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from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizer, AutoTokenizer
import pandas as pd
import numpy as np
import logging
logger = logging.getLogger(__name__)
class DocumentDataset(Dataset):
"""
Dataset class for document classification
with improved preprocessing and batching
"""
def __init__(self, texts, labels, tokenizer_name='bert-base-uncased', max_length=512, num_classes=None):
self.texts = texts
self.labels = labels
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
self.max_length = max_length
if type(labels) is not np.ndarray or type(labels) is not list:
# Validate labels
unique_labels = set(labels)
min_label = min(unique_labels) if unique_labels else 0
max_label = max(unique_labels) if unique_labels else 0
# Log warning if labels might be out of range
if num_classes is not None and (min_label < 0 or max_label >= num_classes):
logger.warning(f"Label Range Error: Labels must be between 0 and {num_classes-1}, "
f"but found range [{min_label}, {max_label}]")
logger.warning(f"Unique label values: {sorted(unique_labels)}")
# Fix labels by remapping them to start from 0 (some datasets might have labels starting from 1)
if min_label != 0:
logger.warning(f"Auto-correcting labels to be zero-indexed...")
label_map = {original: idx for idx, original in enumerate(sorted(unique_labels))}
self.labels = np.array([label_map[label] for label in labels])
logger.warning(f"New unique label values: {sorted(set(self.labels))}")
else:
# If labels is a list or numpy array, there are multiple label columns
# Validate each label column
labels = np.array(labels)
for i in range(labels.shape[1]):
unique_labels = set(labels[:, i])
min_label = min(unique_labels) if unique_labels else 0
max_label = max(unique_labels) if unique_labels else 0
# Log warning if labels might be out of range
if num_classes is not None and (min_label < 0 or max_label >= num_classes):
logger.warning(f"Label Range Error: Labels must be between 0 and {num_classes-1}, "
f"but found range [{min_label}, {max_label}]")
logger.warning(f"Unique label values: {sorted(unique_labels)}")
# Fix labels by remapping them to start from 0
if min_label != 0:
logger.warning(f"Auto-correcting labels to be zero-indexed...")
label_map = {original: idx for idx, original in enumerate(sorted(unique_labels))}
labels[:, i] = np.array([label_map[label] for label in labels[:, i]])
logger.warning(f"New unique label values: {sorted(set(labels[:, i]))}")
self.labels = labels
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
text = str(self.texts[idx])
label = self.labels[idx]
# Tokenize the text with attention mask and truncation
encoding = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=self.max_length,
return_token_type_ids=True,
padding='max_length',
truncation=True,
return_attention_mask=True,
return_tensors='pt'
)
return {
'input_ids': encoding['input_ids'].flatten(),
'attention_mask': encoding['attention_mask'].flatten(),
'token_type_ids': encoding['token_type_ids'].flatten(),
'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 load_data(data_path, text_col='text', label_col: str | list ='label', validation_split=0.1, test_split=0.1, seed=42):
"""
Load data from CSV/TSV and split into train, validation and test sets
"""
# Determine file format based on extension
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.")
# If label_col is a list of columns, do the below but for each column
if isinstance(label_col, list):
labels = None
for idx, label in enumerate(label_col):
if label not in df.columns:
raise ValueError(f"Label column '{label}' not found in the dataset.")
# Convert labels to numeric if they aren't already
if not np.issubdtype(df[label].dtype, np.number):
label_map = {label: idx for idx, label in enumerate(sorted(df[label].unique()))}
df[f'label_numeric_{idx}'] = df[label].map(label_map)
if labels is None:
labels = df[f'label_numeric_{idx}'].values
else:
# Extend the labels array to dim 1
labels = np.column_stack((labels, df[f'label_numeric_{idx}'].values))
# Log the mapping for reference
logger.info(f"Label mapping for column '{label}': {label_map}")
else:
# Check if labels start from 0
labels = df[label].values
min_label = labels.min()
if min_label != 0:
logger.warning(f"Labels don't start from 0 (min={min_label}). Converting to zero-indexed...")
label_map = {label: idx for idx, label in enumerate(sorted(set(labels)))}
labels = np.array([label_map[label] for label in labels])
else: # In case there is only one label column
# 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
# Log the mapping for reference
logger.info(f"Label mapping: {label_map}")
else:
labels = df[label_col].values
# Check if labels start from 0
min_label = labels.min()
if min_label != 0:
logger.warning(f"Labels don't start from 0 (min={min_label}). Converting to zero-indexed...")
label_map = {label: idx for idx, label in enumerate(sorted(set(labels)))}
labels = np.array([label_map[label] for label in labels])
# Create a DataFrame with text and numeric labels
texts = df[text_col].values
# Shuffle and split the data
np.random.seed(seed)
indices = np.random.permutation(len(texts))
test_size = int(test_split * len(texts))
val_size = int(validation_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]
# Log stats about the dataset
logger.info(f"Dataset splits: train={train_size}, val={val_size}, test={test_size}")
logger.info(f"Label distribution in train set: {np.bincount(train_labels)}")
return (train_texts, train_labels), (val_texts, val_labels), (test_texts, test_labels)
def create_data_loaders(train_data, val_data, test_data, tokenizer_name='bert-base-uncased',
max_length=512, batch_size=16, num_classes=None, return_datasets=False):
"""
Create DataLoader objects for training, validation and testing
"""
train_texts, train_labels = train_data
val_texts, val_labels = val_data
test_texts, test_labels = test_data
# Create datasets
train_dataset = DocumentDataset(train_texts, train_labels, tokenizer_name, max_length, num_classes)
val_dataset = DocumentDataset(val_texts, val_labels, tokenizer_name, max_length, num_classes)
test_dataset = DocumentDataset(test_texts, test_labels, tokenizer_name, max_length, num_classes)
if return_datasets:
return train_dataset, val_dataset, test_dataset
# Create data loaders
if len(train_dataset.texts) == 0:
logger.warning("Training dataset is empty. Check your data loading and splitting.")
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. Check your data loading and splitting.")
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. Check your data loading and splitting.")
test_loader = None
else:
test_loader = DataLoader(test_dataset, batch_size=batch_size)
return train_loader, val_loader, test_loader |