src/evaluate_word_segmentation.py

"""
Error analysis script for Vietnamese Word Segmentation (TRE-1).

Loads a trained VLSP 2013 model, predicts on the test set, and performs
detailed error analysis across multiple dimensions:
- Syllable-level confusion (B/I)
- Word-level false splits and false joins
- Error rate by word length
- Top error patterns with context
- Boundary errors (near sentence start/end)

Usage:
    source .venv/bin/activate
    python src/evaluate_word_segmentation.py
    python src/evaluate_word_segmentation.py --model models/word_segmentation/vlsp2013
    python src/evaluate_word_segmentation.py --output results/word_segmentation
"""

import csv
from collections import Counter, defaultdict
from pathlib import Path

import click

PROJECT_ROOT = Path(__file__).parent.parent


# ============================================================================
# Feature Extraction (duplicated from train_word_segmentation.py)
# ============================================================================

FEATURE_GROUPS = {
    "form":      ["S[0]", "S[0].lower"],
    "type":      ["S[0].istitle", "S[0].isupper", "S[0].isdigit", "S[0].ispunct", "S[0].len"],
    "morphology": ["S[0].prefix2", "S[0].suffix2"],
    "left":      ["S[-1]", "S[-1].lower", "S[-2]", "S[-2].lower"],
    "right":     ["S[1]", "S[1].lower", "S[2]", "S[2].lower"],
    "bigram":    ["S[-1,0]", "S[0,1]"],
    "trigram":   ["S[-1,0,1]"],
    "dictionary": ["S[-1,0].in_dict", "S[0,1].in_dict"],
}


def get_all_templates():
    """Return all feature templates (all groups enabled)."""
    templates = []
    for group_templates in FEATURE_GROUPS.values():
        templates.extend(group_templates)
    return templates


def get_syllable_at(syllables, position, offset):
    idx = position + offset
    if idx < 0:
        return "__BOS__"
    elif idx >= len(syllables):
        return "__EOS__"
    return syllables[idx]


def is_punct(s):
    return len(s) == 1 and not s.isalnum()


def load_dictionary(path):
    """Load dictionary from a text file (one word per line)."""
    dictionary = set()
    with open(path, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                dictionary.add(line)
    return dictionary


def extract_syllable_features(syllables, position, active_templates, dictionary=None):
    active = set(active_templates)
    features = {}

    s0 = get_syllable_at(syllables, position, 0)
    is_boundary = s0 in ("__BOS__", "__EOS__")

    if "S[0]" in active:
        features["S[0]"] = s0
    if "S[0].lower" in active:
        features["S[0].lower"] = s0.lower() if not is_boundary else s0
    if "S[0].istitle" in active:
        features["S[0].istitle"] = str(s0.istitle()) if not is_boundary else "False"
    if "S[0].isupper" in active:
        features["S[0].isupper"] = str(s0.isupper()) if not is_boundary else "False"
    if "S[0].isdigit" in active:
        features["S[0].isdigit"] = str(s0.isdigit()) if not is_boundary else "False"
    if "S[0].ispunct" in active:
        features["S[0].ispunct"] = str(is_punct(s0)) if not is_boundary else "False"
    if "S[0].len" in active:
        features["S[0].len"] = str(len(s0)) if not is_boundary else "0"
    if "S[0].prefix2" in active:
        features["S[0].prefix2"] = s0[:2] if not is_boundary and len(s0) >= 2 else s0
    if "S[0].suffix2" in active:
        features["S[0].suffix2"] = s0[-2:] if not is_boundary and len(s0) >= 2 else s0

    s_1 = get_syllable_at(syllables, position, -1)
    s_2 = get_syllable_at(syllables, position, -2)
    if "S[-1]" in active:
        features["S[-1]"] = s_1
    if "S[-1].lower" in active:
        features["S[-1].lower"] = s_1.lower() if s_1 not in ("__BOS__", "__EOS__") else s_1
    if "S[-2]" in active:
        features["S[-2]"] = s_2
    if "S[-2].lower" in active:
        features["S[-2].lower"] = s_2.lower() if s_2 not in ("__BOS__", "__EOS__") else s_2

