dataset_statistics.py

import json
from pathlib import Path
from collections import Counter
import statistics as stats


def load_jsonl(file_path):
    """Load JSONL file and return list of items."""
    items = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            items.append(json.loads(line.strip()))
    return items


def calculate_text_statistics(items):
    """Calculate statistics for text fields."""
    text_lengths = [len(item['text'].split()) for item in items]
    char_lengths = [len(item['text']) for item in items]

    return {
        'avg_words': stats.mean(text_lengths),
        'min_words': min(text_lengths),
        'max_words': max(text_lengths),
        'median_words': stats.median(text_lengths),
        'avg_chars': stats.mean(char_lengths),
        'min_chars': min(char_lengths),
        'max_chars': max(char_lengths),
        'median_chars': stats.median(char_lengths),
    }


def analyze_classification_subset():
    """Analyze classification subset statistics."""
    print("\n" + "="*60)
    print("CLASSIFICATION SUBSET ANALYSIS")
    print("="*60)

    for split in ['train', 'test']:
        file_path = Path(f'data/classification/{split}.jsonl')
        items = load_jsonl(file_path)

        print(f"\n{split.upper()} Split:")
        print(f"  Total examples: {len(items)}")

        # Label distribution
        label_counter = Counter(item['label'] for item in items)
        print("\n  Label Distribution:")
        for label, count in label_counter.most_common():
            percentage = (count / len(items)) * 100
            print(f"    {label:20s}: {count:4d} ({percentage:5.1f}%)")

        # Text statistics
        text_stats = calculate_text_statistics(items)
        print("\n  Text Statistics:")
        print(f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
              f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
              f"Median: {text_stats['median_words']:.1f}")
        print(f"    Chars per text - Avg: {text_stats['avg_chars']:.1f}, "
              f"Min: {text_stats['min_chars']}, Max: {text_stats['max_chars']}, "
              f"Median: {text_stats['median_chars']:.1f}")


def analyze_sentiment_subset():
    """Analyze sentiment subset statistics."""
    print("\n" + "="*60)
    print("SENTIMENT SUBSET ANALYSIS")
    print("="*60)

    for split in ['train', 'test']:
        file_path = Path(f'data/sentiment/{split}.jsonl')
        items = load_jsonl(file_path)

        print(f"\n{split.upper()} Split:")
        print(f"  Total examples: {len(items)}")

        # Sentiment distribution
        sentiment_counter = Counter(item['sentiment'] for item in items)
        print("\n  Sentiment Distribution:")
        for sentiment, count in sentiment_counter.most_common():
            percentage = (count / len(items)) * 100
            print(f"    {sentiment:10s}: {count:4d} ({percentage:5.1f}%)")

        # Text statistics
        text_stats = calculate_text_statistics(items)
        print("\n  Text Statistics:")
        print(f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
              f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
              f"Median: {text_stats['median_words']:.1f}")


def analyze_aspect_sentiment_subset():
    """Analyze aspect-sentiment subset statistics."""
    print("\n" + "="*60)
    print("ASPECT-SENTIMENT SUBSET ANALYSIS")
    print("="*60)

    for split in ['train', 'test']:
        file_path = Path(f'data/aspect_sentiment/{split}.jsonl')
        items = load_jsonl(file_path)

        print(f"\n{split.upper()} Split:")
        print(f"  Total examples: {len(items)}")

        # Multi-aspect analysis
        single_aspect = sum(1 for item in items if len(item['aspects']) == 1)
        multi_aspect = sum(1 for item in items if len(item['aspects']) > 1)
        max_aspects = max(len(item['aspects']) for item in items)

        print(f"\n  Aspect Coverage:")
        print(f"    Single aspect: {single_aspect} ({(single_aspect/len(items))*100:.1f}%)")
        print(f"    Multi-aspect: {multi_aspect} ({(multi_aspect/len(items))*100:.1f}%)")
        print(f"    Max aspects per example: {max_aspects}")

