_batch.numpy())
print(classification_report(all_labels, all_preds))
print("\nTraining LSTM model...")
lstm_model = SentimentLSTM(embedding_matrix)
train_model(lstm_model, train_loader)
evaluate_model(lstm_model, test_loader)
print("\nTraining GRU model...")
gru_model = SentimentGRU(embedding_matrix)
train_model(gru_model, train_loader)
evaluate_model(gru_model, test_loader)
print("\nTraining CNN model...")
cnn_model = SentimentCNN(embedding_matrix)
train_model(cnn_model, train_loader)
evaluate_model(cnn_model, test_loader)
|