    s1 = get_syllable_at(syllables, position, 1)
    s2 = get_syllable_at(syllables, position, 2)
    if "S[1]" in active:
        features["S[1]"] = s1
    if "S[1].lower" in active:
        features["S[1].lower"] = s1.lower() if s1 not in ("__BOS__", "__EOS__") else s1
    if "S[2]" in active:
        features["S[2]"] = s2
    if "S[2].lower" in active:
        features["S[2].lower"] = s2.lower() if s2 not in ("__BOS__", "__EOS__") else s2

    if "S[-1,0]" in active:
        features["S[-1,0]"] = f"{s_1}|{s0}"
    if "S[0,1]" in active:
        features["S[0,1]"] = f"{s0}|{s1}"
    if "S[-1,0,1]" in active:
        features["S[-1,0,1]"] = f"{s_1}|{s0}|{s1}"

    # G8: Dictionary lookup — longest match for bigram windows
    if dictionary is not None:
        n = len(syllables)

        if "S[-1,0].in_dict" in active and position >= 1:
            match = ""
            for length in range(2, min(6, position + 2)):
                start = position - length + 1
                if start >= 0:
                    ngram = " ".join(syllables[start:position + 1]).lower()
                    if ngram in dictionary:
                        match = ngram
            features["S[-1,0].in_dict"] = match if match else "0"

        if "S[0,1].in_dict" in active and position < n - 1:
            match = ""
            for length in range(2, min(6, n - position + 1)):
                ngram = " ".join(syllables[position:position + length]).lower()
                if ngram in dictionary:
                    match = ngram
            features["S[0,1].in_dict"] = match if match else "0"

    return features


def sentence_to_syllable_features(syllables, active_templates, dictionary=None):
    return [
        [f"{k}={v}" for k, v in extract_syllable_features(syllables, i, active_templates, dictionary).items()]
        for i in range(len(syllables))
    ]


# ============================================================================
# Data Loading
# ============================================================================

def load_vlsp2013_test(data_dir):
    """Load VLSP 2013 test set."""
    tag_map = {"B-W": "B", "I-W": "I"}
    sequences = []
    current_syls = []
    current_labels = []

    with open(data_dir / "test.txt", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                if current_syls:
                    sequences.append((current_syls, current_labels))
                    current_syls = []
                    current_labels = []
            else:
                parts = line.split("\t")
                if len(parts) == 2:
                    current_syls.append(parts[0])
                    current_labels.append(tag_map.get(parts[1], parts[1]))
        if current_syls:
            sequences.append((current_syls, current_labels))

    return sequences


# ============================================================================
# Label Utilities
# ============================================================================

def labels_to_words(syllables, labels):
    """Convert syllable sequence and BIO labels back to words."""
    words = []
    current_word = []
    for syl, label in zip(syllables, labels):
        if label == "B":
            if current_word:
                words.append(" ".join(current_word))
            current_word = [syl]
        else:
            current_word.append(syl)
    if current_word:
        words.append(" ".join(current_word))
    return words


def labels_to_word_spans(syllables, labels):
    """Convert BIO labels to word spans as (start_idx, end_idx, word_text)."""
    spans = []
    start = 0
    for i, (syl, label) in enumerate(zip(syllables, labels)):
        if label == "B" and i > 0:
            word = " ".join(syllables[start:i])
            spans.append((start, i, word))
            start = i
    if start < len(syllables):
        word = " ".join(syllables[start:])
        spans.append((start, len(syllables), word))
    return spans


