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Text augmentation module for sentiment analysis
Handles synonym replacement, word swapping, deletion, insertion, and back-translation
"""
import random
import numpy as np
import torch
import nlpaug.augmenter.word as naw
import nlpaug.augmenter.sentence as nas
from tqdm import tqdm
class TextAugmenter:
def __init__(self, aug_methods=['synonym', 'swap', 'delete'], aug_p=0.1):
self.aug_methods = aug_methods
self.aug_p = aug_p
if 'synonym' in aug_methods:
self.synonym_aug = naw.SynonymAug(aug_src='wordnet', aug_p=aug_p)
if 'backtranslation' in aug_methods:
try:
self.back_trans_aug = naw.BackTranslationAug(
from_model_name='facebook/wmt19-en-de',
to_model_name='facebook/wmt19-de-en',
device='cuda' if torch.cuda.is_available() else 'cpu'
)
except Exception as e:
print(f"Warning: Back-translation not available: {e}")
if 'backtranslation' in self.aug_methods:
self.aug_methods.remove('backtranslation')
def random_swap(self, text, n=2):
words = text.split()
if len(words) < 2:
return text
for _ in range(n):
idx1, idx2 = random.sample(range(len(words)), 2)
words[idx1], words[idx2] = words[idx2], words[idx1]
return ' '.join(words)
def random_deletion(self, text, p=0.1):
words = text.split()
if len(words) == 1:
return text
new_words = [word for word in words if random.random() > p]
if len(new_words) == 0:
return random.choice(words)
return ' '.join(new_words)
def random_insertion(self, text, n=1):
words = text.split()
if len(words) == 0:
return text
for _ in range(n):
random_word = random.choice(words)
random_idx = random.randint(0, len(words))
words.insert(random_idx, random_word)
return ' '.join(words)
def synonym_replacement(self, text):
if 'synonym' in self.aug_methods and hasattr(self, 'synonym_aug'):
try:
augmented = self.synonym_aug.augment(text)
# ✅ FIX: Handle nlpaug's variable return types safely
if augmented is None:
return text
elif isinstance(augmented, list):
# nlpaug sometimes returns list even with n=1
if len(augmented) > 0 and isinstance(augmented[0], str) and augmented[0].strip():
return augmented[0]
else:
return text
elif isinstance(augmented, str) and augmented.strip():
return augmented
else:
return text
except Exception as e:
print(f"Synonym augmentation failed for text '{text[:50]}...': {e}")
return text
return text
def back_translation(self, text):
if 'backtranslation' in self.aug_methods and hasattr(self, 'back_trans_aug'):
try:
augmented = self.back_trans_aug.augment(text)
# ✅ FIX: Same safe handling as above
if augmented is None:
return text
elif isinstance(augmented, list):
if len(augmented) > 0 and isinstance(augmented[0], str) and augmented[0].strip():
return augmented[0]
else:
return text
elif isinstance(augmented, str) and augmented.strip():
return augmented
else:
return text
except Exception as e:
print(f"Back-translation failed for text '{text[:50]}...': {e}")
return text
return text
def augment_text(self, text, method=None):
if not text or not isinstance(text, str) or not text.strip():
return text
if method is None:
method = random.choice(self.aug_methods)
if method == 'swap':
return self.random_swap(text, n=max(1, len(text.split()) // 10))
elif method == 'delete':
return self.random_deletion(text, p=self.aug_p)
elif method == 'insert':
return self.random_insertion(text, n=max(1, len(text.split()) // 10))
elif method == 'synonym':
return self.synonym_replacement(text)
elif method == 'backtranslation':
return self.back_translation(text)
else:
return text
def augment_dataset(self, texts, labels, n_aug=1, keep_original=True):
print(f"Augmenting dataset (n_aug={n_aug}, methods={self.aug_methods})...")
augmented_texts = []
augmented_labels = []
if keep_original:
augmented_texts.extend(texts)
augmented_labels.extend(labels)
for text, label in tqdm(zip(texts, labels), total=len(texts), desc="Augmenting"):
for _ in range(n_aug):
aug_text = self.augment_text(text)
augmented_texts.append(aug_text)
augmented_labels.append(label)
print(f"Original size: {len(texts)}")
print(f"Augmented size: {len(augmented_texts)}")
return np.array(augmented_texts), np.array(augmented_labels)
def augment_minority_classes(self, texts, labels, target_ratio=1.0):
from collections import Counter
class_counts = Counter(labels)
max_count = max(class_counts.values())
augmented_texts = list(texts)
augmented_labels = list(labels)
for class_label, count in class_counts.items():
target_count = int(max_count * target_ratio)
n_to_generate = max(0, target_count - count)
if n_to_generate > 0:
class_texts = [text for text, label in zip(texts, labels) if label == class_label]
for _ in tqdm(range(n_to_generate), desc=f"Augmenting class {class_label}"):
text = random.choice(class_texts)
aug_text = self.augment_text(text)
augmented_texts.append(aug_text)
augmented_labels.append(class_label)
print(f"\nOriginal size: {len(texts)}")
print(f"Augmented size: {len(augmented_texts)}")
print(f"Original distribution: {Counter(labels)}")
print(f"Augmented distribution: {Counter(augmented_labels)}")
return np.array(augmented_texts), np.array(augmented_labels)
def augment_training_data(data_dict, n_aug=1, balance_classes=True, aug_methods=['synonym', 'swap', 'delete']):
"""
Augment training data to handle class imbalance
Args:
data_dict: Dictionary with 'train' key containing 'texts' and 'labels'
n_aug: Number of augmentations per sample (if not balancing)
balance_classes: If True, augment minority classes to match majority
aug_methods: List of augmentation methods to use
Returns:
Updated data_dict with augmented training data
"""
augmenter = TextAugmenter(aug_methods=aug_methods, aug_p=0.1)
train_texts = data_dict['train']['texts']
train_labels = data_dict['train']['labels']
if balance_classes:
aug_texts, aug_labels = augmenter.augment_minority_classes(
train_texts, train_labels, target_ratio=1.0
)
else:
aug_texts, aug_labels = augmenter.augment_dataset(
train_texts, train_labels, n_aug=n_aug, keep_original=True
)
data_dict['train']['texts'] = aug_texts
data_dict['train']['labels'] = aug_labels
return data_dict |