        # Aspect-sentiment pair distribution
        aspect_sentiment_pairs = []
        for item in items:
            for asp in item['aspects']:
                aspect_sentiment_pairs.append(f"{asp['aspect']}#{asp['sentiment']}")

        pair_counter = Counter(aspect_sentiment_pairs)
        print("\n  Top 10 Aspect-Sentiment Pairs:")
        for pair, count in pair_counter.most_common(10):
            aspect, sentiment = pair.split('#')
            percentage = (count / len(aspect_sentiment_pairs)) * 100
            print(f"    {aspect:20s} + {sentiment:8s}: {count:4d} ({percentage:5.1f}%)")

        # Overall aspect distribution
        aspect_counter = Counter()
        sentiment_by_aspect = {}

        for item in items:
            for asp in item['aspects']:
                aspect = asp['aspect']
                sentiment = asp['sentiment']
                aspect_counter[aspect] += 1

                if aspect not in sentiment_by_aspect:
                    sentiment_by_aspect[aspect] = Counter()
                sentiment_by_aspect[aspect][sentiment] += 1

        print("\n  Aspect Distribution with Sentiment Breakdown:")
        for aspect, count in aspect_counter.most_common():
            percentage = (count / sum(aspect_counter.values())) * 100
            print(f"\n    {aspect:20s}: {count:4d} ({percentage:5.1f}%)")

            # Sentiment breakdown for this aspect
            sentiments = sentiment_by_aspect[aspect]
            total_aspect = sum(sentiments.values())
            for sentiment in ['positive', 'negative', 'neutral']:
                if sentiment in sentiments:
                    sent_count = sentiments[sentiment]
                    sent_pct = (sent_count / total_aspect) * 100
                    print(f"      - {sentiment:8s}: {sent_count:3d} ({sent_pct:5.1f}%)")


def generate_summary_statistics():
    """Generate overall summary statistics."""
    print("\n" + "="*60)
    print("DATASET SUMMARY")
    print("="*60)

    total_train = len(load_jsonl('data/classification/train.jsonl'))
    total_test = len(load_jsonl('data/classification/test.jsonl'))

    print("\nTotal Dataset Size:")
    print(f"  Train: {total_train} examples")
    print(f"  Test: {total_test} examples")
    print(f"  Total: {total_train + total_test} examples")
    print(f"  Train/Test Ratio: {total_train/total_test:.2f}:1")

    # Available subsets
    print("\nAvailable Subsets:")
    print("  1. Classification: Text → Label (14 banking aspect categories)")
    print("  2. Sentiment: Text → Sentiment (positive/negative/neutral)")
    print("  3. Aspect-Sentiment: Text → Multiple (Aspect, Sentiment) pairs")

    # Data format
    print("\nData Format:")
    print("  - All subsets use JSONL format")
    print("  - UTF-8 encoding")
    print("  - Vietnamese language text")

    # Use cases
    print("\nRecommended Use Cases:")
    print("  - Classification: Banking domain text classification")
    print("  - Sentiment: Customer feedback sentiment analysis")
    print("  - Aspect-Sentiment: Fine-grained aspect-based sentiment analysis")


def save_statistics_report():
    """Save statistics to a markdown file."""
    import sys
    from io import StringIO

    # Capture output
    old_stdout = sys.stdout
    sys.stdout = buffer = StringIO()

    # Run all analyses
    generate_summary_statistics()
    analyze_classification_subset()
    analyze_sentiment_subset()
    analyze_aspect_sentiment_subset()

    # Get output
    output = buffer.getvalue()
    sys.stdout = old_stdout

    # Save to file
    with open('statistics_report.md', 'w', encoding='utf-8') as f:
        f.write("# UTS2017_Bank Dataset Statistics Report\n\n")
        f.write("```\n")
        f.write(output)
        f.write("```\n")

    print("Statistics report saved to statistics_report.md")


if __name__ == "__main__":
    generate_summary_statistics()
    analyze_classification_subset()
    analyze_sentiment_subset()
    analyze_aspect_sentiment_subset()

    print("\n" + "="*60)
    save_statistics_report()