# ============================================================================
# Error Analysis
# ============================================================================

def analyze_syllable_errors(all_true, all_pred):
    """Analyze syllable-level B/I confusion."""
    b_to_i = 0  # false join: predicted I where truth is B
    i_to_b = 0  # false split: predicted B where truth is I
    total_b = 0
    total_i = 0

    for true_labels, pred_labels in zip(all_true, all_pred):
        for t, p in zip(true_labels, pred_labels):
            if t == "B":
                total_b += 1
                if p == "I":
                    b_to_i += 1
            elif t == "I":
                total_i += 1
                if p == "B":
                    i_to_b += 1

    return {
        "total_b": total_b,
        "total_i": total_i,
        "b_to_i": b_to_i,
        "i_to_b": i_to_b,
        "b_to_i_rate": b_to_i / total_b if total_b > 0 else 0,
        "i_to_b_rate": i_to_b / total_i if total_i > 0 else 0,
    }


def analyze_word_errors(all_syllables, all_true, all_pred):
    """Analyze word-level errors: false splits and false joins."""
    false_splits = []  # compound words incorrectly broken apart (I→B)
    false_joins = []   # separate words incorrectly merged (B→I)

    for syllables, true_labels, pred_labels in zip(all_syllables, all_true, all_pred):
        true_spans = set()
        pred_spans = set()

        for start, end, word in labels_to_word_spans(syllables, true_labels):
            true_spans.add((start, end))
        for start, end, word in labels_to_word_spans(syllables, pred_labels):
            pred_spans.add((start, end))

        true_words = labels_to_words(syllables, true_labels)
        pred_words = labels_to_words(syllables, pred_labels)

        # Find words in truth that were split in prediction
        true_span_list = labels_to_word_spans(syllables, true_labels)
        pred_span_list = labels_to_word_spans(syllables, pred_labels)

        for start, end, word in true_span_list:
            n_syls = end - start
            if n_syls > 1 and (start, end) not in pred_spans:
                # This true multi-syllable word was not predicted as a unit
                # Find what the prediction did with these syllables
                pred_parts = []
                for ps, pe, pw in pred_span_list:
                    if ps >= start and pe <= end:
                        pred_parts.append(pw)
                    elif ps < end and pe > start:
                        pred_parts.append(pw)
                if len(pred_parts) > 1:
                    context_start = max(0, start - 2)
                    context_end = min(len(syllables), end + 2)
                    context = " ".join(syllables[context_start:context_end])
                    false_splits.append((word, pred_parts, context))

        for start, end, word in pred_span_list:
            n_syls = end - start
            if n_syls > 1 and (start, end) not in true_spans:
                # This predicted multi-syllable word was not in truth
                # Find what truth had for these syllables
                true_parts = []
                for ts, te, tw in true_span_list:
                    if ts >= start and te <= end:
                        true_parts.append(tw)
                    elif ts < end and te > start:
                        true_parts.append(tw)
                if len(true_parts) > 1:
                    context_start = max(0, start - 2)
                    context_end = min(len(syllables), end + 2)
                    context = " ".join(syllables[context_start:context_end])
                    false_joins.append((word, true_parts, context))

    return false_splits, false_joins


def analyze_errors_by_word_length(all_syllables, all_true, all_pred):
    """Compute error rates broken down by true word length (in syllables)."""
    correct_by_len = Counter()
    total_by_len = Counter()

    for syllables, true_labels, pred_labels in zip(all_syllables, all_true, all_pred):
        true_spans = set()
        pred_spans = set()

        for start, end, word in labels_to_word_spans(syllables, true_labels):
            true_spans.add((start, end))
            n_syls = end - start
            total_by_len[n_syls] += 1

        for start, end, word in labels_to_word_spans(syllables, pred_labels):
            pred_spans.add((start, end))

        for span in true_spans:
            n_syls = span[1] - span[0]
            if span in pred_spans:
                correct_by_len[n_syls] += 1

