scripts/statistics.py

"""
Statistics for UD Vietnamese Dataset (UDD-v0.1)
"""

from collections import Counter
from os.path import dirname, join


def parse_conllu(filepath):
    """Parse CoNLL-U file and return sentences."""
    sentences = []
    current_sentence = {
        'tokens': [],
        'upos': [],
        'deprel': [],
        'head': [],
        'metadata': {}
    }

    with open(filepath, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if not line:
                if current_sentence['tokens']:
                    sentences.append(current_sentence)
                    current_sentence = {
                        'tokens': [],
                        'upos': [],
                        'deprel': [],
                        'head': [],
                        'metadata': {}
                    }
            elif line.startswith('#'):
                # Metadata
                if '=' in line:
                    key, value = line[2:].split('=', 1)
                    current_sentence['metadata'][key.strip()] = value.strip()
            else:
                parts = line.split('\t')
                if len(parts) >= 10:
                    # Skip multi-word tokens (e.g., 1-2)
                    if '-' in parts[0] or '.' in parts[0]:
                        continue
                    current_sentence['tokens'].append(parts[1])
                    current_sentence['upos'].append(parts[3])
                    current_sentence['head'].append(parts[6])
                    current_sentence['deprel'].append(parts[7])

    # Add last sentence if exists
    if current_sentence['tokens']:
        sentences.append(current_sentence)

    return sentences


def compute_statistics(sentences):
    """Compute statistics from parsed sentences."""
    stats = {}

    # Basic counts
    stats['num_sentences'] = len(sentences)
    stats['num_tokens'] = sum(len(s['tokens']) for s in sentences)

    # Sentence length statistics
    sent_lengths = [len(s['tokens']) for s in sentences]
    stats['avg_sent_length'] = sum(sent_lengths) / len(sent_lengths) if sent_lengths else 0
    stats['min_sent_length'] = min(sent_lengths) if sent_lengths else 0
    stats['max_sent_length'] = max(sent_lengths) if sent_lengths else 0

    # UPOS distribution
    all_upos = []
    for s in sentences:
        all_upos.extend(s['upos'])
    stats['upos_counts'] = Counter(all_upos)

    # DEPREL distribution
    all_deprel = []
    for s in sentences:
        all_deprel.extend(s['deprel'])
    stats['deprel_counts'] = Counter(all_deprel)

    # Tree depth statistics
    depths = []
    for s in sentences:
        max_depth = compute_tree_depth(s['head'])
        depths.append(max_depth)
    stats['avg_tree_depth'] = sum(depths) / len(depths) if depths else 0
    stats['max_tree_depth'] = max(depths) if depths else 0

    # Root relation counts
    root_upos = []
    for s in sentences:
        for i, (upos, deprel) in enumerate(zip(s['upos'], s['deprel'])):
            if deprel == 'root':
                root_upos.append(upos)
    stats['root_upos_counts'] = Counter(root_upos)

    return stats


def compute_tree_depth(heads):
    """Compute maximum depth of dependency tree."""
    n = len(heads)
    if n == 0:
        return 0

    depths = [0] * n

    def get_depth(idx):
        if depths[idx] > 0:
            return depths[idx]
        head = int(heads[idx])
        if head == 0:
            depths[idx] = 1
        else:
            depths[idx] = get_depth(head - 1) + 1
        return depths[idx]

    for i in range(n):
        try:
            get_depth(i)
        except (RecursionError, IndexError):
            depths[i] = 1

    return max(depths) if depths else 0


def print_statistics(stats):
    """Print statistics in a nice format."""
    print("=" * 60)
    print("UD Vietnamese Dataset (UDD-v0.1) Statistics")
    print("=" * 60)

    print("\n## Basic Statistics")
    print(f"  Sentences:         {stats['num_sentences']:,}")
    print(f"  Tokens:            {stats['num_tokens']:,}")
    print(f"  Avg sent length:   {stats['avg_sent_length']:.2f}")
    print(f"  Min sent length:   {stats['min_sent_length']}")
    print(f"  Max sent length:   {stats['max_sent_length']}")
    print(f"  Avg tree depth:    {stats['avg_tree_depth']:.2f}")
    print(f"  Max tree depth:    {stats['max_tree_depth']}")

    print("\n## UPOS Distribution")
    print(f"  {'Tag':<10} {'Count':>8} {'Percent':>8}")
    print("  " + "-" * 28)
    total_tokens = stats['num_tokens']
    for tag, count in stats['upos_counts'].most_common():
        pct = count / total_tokens * 100
        print(f"  {tag:<10} {count:>8,} {pct:>7.2f}%")

    print("\n## DEPREL Distribution")
    print(f"  {'Relation':<20} {'Count':>8} {'Percent':>8}")
    print("  " + "-" * 38)
    for rel, count in stats['deprel_counts'].most_common():
        pct = count / total_tokens * 100
        print(f"  {rel:<20} {count:>8,} {pct:>7.2f}%")

    print("\n## Root UPOS Distribution")
    print(f"  {'UPOS':<10} {'Count':>8} {'Percent':>8}")
    print("  " + "-" * 28)
    total_roots = sum(stats['root_upos_counts'].values())
    for tag, count in stats['root_upos_counts'].most_common():
        pct = count / total_roots * 100
        print(f"  {tag:<10} {count:>8,} {pct:>7.2f}%")

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


def main():
    # Find train.conllu file
    base_dir = dirname(dirname(__file__))
    conllu_file = join(base_dir, 'train.conllu')

    print(f"Reading: {conllu_file}")
    sentences = parse_conllu(conllu_file)

    stats = compute_statistics(sentences)
    print_statistics(stats)


if __name__ == "__main__":
    main()