dataset.py

import torch
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.num_classes = num_classes
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
        self.max_length = max_length
        
        if labels is None:
            self.labels = np.zeros(len(texts), dtype=int)
        elif type(labels) is not np.ndarray and 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] if self.labels is not None else torch.tensor(0, dtype=torch.long)

        # 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] if self.labels is not None else None
        }
    
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.reshape(-1, 1)
                else:
                    # Extend the labels array to dim 1
                    labels = np.concatenate((labels, df[f'label_numeric_{idx}'].values.reshape(-1, 1)), axis=1)
                
                # Log the mapping for reference
                logger.info(f"Label mapping for column '{label}': {label_map}")
            else: # Column is already numeric
                current_col_labels = df[label].values # Get current column's data
                # Check if labels start from 0
                min_label = current_col_labels.min()
                if min_label != 0:
                    logger.warning(f"Labels in numeric column '{label}' don't start from 0 (min={min_label}). Converting to zero-indexed...")
                    label_map = {lbl: idx for idx, lbl in enumerate(sorted(set(current_col_labels)))}
                    current_col_labels = np.array([label_map[lbl] for lbl in current_col_labels]) # Apply mapping to current column data

                # Concatenate this column to the main 'labels' array
                if labels is None:
                    # This is the first column encountered (and it's numeric)
                    labels = current_col_labels.reshape(-1, 1)
                else:
                    # Append this numeric column to existing labels
                    labels = np.concatenate((labels, current_col_labels.reshape(-1, 1)), axis=1)
    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
            
            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}")
    # Also print the num_categories being passed
    
    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