    results = {}
    for length in sorted(total_by_len.keys()):
        total = total_by_len[length]
        correct = correct_by_len[length]
        results[length] = {
            "total": total,
            "correct": correct,
            "errors": total - correct,
            "accuracy": correct / total if total > 0 else 0,
            "error_rate": (total - correct) / total if total > 0 else 0,
        }
    return results


def analyze_boundary_errors(all_syllables, all_true, all_pred, window=3):
    """Analyze errors near sentence start/end."""
    start_errors = 0
    start_total = 0
    end_errors = 0
    end_total = 0
    middle_errors = 0
    middle_total = 0

    for syllables, true_labels, pred_labels in zip(all_syllables, all_true, all_pred):
        n = len(syllables)
        for i, (t, p) in enumerate(zip(true_labels, pred_labels)):
            if i < window:
                start_total += 1
                if t != p:
                    start_errors += 1
            elif i >= n - window:
                end_total += 1
                if t != p:
                    end_errors += 1
            else:
                middle_total += 1
                if t != p:
                    middle_errors += 1

    return {
        "start": {"errors": start_errors, "total": start_total,
                  "error_rate": start_errors / start_total if start_total > 0 else 0},
        "end": {"errors": end_errors, "total": end_total,
                "error_rate": end_errors / end_total if end_total > 0 else 0},
        "middle": {"errors": middle_errors, "total": middle_total,
                   "error_rate": middle_errors / middle_total if middle_total > 0 else 0},
    }


def get_top_error_patterns(all_syllables, all_true, all_pred, top_n=20):
    """Find the most common incorrectly segmented syllable pairs."""
    error_patterns = Counter()

    for syllables, true_labels, pred_labels in zip(all_syllables, all_true, all_pred):
        for i, (t, p) in enumerate(zip(true_labels, pred_labels)):
            if t != p:
                syl = syllables[i]
                prev_syl = syllables[i - 1] if i > 0 else "__BOS__"
                next_syl = syllables[i + 1] if i < len(syllables) - 1 else "__EOS__"
                error_type = f"{t}→{p}"
                pattern = (prev_syl, syl, next_syl, error_type)
                error_patterns[pattern] += 1

    return error_patterns.most_common(top_n)


def compute_word_metrics(all_syllables, all_true, all_pred):
    """Compute word-level precision, recall, F1."""
    correct = 0
    total_pred = 0
    total_true = 0

    for syllables, true_labels, pred_labels in zip(all_syllables, all_true, all_pred):
        true_words = labels_to_words(syllables, true_labels)
        pred_words = labels_to_words(syllables, pred_labels)

        total_true += len(true_words)
        total_pred += len(pred_words)

        true_boundaries = set()
        pred_boundaries = set()

        pos = 0
        for word in true_words:
            n_syls = len(word.split())
            true_boundaries.add((pos, pos + n_syls))
            pos += n_syls

        pos = 0
        for word in pred_words:
            n_syls = len(word.split())
            pred_boundaries.add((pos, pos + n_syls))
            pos += n_syls

        correct += len(true_boundaries & pred_boundaries)

    precision = correct / total_pred if total_pred > 0 else 0
    recall = correct / total_true if total_true > 0 else 0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

    return {
        "precision": precision,
        "recall": recall,
        "f1": f1,
        "total_true": total_true,
        "total_pred": total_pred,
        "correct": correct,
    }


# ============================================================================
# Reporting
# ============================================================================

def format_report(syl_errors, word_metrics, false_splits, false_joins,
                  length_errors, boundary_errors, top_patterns,
                  num_sentences, num_syllables):
    """Format error analysis as text report."""
    lines = []
    lines.append("=" * 70)
    lines.append("Word Segmentation Error Analysis — VLSP 2013 Test Set")
    lines.append("=" * 70)
    lines.append("")

