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ae47555 acf97d8 ae47555 acf97d8 ae47555 acf97d8 ae47555 acf97d8 ae47555 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
from collections import Counter
import re
import logging
logger = logging.getLogger(__name__)
class LSTMTokenizer:
"""
Simple tokenizer for LSTM models
"""
def __init__(self, max_vocab_size=30000, max_seq_length=512):
self.word2idx = {}
self.idx2word = {}
self.word2idx['<pad>'] = 0
self.word2idx['<unk>'] = 1
self.idx2word[0] = '<pad>'
self.idx2word[1] = '<unk>'
self.vocab_size = 2 # Start with pad and unk tokens
self.max_vocab_size = max_vocab_size
self.max_seq_length = max_seq_length
def fit(self, texts):
"""Build vocabulary from texts"""
word_counts = Counter()
# Clean and tokenize texts
for text in texts:
words = self._tokenize(text)
word_counts.update(words)
# Sort by frequency and take most common words
vocab_words = [word for word, count in word_counts.most_common(self.max_vocab_size - 2)]
# Add words to vocabulary
for word in vocab_words:
if word not in self.word2idx:
self.word2idx[word] = self.vocab_size
self.idx2word[self.vocab_size] = word
self.vocab_size += 1
logger.info(f"Vocabulary size: {self.vocab_size}")
return self
def _tokenize(self, text):
"""Simple tokenization by splitting on whitespace and removing punctuation"""
text = text.lower()
# Remove punctuation and split on whitespace
text = re.sub(r'[^\w\s]', '', text)
return text.split()
def encode(self, text, padding=True, truncation=True):
"""Convert text to token ids"""
words = self._tokenize(text)
# Truncate if needed
if truncation and len(words) > self.max_seq_length:
words = words[:self.max_seq_length]
# Convert to indices
ids = [self.word2idx.get(word, self.word2idx['<unk>']) for word in words]
# Create attention mask (1 for tokens, 0 for padding)
attention_mask = [1] * len(ids)
# Pad if needed
if padding and len(ids) < self.max_seq_length:
padding_length = self.max_seq_length - len(ids)
ids = ids + [self.word2idx['<pad>']] * padding_length
attention_mask = attention_mask + [0] * padding_length
return {
'input_ids': torch.tensor(ids, dtype=torch.long),
'attention_mask': torch.tensor(attention_mask, dtype=torch.long)
}
class LSTMDataset(Dataset):
"""Dataset for LSTM model"""
def __init__(self, texts, labels, tokenizer):
self.texts = texts
self.labels = labels
self.tokenizer = tokenizer
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
text = str(self.texts[idx])
label = self.labels[idx]
# Tokenize
encoding = self.tokenizer.encode(text)
return {
'input_ids': encoding['input_ids'],
'attention_mask': encoding['attention_mask'],
'label': torch.tensor(label, dtype=torch.long)
}
def get_text_(self, idx):
"""Get original text for a given index"""
return {
'text': self.texts[idx],
'label': self.labels[idx]
}
def prepare_lstm_data(data_path, text_col='text', label_col='label',
max_vocab_size=30000, max_seq_length=512,
val_split=0.1, test_split=0.1, batch_size=32, seed=42, return_datasets=False):
"""
Load data and prepare for LSTM model
"""
# Load data
if data_path.endswith('.csv'):
df = pd.read_csv(data_path)
elif data_path.endswith('.tsv'):
df = pd.read_csv(data_path, sep='\t')
else:
raise ValueError("Unsupported file format. Please provide CSV or TSV file.")
# Convert labels to numeric if they aren't already
if not np.issubdtype(df[label_col].dtype, np.number):
label_map = {label: idx for idx, label in enumerate(sorted(df[label_col].unique()))}
df['label_numeric'] = df[label_col].map(label_map)
labels = df['label_numeric'].values
logger.info(f"Label mapping: {label_map}")
else:
labels = df[label_col].values
# Make sure labels start from 0
min_label = labels.min()
if min_label != 0:
label_map = {label: idx for idx, label in enumerate(sorted(set(labels)))}
labels = np.array([label_map[label] for label in labels])
texts = df[text_col].values
# Split data
np.random.seed(seed)
indices = np.random.permutation(len(texts))
test_size = int(test_split * len(texts))
val_size = int(val_split * len(texts))
train_size = len(texts) - test_size - val_size
train_indices = indices[:train_size]
val_indices = indices[train_size:train_size + val_size]
test_indices = indices[train_size + val_size:]
train_texts, train_labels = texts[train_indices], labels[train_indices]
val_texts, val_labels = texts[val_indices], labels[val_indices]
test_texts, test_labels = texts[test_indices], labels[test_indices]
# Create tokenizer and fit on training data
tokenizer = LSTMTokenizer(max_vocab_size=max_vocab_size, max_seq_length=max_seq_length)
tokenizer.fit(train_texts)
# Create datasets
train_dataset = LSTMDataset(train_texts, train_labels, tokenizer)
val_dataset = LSTMDataset(val_texts, val_labels, tokenizer)
test_dataset = LSTMDataset(test_texts, test_labels, tokenizer)
if return_datasets:
return train_dataset, val_dataset, test_dataset, tokenizer.vocab_size
# Create data loaders
if len(train_dataset.texts) == 0:
logger.warning("Training dataset is empty. Please check your data.")
train_loader = None
else:
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
if len(val_dataset.texts) == 0:
logger.warning("Validation dataset is empty. Please check your data.")
val_loader = None
else:
val_loader = DataLoader(val_dataset, batch_size=batch_size)
if len(test_dataset.texts) == 0:
logger.warning("Test dataset is empty. Please check your data.")
test_loader = None
else:
test_loader = DataLoader(test_dataset, batch_size=batch_size)
return train_loader, val_loader, test_loader, tokenizer.vocab_size |