Dataset Loading and Iteration

/**
 * CNN sentence dataset iterator for sentence classification tasks
 * Provides standardized data preparation for CNN-based text classification
 */
public class CnnSentenceDataSetIterator {
    // CNN-specific dataset iteration with sentence-level batching and preprocessing
}

/**
 * Reuters News Groups dataset iterator
 * Provides access to Reuters news articles with category labels
 */
public class ReutersNewsGroupsDataSetIterator {
    // Iterator for Reuters dataset with automatic downloading and preprocessing
}

/**
 * Reuters News Groups dataset loader
 * Handles downloading, extraction, and preparation of Reuters dataset
 */
public class ReutersNewsGroupsLoader {
    // Dataset loading utilities for Reuters news groups data
}

/**
 * Interface for providing labeled sentences to dataset iterators
 */
public interface LabeledSentenceProvider {
    /**
     * Get total number of labeled sentences
     * @return Total count of available labeled sentences
     */
    int totalNumSentences();
    
    /**
     * Get all available sentence labels
     * @return List of all unique labels in the dataset
     */
    List<String> allLabels();
    
    /**
     * Get sentence at specific index
     * @param index Index of sentence to retrieve
     * @return Sentence string at specified index
     */
    String sentenceAt(int index);
    
    /**
     * Get label for sentence at specific index
     * @param index Index of sentence label to retrieve
     * @return Label string for sentence at specified index
     */
    String labelAt(int index);
}

/**
 * Collection-based labeled sentence provider
 * Provides labeled sentences from in-memory collections
 */
public class CollectionLabeledSentenceProvider implements LabeledSentenceProvider {
    
    /**
     * Create provider from sentence and label collections
     * @param sentences Collection of sentence strings
     * @param labels Collection of corresponding label strings
     */
    public CollectionLabeledSentenceProvider(Collection<String> sentences, Collection<String> labels);
    
    /**
     * Create provider from labeled document collection
     * @param documents Collection of LabelledDocument instances
     */
    public CollectionLabeledSentenceProvider(Collection<LabelledDocument> documents);
}

/**
 * File-based labeled sentence provider
 * Reads labeled sentences from file system with various formats
 */
public class FileLabeledSentenceProvider implements LabeledSentenceProvider {
    
    /**
     * Create provider from file with specified format
     * @param file File containing labeled sentences
     * @param format Format specification for parsing labeled data
     */
    public FileLabeledSentenceProvider(File file, LabeledSentenceFormat format);
    
    /**
     * Create provider from directory with label-based organization
     * @param directory Directory containing subdirectories for each label
     */
    public FileLabeledSentenceProvider(File directory);
}

/**
 * Converter for label-aware data formats
 * Handles conversion between different labeled data representations
 */
public class LabelAwareConverter {
    
    /**
     * Convert label-aware iterator to standard format
     * @param iterator LabelAwareIterator to convert
     * @return Converted data in standard format
     */
    public static ConvertedData convert(LabelAwareIterator iterator);
    
    /**
     * Convert labeled document collection to provider format
     * @param documents Collection of LabelledDocument instances
     * @return LabeledSentenceProvider for the documents
     */
    public static LabeledSentenceProvider convert(Collection<LabelledDocument> documents);
}

import org.deeplearning4j.datasets.iterator.ReutersNewsGroupsDataSetIterator;
import org.deeplearning4j.datasets.loader.ReutersNewsGroupsLoader;
import org.deeplearning4j.iterator.*;
import org.deeplearning4j.iterator.provider.*;

// Reuters News Groups dataset usage
ReutersNewsGroupsDataSetIterator reutersIterator = new ReutersNewsGroupsDataSetIterator(
    32,      // batch size
    100,     // truncate length
    true,    // train set
    new DefaultTokenizerFactory()
);

while (reutersIterator.hasNext()) {
    DataSet batch = reutersIterator.next();
    // Process batch for training
    System.out.println("Batch size: " + batch.numExamples());
}

// Custom labeled sentence provider from collections
Collection<String> sentences = Arrays.asList(
    "This is a positive example",
    "This is a negative example",
    "Another positive case"
);

Collection<String> labels = Arrays.asList(
    "positive",
    "negative", 
    "positive"
);

LabeledSentenceProvider provider = new CollectionLabeledSentenceProvider(sentences, labels);
System.out.println("Total sentences: " + provider.totalNumSentences());
System.out.println("Available labels: " + provider.allLabels());

// Access specific sentences and labels
for (int i = 0; i < provider.totalNumSentences(); i++) {
    String sentence = provider.sentenceAt(i);
    String label = provider.labelAt(i);
    System.out.println("Sentence: " + sentence + " -> Label: " + label);
}

// File-based labeled sentence provider
File labeledDataFile = new File("labeled_data.txt");
FileLabeledSentenceProvider fileProvider = new FileLabeledSentenceProvider(
    labeledDataFile, 
    LabeledSentenceFormat.TAB_SEPARATED // or other format
);

// Directory-based provider (subdirectories as labels)
File dataDirectory = new File("data/");
// Expected structure:
// data/
//   positive/
//     file1.txt
//     file2.txt
//   negative/
//     file3.txt
//     file4.txt

FileLabeledSentenceProvider dirProvider = new FileLabeledSentenceProvider(dataDirectory);

// CNN sentence dataset iterator configuration
LabeledSentenceProvider sentenceProvider = new CollectionLabeledSentenceProvider(
    sentences, labels
);

CnnSentenceDataSetIterator cnnIterator = new CnnSentenceDataSetIterator.Builder()
    .sentenceProvider(sentenceProvider)
    .tokenizerFactory(new DefaultTokenizerFactory())
    .maxSentenceLength(100)
    .minibatchSize(32)
    .build();

// Use with neural network training
while (cnnIterator.hasNext()) {
    DataSet batch = cnnIterator.next();
    // Train CNN model with batch
}

// Reuters dataset downloading and preparation
ReutersNewsGroupsLoader loader = new ReutersNewsGroupsLoader();
// loader.downloadAndExtract(); // Downloads Reuters data if not present

// Advanced iterator configuration with custom preprocessing
TokenizerFactory customTokenizer = new DefaultTokenizerFactory();
customTokenizer.setTokenPreProcessor(new CommonPreprocessor());

CnnSentenceDataSetIterator advancedIterator = new CnnSentenceDataSetIterator.Builder()
    .sentenceProvider(provider)
    .tokenizerFactory(customTokenizer)
    .maxSentenceLength(150)
    .minibatchSize(64)
    .useNormalizedWordVectors(true)
    .build();