    # Summary
    total_syl_errors = syl_errors["b_to_i"] + syl_errors["i_to_b"]
    lines.append("1. Summary")
    lines.append("-" * 40)
    lines.append(f"  Sentences:           {num_sentences:,}")
    lines.append(f"  Syllables:           {num_syllables:,}")
    lines.append(f"  True words:          {word_metrics['total_true']:,}")
    lines.append(f"  Predicted words:     {word_metrics['total_pred']:,}")
    lines.append(f"  Correct words:       {word_metrics['correct']:,}")
    lines.append(f"  Word Precision:      {word_metrics['precision']:.4f} ({word_metrics['precision']*100:.2f}%)")
    lines.append(f"  Word Recall:         {word_metrics['recall']:.4f} ({word_metrics['recall']*100:.2f}%)")
    lines.append(f"  Word F1:             {word_metrics['f1']:.4f} ({word_metrics['f1']*100:.2f}%)")
    lines.append(f"  Syllable errors:     {total_syl_errors:,} / {num_syllables:,} ({total_syl_errors/num_syllables*100:.2f}%)")
    lines.append(f"  Word errors (FN):    {word_metrics['total_true'] - word_metrics['correct']:,}")
    lines.append(f"  Word errors (FP):    {word_metrics['total_pred'] - word_metrics['correct']:,}")
    lines.append("")

    # Syllable confusion
    lines.append("2. Syllable-Level Confusion (B/I)")
    lines.append("-" * 40)
    lines.append(f"  True B, Predicted I (false join):  {syl_errors['b_to_i']:,} / {syl_errors['total_b']:,} ({syl_errors['b_to_i_rate']*100:.2f}%)")
    lines.append(f"  True I, Predicted B (false split): {syl_errors['i_to_b']:,} / {syl_errors['total_i']:,} ({syl_errors['i_to_b_rate']*100:.2f}%)")
    lines.append("")
    lines.append("  Confusion Matrix:")
    lines.append(f"              Pred B    Pred I")
    lines.append(f"  True B    {syl_errors['total_b'] - syl_errors['b_to_i']:>8,}  {syl_errors['b_to_i']:>8,}")
    lines.append(f"  True I    {syl_errors['i_to_b']:>8,}  {syl_errors['total_i'] - syl_errors['i_to_b']:>8,}")
    lines.append("")

    # False splits
    split_counter = Counter()
    for word, parts, context in false_splits:
        split_counter[word] += 1

    lines.append("3. Top False Splits (compound words broken apart)")
    lines.append("-" * 70)
    lines.append(f"  Total false splits: {len(false_splits):,}")
    lines.append(f"  Unique words affected: {len(split_counter):,}")
    lines.append("")
    lines.append(f"  {'Word':<25} {'Count':<8} {'Example context'}")
    lines.append(f"  {'----':<25} {'-----':<8} {'---------------'}")
    for word, count in split_counter.most_common(20):
        # Find an example context for this word
        for w, parts, ctx in false_splits:
            if w == word:
                lines.append(f"  {word:<25} {count:<8} {ctx}")
                break
    lines.append("")

    # False joins
    join_counter = Counter()
    for word, parts, context in false_joins:
        join_counter[word] += 1

    lines.append("4. Top False Joins (separate words merged)")
    lines.append("-" * 70)
    lines.append(f"  Total false joins: {len(false_joins):,}")
    lines.append(f"  Unique words affected: {len(join_counter):,}")
    lines.append("")
    lines.append(f"  {'Merged as':<25} {'Count':<8} {'Should be':<30} {'Context'}")
    lines.append(f"  {'---------':<25} {'-----':<8} {'---------':<30} {'-------'}")
    for word, count in join_counter.most_common(20):
        for w, parts, ctx in false_joins:
            if w == word:
                should_be = " | ".join(parts)
                lines.append(f"  {word:<25} {count:<8} {should_be:<30} {ctx}")
                break
    lines.append("")

    # Error by word length
    lines.append("5. Error Rate by Word Length (syllables)")
    lines.append("-" * 70)
    lines.append(f"  {'Length':<10} {'Total':<10} {'Correct':<10} {'Errors':<10} {'Accuracy':<12} {'Error Rate'}")
    lines.append(f"  {'------':<10} {'-----':<10} {'-------':<10} {'------':<10} {'--------':<12} {'----------'}")
    for length, stats in sorted(length_errors.items()):
        label = f"{length}-syl"
        lines.append(f"  {label:<10} {stats['total']:<10,} {stats['correct']:<10,} {stats['errors']:<10,} {stats['accuracy']*100:>8.2f}%    {stats['error_rate']*100:.2f}%")
    lines.append("")

    # Boundary errors
    lines.append("6. Error Rate by Position in Sentence")
    lines.append("-" * 40)
    for region, stats in boundary_errors.items():
        label = f"{region.capitalize()} (first/last 3 syls)" if region != "middle" else "Middle"
        lines.append(f"  {label:<35} {stats['errors']:,} / {stats['total']:,} ({stats['error_rate']*100:.2f}%)")
    lines.append("")

    # Top error patterns
    lines.append("7. Top Error Patterns (syllable in context)")
    lines.append("-" * 70)
    lines.append(f"  {'Prev syl':<15} {'Current':<15} {'Next syl':<15} {'Error':<8} {'Count'}")
    lines.append(f"  {'--------':<15} {'-------':<15} {'--------':<15} {'-----':<8} {'-----'}")
    for (prev_syl, syl, next_syl, error_type), count in top_patterns:
        lines.append(f"  {prev_syl:<15} {syl:<15} {next_syl:<15} {error_type:<8} {count}")
    lines.append("")

    lines.append("=" * 70)
    return "\n".join(lines)


def save_errors_csv(output_path, false_splits, false_joins, length_errors):
    """Save error details to CSV files."""
    output_dir = output_path.parent

    # False splits CSV
    splits_path = output_dir / "false_splits.csv"
    split_counter = Counter()
    split_examples = {}
    for word, parts, context in false_splits:
        split_counter[word] += 1
        if word not in split_examples:
            split_examples[word] = (parts, context)

    with open(splits_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["word", "count", "predicted_parts", "context"])
        for word, count in split_counter.most_common():
            parts, ctx = split_examples[word]
            writer.writerow([word, count, " | ".join(parts), ctx])

    # False joins CSV
    joins_path = output_dir / "false_joins.csv"
    join_counter = Counter()
    join_examples = {}
    for word, parts, context in false_joins:
        join_counter[word] += 1
        if word not in join_examples:
            join_examples[word] = (parts, context)

    with open(joins_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["merged_word", "count", "true_parts", "context"])
        for word, count in join_counter.most_common():
            parts, ctx = join_examples[word]
            writer.writerow([word, count, " | ".join(parts), ctx])

    # Word length error rates CSV
    length_path = output_dir / "error_by_length.csv"
    with open(length_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["word_length_syllables", "total", "correct", "errors", "accuracy", "error_rate"])
        for length, stats in sorted(length_errors.items()):
            writer.writerow([length, stats["total"], stats["correct"], stats["errors"],
                           f"{stats['accuracy']:.4f}", f"{stats['error_rate']:.4f}"])

    return splits_path, joins_path, length_path


# ============================================================================
# Main
# ============================================================================

@click.command()
@click.option(
    "--model", "-m",
    default=None,
    help="Model directory (default: models/word_segmentation/vlsp2013)",
)
@click.option(
    "--data-dir", "-d",
    default=None,
    help="Dataset directory (default: datasets/c7veardo0e)",
)
@click.option(
    "--output", "-o",
    default=None,
    help="Output directory for results (default: results/word_segmentation)",
)
def main(model, data_dir, output):
    """Run error analysis on VLSP 2013 word segmentation test set."""
    # Resolve paths
    model_dir = Path(model) if model else PROJECT_ROOT / "models" / "word_segmentation" / "vlsp2013"
    data_path = Path(data_dir) if data_dir else PROJECT_ROOT / "datasets" / "c7veardo0e"
    output_dir = Path(output) if output else PROJECT_ROOT / "results" / "word_segmentation"
    output_dir.mkdir(parents=True, exist_ok=True)

    model_path = model_dir / "model.crf"
    if not model_path.exists():
        model_path = model_dir / "model.crfsuite"
    if not model_path.exists():
        raise click.ClickException(f"No model file found in {model_dir}")

    click.echo(f"Model: {model_path}")
    click.echo(f"Data:  {data_path}")
    click.echo(f"Output: {output_dir}")
    click.echo("")

    # Load model
    click.echo("Loading model...")
    model_path_str = str(model_path)
    if model_path_str.endswith(".crf"):
        from underthesea_core import CRFModel, CRFTagger
        crf_model = CRFModel.load(model_path_str)
        tagger = CRFTagger.from_model(crf_model)
        predict_fn = lambda X: [tagger.tag(xseq) for xseq in X]
    else:
        import pycrfsuite
        tagger = pycrfsuite.Tagger()
        tagger.open(model_path_str)
        predict_fn = lambda X: [tagger.tag(xseq) for xseq in X]

    # Load test data
    click.echo("Loading VLSP 2013 test set...")
    test_data = load_vlsp2013_test(data_path)
    click.echo(f"  {len(test_data)} sentences")

    all_syllables = [syls for syls, _ in test_data]
    all_true = [labels for _, labels in test_data]
    num_syllables = sum(len(syls) for syls in all_syllables)
    click.echo(f"  {num_syllables:,} syllables")

    # Load dictionary if available
    dict_path = model_dir / "dictionary.txt"
    dictionary = None
    if dict_path.exists():
        dictionary = load_dictionary(dict_path)
        click.echo(f"  Dictionary: {len(dictionary)} words from {dict_path}")

    # Extract features and predict
    click.echo("Extracting features...")
    active_templates = get_all_templates()
    if dictionary is None:
        active_templates = [t for t in active_templates if t not in FEATURE_GROUPS["dictionary"]]
    X_test = [sentence_to_syllable_features(syls, active_templates, dictionary) for syls in all_syllables]

    click.echo("Predicting...")
    all_pred = predict_fn(X_test)

    # Run analyses
    click.echo("Analyzing errors...")

    # 1. Syllable confusion
    syl_errors = analyze_syllable_errors(all_true, all_pred)

    # 2. Word metrics
    word_metrics = compute_word_metrics(all_syllables, all_true, all_pred)

    # 3. Word-level errors
    false_splits, false_joins = analyze_word_errors(all_syllables, all_true, all_pred)

    # 4. Error by word length
    length_errors = analyze_errors_by_word_length(all_syllables, all_true, all_pred)

    # 5. Boundary errors
    boundary_errors = analyze_boundary_errors(all_syllables, all_true, all_pred)

    # 6. Top error patterns
    top_patterns = get_top_error_patterns(all_syllables, all_true, all_pred, top_n=20)

    # Generate report
    report = format_report(
        syl_errors, word_metrics, false_splits, false_joins,
        length_errors, boundary_errors, top_patterns,
        len(test_data), num_syllables,
    )

    # Print to console
    click.echo("")
    click.echo(report)

    # Save report
    report_path = output_dir / "error_analysis.txt"
    with open(report_path, "w", encoding="utf-8") as f:
        f.write(report)
    click.echo(f"\nReport saved to {report_path}")

    # Save CSVs
    splits_csv, joins_csv, length_csv = save_errors_csv(
        report_path, false_splits, false_joins, length_errors
    )
    click.echo(f"False splits CSV: {splits_csv}")
    click.echo(f"False joins CSV:  {joins_csv}")
    click.echo(f"Error by length:  {length_csv}")


if __name__ == "__main__":
    main()